Sunday, August 31, 2025

Dogra et al.: Does This Radiology Article Help Us Shape Coding & Payment for AI Pathology?

This recent article by Dogra et al., clearly explains the poor fit of today's coding and reimbursement with radiology AI.  The framework could be useful for pathology AI as well.

Find it here:

  • SaaS/AI and Radiology:  Dogra S et al. (2024) Reimbursement in the age of generalist radiology artificial intelligence.  NPJ Dig Med https://www.nature.com/articles/s41746-024-01352-w
  • Writing: We argue that generalist radiology artificial intelligence (GRAI) challenges current healthcare reimbursement frameworks. Unlike narrow AI tools, GRAI’s multi-task capabilities render existing pathways inadequate.



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AI CORNER
This is direct AI output; do not quote without fact checking.

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Here’s a graduate-level summary of Dogra, Silva & Rajpurkar (npj Digital Medicine, 2024), tuned for readers steeped in CMS policy, coding, and reimbursement—plus a few bridges to genomics/digital pathology.

Thesis & scope. The authors argue that generalist radiology AI (GRAI)—multi-task, adaptable, and increasingly multimodal—doesn’t fit today’s reimbursement scaffolding that was built for narrow, task-specific AI. They lay out how coding, valuation, and coverage would need to evolve and sketch several policy-ready pathways to get there.

System backdrop (why status quo strains). Under MPFS, payment flows via RBRVS (work, practice expense, malpractice), with relativity influenced by AMA RUC; hospitals are paid under IPPS/OPPS (MS-DRGs/APCs). CPT is the lingua franca: Cat I for established services; Cat III for emerging tech; and now CPT Appendix S classifies AI as assistive, augmentative, or autonomous. The sticking point for AI—especially generalist software—is practice expense: software licensing, maintenance, and cloud costs don’t map cleanly to the direct/indirect PE constructs, and historical PPIS inputs underweight these categories.

Where AI reimbursement stands today. A small set of SaMD examples illuminate the limits: 92229 (autonomous DR screening) and 75580 (FFR-CT) reached Category I, while additional AI codes remain Category III. On the inpatient side, NTAP has been inconsistently available (e.g., approval for Viz.ai LVO triage; denials where “substantial improvement” wasn’t met). CMS’s TCET pathway could accelerate coverage for Breakthrough-designated tech but is capacity-limited. These experiences show: (1) autonomous tools can have zero work RVU, complicating relativity; (2) software costs are hard to place within PE; and (3) evidence thresholds vary by setting and program.

Core problem GRAI poses. By design, GRAI can adapt tasks (triage, detection, segmentation, reporting), span modalities, and ** shift along Appendix-S levels** (assistive → augmentative → autonomous) over time. That dynamism clashes with static CPT descriptors, fixed RVU components, and coverage frameworks that expect discrete, stable services. The authors stress that GRAI’s value is composite (task breadth, autonomy level, adaptability, multimodal fusion) and cannot be captured by a single “add-on” widget model.

Coding pathways the authors surface.

  • Coordination-style codes (analogy: chronic care management): a single multi-task code that “bridges” activities (triage→detection→reporting) and can be appended across modalities.

  • Modality-series with gradations by task depth (e.g., detection-only vs. full report generation), preserving relativity within existing radiology code families.

  • Modifier strategy (telemedicine analogy): start by modifying existing codes to denote GRAI involvement/level, easing scale-up and avoiding code proliferation.

  • Adaptive descriptors aligned to FDA PCCPs: code language that anticipates pre-authorized algorithm updates, preventing payment from breaking when the model evolves within a PCCP.

Valuation options the authors contemplate.

  • Composite RVU builds that incorporate: (a) radiologist work for oversight/interpretation; (b) task complexity that escalates with autonomy (e.g., full report generation); (c) an adaptability component for multi-modality integration; and (d) a clearer home for software costs in PE.

  • Movement toward outcomes- or access-linked payment where FFS granularization fails (e.g., when GRAI’s contributions cut across multiple discrete services). The paper frames this as complementary—not a wholesale replacement—recognizing CMS’s FFS core.

Coverage & diffusion (what will really drive uptake). Even with codes and RVUs, coverage determines adoption. The authors expect a premium on comparative effectiveness and RWD/RWE, including calls for more randomized trials for AI. Societal guidance (e.g., ACR) will matter, as will harmonization of NCD/LCD expectations for safety, performance, and utilization management. Importantly, coverage criteria must learn to “live with change,” acknowledging PCCP-style evolution without re-litigating basic clinical utility after every minor update.

Practical implications for CMS policy wonks (and why genomics folks should care).

  • Appendix S as a common spine. Whether radiology or genomics/digital pathology, mapping AI functions to assistive/augmentative/autonomous helps anchor code design and evidence tiers. For “dry lab” genomics algorithms (e.g., therapy-ranking engines), Appendix S provides a shared taxonomy to argue work vs. PE allocation and supervision expectations.

  • SaaS friction = PFS pain point. The authors’ critique of PE treatment of software aligns with current CMS PFS SaaS RFI concerns: license/maintenance/cloud costs, thin claims data, and cross-setting consistency. Their proposed paths (crosswalking, composite valuation, adaptive coding) translate cleanly to genomic AI SaaS used across oncology pathways.

  • Coding strategy you can reuse. For generalist digital pathology or molecular decision-support (e.g., multi-task WSI models; pharmacogenomic/therapeutic ranking engines), consider:

    1. Coordinator-style codes that bundle multi-step AI assistance;

    2. Series or modifiers indicating autonomy level;

    3. Descriptors that reference PCCPs, so updates don’t orphan payment.

  • Evidence playbook. Build a laddered evidence plan (technical performance → workflow impact → clinical outcomes → utilization/total cost) with RWE infrastructure from day one, anticipating LCD scrutiny and TCET-like pathways where applicable.

Bottom line. Dogra et al. make a clear case: generalist AI will force adaptive reimbursement design. For CMS and the AMA, that likely means new coding constructs, composite valuation that explicitly prices software & autonomy, and coverage rules that accept regulated evolution of models. For genomics, the blueprint is strikingly portable: many “generalist” decision engines face the same PE blind spots, coding rigidity, and evidence translation hurdles as GRAI—and can borrow these pathways almost verbatim.

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Are there any quick coding fixes?  Sidebar.

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Other views of value in radiology.  

See the Seidel value matrix for diagnostic imaging, 2016.  https://pubmed.ncbi.nlm.nih.gov/27350100  [subscr]

See Antonissen 2025, AI in Radiology, 173 products and their evidence.  https://link.springer.com/article/10.1007/s00330-025-11830-8   [open]

See Jan Beger's essay, citing to Rajpurkar and Topol, on the radiologist/ radiology AI split.  https://www.linkedin.com/posts/janbeger_the-case-for-role-separation-in-ai-human-activity-7359791795406258177-w133/     discussing:  https://pubmed.ncbi.nlm.nih.gov/40728396/   [subscr.]


CMS Asks You to Comment: How to Price AI-Based Services

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In both the Outpatient proposed rule and the Physician Fee Schedule proposed rule, CMS asks for public comment on how to price AI- and software-based services, including diagnostics.  The comment periods run until September 12 & 14.  I provide the relevant pages of Federal Register in a ZIP file, and provide links for submitting comments.

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Comment Fatigue?

You'll be excused for comment fatigue.   (1) AMA seeks comments on how to code and class software-based or  -augmented services (here).  (2) Last spring, HHS ran a comment cycle on  how it could de-regulate and reduce red tape in healthcare services (here).   (3) About a week ago, HHS announced it was setting up a federal advisory committee to guide policy redesign for US healthcare (here).  (4) There's an HHS  effort where digital giants like Amazon and Apple promise to bring digital healthcare improvements to the U.S. (here, point #3).  

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Comment on Pricing Physician Office and Hospital Outpatient AI

  • I wrote a  lengthy blog about this on July 16, so I'll focus on a shorter version today.

Summer Policymaking and Summer Comment

  • Each summer, CMS releases hundreds of pages of policy and updates for the physician office environment ("PFS Rule") and hospital outpatient environment ("OPPS Rule.")  

Key Pages in ZIP File PDFs

The key pages from both PFS and OPPS rule are in a cloud ZIP file here.

  • Fed Reg July 16, 2025.  PFS Rule, 90FR32506-7.  CMS-1832-P.
    • Comment on AI/Software pricing issues ("b.").
    • Also of interest, on page 32374 ("d"), comment on how CMS may increasingly use hospital APC prices to set prices (RVUs) in the office setting.  
    • This builds on PAMA Section 220, 2014 (see also at SSA 1848(c)(2)(M).) 
  • Fed Reg July 17, 2025.  OPPS Rule, 90FR 33565 (F).  CMS-1834-P.
    • Comment on AI/Software pricing issues (F.)

Comment Online

  • Comment on the PFS rule until September 12, here.
  • Comment on the OPPS rule until September 14, here.

Comment on Content

For more nerdy detail and for a sample comment letter, click on my blog of July 16.

See House Health testimony on AI in Healthcare, 9/3/2025.

Comments  should be based on specific questions issued by CMS (90FR32506, 90FR33565), see ZIP file above.  Brief highlights of their questions to us include:

  • PFS
    • "More services include innovative technology...not well accounting for in our practice expense methodology."
    • [AMA data does not provide...] insight into the direct costs associated with this technology.
    • We consider most software, analysis, licensing fees to be indirect costs tied to hardware.
    • What factors should we consider when paying for SaaS?  How may CMS evaluate quality and efficacy of SaaS and AI technology?  We welcome public comment.
  • OPPS
    • Prior to CY2018, SaaS was considered supportive and was packaged [bundled] into payment for the clinical service.
    • Advanced imaging software for MRI was packaged into the payment for MRI.
    • CMS has paid under New Technology APCs.  We do not have a specific payment methodology for SaaS.   These technologies evolve and diversify.
    • Some say that...the lack of consistent policy can be an impediment to patient access...for services approved by FDA.   We welcome public comment.

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CMS expects separate comment letters to be filed with OPPS and PFS.   Should you want to write one consolidated letter, clearly label it as top as your comment on both rules and very clearly demarcate which part of your letter is for which.   

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In addition to PAMA Section 220 legislative text on setting RVUs in new ways, see CMS discussion from November 2014 in PFS final rule CY2015, CMS-1612-FC, click here and search for "220."

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For a discussion of SaaS/AI via Radiology, here:  Dogra S et al. (2024) Reimbursement in the age of generalist radiology artificial intelligence.  NPJ Dig Med https://www.nature.com/articles/s41746-024-01352-w

Writing: We argue that generalist radiology artificial intelligence (GRAI) challenges current healthcare reimbursement frameworks. Unlike narrow AI tools, GRAI’s multi-task capabilities render existing pathways inadequate.

Friday, August 29, 2025

Digital Pathology, AI, and Coding: Why You've Got to Read AMA "TAB 94"

There is an impending boom of new capabilities which will arrive as digital pathology plus AI or machine learning.  

There will be an old fashioned collision:  How will the coding and reimbursement system handle them?

Background - PLA

From about 2021-2024, the AMA CPT made about a half-dozen codes for such services under the PLA coding system - Proprietary Laboratory Analyses.   However, the last digital pathology code under PLA appears to date back to July 2024.   Since then, I've seen dig path codes applied for (AMA publishes quarterly applicants), but they vanish without being finalized (AMA publishes final quarterly codes).

Background - AMA Digital Medicine

Enter the AMA Digital Medicine Payment Advisory Group DMPAG.   They created Appendix S, an initial AMA CPT template for digital medicine or AI-medicine.   

https://www.ama-assn.org/practice-management/cpt/cpt-appendix-s-ai-taxonomy-medical-services-procedures

September 2025 at AMA - "Tab 94"

At the September 2025 AMA CPT meeting, the DMPAG will unveil a new proposal which will have massive impacts on coding and reimbursement for digital pathology.   It's known as "TAB 94."  

  • While you can only access Tab 94 documents to review under confidentiality, I did hear an AMA talk last week at a lab conference, about Tab 94, where Tab 94 was presented without any disclosure requirements.  Still, I'll only briefly allude to Tab 94 and I'll give you instructions step-by-step how to get it yourself.

Tab 94 would create a new section of AMA CPT - not Category I, nor Category III, nor PLA.  It will hold services from any area of medicine - such as radiology, cardiology, and pathology - based on computer output from a digital algorithm.  It is proposed to be called CMAA - Clinically Meaningful Algorithmic Analyses.   Services would require FDA approval.

Questions You Might Ask

Who are the stakeholders here?   Specialty labs like Artera AI?  Large genomic labs with a big push in AI services too - such as Tempus?   Lab stakeholder organizations (think CAP, ACLA?)  Other very large stakeholders, like AdvaMed (radiology, cardiology) and AdvaMed DX (diagnostics)?  And PHRMA (as FDA digital pathology tests become companion diagnostics for FDA drugs).   Should  cardiology-radiology services have one AMA category (always requiring FDA) and pathology another (allowing either FDA or CLIA options)?

Finding Tab 94 - Mini Approach

I think you should be able to find Tab 94 by going to this link, which may require you to get an AMA account using your email before proceeding.

https://cptsmartapp.ama-assn.org/ipdashboard

Be sure you are in the "Interested Party" half of the above website, scroll down, click forward, find Tab 94 and request to review.

Finding Tab 94 - Step by Step Approach

I wouldn't be surprised if just finding the AMA Tab 94 download button is confusing to some.  I also put online detailed instructions step by step.


https://brucedocumentblog.blogspot.com/2025/08/ama-pla-and-tab-94.html

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For a discussion via Radiology, here:  Dogra S et al. (2024) Reimbursement in the age of generalist radiology artificial intelligence.  NPJ Dig Med https://www.nature.com/articles/s41746-024-01352-w

Writing: We argue that generalist radiology artificial intelligence (GRAI) challenges current healthcare reimbursement frameworks. Unlike narrow AI tools, GRAI’s multi-task capabilities render existing pathways inadequate.


Thursday, August 28, 2025

AI Writing Sample: Bio-Rad Holds Webinar on Advances in Droplet Digital PCR (ddPCR)

This past week, Bio-Rad offered a detailed webinar update on its droplet digital PCR offerings.  

As of August 28, the deck is still online here and the webinar video is still online here.

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AI CORNER

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As an experiment in how Chat GPT would handle this, I uploaded the deck, autotranscript, and a couple press releases and asked for a summary [prompt at bottom.] .  The AI material below includes the main analysis, plus a "payor facing" page, plus a nerdy page based on CMS NCD for NGS #90.2.

NOTE!

This is the direct AI output.  I'm offering it as an example of an AI work product, not as an analysis of Bio-Rad.   Do not quote any details without confirming.

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Bio-Rad’s ddPCR expansion (acquisition + portfolio launch): what changed and why it matters

Executive summary

What Bio-Rad did. On July 7, 2025, Bio-Rad announced an expansion of its Droplet Digital PCR (ddPCR) offering built around two pillars: (1) a strategic acquisition of Stilla Technologies and (2) the launch of four ddPCR platforms, notably the QX Continuum™ and the QX700™ series. The move broadens Bio-Rad’s instrument lineup (now spanning QX200™, QX600™, QX ONE™, QXDx™, QX Continuum™, and QX700™ variants) and is paired with a very large assay catalog to smooth migration from qPCR to ddPCR.

Why it matters. ddPCR provides absolute quantification, higher sensitivity, and superior reproducibility versus qPCR, while offering faster, simpler, and more cost-effective workflows than many NGS workflows for targeted, quantitative questions. Bio-Rad’s updated portfolio explicitly targets that middle ground—rare allele detection, precise CNV calling, residual disease/viral reservoir monitoring—and positions ddPCR as a routine tool in research and clinical labs.


What was announced (and when)

  • Acquisition & platform rollout (July 7). Bio-Rad introduced QX Continuum™ (all-in-one, qPCR-like workflow, four-color multiplexing, up to 8 thermal profiles/plate) and the QX700™ series (E/S/HT) featuring seven-color multiplexing, continuous loading, >700 samples/day, and flexible throughput tiers. These instruments complement the existing QX200/QX600/QX ONE/QXDx portfolio.

  • Webinar (Aug 26). Management hosted an investor/analyst webinar on Aug 26, 2025 (10:00 am PT) to walk through the technology, portfolio positioning, and Q&A.


Portfolio at a glance (and who each system is for)

  • QX Continuum™entry/bridge system for academic & translational groups moving from qPCR; run 1–96 samples on standard qPCR plates with the lowest per-sample running cost (same cost whether 1 or 96 wells), easing plastics/inventory burden. Ideal when you want qPCR-like simplicity with ddPCR accuracy.

  • QX700™ series (E / S / HT)integrated, scalable line with highest multiplexing (7-color) and formats matched to 48 / 192 / 384 samples; >700 samples/day possible in high-throughput contexts (biopharma QA/QC, environmental testing, cell & gene therapy).

  • QX600Dx™ / QXDx™registered systems aimed at clinical/LDT labs, including hematologic and solid-tumor monitoring (liquid biopsy) use cases; the deck explicitly maps platform families to Clinical/LDT and BioPharma QA/QC segments.

Positioning map. The 18-page deck shows where each platform lands across entry-level, high-performance research, clinical/LDT, and QA/QC lanes, underscoring a “no-compromise coverage” strategy for all ddPCR use cases.


Why scientists care: the technical and practical deltas

1) Absolute quantification & sensitivity (core ddPCR value).
ddPCR’s partitioning into droplets enables absolute copy number measurements without standards, rare-variant detection, and highly reproducible quantitation across labs. The deck cites independent metrological work and >12,000 publications demonstrating real-world sensitivity, precision, and reproducibility—attributes that matter for MRD, viral reservoir work, and biosurveillance.

2) Fit-for-purpose vs qPCR and NGS.
Compared to NGS, ddPCR offers lower cost, shorter TAT, and simpler analysis for targeted questions; compared to qPCR, it reduces variability, avoids standard curve drift, and reaches rare-event regimes qPCR struggles with. The deck frames ddPCR as the right tool when high sensitivity, absolute measurements, and reproducibility are required.

3) Migration friction is low (assays, plastics, workflows).
Bio-Rad highlights ~490k+ validated assays (with detailed counts for CNV, mutation, gene expression, methylation, infectious disease, cell & gene therapy) and notes ~600k assays designed for qPCR are compatible with ddPCR, making it straightforward to port assays you already trust. Continuum’s use of standard qPCR plates eliminates new-plastic hassles.

4) Throughput & multiplexing choices.
From single-sample to hundreds/day, the refreshed lineup covers low- to high-throughput labs and scales multiplexing from 4-color (Continuum) to 7-color (QX700 series), letting labs right-size instrument capex and consumable pull-through.


Where Bio-Rad sees ddPCR going next

Adoption curve & markets. The deck sizes the absolute-quantitation segment at roughly $600M today, with growth driven by (i) expanding use alongside qPCR/NGS where unmet sensitivity/precision needs remain, and (ii) proven clinical utility pushing ddPCR toward routine diagnostics.

Applications spotlight. Oncology liquid biopsy monitoring, biopharma production/QA (dose control, contamination), and bio-surveillance (e.g., wastewater) are highlighted as “save-lives” applications that prize reproducibility and precision.

Assay/menu strategy. Beyond internal assay design, Bio-Rad references external partnerships to expand clinical menus (e.g., colorectal cancer screening and transplant monitoring programs cited on the webinar), signaling a roadmap to more registered tests through 2026–2027.


Practical implications for labs

For research groups (migrating from qPCR).

  • Start with QX Continuum™ to keep plates, plastics, and per-sample cost familiar while gaining ddPCR accuracy; it’s designed to look and feel qPCR-like.

  • Port your existing assays: large validated menus and qPCR↔ddPCR compatibility minimize method-development time.

  • Scale up to QX700 E/S/HT if you need 7-color multiplexing or high throughput for screening or production workflows.

For clinical/LDT labs.

  • Use registered platforms (QX600Dx / QXDx) to support oncology monitoring (liquid biopsy) and related quantitative assays where absolute measurements and lot-to-lot reproducibility are critical for patient management.

  • Expect gradual adoption: the webinar candidly notes the slow cycle for replacing entrenched, validated, reimbursed methods—so pair platform adoption with a menu strategy and evidence roadmap.


“When should I choose ddPCR over qPCR or NGS?”

  • Choose ddPCR when you need absolute copies (no standards), rare-variant detection, tight CVs across sites, rapid TAT, and cost control for targeted questions (MRD trendlines, viral reservoir quant, CNV precision, low-level contamination).

  • Choose qPCR for fast, low-cost relative quant where the biology tolerates higher variability and less sensitivity.

  • Choose NGS for discovery, high-plex variant surveys, or when you need genome-scale context—and accept longer TAT, higher cost, and heavier bioinformatics.


Evidence & credibility signals to watch

  • Peer-reviewed usage: >12,000 publications span research and a growing share of clinical application papers.

  • Metrology: the deck cites independent inter-lab reproducibility supporting droplet partitioning as the precision benchmark.

  • Company follow-through: Bio-Rad publicly committed to portfolio depth and discussed assay/menu expansion on the August 26 webinar; investor materials remain available via IR.


Risks & adoption hurdles (from Bio-Rad’s own remarks)

  • Clinical adoption is slow—replacing validated, reimbursed methods takes time and evidence; markets into 2026 remain macro-uncertain.

  • Forward-looking: All guidance is subject to change; Bio-Rad flags typical market, regulatory, supply chain and competitive risks in its safe-harbor statements.


Bottom line for potential adopters

  • If you’re qPCR-centric: the QX Continuum™ offers a low-friction on-ramp—same plates, lowest per-sample cost, and absolute quantification to improve rigor and sensitivity in familiar assays.

  • If you’re scaling quant or QA/QC: QX700™ variants provide 7-color multiplexing and throughput headroom (to >700 samples/day) to industrialize targeted quantitation.

  • If you’re clinical: align platform choice with registered systems and plan evidence generation for utility + reimbursement, leveraging Bio-Rad’s growing assay/menu ecosystem.


Source materials reviewed

  • Acquisition + platform rollout press release (July 7, 2025).

  • Webinar announcement (Aug 26, 2025).

  • Webinar auto-transcript & notes.

  • 18-slide deck (“Droplet Digital PCR – Powering the Future,” Aug 26, 2025).

If you want, I can turn this into a 1-page payor-facing brief (or a lab-director buyer’s guide) that distills the platform choice and procurement checklist for different settings.


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Write a one page payor facing brief; why they should care.

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Here’s the same payor-facing one-pager rewritten in paragraph format so it reads as a polished briefing note rather than a bullet summary:


Why Payors Should Care About Bio-Rad’s Expanded Digital PCR Portfolio

Bio-Rad, a leader in molecular diagnostics, has recently expanded its Droplet Digital PCR (ddPCR) offering with new platforms (QX Continuum™, QX700™ series) and through a strategic acquisition that strengthens its technology base. The result is a broad, tiered portfolio that makes ddPCR more accessible to both research and clinical laboratories. For payors, this matters because ddPCR is no longer a niche platform; it is positioned to become a routine tool in oncology, transplantation, infectious disease, and cell and gene therapy monitoring—areas that already represent significant spend and reimbursement pressure.

The importance of this development lies in ddPCR’s ability to fill a precision gap between qPCR and NGS. While qPCR remains inexpensive and fast, it is limited by variability and poor sensitivity for rare variants. NGS, in contrast, is comprehensive but expensive, slow, and analytically complex. ddPCR offers a middle path, providing absolute quantification, high reproducibility, and rare-variant sensitivity at a fraction of the cost of NGS and with turnaround times closer to qPCR. For payors, this means that in many targeted applications ddPCR may substitute for more costly sequencing tests, reducing overall claims burden without compromising accuracy.

The clinical use cases for ddPCR are broadening rapidly. In oncology, ddPCR enables monitoring of minimal residual disease, quantitative tracking of circulating tumor DNA, and precise measurement of therapy response. In transplant medicine, it supports donor-derived cell-free DNA monitoring to detect rejection earlier and more reliably. In infectious disease, it provides precise quantification of viral reservoirs in HIV, HBV, and SARS-CoV-2, where qPCR often falls short. In cell and gene therapy, it is used for copy number validation, dose monitoring, and contamination detection, making it directly relevant to the emerging high-cost therapies payors must manage. These are all coverage-sensitive areas where ddPCR’s reproducibility can reduce downstream costs from false negatives, unnecessary repeat testing, or inappropriate therapy changes.

The evidence base supporting ddPCR is unusually strong. More than 12,000 peer-reviewed publications validate its reproducibility and sensitivity across a wide range of applications. Independent metrology studies confirm that droplet partitioning delivers superior reproducibility compared to qPCR, an important signal of reliability for reimbursement decisions. Bio-Rad has also positioned clinical-grade platforms (QX600Dx, QXDx) for regulated use, with oncology and transplant assays already in use and expanded menus in development. For payors, this reduces the risk of reimbursing “experimental” technology and provides confidence that ddPCR tests are supported by a credible body of evidence.

Adoption barriers are also lower than in previous technology transitions. Bio-Rad has built a catalog of hundreds of thousands of validated assays, many directly compatible with qPCR designs. The QX Continuum™ system accepts standard qPCR plates and delivers the lowest per-sample consumable cost in the ddPCR space, allowing labs to adopt the technology without overhauling workflows or inventories. At the same time, high-throughput options such as the QX700™ series can process more than 700 samples per day, making ddPCR suitable for reference and central labs. These features mean payors can expect faster laboratory uptake, translating into earlier claims activity.

In summary, Bio-Rad’s expansion of its ddPCR portfolio signals a significant shift: digital PCR is moving from specialized research toward routine clinical practice. For payors, the implications are clear. Coverage requests for ddPCR-based tests will increase, particularly in oncology, transplant, and advanced therapy monitoring. ddPCR offers a cost-effective alternative to NGS for many quantitative applications, and its reproducibility makes it a strong candidate for demonstrating clinical utility. Payors who establish frameworks for ddPCR coverage now will be better prepared to manage its adoption in a way that balances patient access, clinical benefit, and cost containment.


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Here's a nerdy extrapolation

 Medicare is a large part of the oncology marketplace, and has a National Coverage Determination NCD 90.2, on NGS in Oncology, which places restrictions on the use, especially the serial use, of NGS. However, ddPCR is not NGS and ddPCR tests are exempt from the rules of NCD 90.2 Discuss why this may matter to clinicians and others.

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That’s a very astute observation. Here’s how I’d frame it clearly for clinicians, payors, and policy readers:


Why ddPCR’s Exemption from 

NCD 90.2 Matters

Background.
Medicare’s National Coverage Determination (NCD) 90.2 sets strict boundaries around next-generation sequencing (NGS) in oncology, particularly limiting coverage for repeat or serial use of NGS in the same patient. The intent was to control cost and ensure NGS is applied in situations with clear clinical utility—typically initial genomic profiling for advanced cancer. However, the NCD applies only to NGS technologies, not to other molecular methods. Because digital PCR (ddPCR) is not classified as NGS, ddPCR-based tests are not restricted by the rules of NCD 90.2.

Why this matters to clinicians.
Oncologists often need to track a single known variant or biomarker over time—for example, monitoring minimal residual disease, watching for resistance mutations, or quantifying ctDNA to assess therapy response. Under NCD 90.2, ordering repeated NGS panels is difficult, expensive, and sometimes non-covered. ddPCR provides a precise, low-cost, reimbursable alternative for longitudinal monitoring without running afoul of NCD rules. Clinicians can therefore continue to manage patient care with high sensitivity and reproducibility, while avoiding denials linked to inappropriate serial NGS use.

Why this matters to payors.
For Medicare and commercial payors, the ddPCR exemption has two important implications. First, it means that oncology monitoring tests built on ddPCR can bypass NCD 90.2 restrictions, potentially gaining easier coverage through local MAC or MolDx determinations. Second, ddPCR provides a cost-control mechanism: instead of repeat $3,000–$5,000 NGS panels, a lab can offer a focused ddPCR assay at a fraction of the price, while still delivering the clinical information needed. This positions ddPCR as a more economically sustainable technology for repeat testing in chronic oncology management.

Why this matters to the broader ecosystem.
For labs, the exemption creates regulatory headroom to innovate ddPCR-based assays without being bound by the national restrictions that weigh down NGS. For biopharma, it opens the door to companion monitoring tests that can be reimbursed on a per-use basis, rather than being constrained to a single test per patient lifetime. For patients, it means access to serial, sensitive monitoring that can guide therapy adjustments in real time.

Bottom line.
ddPCR’s exemption from NCD 90.2 turns it into a strategic tool: clinicians get flexibility, payors get cost control, and labs/biopharma get a reimbursement pathway not tied up in NGS policy constraints. In effect, it creates a “green lane” for oncology monitoring that is clinically necessary but financially and regulatorily impractical to manage with NGS alone.


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Original prompt (from top)

Please study this press release of a Biorad acquisition, press release of a webinar, auto-transcript of the webinar, and 18 page deck for the webinar. Please write up a detailed report on what Biorad did and why it is of interest to scientists in both research and clinical labs that may need digital pcr or migrate to digital pcr.

MOLDX Proposes Two LCDs: Thrombophilia Testing, Liver Disease Steatosis Risk

MolDx has released two proposed LCDs, DL40238 for genetic testing for hereditary thrombophilia, and DL40187 for risk stratification of metabolic dysfunction with steatotic liver disease.

The liver LCD has been released for all 4 MolDx MACs, the thrombophilia one for 3 MolDx MACs (not WPS, so their comment period will lag).  

Comment periods for the proposals in release run August 28 to October 12, 2025.

UPDATE.  MolDx also released a third LCD, on PCR tumor testing, on August 28.  I reported on that one, on September 9.

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LIVER

The liver LCD 40187 is here.  Note that MASLD has replaced NASH.

It has a request letter from Margaret Connelly, LabCorp (9/2023), for the risk test NIS4.    There is also a request from Hilary Imai, Siemens (7/2024), for the ELF test.

The five main criteria proposed are, guideline-based suspicion of MASLD, initial risk testing with non-molecular or non-proteomic test (e.g. FIB4), liver stiffness is "indeterminate or not performed," and results will aid management (referal, Rx).  There are 46 references.

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THROMBOPHILIA

The thrombophilia LCD 40259 was internally generated.  

The patient has a venous thromboembolism with certain risk factors.  In addition, genetic testing will guide management eg anticoagulation, contains defined minimum genetic content, and does not contain improperly validated content that would "reasonably expect" misuse.  There are 45 references.  

The most heavily used citation is #11 Middeldorp et al. 2023, the ASH current guideline.  The LCD has extensive discussion of low-value or low-impact use of genetic testing, but the actual CMS coverage is simple and pretty broad (top section of LCD).

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AI CORNER

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Direct AI output.  Do not quote without checking details. Chat GPT 5.

Here’s a structured expert-level review of the two new proposed MolDX LCDs you provided.


1. DL40187 – Biomarker Testing for Risk Stratification in MASLD/MASH

Scope and Coverage

  • This LCD covers molecular and proteomic biomarker tests used for risk stratification of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) and Metabolic Dysfunction-Associated Steatohepatitis (MASH).

  • MASLD/MASH terminology is aligned with recent AASLD guidance, replacing the older NAFLD/NASH labels.

  • Tests considered include proteomics assays (e.g., ELF test) and molecular biomarker panels (e.g., NIS4, multi-analyte assays with algorithmic analysis).

Coverage Criteria

  • Applies to adults with MASLD/MASH where:

    1. FIB-4 (initial clinical risk tool) suggests intermediate/high risk.

    2. Imaging (VCTE, MRE) is indeterminate or not performed.

    3. Test results will guide management (biopsy, drug eligibility, specialist referral).

    4. Test is not repeated within 12 months or after biopsy.

    5. Analytical and clinical validity must be shown in peer-reviewed studies.

    6. Algorithm validation must be independent of the development cohort.

    7. A MolDX Technical Assessment must be completed.

Evidence Base

  • Justifies policy via:

    • High prevalence of MASLD (35–48% in U.S. adults, higher in T2D and Hispanic populations).

    • Increasing progression to cirrhosis and liver transplant.

    • Recent FDA approval of resmetirom (March 2024) for F2–F3 MASH fibrosis, creating immediate demand for non-biopsy diagnostic stratification tools.

  • ELF test and NIS4 specifically discussed, including AUROC values and comparative performance to elastography.

Billing Guidance (DA60205)

  • Limits to 1 test per 12 months.

  • Requires DEX Z-Code® submission.

  • Accepts CPT codes 81479, 81517, 81599, 0003M, 0166U, etc..

Comment Period

  • Open August 28, 2025 – October 12, 2025 (per LCD posting).

External Requests

  • Labcorp (Sept 18, 2023): Requested LCD for NIS4 blood test to identify at-risk NASH.

  • Siemens Healthineers (July 1, 2024): Requested LCD for ELF test, citing FDA De Novo clearance (2021), guideline endorsement, and disruption due to MolDX Z-code billing enforcement.


2. DL40259 – Genetic Testing for Hereditary Thrombophilia

Scope and Coverage

  • Defines coverage for hereditary thrombophilia testing via LDTs, FDA-cleared, or FDA-approved tests.

  • Includes single variant (familial) or NGS-based multi-gene panels.

  • Focused on germline testing (not somatic or acquired thrombophilias).

Coverage Criteria
Genetic testing covered when ALL are true:

  1. Patient has VTE in specific contexts:

    • VTE associated with major transient or hormonal risk factors (where anticoagulation duration is uncertain).

    • Cerebral or splanchnic venous thrombosis where short-term therapy is standard, but test result may support extended therapy.

  2. Test will directly guide anticoagulation management.

  3. Test includes minimum content with well-established evidence (per 2023 ASH Thrombophilia Testing Guidelines).

  4. No extra genetic content of unclear clinical validity.

  5. No conflict with repeat germline testing rules.

  6. Must complete MolDX Technical Assessment.

Evidence Base

  • Notes VTE incidence (~1–1.5/1000 person-years) and 30% 10-year recurrence risk.

  • Recognizes genetic contribution (up to 60% heritability), with Factor V Leiden and prothrombin 20210G>A most common.

  • However, emphasizes risks of overdiagnosis, inappropriate anticoagulation, and bleeding.

  • Cites 2023 ASH Guidelines, which recommend against routine thrombophilia testing in most clinical scenarios, allowing conditional use only when it would alter management.

Billing Guidance (DA60294)

  • Codes: 81240 (prothrombin), 81241 (Factor V Leiden), 81479.

  • Requires DEX Z-Code™.

  • Broad set of ICD-10 VTE/embolism codes included.

Comment Period

  • Open August 28, 2025 – October 12, 2025 (parallel to MASLD LCD).

External Requests

  • No external request letters identified in your documents; this appears internally initiated by MolDX based on ASH guidelines and utilization concerns.


3. Comparison of DL40187 (MASLD) vs. DL40259 (Thrombophilia)

Common Features

  • Both are MolDX LCDs with draft billing/coding articles attached.

  • Both restrict testing frequency (no repeats without justification).

  • Both require DEX Z-Codes and MolDX Technical Assessments.

  • Both have the same public comment window: Aug 28 – Oct 12, 2025.

Key Differences

  • Clinical Area:

    • DL40187 addresses liver disease risk stratification in a growing population with new drug eligibility implications.

    • DL40259 addresses genetic thrombophilia in VTE patients, a narrower but clinically high-stakes population.

  • Evidence Drivers:

    • MASLD LCD is motivated by new FDA-approved therapies (resmetirom) and the clinical need for non-biopsy fibrosis risk tools.

    • Thrombophilia LCD is driven by guideline caution (ASH 2023), emphasizing that most thrombophilia tests lack clinical utility in routine care.

  • Industry Requests:

    • MASLD LCD arose from external manufacturer requests (Labcorp for NIS4, Siemens for ELF) dating back to 2023–2024.

    • Thrombophilia LCD appears MAC-initiated, with no external sponsor request.

  • Policy Stance:

    • MASLD LCD is expansive, creating a pathway for multiple biomarker assays if they meet MolDX validity standards.

    • Thrombophilia LCD is restrictive, limiting coverage only to scenarios where test results meaningfully guide anticoagulation management.


In summary:

  • DL40187 (MASLD/MASH) is a coverage-enabling policy shaped by industry requests and clinical innovation.

  • DL40259 (Thrombophilia) is a coverage-restricting policy aligned with guideline skepticism about routine use.

  • Together, they illustrate MolDX’s dual role: expanding access where strong clinical/economic rationale exists, while curbing low-value or over-used genetic testing.


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Is the workup for venous thromboembolism and suspected thrombophilia primarily Factor V genetics or are other genes routinely needed?

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The workup for venous thromboembolism (VTE) and suspected inherited thrombophilia is broader than just Factor V Leiden.

Here’s the usual approach in clinical practice:


1. Factor V Leiden

  • The most common inherited thrombophilia in Caucasian populations.

  • Testing is usually by PCR for the G1691A mutation.


2. Prothrombin Gene Mutation (G20210A)

  • The second most common genetic risk factor.

  • Like Factor V Leiden, usually tested by PCR.


3. Other Genetic Factors

  • Antithrombin deficiency, Protein C deficiency, and Protein S deficiency are considered inherited thrombophilias, but these are generally tested functionally (activity/antigen assays), not primarily by gene sequencing.

  • Genetic confirmation is possible but not routinely ordered unless there is a strong family history or atypical lab results.


4. Non-genetic Workup

  • Testing for antiphospholipid syndrome (lupus anticoagulant, anticardiolipin antibody, anti-β2 glycoprotein I antibody) is often included in the evaluation because it is an acquired cause of thrombosis.

  • Homocysteine and MTHFR mutations are no longer routinely recommended, as they don’t strongly predict VTE risk in most patients.


5. Practical Clinical Use

  • Most “routine” thrombophilia panels ordered after a first unprovoked clot typically include:

    • Factor V Leiden

    • Prothrombin G20210A

    • Protein C activity

    • Protein S activity

    • Antithrombin activity

    • Antiphospholipid antibody panel

  • Full next-generation sequencing panels (with multiple rare gene variants) are not routine outside research or very unusual cases.


Summary:
The primary genetic tests are Factor V Leiden and Prothrombin G20210A

Protein C, Protein S, and Antithrombin are tested functionally rather than genetically. Other genes are not routinely needed. Workup is broadened depending on age of onset, family history, recurrence, and unusual clot location.


Would you like me to put together a table of “core” vs “optional/rarely indicated” tests for suspected thrombophilia? That way you’d have a quick reference.




Wednesday, August 27, 2025

Artera, Owkin, on Digital Pathology in Cancer Management

Whole-slide imaging codes appear to be on hiatus at AMA CPT (here). but in the real world, the field is moving ahead steadily.  

Artera AI - Prostate

Artera AI won FDA de novo approval for AI prognostics based on prostate cancer slides.   See the press release here.   See Artera's diagnostics web page here.  See "Urology Times" here, which summarizes several published studies.

You can find the FDA webpage for DEN240068 here.  It can take a few months before FDA approval documents show up.  They will be interesting when they appear, as they'll reflect the FDA's current thinking on WSI & AI.

For now, Artera summarizes as, "the first and only AI-powered software authorized to prognosticate long-term outcomes for patients with non-metastatic prostate cancer."



Owkin - Breast Cancer (Barberis et al.)

Owkin publishes a paper on slide imaging for metastatic relapse risk in breast cancer.

See a Linked In article by Owkin's Victor Aubert here.  

Find the new publication, Barberis et al., in Nature Communications, here"Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides."


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PubMed offered a "related title" to Barberis as being, "External validation of precisebreast, a digital prognostic test for predicting breast cancer recurrence, in an early-stage cohort from the Netherlands."  Westenend et al. 2025, here.  See more under "AI Corner."

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AI CORNER

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I asked Chat GPT to compare and contrast the two 2025 breast cancer WSI papers, Barberis 2025 and Westenend 2025.  

Below are direct AI output; check details before quoting.

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Brief Compare

Both new AI-based pathology models address ER+/HER2− early breast cancer, but with different strengths for payor assessment. 

RlapsRisk-BC (Garberis et al.) predicts 5-year metastasis-free survival directly from routine slides, delivering clear incremental value over standard clinicopathologic factors (ΔC-index +0.05 overall, +0.08 in intermediate risk), which supports its potential role as a lower-cost triage or complement to genomic assays

PreciseBreast/PDxBR (Westenend et al.) predicts longer-term invasive disease-free survival and shows strong performance against MINDACT/MammaPrint clinical models, especially for grade 2 re-stratification, but adds less value beyond robust clinical-only models. 

For payors, the practical message is that RlapsRisk-BC demonstrates measurable utility versus clinical baselines and could reduce reliance on expensive molecular tests.  PDxBR’s appeal lies in refining ambiguous grade-2 cases and offering an alternative to genomic classifiers in longer-horizon risk planning.


###

Side-by-side for cancer experts: 

RlapsRisk-BC (Garberis et al., Nat Commun 2025) vs PreciseBreast/PDxBR (Westenend et al., Breast Cancer Res 2025)

Clinical question & endpoint.

  • RlapsRisk-BC targets ER+/HER2− early breast cancer and learns directly from H&E/HES whole-slide images (WSIs) to predict 5-year metastasis-free survival (MFS)—i.e., distant recurrence risk. Only ER+/HER2− patients were included, and analyses focus on 5-year MFS.

  • PDxBR also studies ER+/HER2− EBC, but its primary endpoint is invasive disease-free survival (IDFS) over longer follow-up (median 8.8 years); events include local/regional recurrence, distant metastasis, and death (drives higher event counts).

Takeaway: the endpoints differ (MFS vs IDFS), so absolute risk and discrimination cannot be compared one-to-one.


Populations & data

  • RlapsRisk-BC external validation (CANTO): n=1,169 ER+/HER2− patients; H&E and HES slides from multiple centers.

  • PDxBR (Netherlands NTH cohort): n=739 ER+/HER2−; treatments were typical of contemporary practice (endocrine 64%, chemo 29%, RT 68%) with 130 IDFS events (24 LRR/second primaries, 25 distant mets, 81 deaths).


Model architecture & inputs

  • RlapsRisk-BC is an end-to-end survival model trained to predict MFS directly from WSIs (no handcrafted features or molecular inputs). It later combines the WSI score with age, tumor size, grade, nodes, Ki-67 to form RR Combined. A calibration step (Uniform Piecewise Approximation) is required when deploying to a new lab—30 local slides (10 G1/15 G2/5 G3) align the score distribution across staining/scanner protocols.

  • PDxBR integrates an AI-derived grading signal (learned morphologic/protein-expression features from pathology images) with clinicopathologic variables (age, size, stage, nodal status). Imaging features contribute ~51% of the final score; gland/tubule architecture is the dominant image driver, while HR status and grade were dropped during feature selection (their signal captured by AI features).


Headline performance (external)

  • RlapsRisk-BC (CANTO, MFS): Adding the WSI score to clinical factors (RR Combined) improved Harrell’s C-index from 0.76→0.81 overall and 0.78→0.86 in the intermediate-risk subgroup (permutation p<0.05). It also outperformed PREDICT Breast (C-indices ~0.70–0.75).

    • With prespecified cutoffs (>5% predicted 5-yr MFS event = high risk), low- vs high-risk HRs were strong (e.g., HR 8.01 for RR Combined), with low-risk event rate ~1–2% at 5 years.

  • PDxBR (NTH, IDFS): C-index/AUC 0.71 overall; PDxBR Clinical 0.69, AI-grade 0.66, MINDACT clinical model 0.60. PDxBR significantly beat MINDACT (p<1e-5) and MammaPrint in the subset with genomic testing (n=252; p=0.0004). Notably, PDxBR vs PDxBR-Clinical was not statistically different (DeLong p=0.49).

Subgroups and re-stratification.

  • RlapsRisk-BC delivered the largest gains in grade 2 and intermediate-clinical-risk patients (sensitivity +21–28 points vs clinical-only), but premenopausal performance was limited.

  • PDxBR reclassifies histologic grade 2 (52% of cases) more evenly using the AI-grade and identifies high-risk cases missed by MINDACT/MammaPrint.


Cut-points & operating characteristics

  • RlapsRisk-BC used prespecified training-set cutoffs and validated with KM analyses; for CANTO, high risk was >5% 5-yr MFS event probability. At this setting, low-risk had ~1.3–1.6% 5-yr metastasis events.

  • PDxBR uses a fixed score threshold of 58, yielding HR 3.05, sensitivity 0.70, specificity 0.59, NPV 0.90, PPV 0.27 for IDFS in NTH (vs prior validation HR 4.4; 6-year IDFS).


Interpretability

  • RlapsRisk-BC: Pathologist-reviewed tile clusters showed increased risk signals for high tumor cell content, nuclear pleomorphism, mitoses, massive/trabecular architecture, and decreased risk with fibrosis or vascular structures, supporting biologic face validity.

  • PDxBR: Quantified feature attributions highlight gland differentiation/tubule formation as the largest single contributor to the score, with nuclear pleomorphism and mitotic figures also important; imaging features explained ~51% of the score.


Implementation & generalizability

  • RlapsRisk-BC explicitly addresses domain shift with the 30-slide calibration (UPA) and shows concordant performance across H&E and HES; authors note weaker performance in premenopausal women and call for head-to-head vs genomic assays and prospective trials.

  • PDxBR deployed on a different national cohort with longer follow-up and demonstrated stable discrimination, significant superiority to MINDACT, and practical grade-2 re-stratification; however, combining images+clinical did not significantly outperform clinical-only in this dataset.


How these results might be used

  • If your clinic struggles with grade-2 and “intermediate-risk” calls: Both tools help, but RlapsRisk-BC showed added value over a strong clinical model (ΔC-index +0.05 overall; +0.08 in intermediate risk) and better sensitivity/specificity tradeoffs at a 5-yr MFS cutpoint.

  • If you need a model aligned to broader IDFS events and longer horizons: PDxBR provides solid 8.8-year IDFS discrimination (C-index 0.71) and beats the MINDACT clinical model, with clear, interpretable morphologic drivers and a fixed pragmatic threshold (58).

  • Genomics positioning: RlapsRisk-BC explicitly proposes WSI as a pre-screen or complement to genomic assays, while PDxBR shows cases misclassified by MammaPrint/MINDACT that its image+clinical approach captures.


Key contrasts in one glance

  • Endpoint focus: MFS (RlapsRisk-BC) vs IDFS (PDxBR).

  • Strength vs clinical baseline: RlapsRisk-BC adds significant incremental value to a multi-variable clinicopath model; PDxBR’s combined model did not significantly exceed its clinical-only model in NTH.

  • Deployment needs: RlapsRisk-BC requires site calibration (30 slides); PDxBR uses a fixed score threshold (58) with standard WSI inputs.

  • Interpretability: Both lean on classic histopathology (glands/tubules, pleomorphism, mitoses); RlapsRisk-BC also flags stroma/vascular correlates of lower risk.


Bottom line for practice & trials

  • For adjuvant-therapy decision support in ER+/HER2− EBC, RlapsRisk-BC currently shows clear incremental prognostic value over clinical factors for 5-yr distant relapse, especially in grade-2/intermediate-risk cases—useful where the question is chemotherapy vs no chemo in the near term. Prospective utility studies (and operationalizing the calibration step) are the next barrier.

  • PDxBR offers a long-horizon, event-inclusive measure that outperforms MINDACT-style clinical modeling and reclassifies ambiguous grades, which may matter for extended endocrine therapy or surveillance planning; however, its combined model didn’t beat clinical-only here, so payer/HTA arguments may hinge on grade-2 re-stratification and MINDACT/MammaPrint deltas rather than pure incremental AUC vs clinical.

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Decibio Webinar on NGS Trends in Oncology; September 16, 9 Pacific

If you track Linked In, several times a month the consultancy Decibio posts a few key graphics from its industry research.  

Let DecibBio catch you up on all that with a free webinar on September 16, 2025 (9 pt, 12 et).   

Find it here.


From the website:

Next-generation sequencing (NGS) is transforming oncology care, but adoption varies across specialties, institutions, and testing contexts. Join DeciBio for a data-driven discussion on the current state of NGS in oncology and where adoption is headed next.

The session will open with two brief presentations from our team:

• DxBooks dataset overview: the latest insights on diagnostic test utilization trends.

• Genomics tracker findings: fresh perspectives on adoption dynamics across oncology practice.

We’ll then transition into a stakeholder panel discussion, bringing together oncologists, pathologists, and industry leaders to share real-world perspectives on drivers and barriers to NGS adoption, evolving clinical use cases, and the future of genomic testing in cancer care.

Tuesday, August 26, 2025

Verily Closes Medical Device Efforts


Verily, "eliminated its entire medical devices program" this week.  See an open-access story at TechCrunch and a subscription article at Business Insider, Alphabet's life sciences arm,    

Alphabet has a market cap of about $2 trillion, of which Google Search is about 80% of revenue.  Its life sciences businesses (Verily, Calico) are tiny by comparison.

What Verily Did/Does?

According to a web search by Chat GPT 5, experimental or discontinued projects at Verily included a spoon for tremors (acquied Liftware), a glucose-sensing contact lens, a retinal camera, and miniatured glucose monitors in collaboration with Dexcom.   

In a future that is absent "devices," Verily life sciences interests could include AI oriented toward digital health startups, pharma, healthcare systems improvements.  Lightpath, for example, is an AI-powered chronic care app in development.  

##

In mid-2024, Verily announced it was shifting its headquarters to Dallas-Ft. Worth.  Nicole Ward at DallasInnovates.com writes,

  • Verily, born as a moonshot project at Google X, has consistently pursued ambitious goals in healthcare technology. 
    • The company develops advanced tools and solutions to help patients, providers, and researchers navigate the healthcare landscape. Its offerings range from consumer-facing products like diabetes management apps and retinal screening devices to sophisticated data analytics platforms for healthcare professionals.
  • Since establishing its Dallas office, Verily has rapidly emerged as a force in Dallas-Fort Worth’s growing healthcare and tech ecosystem.
Separate from Verily, Ward notes an ARPA-H hub was expected at DFW-Pegasus Park, and a $5B investment in pediatric health and innovation was expected at DFW-UT-SW.

##

Past leadership at Verily included Dr Amy Abernethy and Dr Andrew Conrad (now at S32 VC).  Stephen Gillett is CEO since 2023; prior positions included Symantec (as COO) and Best Buy.

I occassionally hesitate between "Dr Conrad" (early CEO at Veriy, now in VC) and "Dr Conway," who went from CMS to Optum.

Monday, August 25, 2025

MolDx New Transplant Rejection Policy - Comment til August 31

In a July 17, 2025 blog, I noted that MolDx had rolled out a new proposed solid organ transplant policy, for molecular tests to detect rejection (e.g. DL40058).   

The comment period is wrapping up soon, August 31, 2025.

All the MolDx MACs (WPS, CGS, Palmetto, Noridian) have the same dates on this one.

Original blog here:

https://www.discoveriesinhealthpolicy.com/2025/07/new-clia-policies-at-cms-new-moldx.html

Find the LCD with comment instructions, here.

My thoughts -

  • ALL NEW LCD
    • MolDx is rolling out an entirely new LCD on this topic.
    • Presumably, when this is finalized, the "current" LCD will be deleted.
    • Rolling out an entirely new LCD makes it harder to judge changes in coverage, since you can't just redline the older and newer versions against each other.
  • BILLING ARTICLE
    • The billing article is very important - at the LCD, at bottom, see DA60146.
  • TWO SCENARIOS
    • The billing article makes it very clear that there are two scenarios, testing-for-cause and testing-per-protocol (aka surveillance.)  Also, tests in this field are often given two Z codes, one for surveillance and one for cause.
    • The LCD itself could be more clearly structured around "for cause" and "surveillance" testing.  
      • Why make the billing article clear about 2 scenarios but not the LCD??
    • For example, For Cause testing means XYZ and it is covered when (5 bullet points).
    • For example, Surveillance testing means XYZ and it is covered when (5 bullet points).
  • "SURVEILLANCE"
    • I would suggest using "surveillance" quarterly testing, as this term is also used in post-op cancer surveillance ("MRD" testing) and, in Z code definitions (at DEX Registry).   
    • Surveillance directs the reader's mind to one kind of testing and is intrinsically meaningful.  
    • "Per protocol" can mean anything (McDonald's makes Big Macs per protocol.)


Keeping Up with MAGA HHS: (1) MAHA Dashboard, (2) CMMI & MAHA, (3) Techno-CMS. (4) CMS & Fraud, (5) NIH Golden Science Plan

 A few days ago, I noted that HHS is recruiting a panel of experts that will help HHS reform the US provider and insurance system.  Here.

Also in the news this week - 

  1. HHS posts a collated dashboard for its MAHA activities.  
    1. E.g. food dyes; cause of autism; etc.
    2. Here.
  2. CMMI (Center for Innovation) gets advice on several priorities for success.
    1. By health policy expert Joshua Liao; see Health Affairs.
    2. Here.
  3. CMS and Big Tech: Make HealthTech Great Again" (MHTGA)
    1. Amazon, Apple, Google, OpenAI, Anthropic commit to healthcare tech improvements with HHS.  Seema Verma remarks.
    2. Here.
  4. "Crushing Fraud Cook-off Competition" is launched at CMS.
    1. Unlimited outside teams (companies) can make suggestions to CMS.
    2. 10 entrants will be selected for "round two."
    3. Timetable August to December.
    4. Here.
  5. "NIH Gold Standard Science"
    1. NIH publishes 16-page PDF, how to improve science as "gold standard science."
    2. Here.

OpenEvidence (AI for Docs) Gets 100% on US Medical Licensing Exam

Open Evidence is AI for clinicians, trained on medical data and with direct access to text in major journals like JAMA and NEJM.   I understand it's free registration if you have an NPI.

Press release, that Open Evidence gets a perfect 100% on the US Medical Licensing Exam, USMLE.

A competing general model, GPT 5, got 97%.


##

I don't have an NPI and haven't used OpenEvidence directly.  

I've used Scite.ai, which is based on PubMed plus many open access journals.



Sunday, August 24, 2025

Cost Effectiveness of CGP in Lung Cancer: Spencer et al.

Spencer et al. publish a nice health economics paper on the cost effectiveness of CGP.  It compares US and German cost-effectiveness in several scenarios.

  • Absolute increase in survival between standard panel and CGP large panel was 0.1 year (5 weeks survival).
  • Cost per QALY was $175,000 (implying circa $17,000 cost per that 0.1 year).
  • Authors use 3,800 patientts in Syapse for data.

It's not open access but J Molec Dx comes with a membership in AMP.

https://pubmed.ncbi.nlm.nih.gov/40701291/

Cost-Effectiveness Analysis of Comprehensive Genomic Profiling in Patients with Advanced Non-Small-Cell Lung Cancer Using Real-World Data

Scott Spencer 1, Weicheng Ye 2, Siyang Peng 3, Denise Zou 2  [ILMN & TFS]

PMID: 40701291 https://www.jmdjournal.org/article/S1525-1578(25)00165-5/fulltext

Abstract

Cancer treatment costs pose a significant global economic burden. By facilitating treatment plans tailored to the genomic profile of patients' cancer, genomic testing has the potential to reduce health care costs. Using real-world evidence, this study compared the cost-effectiveness of comprehensive genomic profiling (CGP) versus small panel (SP) testing in patients with advanced non-small-cell lung cancer in the United States and Germany. 

A partitioned survival model was developed to estimate the life years and drug acquisition costs associated with CGP and SP testing in patients receiving matched targeted therapy, matched immunotherapy, or no matched therapy/untreated. 

Key model parameters were informed by real-world data derived from the Syapse study. Scenario and sensitivity analyses were conducted. CGP improved the average overall survival by 0.10 years compared with SP. 

CGP was associated with higher health care costs because of a higher percentage of patients receiving targeted therapies. The estimated incremental cost-effectiveness ratio (ICER) of CGP versus SP was $174,782 and $63,158 per life-year gained in the United States and Germany, respectively. Increasing the number of patients receiving treatment decreased the ICERs ($86,826 in the United States and $29,235 in Germany), while switching from immunotherapy plus chemotherapy to chemotherapy alone increased the ICERs ($223,226 in the United States and $83,333 in Germany). 

Altogether, CGP has the potential to improve patient outcomes and is more cost-effective than SP.

###

###  AI CORNER ###

I asked Chat GPT 5 for its take on the article.

###

Does CGP Really Make a Difference Over Small Panels?

A new JMD cost-effectiveness study asks the pointed question: does broad comprehensive genomic profiling (CGP) really improve outcomes over more focused small panel (SP) testing in advanced NSCLC?

The answer, based on real-world data, is: yes, but only a little. Patients tested with CGP lived an average of 0.1 year longer — about five weeks of added survival — compared with those tested on small panels. That’s not nothing, but it’s a modest gain, especially when seen against the costs.

On the economic side, the authors report that CGP cost $175,000 per life-year gained in the U.S.. That’s on the edge (or beyond) the usual willingness-to-pay thresholds used in U.S. health economics. The main driver? Patients identified by CGP are more likely to receive expensive targeted drugs, raising overall treatment costs.

Interestingly, the story looks better in Germany, where drug prices are regulated and significantly lower. There, the incremental cost-effectiveness ratio was only $63,000 per life-year gained — a far more palatable number. The difference highlights how drug pricing policy, not just diagnostic technology, shapes cost-effectiveness.

Policy takeaway: Whether CGP “makes a difference” depends on how you measure it. Clinically, the added benefit over small panels is modest. Economically, in the U.S. it comes at a very high cost per year of life gained, while in Germany it falls into a range that would generally be considered cost-effective. This underscores a bigger point: debates over the value of CGP are really debates over how much we’re willing to pay for marginal gains in survival — and whether drug prices should be allowed to dominate that equation.


Pricing was a little quirky.  

According to Chat GPT, they used standard CMS CLFS pricing for large and small panels ($2919, $597).   According to Chat GPT, the same price (for German code 19424) was used for either panel size ,being (E) 2973 or $3218.  



Reforming the US Healthcare System: Again

HHS and CMS ran call-for-suggestions operations last spring, how can CMS, or HHS as a whole, be revised, reformed, and more effective.

Now, HHS forms a Federal Healthcare Advisory Committee to guide policy redesign for US healthcare.

HHS press release here:

https://www.cms.gov/newsroom/press-releases/hhs-drives-reform-restore-patient-centered-care-announces-request-nominations-members-serve-federal

Early coverage, from Fierce Healthcare here:

https://www.fiercehealthcare.com/regulatory/hhs-form-committee-offers-guidance-medicare-medicaid

Here's a brief quote from Fierce Healthcare:

  • The HHS announced Thursday that the Healthcare Advisory Committee will offer recommendations to Secretary Robert F. Kennedy Jr. and Centers for Medicare & Medicaid Services (CMS) Administrator Mehmet Oz, M.D., seeking to "improve how care is financed and delivered" across Medicare, Medicaid, the Children's Health Insurance Program and the Affordable Care Act's exchanges. 
  • The CMS is currently accepting nominations for committee members and is looking for experts in chronic disease management, financing in federal health programs and delivery system reform. Individuals can either be nominated by an organization or submit a nomination for themselves. 
  • "This is a moment for action," Oz said in an announcement.
The article closes by noting:

  • CMS said in the announcement that nominations will be accepted for 30 days after the call goes live on the Federal Register, a date set for Aug. 22, with the full list of committee members to be published later this year.

  • "Every American high-quality, affordable care—without red tape, corporate greed, or excessive costs," said RFK Jr. "This new advisory committee will unite the best minds in healthcare to help us deliver real results, hold the system accountable, and drive forward our mission to Make America Healthy Again."
The actual Federal Register announcement, just two pages but bulleting a few goals, is here:

Submissions by September 22.  

Writing, at 90 Fed Reg 41089-90:

The Committee is tasked with advising the Secretary of HHS and the Administrator of CMS on the following issues as they relate to strategies for improving the operations and outcomes of federally administered healthcare insurance and payment programs: 

• Developing a set of actionable policy initiatives that can promote chronic disease prevention and management, as consistent with the Make America Healthy Again policy agenda; 
• Identifying opportunities to move towards a regulatory framework of accountability for safety and outcomes that reduce unnecessary red tape and allow providers to focus on improving patient health outcomes not filling out paperwork; 
• Sharing actionable levers to advance a real-time data system, enabling a new standard of excellence in care, rapid claims processing, rapid quality measurement and rewards; 
• Identifying structural opportunities to improve quality for the most vulnerable in the Medicaid program (outside of more funding for the current system); and 
• Securing the sustainability of the Medicare Advantage program, specifically identifying opportunities to modernize risk adjustment and quality measures that assess and improve health outcomes.

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Not directly related to the above panel of experts, I noticed the headline CMS gave to the July annual proposed outpatient policy rule, a document of usually-numbing-detail and trivia.



AMP, CAP, ACCC: New Consensus for HRD Testing

See Hsiao et al., August 2025, for a joint consensus recommendation for HRD testing - homologous recombination deficiency.

It's open access at J Molec Diagnostics and runs 20 pages.

https://www.jmdjournal.org/action/showPdf?pii=S1525-1578%2825%2900136-9


The authors' abstract reads:

Homologous recombination deficiency (HRD) is a genomic feature present in some malignant neoplasms and is attributed to the failure of the homologous recombination repair pathway. Tumors with an HRDpositive status may have a distinct prognosis and/or response to therapies, including poly (ADP-ribose) polymerase inhibitors. 

The Association for Molecular Pathology assembled an expert panel to examine current practice and perform a scoping review of the medical literature pertaining to the molecular detection of HRD in the clinical setting. The expert panel examined the following topics: components of existing and proposed HRD and genomic instability biomarkers (including mutational signatures, loss of heterozygosity, mutations in homologous recombination repair associated genes, and epigenetic silencing of RAD51C, BRCA1, or BRCA2); technical considerations for identifying genomic scars from tumor and germline next-generation sequencing results; guidelines on interpretation and caveats when reporting assessments of genomic instability and HRD scores; and the clinical significance of HRD. 

The panel formulated a set of expert consensus opinion recommendations regarding HRD assay design and validation to guide laboratories in developing HRD tests to ensure high-quality and reproducible results.

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From the introduction,

[T]here is no universal definition of what constitutes HRD, which makes it difficult for laboratories to offer such testing. In the United States, there is currently one FDA-approved agent whose labeling defines HRD, describing it as a deleterious or suspected deleterious mutation in BRCA and/or genomic instability (olaparib package insert, per manufacturer instructions); however, genomic instability also has no universally agreed upon definition. At the time of this publication, one commercially available test is approved by the FDA to assess HRD (Myriad Genetics MyChoice CDx, Salt Lake City, UT). This assay uses a composite of tumor BRCA status and LOH, TAI, and LST to assess genomic instability.