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Sequential Diagnosis with Language Models

T0 review · 1 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Orchestrated prompting of a language model as a virtual physician panel reaches about 80 percent diagnostic accuracy on hard sequential cases—roughly four times the average of generalist physicians—while reducing estimated test costs by…

desk verdict A genuinely new interactive diagnostic benchmark with a strong orchestration result, but the headline 80%-vs-20% comparison rests on a Gatekeeper that knows the answer and can synthesize confirmatory findings — needs a blinded control before the claim is taken at face value. read the letter →

arxiv 2506.22405 v2 pith:YD5S4R6B submitted 2025-06-27 cs.CL

Harsha Nori , Mayank Daswani , Christopher Kelly , Scott Lundberg , Marco Tulio Ribeiro , Marc Wilson , Xiaoxuan Liu , Viknesh Sounderajah
show 7 more authors
Jonathan Carlson Matthew P Lungren Bay Gross Peter Hames Mustafa Suleyman Dominic King Eric Horvitz
This is my paper · ORCID
classification cs.CL
keywords sequentialdiagnosislanguagemodelsmulti-agentorchestrationdiagnosticaccuracycost-effectivetestingNEJMCPCcasesgatekeeperbenchmarkdifferential
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Sequential diagnosis—asking questions, ordering tests, and revising hypotheses as evidence arrives—is closer to real clinical work than the one-shot vignettes used to evaluate most medical AI. This paper argues that the missing element matters, and builds a benchmark around it: 304 difficult clinicopathological cases become interactive encounters, with a clinician or AI requesting each piece of information from a gatekeeper before proposing a diagnosis. The central claim is that orchestrated prompting of a large language model, using a virtual panel of physician roles and cost tracking, beats both off-the-shelf models and practicing physicians on accuracy and cost at the same time. In the strongest configuration, the system reaches roughly 80 percent diagnostic accuracy against about 20 percent for the generalist physicians studied, while ordering tests that cost about 20 percent less than the physicians' work-ups. The reason to care is that if this holds, better diagnostic support may be achievable with today's models, without retraining or new infrastructure.

What carries the argument

The central object is the Sequential Diagnosis Benchmark (SDBench), an interactive encounter in which a diagnostic agent starts from a short case abstract and can ask questions, order tests, or commit to a diagnosis. The load-bearing component inside the benchmark is the Gatekeeper, a language model with access to the full case file that releases findings only when explicitly queried and synthesizes clinically plausible answers when a requested test was not part of the original case write-up; a Judge model scores the final diagnosis against a five-point clinical rubric, and a cost estimator converts each ordered test into CPT-coded dollar amounts. The offered method is MAI-DxO, a model-agnostic orchestrator that simulates a panel of five physician personas, runs a structured chain of debate, updates a differential after each finding, and optionally tracks a test budget before choosing up to three tests per round.

What would settle it

Run a leakage probe: give the Gatekeeper a query for a test whose result is pathognomonic for the true diagnosis in cases where the original write-up never mentions that test, and see whether the synthesized result is systematically diagnostic. A concrete version: ask for a genetic marker or biopsy feature absent from the case text, then have blinded clinicians guess the final diagnosis from the answers; above-chance guessing would show the oracle is leaking ground truth.

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Extended reading notes

Core claim

The paper's central claim is that a language model prompted to play five physician roles—one maintaining a probability-ranked differential, one choosing discriminating tests, one challenging the leading hypothesis, one policing cost, and one checking consistency—outperforms both unassisted frontier models and individual generalist physicians on a sequential diagnostic task, on both accuracy and estimated test cost. The authors state this as a new Pareto frontier: for every level of spending, their orchestrator achieves at least as high accuracy as any off-the-shelf model or physician they compared, and for every accuracy level it spends no more. They also claim the effect is model-agnostic, lifting accuracy by an average of 11 percentage points across models from six model families, and that the gains persist on a held-out set of cases published mostly after training cutoffs, which they take as evidence against memorization.

Load-bearing premise

The load-bearing premise is that the Gatekeeper's synthetic answers for tests not in the original case file behave like real clinical results and do not encode the ground-truth diagnosis; if that fails, the benchmark measures interrogation of an answer-aware oracle rather than real sequential diagnosis.

Editorial extensions

If this is right

  • If the gains hold, diagnostic accuracy of current frontier models can be improved by changing how they are prompted, rather than by training new models.
  • Cost-conscious orchestration can cut test spending by roughly 70 percent relative to an off-the-shelf reasoning model at similar accuracy, which matters in resource-limited settings.
  • The benchmark provides a reusable way to evaluate not just what a model knows but whether it seeks the right information at the right time under explicit cost pressure.
  • The held-out test set result suggests the reported gains are not simply memorized answers from published cases.
  • Orchestration improves weaker models more, so the approach may narrow the gap between weaker and stronger language models on medical reasoning.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Our reading: the benchmark's numbers depend on the Gatekeeper faithfully simulating unreported test results; if synthetic answers leak the final diagnosis in subtle ways, reported accuracy would overstate real-world performance, and a direct leakage audit would settle it.
  • We would expect the accuracy-cost ordering to shift in routine primary care, where most cases are common, whereas the physician comparison studied generalists on unusually hard and rare cases.
  • The same gatekeeper-plus-synthetic-findings design could be reused to generate unlimited interactive practice cases for medical education, an extension the authors note but do not develop.
  • Because costs are based on US CPT pricing, relative rankings may change under other health-system price schedules; absolute dollar figures should not be read as universal.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

1 major / 6 minor

Summary. This paper introduces SDBench, a sequential diagnostic benchmark built from 304 NEJM clinicopathological conference cases, in which a diagnostic agent (human or AI) starts from a brief vignette and iteratively requests history, exam, and test information from a language-model Gatekeeper before committing to a final diagnosis. The Gatekeeper has access to the full case file, including the final diagnosis, and synthesizes findings for tests not mentioned in the original case. The authors also present MAI-DxO, a model-agnostic multi-agent orchestrator that role-plays a panel of physicians, and report that when paired with o3 it achieves 80% accuracy versus 20% for generalist physicians, with cost reductions of 20% versus physicians and 70% versus off-the-shelf o3. Accuracy gains generalize across several model families and persist on a held-out test set of 56 recent cases.

Significance. If the central accuracy and cost claims hold, SDBench would be a valuable step beyond static multiple-choice medical benchmarks, and MAI-DxO would demonstrate that prompt-based orchestration can substantially improve both diagnostic accuracy and cost-efficiency of frontier LLMs. The paper's strengths include a held-out test set of cases published near the model training cutoff, permutation tests for the orchestration gains, physician review of Gatekeeper responses, a physician-validated Judge rubric with reported inter-rater agreement, and a model-agnostic design. These features make the internal comparisons carefully executed. However, the headline claims depend critically on the Gatekeeper's synthetic findings being independent of the ground-truth diagnosis; because the Gatekeeper conditions on that diagnosis, there is a credible leakage channel that could inflate the reported accuracy. Until that channel is controlled, the significance of the results is conditional.

major comments (1)
  1. [Section 2 ('Gatekeeper'), Appendix C, Section 4] The central claim of Section 4 and the abstract—that MAI-DxO reaches 80% accuracy versus 20% for generalist physicians—depends on the assumption that the Gatekeeper's synthetic findings are independent of the ground-truth diagnosis. Section 2 states that the Gatekeeper (o4-mini) has access to the full NEJM CPC case file, including the final diagnosis, and that it 'can synthesize additional case-consistent information for tests not described in the original CPC narrative.' Because the synthesis is conditioned on the final diagnosis, the Gatekeeper can produce pathognomonic or confirmatory results for tests on the correct diagnostic path while never producing the false positives, nondiagnostic results, or false negatives that a real work-up would contain. Appendix C shows synthetic findings (e.g., the perirenal biopsy in the Erdheim-Chester case) that are strongly confirmatory, and the agent interface hides the synthetic/real label. The physician review of 508 responses checks only for human-detectable leakage and cannot control for a systematic positivity bias in synthetic results. Without a diagnosis-blinded control condition (e.g., a Gatekeeper variant that does not receive the final diagnosis, or an analysis of accuracy split by real vs. synthetic test results), the reported Pareto frontier may measure 'interrogation of an answer-aware oracle' rather than real sequential diagnostic ability. This is a load-bearing issue for the headline accuracy and cost claims.
minor comments (6)
  1. [Abstract, Section 4] The abstract quotes 80% accuracy for MAI-DxO with o3, but Section 4 reports 81.9% for the no-budget configuration and 79.9% for a budgeted configuration; please specify which configuration and data split the headline number refers to.
  2. [Figure 7 caption, Section 3.3] In Figure 7, AI agents are evaluated on all 304 cases while physicians are evaluated only on the 56-case test set; the caption should state this asymmetry explicitly, and Figure 9 should be referenced as the primary like-for-like comparison.
  3. [Section 4, Section 5.3] Section 5.3 acknowledges that only generalists were recruited, but the Section 4 statement that MAI-DxO 'outperforms ... physicians at every cost or accuracy level' does not repeat this qualifier; please add the qualifier there.
  4. [Section 2 ('Estimating costs')] The cost model treats all physician visits as $300 regardless of the number of questions asked within a visit; since the number of questions varies widely across agents, this simplification could affect the relative cost comparisons.
  5. [Section 2 ('Judging diagnoses')] The Judge agent is given full access to the case file during adjudication; although inter-rater agreement with physicians is reported for a sample, consider reporting Judge scores on a random sample of diagnoses from all agent types to rule out systematic rubric bias.
  6. [Appendix C, Section 2] Appendix C reveals that synthetic vs. real findings are labeled in the transcript, but the main text of Section 2 does not state that these labels are hidden from agents; please clarify this in the main text.

Circularity Check

1 steps flagged · score 5.0 of 10

The Gatekeeper generates synthetic test findings from a model that has the final diagnosis, so the headline accuracy is partly a measure of eliciting label-conditioned evidence.

  1. other [Section 2, 'Gatekeeper' (SDBench construction); Appendix C (synthetic/real labels hidden from agents)]
    "We implemented the Gatekeeper using a language model (o4-mini) with access to the full NEJM CPC case file, including the final diagnosis. ... we changed the Gatekeeper to return realistic synthetic findings for queries not covered in the original text. These findings are numerically or descriptively consistent with the rest of the case, with no indication that they are synthetic."

    The evidence an agent receives is generated by a model conditioned on the target diagnosis: 'consistent with the rest of the case' means consistent with the known final answer. When an agent orders a test on the correct diagnostic path, the Gatekeeper can return confirmatory, even pathognomonic, results, while off-path tests can be returned as non-diagnostic. Thus the evidential basis for the agent's diagnosis is constructed from the diagnosis itself, rather than from independent patient data. The physician review checked only for explicitly inappropriate responses and 'clues that could leak diagnostic information,' not for a systematic positivity bias in synthetic findings, and Appendix C confirms the real/synthetic labels are hidden from the agents.

full rationale

The paper's orchestration results, cross-model generalization, judge validation, and cost comparisons are empirical and are not defined in terms of the headline metric, so there is no self-citation or definitional circularity in the MAI-DxO derivation itself. However, the benchmark's evidence-generation loop is partially circular: the Gatekeeper is an LLM with access to the full NEJM case file including the final diagnosis, and it synthesizes test results for queries not covered in the original text. Because those synthetic findings are 'consistent with the rest of the case' and the case includes the ground-truth diagnosis, the information used by agents to reach a diagnosis is generated from the very outcome the benchmark measures. This compromises the independence of the evaluation: it is not equivalent to diagnosing from a real, answer-unaware clinical record, and the paper's own Appendix C acknowledges that the synthetic/real distinction is hidden from agents. The physician review of 508 responses is a useful but incomplete check, as it looked for explicit clues rather than systematic confirmatory bias. We therefore score this as partial circularity (5), not as a fully forced or self-citation-driven result.

Assumptions & free parameters 4 free parameters · 4 assumptions · 2 invented entities

The central accuracy and cost results depend on several hand-set thresholds (Judge cutoff, $300 per visit, test limits) and on the Gatekeeper's ability to produce answer-independent synthetic findings. Until code, prompts, and transcripts are released, these are unverifiable design choices.

free parameters (4)
  • Judge correctness threshold = score >= 4 on a 5-point Likert scale
    Chosen by the authors as the cutoff for 'correct'; the reported accuracy numbers depend directly on this hand-set threshold.
  • Cost per physician visit = $300
    Fixed cost assigned to each visit; all cost comparisons use this assumed value.
  • Maximum tests and questions per round = 3 tests, 5 questions
    MAI-DxO design choice that shapes test selection and cost; the ablation in Appendix A shows it affects cost.
  • Gatekeeper and Judge model choices = o4-mini for Gatekeeper, o3 for Judge
    The behavior of both agents depends on the underlying model, which changes what information is surfaced and how diagnoses are graded.
assumptions (4)
  • domain assumption NEJM CPC case files and Gatekeeper-synthesized responses provide a valid proxy for real clinical evidence gathering.
    The entire benchmark and the reported accuracy/cost results rest on this equivalence, stated in Section 2.
  • domain assumption The LLM Judge with a 5-point rubric correctly adjudicates whether a candidate diagnosis matches ground truth.
    Section 2; validated on only 112 physician-graded diagnoses, with kappa of 0.70 and 0.87.
  • domain assumption Held-out test cases published in 2024-2025 are outside the training corpora of the evaluated models.
    Section 3; plausible because of publication dates, but not verified per model.
  • domain assumption CPT-based cost estimation from a single U.S. health system's 2023 price table approximates relative diagnostic costs.
    Section 2; cost comparisons depend on this assumption, and the authors note it is a first-order approximation.
invented entities (2)
  • Information Gatekeeper (LM oracle)
    purpose: Discloses clinical findings only when explicitly queried and synthesizes case-consistent findings for tests not in the original case.
    The Gatekeeper has access to the ground-truth diagnosis and no released transcript or prompt audit is available, so its outputs are not independently verified.
  • MAI-DxO virtual physician personas
    purpose: Five roles (Hypothesis, Test-Chooser, Challenger, Stewardship, Checklist) drive sequential test selection and diagnosis.
    The personas are prompt-based constructs; individual contributions are not validated against external benchmarks or ablations.

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Cite this review

Pith. "Pith review of Sequential Diagnosis with Language Models." pith.science (2026). https://pith.science/paper/YD5S4R6B

@misc{pith2026250622405,
  author       = {Pith},
  title        = {Pith review of: Sequential Diagnosis with Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YD5S4R6B}},
  note         = {Machine review of arXiv:2506.22405}
}
read the original abstract

Artificial intelligence holds great promise for expanding access to expert medical knowledge and reasoning. However, most evaluations of language models rely on static vignettes and multiple-choice questions that fail to reflect the complexity and nuance of evidence-based medicine in real-world settings. In clinical practice, physicians iteratively formulate and revise diagnostic hypotheses, adapting each subsequent question and test to what they've just learned, and weigh the evolving evidence before committing to a final diagnosis. To emulate this iterative process, we introduce the Sequential Diagnosis Benchmark, which transforms 304 diagnostically challenging New England Journal of Medicine clinicopathological conference (NEJM-CPC) cases into stepwise diagnostic encounters. A physician or AI begins with a short case abstract and must iteratively request additional details from a gatekeeper model that reveals findings only when explicitly queried. Performance is assessed not just by diagnostic accuracy but also by the cost of physician visits and tests performed. We also present the MAI Diagnostic Orchestrator (MAI-DxO), a model-agnostic orchestrator that simulates a panel of physicians, proposes likely differential diagnoses and strategically selects high-value, cost-effective tests. When paired with OpenAI's o3 model, MAI-DxO achieves 80% diagnostic accuracy--four times higher than the 20% average of generalist physicians. MAI-DxO also reduces diagnostic costs by 20% compared to physicians, and 70% compared to off-the-shelf o3. When configured for maximum accuracy, MAI-DxO achieves 85.5% accuracy. These performance gains with MAI-DxO generalize across models from the OpenAI, Gemini, Claude, Grok, DeepSeek, and Llama families. We highlight how AI systems, when guided to think iteratively and act judiciously, can advance diagnostic precision and cost-effectiveness in clinical care.

Figures

Figures reproduced from arXiv: 2506.22405 by the authors.

Figure 1
Figure 1. Example of an AI agent solving a sequential-diagnosis reasoning problem. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Multiagent orchestration in the SDBench benchmark. A corpus of NEJM CPC cases [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Participating physicians and models are provided with a case abstract to begin the [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Prompt used for baseline performance estimation. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Overview of the MAI-Dx Orchestrator With input from physicians, we developed a system that emulates a virtual panel of doctors working collaboratively to solve diagnostic cases (MAI-DxO). As shown in [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: Interface developed for physicians to attempt cases from SDBench. [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: Pareto-frontier showing diagnostic accuracy versus average cumulative medical cost [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Accuracy improvements delivered by MAI-DxO (no budget constraints) across [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 9
Figure 9. Figure 9: Pareto frontier curves of MAI-DxO and baseline prompting across validation and [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]
Figure 10
Figure 10. Figure 10: Case level scores of MAI-DxO variants and clinicians across the 56 case test set. All [PITH_FULL_IMAGE:figures/full_fig_p023_10.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

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Reference graph

Works this paper leans on

21 extracted references · 21 canonical work pages · cited by 10 Pith papers

  1. [1]

    Can you describe in your own words the main problems that brought you in today and when each one began? REAL: Primary complaints: progressive dyspnea on exertion for 2 months, dry cough for approximately 6 weeks, orthopnea requiring three pillows, lower- extremity edema, abdominal distention, anorexia, postprandial G A T E K E E P E R 25 nausea, and a 2-m...

    work page

  2. [2]

    Orthopnea developed concurrently

    How have your breathlessness and body swelling changed over time—what was the exact onset, how rapidly did they progress, what makes them better or worse, and how severe are they now? REAL: Dyspnea began about 2 months ago during exertion, progressed over 2 weeks to occur with minimal activity, and now limits routine exercise and sleep. Orthopnea develope...

    work page

  3. [3]

    fast heart rates

    Besides shortness of breath and edema, have you noticed any chest pain, cough, palpitations, fever, night sweats, weight change, abdominal discomfort, change in urine or bowel habits, or any other new or unusual symptoms? REAL: Reports dry cough worse when supine and intermittent palpitations described as “fast heart rates.” Denies chest pain or pressure,...

    work page

  4. [4]

    Please list every prescription medication, over-the-counter drug, vitamin, supplement, or herbal product you take, including dosages and the approximate start dates for each. SYNTHETIC: Metformin 500 mg orally twice daily (started ~5 years ago); calcium carbonate 600 mg orally once daily (started ~2 years ago); daily multivitamin (started ~5 years ago); f...

    work page

  5. [5]

    No implanted devices or indwelling catheters reported

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    work page

  6. [6]

    Have you experienced dryness or grittiness in your eyes, enlarged or painful salivary glands, difficulty swallowing dry foods, or recurrent dental cavities? SYNTHETIC: Patient reports a 2-month history of dry mouth but denies dryness or grittiness in the eyes, salivary gland enlargement or pain, difficulty swallowing dry foods, or recurrent dental cavities

    work page

  7. [7]

    She denies morning stiffness beyond her usual baseline, muscle weakness, skin rashes, Raynaudʼs phenomenon, photosensitivity, or swelling or pain in the parotid glands

    Do you have joint pains, morning stiffness, muscle weakness, skin rashes (especially on the face or hands), Raynaudʼs phenomenon, photosensitivity, or episodes of swelling or pain in the parotid glands? SYNTHETIC: Patient reports ongoing hand-joint stiffness with swan-neck deformities similar to her baseline rheumatoid arthritis. She denies morning stiffn...

    work page

  8. [8]

    SYNTHETIC: Patient works in an office, lives with her husband in a wooded area of New England, and has no recent travel

    Please describe your occupation, hobbies, recent travel, home environment (molds, new construction), and any exposures to chemicals, dusts, animals, or infectious contacts. SYNTHETIC: Patient works in an office, lives with her husband in a wooded area of New England, and has no recent travel. She reports no known exposures to molds, new construction, chem...

    work page

Show all 21 references
  1. [9]

    Do you smoke cigarettes or use tobacco, drink alcohol, or use recreational drugs? If yes, how much and for how long? REAL: Patient denies tobacco or recreational drug use and reports drinking three glasses of wine per week

  2. [10]

    Does anyone in your family have autoimmune diseases, heart or kidney disease, cancer, or similar symptoms of swelling and shortness of breath? REAL: Family history is notable for coronary artery disease and colon cancer in her father, systemic lupus erythematosus in her son, a...

  3. [11]

    Desmopressin was prescribed 8 days ago after endocrinology confirmed central diabetes insipidus

    Can you estimate your daily urine volume (for example, number of times you void and approximate volume each time) and explain why desmopressin was prescribed 8 days ago—was a diagnosis such as diabetes insipidus made? SYNTHETIC: Patient reported approximately 8–10 voids per da...

  4. [12]

    Have you had any imaging studies (CT, MRI, ultrasound) of the chest, abdomen, or pelvis in the past few months? If so, what findings were reported, especially regarding the pancreas, kidneys, aorta, or lymph nodes? REAL: Underwent CT of the abdomen and pelvis 8 days ago showin...

  5. [13]

    Have you noticed any abdominal or back pain, flank discomfort, early satiety, jaundice, change in stool color, itchy skin, or episodes of pancreatitis? SYNTHETIC: Denies abdominal pain, back or flank discomfort, early satiety, jaundice, changes in stool color, pruritus, or any...

  6. [14]

    Have you ever been told you had elevated liver enzymes, abnormal kidney function, or protein in your urine? If yes, what were the results and when? SYNTHETIC: Denies any prior notification of elevated liver enzymes, abnormal kidney function, or proteinuria

  7. [15]

    Lead-Dx Summary 1 IgG4-related disease 2 Erdheim-Chester disease 3 Infiltrative lymphoma

    Do you currently have or have you ever had painless swellings or G A T E K E E P E R 28 lumps around your eyes, salivary glands (parotid/submandibular), thyroid, or any other soft-tissue mass noticed on exam or imaging? SYNTHETIC: Denies any painless swellings or lumps around ...

  8. [16]

    Serum IgG4 concentration SYNTHETIC: Serum IgG4 concentration: 62 mg per deciliter (reference range 4–86 mg per deciliter)

  9. [17]

    Complete blood count with differential & basic metabolic panel REAL: White-cell count: 7150 per µl (normal); differential count: normal; sodium: 139 mmol/L; potassium: 4.9 mmol/L; chloride: 105 mmol/L; carbon dioxide: 23 mmol/L; urea nitrogen: 13 mg/dL; creatinine: 0.50 mg/dL;...

  10. [18]

    Decisive test: Perirenal soft-tissue biopsy for histology/IHC—foamy CD68+ CD1a− histiocytes would prove Erdheim-Chester

    ESR and CRP REAL: Erythrocyte sedimentation rate: 70 mm/hr; C-reactive protein: 47.1 mg/L G A T E K E E P E R Lead-Dx Summary 1 Erdheim-Chester disease 2 Infiltrative lymphoma 3 IgG4-related disease. Decisive test: Perirenal soft-tissue biopsy for histology/IHC—foamy CD68+ CD1...

  11. [19]

    Thin- walled vessels and scattered lymphocytes are present; no granulomas or giant cells are seen

    CT-guided perirenal (or periaortic) soft-tissue core biopsy with histopathology and immunohistochemistry (CD68, CD1a, S100, IgG4, factor XIIIa) SYNTHETIC: Histologic sections of core biopsy show a proliferation of histiocytoid cells with small ovoid nuclei and abundant eosinop...

  12. [20]

    Joint spaces are preserved

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  13. [21]

    Rapid molecular assay on formalin-fixed paraffin-embedded tissue confirms the presence of a BRAF V600E/D mutation

    BRAF V600E mutation analysis on biopsy tissue (PCR or IHC) REAL: Immunohistochemical staining for BRAF V600E shows diffuse positivity in lesional histiocytes. Rapid molecular assay on formalin-fixed paraffin-embedded tissue confirms the presence of a BRAF V600E/D mutation. G A...

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Reviewed August 6, 2026 · model on record in the stance chip above.