Pith. sign in

REVIEW 5 major objections 6 minor 24 references

Developing an Artificial Intelligence Tool for Personalized Breast Cancer Treatment Plans based on the NCCN Guidelines

T0 review · 5 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that Agentic-RAG, a three-step LLM retrieval pipeline, achieved 100% adherence to NCCN breast cancer treatment guidelines on 24 test questions, with no hallucinations and no incorrect or missing treatments.

desk verdict Sensible RAG engineering, but the 100% adherence claim is unsupported by an unreleased, internally inconsistent evaluation. read the letter →

arxiv 2502.15698 v1 pith:N5LT3BCU submitted 2025-01-06 cs.IR

Abdul M. Mohammed , Iqtidar Mansoor , Sarah Blythe , Dennis Trujillo This is my paper · ORCID
classification cs.IR
keywords breastcancerNCCNguidelinesretrieval-augmentedgenerationagenticRAGGraph-RAGclinicaldecisionsupportLLMevaluationtreatmentrecommendation
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

The paper claims that an AI pipeline called Agentic-RAG can generate breast cancer treatment plans that fully follow the NCCN clinical guidelines on a test set of 24 patient questions. The system selects relevant guideline titles, retrieves the matching structured content, and iteratively checks its own recommendation until it is complete. The authors report 100% adherence, no hallucinations, no incorrect or missing treatments, and exact page-number references for every recommendation. They compare this with a graph-based retrieval system and with ChatGPT-4, which scored lower on adherence and sometimes omitted or added treatments. The claim matters because oncologists must keep up with frequently updated, flowchart-based guidelines, and a transparent, source-referencing tool could make guideline-compliant recommendations easier to verify.

What carries the argument

The central mechanism is the three-step Agentic-RAG loop. A first LLM call selects clinical guideline titles from the patient description and question; a retrieval step pulls the corresponding JSON objects, which were created by converting NCCN flowchart and table pages into structured form; a second LLM call generates a recommendation following a fixed template; and a third LLM call checks whether the recommendation covers all required care aspects, sending the system back for another generation when something is missing. The JSON conversion of guideline flowcharts is what makes the retrieval precise enough to reference exact pages.

What would settle it

Take a set of patient cases that exercise less common branches of the breast cancer flowcharts, convert the same pages to JSON, and have two independent oncologists score the outputs; if any recommendation is judged non-adherent by either oncologist, or if a treatment path present in the original PDF is absent from the JSON, the perfect-adherence claim fails.

Watch

Extended reading notes

Core claim

In the authors' own terms, the central discovery is that breaking the task into three separate LLM steps—title selection, treatment generation, and insufficiency checking—lets an LLM system stay fully aligned with the NCCN breast cancer guidelines. On every one of the 24 questions derived from patient descriptions, Agentic-RAG produced treatment recommendations that a board-certified physician judged as strictly guideline-adherent, with each treatment traceable to a specific NCCN document and page number. The same retrieval and generation machinery also eliminated hallucinations for Graph-RAG and ChatGPT-4 in this evaluation, but only Agentic-RAG recovered every guideline-relevant treatment without adding unnecessary ones. The authors interpret this as evidence that structured, iterative retrieval from a high-quality guideline-derived knowledge base is sufficient to make LLM recommendations both complete and clinically actionable.

Load-bearing premise

The result depends on the assumption that the conversion of NCCN flowchart pages into JSON objects captures every treatment-relevant branch of the guidelines, and that one physician's adherence rating is a reliable gold standard.

Editorial extensions

If this is right

  • If the result holds, a small LLM pipeline can deliver guideline-adherent breast cancer recommendations that a clinician can verify against the source document.
  • Source page references in every recommendation give clinicians a concrete audit trail, which the paper argues is essential for clinical use.
  • The iterative insufficiency check is the component that appears to close the gap: Graph-RAG, which lacks it, missed four treatments on the same questions.
  • The authors state the method is intended to extend to other cancer types, since NCCN guidelines share the same flowchart structure.

Reading between the lines

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

  • The reported 100% is tied to the completeness of the JSON conversion; a missing pathway in that conversion would make a wrong answer look correct, because the LLM can only retrieve what was captured.
  • The strongest reading of the reported numbers is limited to the 24 questions actually tabulated; the manuscript also describes a larger 16-patient-by-4-question design, and the difference between those counts is not reconciled in the text.
  • A single board-certified physician's judgment is the gold standard here; a second oncologist could plausibly score borderline recommendations differently, so the headline number is as fragile as that judgment is shared.
  • Because the reference mechanism is exact page numbers, the system could be extended to automatically flag when guidelines are updated, by re-running retrieval on new guideline versions.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 6 minor

Summary. The paper proposes two retrieval-augmented generation systems, Agentic-RAG and Graph-RAG, which convert NCCN breast cancer guideline flowcharts into JSON and use LLM-based retrieval and generation to produce personalized treatment recommendations. The systems are evaluated against ChatGPT-4 on a small set of curated patient descriptions and adjudicated by a single physician. The central claim is that Agentic-RAG achieves 100% guideline adherence with no hallucinations or incorrect treatments, while Graph-RAG and ChatGPT-4 achieve slightly lower adherence. The manuscript reports favorable results for the proposed systems but provides neither the test data nor enough methodological detail to verify those results.

Significance. If the 100% adherence and zero-hallucination claims were supported by a reproducible and internally consistent evaluation, this would be a practically valuable contribution to clinical decision support and to the literature on retrieval-augmented LLMs for medicine. The paper also has a plausible architecture: separating title selection, JSON retrieval, recommendation generation, and insufficiency checking is a sensible way to ground LLM outputs in structured guideline content. However, the evaluation as presented is not credible: the reported numerators and denominators disagree, the ground-truth standard is the same source from which the system retrieves, and the single-rater, unblinded design does not establish independent clinical validity. The significance of the paper therefore remains contingent on a substantially stronger empirical study.

major comments (5)
  1. [Abstract and Table 1] The reported adherence rates are internally inconsistent. The abstract states that Agentic-RAG achieved 24/24 (100%), Graph-RAG 23/24 (95.8%), and ChatGPT-4 22/24 (91.6%), and that Graph-RAG produced one incorrect treatment and ChatGPT-4 produced two wrong treatments. Table 1, however, lists adherence rates of 100%, 92%, and 94% for the same three systems, and reports 'Wrong treatments 0' for all three. These contradictions are not explained anywhere, and they make it impossible to determine which numbers support the paper's headline claims.
  2. [Sections 3.1 and 4.1] The test-set denominator is ambiguous. Section 3.1 describes 16 patient descriptions with four question variations each, which would yield 64 question-answer pairs, yet the abstract reports 24 total evaluations and Section 4.1 states that Agentic-RAG 'correctly identified all 50 treatment recommendations.' No explanation reconciles these numbers, so the reported 100% adherence cannot be interpreted as a proportion of a well-defined test set.
  3. [Section 3.2] The evaluation relies on a single board-certified physician who is not described as blinded to system identity, and no inter-rater reliability is reported. Because the ground truth is the same NCCN guideline content from which the JSON objects in Section 2.1 were derived, the adherence measure largely reflects whether the retrieval system returns the stored content, not whether the recommendations are clinically correct in an independent sense. The paper's own future-work paragraph (Section 5.1) acknowledges that 'involving more oncologists in the evaluation process' is needed, which reinforces that the current single-rater design is insufficient to support the claimed 100% adherence.
  4. [Section 2.1] The conversion of NCCN flowchart pages into JSON objects is a critical unvalidated step. The paper does not specify the number of pages converted, the extraction protocol, or any quality-assurance check that the JSON faithfully preserves all treatment branches. If the conversion drops or distorts a pathway, both the Agentic-RAG output and the physician's adherence check can agree with each other while diverging from the actual NCCN guideline. No data or validation is provided to rule out this failure mode.
  5. [Sections 2-4] The central empirical claim is not reproducible. The manuscript provides no patient descriptions, question variations, JSON conversion code, prompts, raw system outputs, or reference annotations. Without any of these artifacts, a reader cannot verify a single adherence judgment, check whether the 24 (or 50, or 64) evaluated items are representative, or audit the claimed absence of hallucinations and incorrect treatments. The paper does not even include an appendix with representative examples of the generated treatment plans.
minor comments (6)
  1. [Section 5] There is a typo in the conclusion: 'AgGentic-RAG' should be 'Agentic-RAG'.
  2. [Abstract and throughout] The name of the baseline model is inconsistently written as 'Chat GPT-4' in the abstract and 'ChatGPT-4' in the body and Table 1; please standardize.
  3. [Section 2.2.3 and Figure 1] The generation step is described as using 'o1-preview' in Section 2.2.3, while Figure 1 and the abstract label the system as 'GPT-4o' and Section 2.2.1 says title selection uses GPT-4o. Clarify which model performs each LLM call.
  4. [Section 3.2] The paper states that the evaluation framework 'followed the benchmarking methodology described by Chen et al. [24],' but reference [24] is a JAMA Oncology article about chatbots providing cancer treatment information, not a benchmarking methodology paper. The citation appears inaccurate and should be corrected or the statement removed.
  5. [Section 2.1] The specific NCCN guideline version is not stated in the methodology. Reference [21] gives a version, but the text should explicitly identify the PDF release date and version used for JSON conversion so that the retrieval and evaluation can be reproduced.
  6. [General] The paper does not include a data-availability or conflict-of-interest statement, which is surprising given the authors' corporate affiliation and the claim that the system is ready for clinical workflows; these statements should be added.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the pipeline is built from NCCN-derived JSON and evaluated against the same guidelines, but the paper contains no first-principles derivation whose output reduces to its input.

full rationale

The paper's claimed contribution is an applied retrieval-and-generation system, not a derived quantitative prediction. The systems ingest JSON objects converted from NCCN guideline pages and generate treatment recommendations; the adherence metric compares those recommendations with the same NCCN guidelines. This is a consistency and retrieval-fidelity evaluation rather than an independent clinical validation, and the missing test set, denominator inconsistencies, and single-rater design are serious reproducibility concerns. However, circularity in the sense of an equation reducing to its inputs, a fitted parameter renamed as a prediction, or a load-bearing self-citation chain is not present: no parameter is fitted to the evaluation set, no derived result is defined in terms of itself, and the cited frameworks (Graph-RAG, Chen et al.) are external. The '100% adherence' claim may overstate the strength of a self-referential benchmark, but overstatement is a validity and correctness issue, not a circularity of derivation. Therefore the circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the validity of the NCCN-as-ground-truth evaluation, the faithfulness of the JSON conversion, and the reliability of a single-physician assessment. None of these are independently verified in the paper.

assumptions (4)
  • domain assumption NCCN guidelines are the correct, complete standard for breast cancer treatment selection
    The entire evaluation uses NCCN as ground truth; if guidelines are incomplete or not the best practice, the system's adherence does not imply clinical correctness.
  • domain assumption The JSON conversion of NCCN PDF pages preserves all treatment-relevant information
    Section 2.1 states pages were converted into JSON objects capturing each page's information, but no validation of completeness is provided.
  • domain assumption A single board-certified physician's manual adherence judgment is reliable ground truth
    Section 3.2 describes manual evaluation by one physician; no inter-rater reliability or adjudication is reported.
  • domain assumption The curated patient descriptions are representative of real clinical scenarios
    Section 3.1 says 16 unique patient descriptions were 'initially curated' but gives no selection criteria or external source.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Developing an Artificial Intelligence Tool for Personalized Breast Cancer Treatment Plans based on the NCCN Guidelines." pith.science (2026). https://pith.science/paper/N5LT3BCU

@misc{pith2026250215698,
  author       = {Pith},
  title        = {Pith review of: Developing an Artificial Intelligence Tool for Personalized Breast Cancer Treatment Plans based on the NCCN Guidelines},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/N5LT3BCU}},
  note         = {Machine review of arXiv:2502.15698}
}
read the original abstract

Cancer treatments require personalized approaches based on a patient's clinical condition, medical history, and evidence-based guidelines. The National Comprehensive Cancer Network (NCCN) provides frequently updated, complex guidelines through visuals like flowcharts and diagrams, which can be time consuming for oncologists to stay current with treatment protocols. This study presents an AI (Artificial Intelligence)-driven methodology to accurately automate treatment regimens following NCCN guidelines for breast cancer patients. We proposed two AI-driven methods: Agentic-RAG (Retrieval-Augmented Generation) and Graph-RAG. Agentic-RAG used a three-step Large Language Model (LLM) process to select clinical titles from NCCN guidelines, retrieve matching JSON content, and iteratively refine recommendations based on insufficiency checks. Graph-RAG followed a Microsoft-developed framework with proprietary prompts, where JSON data was converted to text via an LLM, summarized, and mapped into graph structures representing key treatment relationships. Final recommendations were generated by querying relevant graph summaries. Both were evaluated using a set of patient descriptions, each with four associated questions. As shown in Table 1, Agentic RAG achieved a 100% adherence (24/24) with no hallucinations or incorrect treatments. Graph-RAG had 95.8% adherence (23/24) with one incorrect treatment and no hallucinations. Chat GPT-4 showed 91.6% adherence (22/24) with two wrong treatments and no hallucinations. Both Agentic RAG and Graph-RAG provided detailed treatment recommendations with accurate references to relevant NCCN document page numbers.

Figures

Figures reproduced from arXiv: 2502.15698 by the authors.

Figure 1
Figure 1. Agentic RAG-based Cancer Treatment Recommendation System using NCCN Data. The system incorporates three key LLM [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. GraphRAG-based Cancer Treatment Recommendation System using NCCN Data. The system processes NCCN documents [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

24 extracted references · 21 canonical work pages

  1. [1]

    Global Cancer Statistics 2024: GLOBOCAN Estimates of Incidence and Mor- tality Worldwide for 36 Cancers in 185 Countries

    Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjo- mataram I, Jemal A, et al. Global Cancer Statistics 2024: GLOBOCAN Estimates of Incidence and Mor- tality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2024;74(1):29-52

    work page 2024

  2. [2]

    The global burden of cancer attributable to risk factors, 2010–2019: a systematic analysis for the Global Burden of Disease Study 2019

    Cao B, Soerjomataram I, Bray F, Fidler-Benaoudia MM. The global burden of cancer attributable to risk factors, 2010–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2023;401(10376):563-585

    work page 2010

  3. [3]

    NCCN Guidelines for Breast Cancer

    National Comprehensive Cancer Network. NCCN Guidelines for Breast Cancer. Available from: https://www.nccn.org/professionals/physician gls/default.aspx#breast

    work page

  4. [4]

    Artificial intelligence in healthcare

    Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018 Oct;2(10):719- 731

    work page 2018

  5. [5]

    Challenges and perspectives in use of artificial intelligence to support treatment recom- mendations in clinical oncology

    Duwe G, Mercier D, Wiesmann C, Kauth V , Moench K, Junker M, et al. Challenges and perspectives in use of artificial intelligence to support treatment recom- mendations in clinical oncology. Cancer Medicine. 13

    work page

  6. [6]

    Language Models are Few-Shot Learners

    Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, et al. Language Models are Few-Shot Learners. arXiv preprint arXiv:2005.14165

    arXiv 2005

  7. [7]

    Capacity of ChatGPT to Identify Guideline-Based Treatments for Advanced Solid Tumors

    Schulte B. Capacity of ChatGPT to Identify Guideline-Based Treatments for Advanced Solid Tumors. Cureus. 2023 Apr 21;15(4):e37938

    work page 2023

  8. [8]

    The Consistency and Quality of ChatGPT Responses Compared to Clinical Guide- lines for Ovarian Cancer: A Delphi Approach

    Piazza D, Martorana F, Curaba A, Sambataro D, Vale- rio MR, Firenze A, et al. The Consistency and Quality of ChatGPT Responses Compared to Clinical Guide- lines for Ovarian Cancer: A Delphi Approach. Current Oncology. 2024;31(5):2796-2804

    work page 2024

Show all 24 references
  1. [9]

    Artificial intelligence in medi- cal decision making: challenges for research and prac- tice

    Koopman B, Zuccon G. Artificial intelligence in medi- cal decision making: challenges for research and prac- tice. Med J Aust. 2023;219(6):263-265

    2023

  2. [10]

    Tim Berners-Lee’s Semantic Web

    Kuck G. Tim Berners-Lee’s Semantic Web. South African Journal of Information Management. 2004;6

    2004

  3. [11]

    Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

    Lewis P, Perez E, Piktus A, Petroni F, Karpukhin V , Goyal N, et al. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Adv Neural Inf Pro- cess Syst. 2023;33:9459-9474

    2023

  4. [12]

    Graph Retrieval-Augmented Gen- eration for Large Language Models: A Survey

    Procko T, Ochoa O. Graph Retrieval-Augmented Gen- eration for Large Language Models: A Survey. Avail- able at SSRN: https://ssrn.com/abstract=4895062

  5. [13]

    Optimizing RAG Systems for Technical Support with LLM-based Relevance Feedback and Multi-Agent Patterns

    Fins ˚as M, Maksim J. Optimizing RAG Systems for Technical Support with LLM-based Relevance Feedback and Multi-Agent Patterns. Master’s the- sis, Norwegian University of Science and Technology (NTNU)

  6. [14]

    Improved precision oncology question- answering using agentic LLM

    Das R, Maheswari K, Siddiqui S, Arora N, Paul A, Nanshi J, et al. Improved precision oncology question- answering using agentic LLM. medRxiv

  7. [15]

    From Local to Global: A Graph RAG Ap- proach to Query-Focused Summarization

    Edge D, Trinh H, Cheng N, Bradley J, Chao A, Mody A, et al. From Local to Global: A Graph RAG Ap- proach to Query-Focused Summarization. Microsoft Research

  8. [16]

    Prompt Engineering for Healthcare: Methodologies and Applications

    Wang J, Shi E, Yu S, Wu Z, Ma C, Dai H, et al. Prompt Engineering for Healthcare: Methodologies and Applications. Journal of Biomedical Informatics. 2021;14(8):1

    2021

  9. [17]

    A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applica- tions

    Sahoo P, Singh AK, Saha S, Jain V , Mondal S, Chadha A. A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applica- tions. arXiv:2402.07927

    arXiv

  10. [18]

    Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review

    Chen B, Zhang Z, Langren ´e N, Zhu S. Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review. arXiv:2310.14735

    arXiv

  11. [19]

    NCCN Clinical Practice Guidelines in Oncology: Multiple Myeloma, Version 2.2024

    National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology: Multiple Myeloma, Version 2.2024. Journal of the National Comprehensive Cancer Network. 2023;21(12):1281- 1320

    2024

  12. [20]

    Manage- ment of Immunotherapy-Related Toxicities, Version 1.2024

    National Comprehensive Cancer Network. Manage- ment of Immunotherapy-Related Toxicities, Version 1.2024. Journal of the National Comprehensive Can- cer Network. 2023;21(5.5):556-567

    2024

  13. [21]

    Breast Cancer, Version 3.2024

    National Comprehensive Cancer Network. Breast Cancer, Version 3.2024. Journal of the National Com- prehensive Cancer Network. 2024;22(5):331-339

    2024

  14. [22]

    Introduction to GPT-4o

    OpenAI. Introduction to GPT-4o. OpenAI Cookbook

  15. [23]

    O1-small Model

    OpenAI. O1-small Model. OpenAI

  16. [24]

    Use of Artificial Intelligence Chatbots for Cancer Treatment Informa- tion

    Chen S, Kann BH, Foote MB, et al. Use of Artificial Intelligence Chatbots for Cancer Treatment Informa- tion. JAMA Oncol. 2023;9(10):1459-1462

    2023

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.