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Record W3151903848 · doi:10.1093/jnci/djab055

RE: Advanced Breast Cancer Definitions by Staging System Examined in the Breast Cancer Surveillance Consortium

2021· letter· en· W3151903848 on OpenAlexaff
Etta D. Pisano, Constantine Gatsonis, Joseph A. Sparano, Melissa A. Troester, Martin J. Yaffe, Elodia B. Cole, Mitchell D. Schnall

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2021
Typeletter
Languageen
FieldComputer Science
TopicAI in cancer detection
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersNational Institute of Environmental Health SciencesNational Cancer InstituteECOG-ACRIN Cancer Research Group
KeywordsBreast cancerMedicineCancerOncologyMedical physicsRadiologyInternal medicine

Abstract

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As investigators for ECOG-ACRIN’s Tomosynthesis Mammographic Imaging Screening Trial (TMIST) trial, we are writing to draw attention to conceptual issues in the outcome definitions and study population in Kerlikowske et al. (1), which limit inferences with respect to the TMIST trial. Kerlikowske et al. (1) converted the TMIST primary outcome definition into a staging system for breast cancer and compared it with other staging systems in association with 5-year breast cancer mortality. However, the primary outcome of TMIST is not a cancer staging system but simply a binary classification of cancers as “advanced” or not. TMIST’s endpoint of advanced cancers was defined to identify cancers that generally require chemotherapy, because although chemotherapy prevents many cancer-related deaths, it is also associated with clinically significant morbidity. Reducing chemotherapy-related morbidity is a valuable goal of breast cancer screening. The authors constructed an ordinal categorical response using elements of the TMIST binary endpoint and performed Receiver Operating Characteristic (ROC) analysis on this ordinal categorical response (1). Although ROC analysis cannot be performed as a binary outcome, the relevance of the ordinal comparison for TMIST is not clear. A more relevant comparison would be conducted with the binary assessment that would result from using an American Joint Committee on Cancer stage as threshold for advanced cancer. For example, if stage IIA or IIB is used as the threshold, as was done by Kerlikowske et al. (1), one can estimate measures of performance that are appropriate for binary tests. The relevant measures for predicting cancer death in 5 years, given at the bottom of Table 2 in the JNCI article for the American Joint Committee on Cancer staging systems and at the bottom of Table 3 for the TMIST definition (1), are combined in Table 1 here. Measures for predicting cancer death in 5 years by AJCC staging systems and by TMIST definitiona AJCC = American Joint Committee on Cancer; AJCC Anat = American Joint Committee on Cancer Anatomic stage; AJCC Progn = American Joint Committee on Cancer Prognostic Pathologic stage; TMIST = Tomosynthesis Mammographic Imaging Screening Trial. Measures for predicting cancer death in 5 years by AJCC staging systems and by TMIST definitiona AJCC = American Joint Committee on Cancer; AJCC Anat = American Joint Committee on Cancer Anatomic stage; AJCC Progn = American Joint Committee on Cancer Prognostic Pathologic stage; TMIST = Tomosynthesis Mammographic Imaging Screening Trial. Another important difference in the outcomes relates to follow-up. The article considers 5-year risk of death, which overrepresents deaths from Estrogen Receptor (ER)-negative cancer and neglects longer term risk of ER+ deaths. The majority of screen-detected breast cancers are ER+, and it is important to address mortality from these cancers. The 2-county trial in Sweden showed that more than 15 years of follow-up was needed to demonstrate the full mortality reduction of breast cancer screening and showed that even at 10 years, fewer than one-half of the averted deaths had been observed (2-4). Finally, Kerlikowske et al. (1) report a large (approximately 60%) proportion of advanced cancer in the Breast Cancer Surveillance Consortium (BCSC) population (Table 3), underscoring that the study population was probably not a pure screening population and likely includes symptomatic women, as commonly seen in practice-based (nontrial) data (5). These important conceptual differences limit the implications of Kerlikowske et al. (1) for TMIST. TMIST is conducted by the ECOG-ACRIN Cancer Research Group (Peter J. O’Dwyer, MD, and Mitchell D. Schnall, MD, PhD, Group Co-Chairs) and supported by the National Cancer Institute of the National Institutes of Health (NIH) (award number: UG1CA189828). Role of the funder: The funder had no role in the writing of the correspondence or decision to submit it for publication. Disclosures: The authors all receive funding from ECOG-ACRIN for their work, but have no other disclosures. Author contributions: Conceptualization: EDP, CG, JS, MAT, MY, MDS. Data Curation: CG. Formal Analysis: CG, MAT, MY. Funding Acquisition: EDP, CG, EC, MDS. Investigation: MAT, MY. Methodology: EDP, CG, MAT, MY, EC, MDS. Project Administration: EDP, CG, MAT, MY, EC, MDS. Resources: EDP, CG, MAT, MY, EC. Software: CG, MY. Supervision: EDP, CG, MAT, MY, EC. Validation: CG. Visualization: CG. Writing, original draft: EDP, CG. Writing, review and edit: EDP, CG, JS, MAT, MY, EC, MDS. Disclaimer: The content is solely the opinion of the authors and does not necessarily represent the views of the NIH. The data underlying this correspondence are available in the correspondence itself.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0260.019
Insufficient payload (model declined to judge)0.0170.018

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.050
GPT teacher head0.291
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractno

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