Abstract PO3-19-03: Addressing USPSTF 2023 Identified Key Gaps in Knowledge in Breast Cancer Screening through TMIST (ECOG-ACRIN EA1151) or its Ancillary Studies
Bibliographic record
Abstract
Abstract Background The United States Preventative Services Task Force in their 2023 recommendations identified areas where more research data is needed to inform future breast cancer screening recommendations. Research areas identified are: improve clinicians and patients understanding and evaluation of dense breast tissue on a screening mammogram, benefits and harms of supplemental screening using ultrasound or MRI for women with dense breasts, health outcomes such as rates of breast cancer diagnosis requiring treatment, rates of advanced breast cancers diagnosed across consecutive screening rounds, and breast cancer-associated morbidity and mortality, causes of increased risk of breast cancer mortality in black women across spectrum of stages and biomarker patterns, understand why black women are more likely to be diagnosed with breast cancers that have biomarker patterns that are indicative of poor health outcomes, assess benefits/harms differences between annual and biennial screening for breast cancer in women overall and if there are differences between black and white women, approaches to reduce the risk of overdiagnosis leading to overtreatment of breast lesions found at screening that may not cause morbidity and mortality, natural history of DCIS, and identify prognostic indicators of breast tumors that are unlikely to affect quality or length of life. Methods The ongoing TMIST study, currently with 88,801 asymptomatic women presenting for screening mammography ages 45-74 enrolled out of 128,905, could contribute to scientific evidence to support the above research areas through existing study aims and planned ancillary studies. Supplemental Screening with US and MRI: TMIST PreSCRIB will utilize Machine Learning applied to TMIST and All of Us data, including genetics, mammograms, social determinates of health and other data to recommend individualized screening strategies for women. DxMRI is a study where women will get AbMRI at time of Dx work-up. There are plans to use these examinations plus supplemental screening MRIs performed on TMIST subjects in an enriched reader study to evaluate the role of supplemental screening MRI in moderate risk women. Rates of breast cancer treatment, consecutive screening, morbidity, and mortality: TMIST’s primary outcome is the proportion of women experiencing an advanced breast cancer and needing treatment. TMIST is also collecting information on health care utilization following a cancer diagnosis, including types of treatment given, and costs data from the screening and diagnostic work-up visits, and mortality data for study participants. Increased risk of breast cancer mortality in black women: TMIST is performing PAM50 plus p53 status, immune profile, DNA repair phenotype, and 21-gene recurrence assay on all breast cancers and a subset of benign tissue. Blood and buccal smears might also help address this issue. Ongoing work, funded by the Susan B. Komen Foundation, focuses on improving Black participation in TMIST Biorepository (currently about 45% participation of the 21% of TMIST US black subjects). Surveys are planned on perceived racism and social determinates of health as part of DxMRI Study. Screening Frequency: We are developing a collaboration with the UK-based clinical trial PROSPECTS to compare rates of all cancers and advanced cancers for annual, biennial, and 3-year screening. Overdiagnosis, natural history of DCIS, prognostic indicators of breast tumors not impacting quality of life: PRoGram- will use radiomics, genomics and pathomics to develop a greater understanding of the variability of the non-advanced cancers diagnosed in the TMIST population, including DCIS. It is hoped that this model will provide greater understanding of the risk of poor outcomes for women diagnosed with lower risk cancers, including DCIS. Citation Format: Etta Pisano, Constantine Gatsonis, Mitchell Schnall, Melissa Troester, Elodia Cole, Jean Cormack, Ilana Gareen, Martin Yaffe, Laura Collins, Amarinthia Curtis, Ruth Carlos, Kathy Miller, Christopher Comstock. Addressing USPSTF 2023 Identified Key Gaps in Knowledge in Breast Cancer Screening through TMIST (ECOG-ACRIN EA1151) or its Ancillary Studies [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-19-03.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.206 | 0.316 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.017 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".