Decoding the Prevalent High-Risk Breast Cancers: Demographics, Pathological, Imaging Insights, and Long-Term Outcome
Bibliographic record
Abstract
Objective: To investigate the features and outcomes of breast cancer in high-risk subgroups. Materials and Methods: REB approved an observational study of women diagnosed with breast cancer from 2010 to 2019. Three radiologists, using the BI-RADS lexicon, blindly reviewed mammogram and MRI screenings without a washout period. Consensus was reached with 2 additional reviewers. Inter-rater agreement was measured by Fleiss Kappa. Statistical analysis included Mann-Whitney U, Chi-square tests for cohort analysis, and Kaplan-Meier for survival rates, with a Cox model for comparative analysis using gene mutation as a reference. Results: The study included 140 high-risk women, finding 155 malignant lesions. Significant age differences noted: chest radiation therapy (median age 44, IQR: 37.0-46.2), gene mutation (median age 49, IQR: 39.8-58.0), and familial risk (median age 51, IQR: 44.5-56.0) ( P = .007). Gene mutation carriers had smaller ( P = .01), higher-grade tumours ( P = .002), and more triple-negative ER- ( P = .02), PR- ( P = .002), and HER2- ( P = .02) cases. MRI outperformed mammography in all subgroups. Substantial to near-perfect inter-rater agreement observed. Over 10 years, no deaths occurred in chest radiation group, with no significant survival difference between gene mutation and familial risk groups, HR = 0.93 (95% CI: 0.27, 3.26), P = .92. Conclusion: The study highlights the importance of age and specific tumour characteristics in identifying high-risk breast cancer subgroups. MRI is confirmed as an effective screening tool. Despite the aggressive nature of cancers in gene mutation carriers, early detection is crucial for survival outcomes. These insights, while necessitating further validation with larger studies, advocate for a move toward personalized medical care, strengthening the existing healthcare guidelines.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".