Deep learning analysis of hematoxylin and eosin-stained benign breast biopsies to predict future invasive breast cancer
Bibliographic record
Abstract
BACKGROUND: Benign breast disease is an important risk factor for breast cancer development. In this study, we analyzed hematoxylin and eosin-stained whole-slide images from diagnostic benign breast disease biopsies using different deep learning approaches to predict which individuals would subsequently developed breast cancer (cases) or would not (controls). METHODS: We randomly divided cases and controls from a nested case-control study of 946 women with benign breast disease into training (331 cases, 331 control individuals) and test (142 cases, 142 control individuals) groups. We employed customized VGG-16 and AutoML machine learning models for image-only classification using whole-slide images, logistic regression for classification using only clinicopathological characteristics, and a multimodal network combining whole-slide images and clinicopathological characteristics for classification. RESULTS: Both image-only (area under the receiver operating characteristic curve [AUROC] = 0.83 [SE = 0.001] and 0.78 [SE = 0.001] for customized VGG-16 and AutoML models, respectively) and multimodal (AUROC = 0.89 [SE = 0.03]) networks had high discriminatory accuracy for breast cancer. The clinicopathological-characteristics-only model had the lowest AUROC (0.54 [SE = 0.03]). In addition, compared with the customized VGG-16 model, which performed better than the AutoML model, the multimodal network had improved accuracy (AUROC = 0.89 [SE = 0.03] vs 0.83 [SE = 0.02]), sensitivity (AUROC = 0.93 [SE = 0.04] vs 0.83 [SE = 0.003]), and specificity (AUROC = 0.86 [SE = 0.03] vs 0.84 [SE = 0.003]). CONCLUSION: This study opens promising avenues for breast cancer risk assessment in women with benign breast disease. Integrating whole-slide images and clinicopathological characteristics through a multimodal approach substantially improved predictive model performance. Future research will explore deep learning techniques to understand benign breast disease progression to invasive breast cancer.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".