Enhanced Prognostication of Early Breast Cancer Outcomes Using Deep Learning on Merged Multistain and Multicolor-Depth Tumor Histopathology
Bibliographic record
Abstract
Accurate breast cancer prognosis helps clinicians in selecting optimal treatments, potentially improving patient survival. We tested whether combining deep learning with tumor histopathology images could reliably predict cancer spread. Advantages of this study include the use of deep learning, which often outperforms traditional methods, and the analysis of tumor histopathology images that offer higher resolution than MRI or CT. We also optimized tumor immunostaining by separately staining slides with AE1/AE3 pan-cytokeratin and hematoxylin and eosin (H&E), and evaluated different image color-depth representations (color, grayscale, and binary) for their prognostic utility. The results indicate that grayscale images outperformed both color and binary formats. Grayscale pan-CK-stained images achieved 94.4% accuracy [area under the curve (AUC) = 0.982], while grayscale H&E-stained images reached 85.7% accuracy (AUC = 0.992) on the test set. Notably, training the ResNet-50 model with experimentally augmented data comprising six distinct datasets differing in staining type and color depth, totaling 2,646 images, further enhanced performance, to 100% accuracy (AUC of 1.0). Importantly, our pipeline ensured no contamination between the development and test sets. Deep learning applied to tumor histopathology images of early-stage breast cancer patients using two stains and varying color depths achieved exceptional prognostic accuracy and robust generalization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".