Toward Accurate Deep Learning-Based Prediction of Ki67, ER, PR, and HER2 Status From H&E-Stained Breast Cancer Images
Bibliographic record
Abstract
Despite improvements in machine learning algorithms applied to digital pathology, only moderate accuracy, to predict molecular information from histology alone, has been achieved so far. One of the obstacles is the lack of large data sets to properly train machine learning models. We therefore built a data set of 185,538 breast cancer (BC) including hematoxylin and eosin (H&E) and associated immunohistochemistry (IHC) images of the proliferative marker Ki67, estrogen receptor (ER), progesterone receptor (PR), and the human epidermal growth factor receptor 2 (HER2). Optimal registration of H&E and IHC pairs was achieved. Ki67, ER, and PR IHC labels, to be predicted, were extracted from IHC assays using image analysis. These labels were ordinaly classified with incremental thresholds (cumulative logit models with balanced and partial proportional odds). HER2 label was determined as follows: positive if tumor IHC 3+ pattern is identified and otherwise negative. Cases with IHC equivocal score (2+) were excluded. A vision transformer (ViT)-based pipeline, trained with this data set, achieved prediction performance of 90% in terms of area under the curve (AUC) of the receiver operating characteristic (ROC) curves. ViT outperformed the weakly supervised clustering-constrained attention multiple instance learning (CLAM) which was developed to automatically identify subregions of high diagnostic value in whole slide. As a first step to "explain" artificial intelligence (AI), we evaluated the ability of both classifiers to localize these high diagnostic value subregions by inspecting their respective "attention" heat-maps. Despite high ViT AUC-ROC results, heat-maps do not obviously match areas of high diagnostic value subregions; it might however provide direction for future work to improve AI attention within whole slide images. Our proposed data set is publicly available.
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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".