Predicting cancer content in tiles of lung squamous cell carcinoma tumours with validation against pathologist labels
Bibliographic record
Abstract
A growing body of research is using deep learning to explore the relationship between treatment biomarkers for lung cancer patients and cancer tissue morphology on digitized whole slide images (WSIs) of tumour resections. However, these WSIs typically contain non-cancer tissue, introducing noise during model training. As digital pathology models typically start with splitting WSIs into tiles, we propose a model that can be used to exclude non-cancer tiles from the WSIs of lung squamous cell carcinoma (SqCC) tumours. We obtained 116 WSIs of tumours from 35 different centres from the Cancer Genome Atlas. A pathologist completed or reviewed cancer contours in four regions of interest (ROIs) within each WSIs. We then split the ROIs into tiles labelled with the percentage of cancer tissue within them and trained VGG16 to predict this value, and then we calculated regression error. To measure classification performance and visualize the classification results, we thresholded the predictions and calculated the area under the receiver operating characteristic curve (AUC). The model's median regression error was 4% with a standard deviation of 35%. At a cancer threshold of 50%, the model had an AUC of 0.83. False positives tended to be in tissues that surround cancer, tiles with <50% cancer, and areas with high immune activity. False negatives tended to be microtomy defects. With further validation for each specific research application, the model we describe in this paper could facilitate the development of more effective research pipelines for predicting treatment biomarkers for lung SqCC. • CNN can predict cancer content in lung SqCC tumor tiles using VGG16. • Validated on multi-center dataset, achieving AUC of 0.83 for cancer classification. • Single whole slide demo processed in ∼1/9th time of a pathologist, AUC = 0.85. • False positives are common near cancer edges and in high immune activity zones. • Model could aid biomarker research, with consistent and explainable errors.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".