Development And Validation Of An AI-Based Pathomics Biomarker To Predict Response To First-Line Treatment In Metastatic Colorectal Cancers
Bibliographic record
Abstract
Microsatellite stable metastatic colorectal cancer (mCRC) patients are treated with a "one-fits-all" standard of care chemotherapy.However, responses occur in 20-30% of patients, while 15-20% are refractory.The latter are exposed to side effects that lower their quality of life.Therefore, the aim of this work is to develop a predictive biomarker, based on digital pathology images, that can help stratify patients according to their risk of resistance.Hematoxylin and eosin-stained (H&E) slides of mCRC resections were digitalized.Patches were extracted from the resulting whole slide images and automatically classified as belonging to one out of 9 classes, including the tumoral one, using a deep learning model.Based on texture features, clusters of patches were computed and were used to create the Bag of words (BoWs) that were subsequently used to train several machine learning classifiers.The best performances were obtained by a support vector machine, reaching a negative predictive value (NPV) of 90% (44/49; 95%CI=79-95%) and 82% (14/17; ICBB 122-2 95%CI=63-93%), in the training and validation sets, respectively.From a clinical perspective, NPV is the most relevant metric to ensure that sensitive patients are not wrongly prevented from receiving treatment.These preliminary findings should be further validated on a larger cohort of patients that we are collecting through a multi-institutional study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".