Prediction of distant melanoma recurrence from primary tumor digital H&E images using deep learning.
Bibliographic record
Abstract
9577 Background: Patients with resected melanoma are at high risk for systemic recurrence. Quantifying the risk of recurrence can help identify the need for adjuvant immunotherapies, and accelerate the acquisition of survival statistics in clinical trials. Methods: 75 patients with stages I, II, and III melanoma seen at Columbia University Medical Center between 2001 and 2014 and The Icahn School of Medicine at Mount Sinai between 2000 and 2010 were included based on availability of tissue and 24 months of clinical follow-up. Images were scanned into tiff files (Aperio Biosystems). Deep neural net (DNN) architecture was designed consisting of convolutional and recurrent neural networks (CNN, RNN). Using QuPath open source software for nuclear segmentation and cell classification, we generated cell location, density, and clustering features to identify tissue areas for training of network parameters. DNN analyzes image and feature information locally within an H&E image, generating a prediction vote per region, and votes are averaged. Results: The model was validated on two independent external sets of stage I-III primary melanomas. Cohort 1 (Yale Medical Center), had n = 86 patients, of whom 49 were alive or had no evidence of disease at death (no DMR) and 37 died from melanoma. The second set, Cohort 2 (Geisnger Health Systems), had n = 29 patients, 15 without DMR and 14 with DMR. Prediction scores correlated with DMR status in both sets (AUC = 0.94 and 0.77 for Cohorts 1 and 2, respectively). A multivariable Cox proportional hazard model showed DNN recurrence prediction to be an independent prognostic factor for both Cohort 1 (HR = 2.54, 95% CI: 1.54-4.19, p = .0004***) and Cohort 2 (HR = 8.43, 95% CI: 2.58-27.51, p = .001**). Conclusions: We designed a DNN for quantitative prediction of melanoma recurrence from a H&E stained tissue. The prediction score warrants further study in larger patient cohorts and may constitute a novel digital pathology tool for the selection of melanoma patients for adjuvant immunotherapy. [Table: see text]
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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 | 0.001 |
| 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) | 0.002 | 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".