Development and validation of a prognostic AI biomarker using multi-modal deep learning with digital histopathology in localized prostate cancer on NRG Oncology phase III clinical trials.
Bibliographic record
Abstract
222 Background: Prognostication in localized prostate cancer is reliant on non-specific tools, an issue that leads to the over- and under-treatment of patients. Various tissue-based molecular biomarkers have attempted to fill this unmet need, but most lack prospective randomized trial validation. Herein, we train and validate prognostic biomarkers in localized prostate cancer using five phase III randomized trials, by leveraging multi-modal deep learning on digital histopathology. Methods: Histopathology image data was generated from pre-treatment biopsy slides in five NRG Oncology phase III randomized radiotherapy prostate cancer trials (RTOG 9202, 9408, 9413, 9910, and 0126). The trials were randomly split into training (80%) and validation (20%) cohorts. A multi-modal artificial intelligence (MMAI) architecture was developed to take clinicopathologic and image-based (histopathology) data as input and predict binary outcomes. Using this architecture, various models were trained to predict relevant clinical endpoints: biochemical recurrence (BCR), distant metastasis (DM), prostate cancer-specific survival (PCaSS), and overall survival (OS). These models were then validated for measures of prognostic discrimination using the time-based area under the curve (AUC) method. Results: Clinicopathologic and histopathology image data was available for 5,654 of 7,957 eligible patients (71.1%), yielding 16.1 TB of data from 16,204 histopathology slides of pretreatment biopsy samples. After training the models, locking them, and evaluating them on the validation cohort, we found that the MMAI prognostic model had superior discrimination compared to the NCCN model (PSA, T-stage, and Gleason score) for 5-year DM (AUC of 0.84 vs 0.73), 5-year BCR (AUC of 0.69 vs 0.58), 10-year PCaSS (AUC of 0.79 vs 0.66), and 10-year OS (AUC of 0.65 vs 0.58). Within each of the individual trials in the validation cohort, the MMAI-model had superior performance compared to NCCN risk groups for all clinical endpoints. Conclusions: This represents the first ever development and validation of prognostic biomarkers in localized prostate cancer using multiple large phase III clinical trials. We have successfully validated that our MMAI-prognostic biomarkers are superior to standard clinical and pathologic variables in identifying future BCR, DM, PCaSS, and OS. This massively scalable technology is feasible and can help personalize the management of prostate cancer patients. Funding: This project was supported by grants U10CA180868 (NRG Oncology Operations), U10CA180822 (NRG Oncology SDMC), UG1CA189867 (NCORP), U24CA196067 (NRG Specimen Bank) from the National Cancer Institute (NCI).
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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 | 0.022 | 0.017 |
| 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 | 0.001 |
| 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 | 0.000 |
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".