Patient-level data meta-analysis of a multi-modal artificial intelligence (MMAI) prognostic biomarker in high-risk prostate cancer: Results from six NRG/RTOG phase III randomized trials.
Bibliographic record
Abstract
299 Background: Recently, an MMAI prognostic biomarker, ArteraAI Prostate, was trained and validated in localized prostate cancer to more accurately risk stratify patients for multiple endpoints compared to NCCN risk groups (Esteva et al., 2022). Prognostication within an NCCN risk group remains clinically important given the multiple treatment decisions required within each risk group (e.g., radiotherapy dose or hormone therapy use). Herein, we validated the MMAI biomarker in high-risk prostate cancer where an increasing number of therapeutic decisions is required. Methods: This study leveraged histopathology image and clinical data from patients with at least one high-risk feature (HRF; cT3-cT4, Gleason 8-10, PSA > 20 ng/mL, primary Gleason pattern 5) from six NRG/RTOG phase III randomized trials (n=1,088). Patients from two trials not part of the initial MMAI biomarker training/validation (RTOG 0521 [n=344] and 9902 [n=318]) and the MMAI validation cohort (RTOG 9202, 9408, 9413, and 9910 [n=426]) were included. Fine-Gray, cumulative incidence, and time dependent area under the curve (tdAUC) analyses were performed for time to distant metastasis (DM) and prostate cancer-specific mortality (PCSM) for standard clinicopathologic variables (age, PSA, Gleason score, T-stage, number of HRFs) and the MMAI model, as a continuous score (per standard deviation increase) and categorically by quartile. Death from other causes were treated as competing risks. Results: The analyzed cohort had a median follow-up of 10.4 years. Median PSA was 21 ng/mL, 60% had Gleason 8-10 disease, 37% had cT3-T4 disease, and 20% were African American. On univariable analysis, the MMAI model was significantly associated with DM (subdistribution hazard ratio [sHR] 2.05, 95% CI 1.74-2.43, p<0.001) and PCSM (sHR 2.04, 95% CI 1.73-2.42, <0.001). On multivariable analysis, the MMAI model, adjusting for either age, PSA, Gleason score, T-stage, or number of HRFs, was the only variable significantly associated with DM. TdAUC was highest for the MMAI biomarker for both 5-year DM (0.71), compared to PSA (0.56), Gleason score (0.61), T-stage (0.63), or number of HRFs (0.64), and for 5-year PCSM (0.75), compared to clinicopathologic variables (range 0.53-0.63). The estimated 10-year DM and 15-year PCSM rates for MMAI quartile 1 vs 4 were 8% vs 31% and 8% vs 34%, respectively. Conclusions: Our novel MMAI prognostic biomarker was successfully validated across six phase III randomized trials with long-term follow-up to be independently prognostic over standard clinical and pathologic variables for men with high-risk prostate cancer. Despite all patients having high-risk disease, the MMAI biomarker identified those with highly variable risks for DM and PCSM. This tool can help enable personalized, shared decision making for patients and providers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.058 | 0.288 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".