Development and Validation of a Multimodal Artificial Intelligence–derived Digital Pathology–based Biomarker Predicting Metastasis Among Patients with Biochemical Recurrence After Radical Prostatectomy in NRG/RTOG Trials
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Biochemical recurrence (BCR) after radical prostatectomy (RP) is a heterogeneous disease state in prostate cancer with multiple treatment options. Improved risk stratification could enable more personalized decision-making. We developed and validated a digital pathology-based multimodal artificial intelligence (MMAI) model to predict outcomes in post-RP BCR patients undergoing salvage therapy. METHODS: An MMAI model was trained to predict distant metastasis (DM) using prostate histopathology image features and clinical variables (pathologic grade group, pathologic T stage, prostate-specific antigen level before salvage radiotherapy [SRT], age, and surgical margin). The locked model was validated in 533 patients from NRG/RTOG 9601 and 0534 treated with SRT ± hormone therapy (HT), using Cox regression and time-dependent area under the receiver operating characteristic curve. KEY FINDINGS AND LIMITATIONS: With a median follow-up of 9.3 yrs, MMAI score was significantly associated with DM (subdistribution hazard ratio = 2.17 per standard deviation [95% confidence interval 1.65-2.85]; p < 0.001) and remained independently prognostic after adjusting for clinical variables and treatment. The 10-yr time-dependent area under the receiver operating characteristic curve for MMAI was 0.74 compared with 0.68 for a clinical nomogram. Binary risk categorization demonstrated higher 10-yr DM incidence in the MMAI high-risk (25%) than in the low-risk (8.8%) group. The absolute reduction in 10-yr DM incidence with HT plus SRT versus SRT alone was 21% in the high-risk group versus 2.5% in the low-risk group. Limitations include the use of archived trial cohorts. CONCLUSIONS AND CLINICAL IMPLICATIONS: The post-RP MMAI model provides individualized risk estimates after SRT ± HT and may support shared decision-making about salvage treatment. External and prospective validation are ongoing.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".