A Multimodal Artificial Intelligence Model to Guide Use of Whole-Pelvic Radiation Therapy in Patients with Localized Prostate Cancer: Exploratory Analysis of RTOG 9413

Background/Objectives: This study explored whether a multimodal artificial intelligence (MMAI) model integrating digitized histopathology and clinical features can identify prostate cancer patients who may benefit from neoadjuvant hormonal therapy (NHT) and whole-pelvic radiotherapy (WPRT). Methods: This secondary analysis of NRG/RTOG 9413 included NHT-treated patients with digitized biopsy slides and clinical data who were not part of the MMAI model optimization. A previously validated MMAI model estimated long-term risk, and Fine-Gray models evaluated interactions between MMAI-derived scores and the radiation field (WPRT vs. prostate-only RT [PORT]) for biochemical failure (BF), chosen over progression-free survival because of extended follow-up and distant metastasis (DM), with subgroup analyses by predefined MMAI strata. Results: Among 81 eligible patients, the MMAI-by-treatment interaction for BF did not confirm a differential effect (p = 0.30). Therefore, subgroup findings should be interpreted as descriptive and hypothesis-generating. Nevertheless, the magnitude effect of WPRT was numerically greater in the MMAI high-risk subgroup (5-yr: 41% vs. 79%; 10-yr: 47% vs. 79%; aHR 0.35 [0.14–0.86]) than in the low–intermediate group (5-yr: 18% vs. 33%; 10-yr: 44% vs. 57%; aHR 0.66 [0.29–1.48]). Conclusions: Although no statistically significant treatment-by-MMAI interaction was demonstrated, these findings are hypothesis-generating and support further investigation of MMAI approaches for guiding WPRT in NRG/RTOG 0534, 0924, GETUG-01, and POP-RT trials.