A Machine Learning Approach for Predicting Biochemical Outcome After PSMA-PET-Guided Salvage Radiotherapy in Recurrent Prostate Cancer After Radical Prostatectomy: Retrospective Study

Janbain, Ali; Farolfi, Andrea; Guenegou-Arnoux, Armelle; Romengas, Louis; Scharl, Sophia; Fanti, Stefano; Serani, Francesca; Peeken, Jan C; Katsahian, Sandrine; Strouthos, Iosif; Ferentinos, Konstantinos; Koerber, Stefan A; Vogel, Marco E; Combs, Stephanie E; Vrachimis, Alexis; Morganti, Alessio Giuseppe; Spohn, Simon K B; Grosu, Anca-Ligia; Ceci, Francesco; Henkenberens, Christoph

doi:10.5167/uzh-263264

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Janbain, A., Farolfi, A., Guenegou-Arnoux, A., Romengas, L., Scharl, S., Fanti, S., Serani, F., Peeken, J. C., Katsahian, S., Strouthos, I., Ferentinos, K., Koerber, S. A., Vogel, M. E., Combs, S. E., Vrachimis, A., Morganti, A. G., Spohn, S. K. B., Grosu, A.-L., Ceci, F., … et al. (2024). A Machine Learning Approach for Predicting Biochemical Outcome After PSMA-PET-Guided Salvage Radiotherapy in Recurrent Prostate Cancer After Radical Prostatectomy: Retrospective Study. JMIR Cancer, 10, e60323. https://doi.org/10.2196/60323

Abstract

BACKGROUND Salvage radiation therapy (sRT) is often the sole curative option in patients with biochemical recurrence after radical prostatectomy. After sRT, we developed and validated a nomogram to predict freedom from biochemical failure.

OBJECTIVE This study aims to evaluate prostate-specific membrane antigen-positron emission tomography (PSMA-PET)-based sRT efficacy for postprostatectomy prostate-specific antigen (PSA) persistence or recurrence. Objectives include developing a random survival forest (RSF) model for predicting bioc