TY  - JOUR
AU  - Janbain, Ali
AU  - Farolfi, Andrea
AU  - Guenegou-Arnoux, Armelle
AU  - Romengas, Louis
AU  - Scharl, Sophia
AU  - Fanti, Stefano
AU  - Serani, Francesca
AU  - Peeken, Jan C
AU  - Katsahian, Sandrine
AU  - Strouthos, Iosif
AU  - Ferentinos, Konstantinos
AU  - Koerber, Stefan A
AU  - Vogel, Marco E
AU  - Combs, Stephanie E
AU  - Vrachimis, Alexis
AU  - Morganti, Alessio Giuseppe
AU  - Spohn, Simon KB
AU  - Grosu, Anca-Ligia
AU  - Ceci, Francesco
AU  - Henkenberens, Christoph
AU  - Kroeze, Stephanie GC
AU  - Guckenberger, Matthias
AU  - Belka, Claus
AU  - Bartenstein, Peter
AU  - Hruby, George
AU  - Emmett, Louise
AU  - Omerieh, Ali Afshar
AU  - Schmidt-Hegemann, Nina-Sophie
AU  - Mose, Lucas
AU  - Aebersold, Daniel M
AU  - Zamboglou, Constantinos
AU  - Wiegel, Thomas
AU  - Shelan, Mohamed
PY  - 2024
DA  - 2024/9/20
TI  - A Machine Learning Approach for Predicting Biochemical Outcome After PSMA-PET–Guided Salvage Radiotherapy in Recurrent Prostate Cancer After Radical Prostatectomy: Retrospective Study
JO  - JMIR Cancer
SP  - e60323
VL  - 10
KW  - cancer
KW  - oncologist
KW  - metastases
KW  - prostate
KW  - prostate cancer
KW  - prostatectomy
KW  - salvage radiotherapy
KW  - PSMA-PET
KW  - prostate-specific membrane antigen–positron emission tomography
KW  - prostate-specific membrane antigen
KW  - PET
KW  - positron emission tomography
KW  - radiotherapy
KW  - radiology
KW  - radiography
KW  - machine learning
KW  - ML
KW  - artificial intelligence
KW  - AI
KW  - algorithm
KW  - algorithms
KW  - predictive model
KW  - predictive models
KW  - predictive analytics
KW  - predictive system
KW  - practical model
KW  - practical models
KW  - deep learning
AB  - 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 biochemical failure, comparing it with a Cox model, and assessing predictive accuracy over time. Multinational cohort data will validate the model’s performance, aiming to improve clinical management of recurrent prostate cancer. Methods: This multicenter retrospective study collected data from 13 medical facilities across 5 countries: Germany, Cyprus, Australia, Italy, and Switzerland. A total of 1029 patients who underwent sRT following PSMA-PET–based assessment for PSA persistence or recurrence were included. Patients were treated between July 2013 and June 2020, with clinical decisions guided by PSMA-PET results and contemporary standards. The primary end point was freedom from biochemical failure, defined as 2 consecutive PSA rises >0.2 ng/mL after treatment. Data were divided into training (708 patients), testing (271 patients), and external validation (50 patients) sets for machine learning algorithm development and validation. RSF models were used, with 1000 trees per model, optimizing predictive performance using the Harrell concordance index and Brier score. Statistical analysis used R Statistical Software (R Foundation for Statistical Computing), and ethical approval was obtained from participating institutions. Results: Baseline characteristics of 1029 patients undergoing sRT PSMA-PET–based assessment were analyzed. The median age at sRT was 70 (IQR 64-74) years. PSMA-PET scans revealed local recurrences in 43.9% (430/979) and nodal recurrences in 27.2% (266/979) of patients. Treatment included dose-escalated sRT to pelvic lymphatics in 35.6% (349/979) of cases. The external outlier validation set showed distinct features, including higher rates of positive lymph nodes (47/50, 94% vs 266/979, 27.2% in the learning cohort) and lower delivered sRT doses (<66 Gy in 57/979, 5.8% vs 46/50, 92% of patients; P<.001). The RSF model, validated internally and externally, demonstrated robust predictive performance (Harrell C-index range: 0.54-0.91) across training and validation datasets, outperforming a previously published nomogram. Conclusions: The developed RSF model demonstrates enhanced predictive accuracy, potentially improving patient outcomes and assisting clinicians in making treatment decisions. 
SN  - 2369-1999
UR  - https://cancer.jmir.org/2024/1/e60323
UR  - https://doi.org/10.2196/60323
DO  - 10.2196/60323
ID  - info:doi/10.2196/60323
ER  -