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

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

For each seed, to provide an accurate assessment of RSF internal validity, we divided 30 times the learning dataset into training and internal validation datasets (ratio 75:25) with stratified random sampling for stratification factors (see e Table 4 in Supplement 1 in Zamboglou et al [15]), allowing the use of common attributes in the data to form strata before sampling, resulting in a more representative and general sample. Each model resulted from an RSF that was grown using 1000 trees.

Ali Janbain, Andrea Farolfi, Armelle Guenegou-Arnoux, Louis Romengas, Sophia Scharl, Stefano Fanti, Francesca Serani, Jan C Peeken, Sandrine Katsahian, Iosif Strouthos, Konstantinos Ferentinos, Stefan A Koerber, Marco E Vogel, Stephanie E Combs, Alexis Vrachimis, Alessio Giuseppe Morganti, Simon KB Spohn, Anca-Ligia Grosu, Francesco Ceci, Christoph Henkenberens, Stephanie GC Kroeze, Matthias Guckenberger, Claus Belka, Peter Bartenstein, George Hruby, Louise Emmett, Ali Afshar Omerieh, Nina-Sophie Schmidt-Hegemann, Lucas Mose, Daniel M Aebersold, Constantinos Zamboglou, Thomas Wiegel, Mohamed Shelan

JMIR Cancer 2024;10:e60323

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