This paper is in the following e-collection/theme issue:
Cancer Prognosis Models and Machine Learning (14) Innovations and Technology in Cancer Care (431) Artificial Intelligence (1421) Machine Learning (1501)Published on in Vol 10 (2024)
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/60323, first published
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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
Authors of this article:
Ali Janbain1 ; Andrea Farolfi2 ; Armelle Guenegou-Arnoux1 ; Louis Romengas1 ; Sophia Scharl3 ; Stefano Fanti2 ; Francesca Serani2 ; Jan C Peeken4 ; Sandrine Katsahian1 ; Iosif Strouthos5 ; Konstantinos Ferentinos5 ; Stefan A Koerber6 ; Marco E Vogel4 ; Stephanie E Combs4 ; Alexis Vrachimis5 ; Alessio Giuseppe Morganti7 ; Simon KB Spohn8 ; Anca-Ligia Grosu8 ; Francesco Ceci9 ; Christoph Henkenberens10 ; Stephanie GC Kroeze11 ; Matthias Guckenberger11 ; Claus Belka12 ; Peter Bartenstein13 ; George Hruby14 ; Louise Emmett15 ; Ali Afshar Omerieh16 ; Nina-Sophie Schmidt-Hegemann17 ; Lucas Mose18 ; Daniel M Aebersold18 ; Constantinos Zamboglou5 ; Thomas Wiegel3 ; Mohamed Shelan18There are no citations yet available for this article according to Crossref .