A Machine Learning Approach for Predicting Biochemical OutcomeAfter PSMA-PET-Guided Salvage Radiotherapy in RecurrentProstate Cancer after Radical Prostatectomy: Retrospective Study

Ali Janbain,Andrea Farolfi,Armelle Guenegou-Arnoux, Louis Romenga,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 K. B. Spohn,Anca-Ligia Grosu,Francesco Ceci,Christoph Henkenberens, Stephanie G. C. Kroeze,Matthias Guckenberge,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）

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Key words

cancer,oncologist,metastases,prostate,prostate cancer,prostatectomy,salvage radiotherapy,PSMA-PET,prostate-specific membrane antigen-positron emission tomography,prostate-specific membrane antigen,PET,positron emissiontomography,radiotherapy,radiology,radiography,machine learning,ML,artificial intelligence,AI,algorithm,algorithms,predictive model,predictive models,predictive analytics,predictive system,practical model,practical models,deep learning

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