Could Machine Learning Improve the Prediction of Pelvic Nodal Status of Prostate Cancer Patients? Preliminary Results of a Pilot Study. (3rd July 2015) - De Bari, B.; Vallati, M.; Gatta, R.; Simeone, C.; Girelli, G.; Ricardi, U.; Meattini, I.; Gabriele, P.; Bellavita, R.; Krengli, M.; Cafaro, I.; Cagna, E.; et al.
ABSTRACT We tested and compared performances of Roach formula, Partin tables and of three Machine Learning (ML) based algorithms based on decision trees in identifying N+ prostate cancer (PC). 1, 555 cN0 and 50 cN+ PC were analyzed. Results were also verified on an independent population of 204 operated cN0 patients, with a known pN status (187 pN0, 17 pN1 patients). ML performed better, also when tested on the surgical population, with accuracy, specificity, and sensitivity ranging between 48–86%, 35–91%, and 17–79%, respectively. ML potentially allows better prediction of the nodal status of PC, potentially allowing a better tailoring of pelvic irradiation.