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.
Lingua originale | Inglese |
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pagine (da-a) | 232-240 |
Numero di pagine | 9 |
Rivista | Cancer Investigation |
Volume | 33 |
Numero di pubblicazione | 6 |
DOI | |
Stato di pubblicazione | Pubblicato - 3 lug 2015 |