| Literature DB >> 25950849 |
B De Bari1, M Vallati, R Gatta, C Simeone, G Girelli, U Ricardi, I Meattini, P Gabriele, R Bellavita, M Krengli, I Cafaro, E Cagna, F Bunkheila, S Borghesi, M Signor, A Di Marco, F Bertoni, M Stefanacci, N Pasinetti, M Buglione, S M Magrini.
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.Entities:
Keywords: Machine Learning; Nodal metastases; Pelvic irradiation; Prostate cancer; Radiotherapy
Mesh：
Year: 2015 PMID： 25950849 DOI： 10.3109/07357907.2015.1024317
Source DB: PubMed Journal: Cancer Invest ISSN： 0735-7907 Impact factor: 2.176