Could machine learning improve the prediction of pelvic nodal status of prostate cancer patients? Preliminary results of a pilot study

B. De Bari, 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. BuglioneS. M. Magrini

Risultato della ricerca: Contributo su rivistaArticolo in rivistapeer review

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 originaleInglese
pagine (da-a)232-240
Numero di pagine9
RivistaCancer Investigation
Volume33
Numero di pubblicazione6
DOI
Stato di pubblicazionePubblicato - 3 lug 2015

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