TY - JOUR
T1 - Could Machine Learning Improve the Prediction of Pelvic Nodal Status of Prostate Cancer Patients?
T2 - Preliminary Results of a Pilot Study
AU - De Bari, B.
AU - Vallati, M.
AU - Gatta, R.
AU - Simeone, C.
AU - Girelli, G.
AU - Ricardi, U.
AU - Meattini, I.
AU - Gabriele, P.
AU - Bellavita, R.
AU - Krengli, M.
AU - Cafaro, I.
AU - Cagna, E.
AU - Bunkheila, F.
AU - Borghesi, S.
AU - Signor, M.
AU - Di Marco, A.
AU - Bertoni, F.
AU - Stefanacci, M.
AU - Pasinetti, N.
AU - Buglione, M.
AU - Magrini, S. M.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Machine Learning
KW - Nodal metastases
KW - Pelvic irradiation
KW - Prostate cancer
KW - Radiotherapy
UR - http://www.scopus.com/inward/record.url?scp=84934287734&partnerID=8YFLogxK
UR - http://www.tandfonline.com/toc/icnv20/current
U2 - 10.3109/07357907.2015.1024317
DO - 10.3109/07357907.2015.1024317
M3 - Article
C2 - 25950849
AN - SCOPUS:84934287734
VL - 33
SP - 232
EP - 240
JO - Cancer Investigation
JF - Cancer Investigation
SN - 0735-7907
IS - 6
ER -
