Could machine learning improve the prediction of pelvic nodal status of prostate cancer patients? Preliminary results of a pilot study.
- 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.
- Bunkheila, F.
- Borghesi, S.
- Signor, M.
- Di Marco, A.
- Bertoni, F.
- Stefanacci, M.
- Pasinetti, N.
- Buglione, M.
- Magrini, S.M.
Publication date
January 2015
DOI ISSN
0735-7907 Citation count (estimate)
2Abstract
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
Add to ORKG