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

Written by:
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 SM.   Are you the author?
Istituto del Radio "O. Alberti", Radiotherapy Department, Spedali Civili di Brescia and University of Brescia, Brescia, Italy.

Reference: Cancer Invest. 2015 May 7. Epub ahead of print.
doi: 10.3109/07357907.2015.1024317

PubMed Abstract
PMID: 25950849 Prostate Cancer Section