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


Newsletter subscription

Free Daily and Weekly newsletters offered by content of interest

The fields of GU Oncology and Urology are rapidly advancing. Sign up today for articles, videos, conference highlights and abstracts from peer-review publications by disease and condition delivered to your inbox and read on the go.