Could Machine Learning Improve the Prediction of Pelvic Nodal Status of Prostate Cancer Patients? Preliminary Results of a Pilot Study. (3rd July 2015)

• Record Type: Journal Article
• Authors: 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.
• Is Part Of: Cancer investigation. Volume 33:Number 6(2015)
• Page Start: 232
• Page End: 240
• Publication Date: 2015-07-03
• Subjects: Cancer -- Periodicals; Oncology -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- Periodicals; 616.994
• DOI: 10.3109/07357907.2015.1024317
• Languages: English
• ISSNs: 0735-7907

Abstract

<abstract> <title>ABSTRACT</title> <p>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).</p> <p>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.</p> </abstract>