Article: article from journal or magazin.
Could machine learning improve the prediction of pelvic nodal status of prostate cancer patients? Preliminary results of a pilot study.
Publication types: Journal ArticlePublication Status: ppublish
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.
Aged, Aged, 80 and over, Algorithms, Artificial Intelligence, Decision Trees, Humans, Lymphatic Metastasis/diagnosis, Male, Middle Aged, Pelvis/pathology, Pilot Projects, Prostatic Neoplasms/pathology, Sensitivity and Specificity
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