Open AccessPredicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-Assisted Decision-Support Models
Author(s) -
Hailang Liu,
Kun Tang,
Ejun Peng,
Liang Wang,
Xia Ding,
Zhiqiang Chen
Publication year - 2020
Publication title -
cancer management and research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.024
H-Index - 40
ISSN - 1179-1322
DOI - 10.2147/cmar.s286167
Subject(s) - logistic regression , lasso (programming language) , medicine , confidence interval , prostate cancer , receiver operating characteristic , area under the curve , prostatectomy , support vector machine , urology , artificial intelligence , machine learning , statistics , mathematics , cancer , computer science , world wide web
This study aimed to develop a machine learning (ML)-assisted model capable of accurately predicting the probability of biopsy Gleason grade group upgrading before making treatment decisions.