Machine-learning predicts time-series prognosis factors in metastatic prostate cancer patients treated with androgen deprivation therapy.

ABSTRACT: Machine learning technology is expected to support diagnosis and prognosis prediction in medicine. We used machine learning to construct a new prognostic prediction model for prostate cancer patients based on longitudinal data obtained from age at diagnosis, peripheral blood and urine tests of 340 prostate cancer patients. Random survival forest (RSF) and survival tree were used for machine learning. In the time-series prognostic prediction model for metastatic prostate cancer patients, the RSF model showed better prediction accuracy than the conventional Cox proportional hazards model for almost all time periods of progression-free survival (PFS), overall survival (OS) and cancer-specific survival (CSS). Based on the RSF model, we created a clinically applicable prognostic prediction mode

SUBMITTER: Saito S 

PROVIDER: S-EPMC10113215 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature