An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: Preliminary findings.

ABSTRACT:

Objectives

To evaluate the added benefit of integrating features from pre-treatment MRI (radiomics) and digitized post-surgical pathology slides (pathomics) in prostate cancer (PCa) patients for prognosticating outcomes post radical-prostatectomy (RP) including a) rising prostate specific antigen (PSA), and b) extraprostatic-extension (EPE).

Methods

Multi-institutional data (N = 58) of PCa patients who underwent pre-treatment 3-T MRI prior to RP were included in this retrospective study. Radiomic and pathomic features were extracted from PCa regions on MRI and RP specimens delineated by expert clinicians. On training set (D1, N = 44), Cox Proportional-Hazards models M_R, M_P and M_RaP were trained using radiomics, pathomics, and their combinatio

SUBMITTER: Hiremath A 

PROVIDER: S-EPMC11044050 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature