Multiparametric MRI-based radiomics model to predict pelvic lymph node invasion for patients with prostate cancer.

ABSTRACT:

Objective

To identify which patient with prostate cancer (PCa) could safely avoid extended pelvic lymph node dissection (ePLND) by predicting lymph node invasion (LNI), via a radiomics-based machine learning approach.

Methods

An integrative radiomics model (IRM) was proposed to predict LNI, confirmed by the histopathologic examination, integrating radiomics features, extracted from prostatic index lesion regions on MRI images, and clinical features via SVM. The study cohort comprised 244 PCa patients with MRI and followed by radical prostatectomy (RP) and ePLND within 6 months between 2010 and 2019. The proposed IRM was trained in training/validation set and evaluated in an internal independent testing set. The model's performance was measured by area under the curve (AUC),

SUBMITTER: Zheng H 

PROVIDER: S-EPMC9283224 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature