Multiparametric MRI-based model for prediction of local progression of hepatocellular carcinoma after thermal ablation.

ABSTRACT:

Purpose

To develop a deep learning radiomics of multiparametric magnetic resonance imaging (DLRMM)-based model that incorporates preoperative and postoperative signatures for prediction of local tumor progression (LTP) after thermal ablation (TA) in hepatocellular carcinoma (HCC).

Methods

From May 2017 to October 2021, 417 eligible patients with HCC were retrospectively enrolled from three hospitals (one primary cohort [PC, n = 189] and two external test cohorts [ETCs][n = 135, 93]). DLRMM features were extracted from T1WI + C, T2WI, and DWI using ResNet18 model. An integrative model incorporating the DLRMM signature with clinicopathologic variables were further built to LTP risk stratification. The performance of these models were compared by areas under receiver operating

SUBMITTER: Chen C 

PROVIDER: S-EPMC10524055 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature