A Nomogram Combining MRI Multisequence Radiomics and Clinical Factors for Predicting Recurrence of High-Grade Serous Ovarian Carcinoma.

ABSTRACT:

Objective

To develop a combined nomogram based on preoperative multimodal magnetic resonance imaging (mMRI) and clinical information for predicting recurrence in patients with high-grade serous ovarian carcinoma (HGSOC).

Methods

This retrospective study enrolled 141 patients with clinicopathologically confirmed HGSOC, including 65 patients with recurrence and 76 without recurrence. Radiomics features were extracted from the mMRI images (FS-T2WI, DWI, and T1WI+C). L1 regularization-based least absolute shrinkage and selection operator (LASSO) regression was performed to select radiomics features. A multivariate logistic regression analysis was used to build the classification models. A nomogram was established by incorporating clinical risk factors and radiomics Radscores. Th

SUBMITTER: Li C 

PROVIDER: S-EPMC9095390 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature