Preoperative multiparameter MRI-based prediction of Ki-67 expression in primary central nervous system lymphoma.

ABSTRACT:

Background

Ki-67 is a key marker of tumor proliferation. This study aimed to develop machine learning models using single- and multi-parameter MRI radiomic features for the preoperative prediction of Ki-67 expression in primary central nervous system lymphoma (PCNSL), aiding prognosis and individualized treatment planning.

Methods

A retrospective analysis of 74 patients was conducted using MRI scans, including T1, contrast-enhanced T1, T2, T2-FLAIR, DWI, and ADC sequences. Patients were categorized into high-expression (Ki-67 > 70%) and low-expression (Ki-67 ≤ 70%) groups. Tumor volumes of interest (VOIs) were manually delineated by radiologists, and 851 radiomic features were extracted using 3DSlicer. After preprocessing, including bias field correction and normalization, f

SUBMITTER: Xu J 

PROVIDER: S-EPMC12559913 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature