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作者

Jian Xu Lili Zhang Qingzeng Liu Jian Zhu

作者单位

²Liaocheng People's Hospital of Shandong Province Liaocheng P. R. China ¹Shandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences Jinan P. R. China

刊名

Precision Radiation Oncology

年份

2025

卷号

Vol.9 No.1

页码

23-34

关键词

Ki‐67 Lymphoma Machine Learning Magnetic Resonance Imaging Radiomics

摘要

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 , 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 int...更多

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 , 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 and low‐expression groups. Tumor volumes of interest were manually delineated by radiologists, and 851 radiomic features were extracted using 3DSlicer. After preprocessing, including bias field correction and normalization, feature selection was performed using SelectKBest and ANOVA. Eight machine learning classifiers, including Logistic Regression, Random Forest, and SVM, were applied to single‐ and multi‐parameter datasets. Results Multiparameter models, particularly Naive Bayes and Logistic Regression, demonstrated superior predictive performance compared to single‐parameter models. Decision curve analysis highlighted that Logistic Regression provides the highest net benefit, followed by Naive Bayes. Conclusion Multiparameter MRI models are more accurate and stable for predicting Ki‐67 expression in PCNSL, supporting clinical decision‐making.收起

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