Radiomics machine learning model based on multi-sequence MRI for predicting Ki-67 expression level in glioblastoma.

Abstract

Introduction: Glioblastoma (GBM) is the most aggressive primary malignant brain tumor in adults, with median overall survival around 15 months. The Ki- 67 proliferation index is an important marker of proliferative activity and has prognostic relevance in GBM; however, its assessment requires surgical tissue and may be affected by sampling bias. Developing a non-invasive preoperative method to estimate Ki-67 expression may provide adjunctive information for risk assessment, while it should not be regarded as a stand-alone determinant of treatment decisions.

Methods: This retrospective study included 269 histologically confirmed GBM patients from The Fourth Affiliated Hospital of Xinjiang Medical University. Patients were classified into low (n = 61) and high (n = 208) Ki-67 expression groups using a 20% threshold, selected according to published GBM Ki-67 stratification literature and local pathology reporting practice. Multi-sequence MRI scans were acquired at 3.0T, including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), fluid-attenuated inversion recovery (FLAIR), and contrast-enhanced T1WI (CET1WI). A total of 8,612 radiomic features were extracted from three-dimensional tumor volumes across all sequences. After reducing dimensionality via Pearson correlation coefficient filtering and LASSO regression, machine learning models were built using BernoulliNB, GaussianNB, SVM, and AdaBoost classifiers for each sequence independently and a Combined model integrating all four sequences. Model performance metrics included AUC, accuracy, sensitivity, specificity, F1 score, calibration curves, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) were used to interpret model predictions.

Results: Baseline demographics (gender, BMI, age) did not differ significantly between groups. The Combined model integrating all sequences achieved the highest predictive accuracy, with AUCs of 0.915 (95% CI: 0.874-0.956) in the training set and 0.746 (95% CI: 0.614-0.879) in the test set, outperforming single-sequence models. Notably, the Combined model attained sensitivity of 0.930 and negative predictive value of 0.972 in training, indicating strong identification of high Ki-67 expression. Calibration curves and DCA demonstrated good model calibration and clinical utility.

Discussion: A radiomics-based multi-sequence MRI machine learning model may serve as a non-invasive adjunct for preoperative Ki-67 estimation in GBM. The combined sequence approach outperformed single modalities, and SHAP analysis enhanced interpretability; however, clinical application requires external validation and outcome-based evidence before use in treatment decision-making.