@article{
  author = {Yang, Dr. Yang; Lv, Mr. Yang; Li, Dr. Zhongying; Wang, Mr. Dasheng; Hu, Mr. Longfei; Guan, Dr. yong; Wang, Prof. Fei; Jiang, Prof. Zheng; Dong, Prof. Yongfei; },
  title = {Dual-Center Study: Noninvasive Prediction of Ki‑67 Status in Intracranial Tumors from Contrast-Enhanced MRI with Explainable XGBoost},
  keywords = {Magnetic resonance imaging;Intracranial Tumors;Ki-67 Prediction;Machine Learning;XGBoost;Deep Learning Features;Explainable Artificial Intelligence;Shapley Additive Explanations;},
  abstract = {Background and Objective: The Ki-67 proliferation index is a key biomarker for assessing the malignancy and prognosis of intracranial tumors, but its acquisition relies on invasive surgery. This study aimed to develop an explainable machine-learning model that non-invasively predicts Ki-67 status in intracranial tumors using preoperative contrast-enhanced T1 (T1CE)-weighted magnetic resonance imaging (MRI). Methods: We retrospectively analyzed a final cohort of 573 pathologically confirmed cases, including 394 from the First Affiliated Hospital of USTC and 179 from the First Affiliated Hospital of Anhui Medical University, across five tumor types: glioma (n=369), meningioma (n=53), acoustic neuroma (n=52), metastatic tumor (n=50), and sellar lesion (n=49). Patients were dichotomized into high- (Ki-67 >=10%) and low-expression groups. After standardized preprocessing and nnU-Net-based tumor segmentation, we extracted clinical (age, gender), hand-crafted features (n=23), and deep-learning (DL) features (n=256) from T1CE. A selection strategy identified 46 features (2 clinical, 14 hand-crafted, 30 DL) for model development. Results: Using 5-fold stratified cross-validation, an XGBoost classifier achieved an out-of-fold AUC of 0.804 (95% CI: 0.769-0.838), accuracy 0.743, sensitivity 0.785, and specificity 0.691, with acceptable probability calibration. SHapley Additive exPlanations (SHAP) provided global and local interpretability, highlighting location, enhancement, and ring-related descriptors (e.g., skullbase_distance_mm, rim_core_entropy_diff, ring_continuity_pct) alongside influential DL features. Conclusion: These results indicate that a T1CE-based, explainable XGBoost model shows promise for non-invasively predicting Ki-67 status across diverse intracranial tumor types, particularly for gliomas, offering imaging-derived evidence to support clinical decision-making.},
  doi = {10.12074/202604.00129},
  url = {https://chinaxiv.org/abs/202604.00129},
  timestamp = {2026-05-05},
}
