Development of a predictive model for Ki-67 index of meningiomas by integrating deep-learning, radiomics and clinical features utilizing fully automated segmentation results.

PURPOSE: To investigate the efficacy of clinical information, traditional radiological, radiomics and deep-learning features combinations for constructing a predictive model for the Ki-67 index of meningiomas. MATERIAL AND METHODS: This study acquired retrospective (198 cases) and prospective (22 cases) meningioma data between 2015 and 2020. Within the retrospective data, 160 cases were utilized for training, while 38 were allocated to an independent test. Ki-67 expression levels were dichotomized into low and high groups using a 4% threshold based on previous research. The study developed and evaluated five classifier models combining clinical information, radiomics and deep-learning features to predict Ki-67 expression levels. Model performance was evaluated via the receiver operating characteristic (ROC) curves and the area under the curve (AUC), obtaining a 95% confidence interval (CI) using DeLong testing. Subsequently, the most effective model was validated using prospective data from 22 cases. RESULTS: The eXtreme Gradient Boosting (XGBoost) classifier model showed optimal performance among the five classifier models. The AUC for the independent test dataset was 0.717 (CI: 0.575-0.858). After optimization, the AUC of the test dataset is 0.767 (CI: 0.631-0.903). The AUC for the prospective test data set was 0.773 (CI: 0.590-0.955). Decision curve analysis (DCA) showed that combining clinical information, radiomics, and deep-learning features resulted in the best predictive performance of the XGBoost classifier. CONCLUSION: An integrated radiomics model enables Ki-67 prediction and has great potential to estimate the risk of tumor regrowth and recurrence non-invasively.