A multi-modal deep learning model for prediction of Ki-67 for meningiomas using pretreatment MR images - Chaoyue Chen; Yanjie Zhao; Linrui Cai; Haoze Jiang; Yuen Teng; Yang Zhang; Shuangyi Zhang; Junkai Zheng; Fumin Zhao; Zhouyang Huang; Xiaolong Xu; Xin Zan; et al. (npj Precision Oncology)
Abstract This study developed and validated a deep learning network using baseline magnetic resonance imaging (MRI) to predict Ki-67 status in meningioma patients. A total of 1239 patients were retrospectively recruited from three hospitals between January 2010 and December 2023, forming training, internal validation, and two external validation cohorts. A representation learning framework was utilized for modeling, and performance was assessed against existing methods. Furthermore, Kaplan–Meier survival analysis was conducted to investigate whether the model could be used for tumor growth prediction. The model achieved superior results, with areas under the curve (AUCs) of 0.797 for internal testing and 0.808 for generalization, alongside 0.756 and 0.727 for 3- and 5-year tumor growth predictions, respectively. The prediction was significantly associated with the growth of asymptomatic small meningiomas. Overall, the model provides an effective tool for early prediction of Ki-67 and tumor volume growth, aiding in individualized patient management.