In Laboratory investigation; a journal of technical methods and pathology ; h5-index 42.0
Determination of 1p/19q co-deletion status is important for the classification, prognostication, and personalized therapy in diffuse lower-grade gliomas (LGG). We developed and validated a deep learning imaging signature (DLIS) from preoperative magnetic resonance imaging (MRI) for predicting the 1p/19q status in patients with LGG. The DLIS was constructed on a training dataset (n = 330) and validated on both an internal validation dataset (n = 123) and a public TCIA dataset (n = 102). The receiver operating characteristic (ROC) analysis and precision recall curves (PRC) were used to measure the classification performance. The area under ROC curves (AUC) of the DLIS was 0.999 for training dataset, 0.986 for validation dataset, and 0.983 for testing dataset. The F1-score of the prediction model was 0.992 for training dataset, 0.940 for validation dataset, and 0.925 for testing dataset. Our data suggests that DLIS could be used to predict the 1p/19q status from preoperative imaging in patients with LGG. The imaging-based deep learning has the potential to be a noninvasive tool predictive of molecular markers in adult diffuse gliomas. The authors developed a deep learning model predictive of 1p/19q status from preoperative imaging in 555 lower-grade gliomas (LGG), and achieved an area under the curve (AUC) of 0.983 in the testing dataset. They reveal that developing deep learning imaging signatures could be a noninvasive tool for predicting molecular markers in LGG.
Yan Jing, Zhang Shenghai, Sun Qiuchang, Wang Weiwei, Duan Wenchao, Wang Li, Ding Tianqing, Pei Dongling, Sun Chen, Wang Wenqing, Liu Zhen, Hong Xuanke, Wang Xiangxiang, Guo Yu, Li Wencai, Cheng Jingliang, Liu Xianzhi, Li Zhi-Cheng, Zhang Zhenyu
2022-Feb
