Deep learning and pathomics analyses predict prognosis of high-grade gliomas by Yuchen Zhu, Yuchen Zhu, Yuxi Gong, Weilin Xu, Xingjian Sun, Xingjian Sun, Gefei Jiang, Gefei Jiang, Lei Qiu, Lei Qiu, Kexin Shi, Kexin Shi, Mengxing Wu, Mengxing Wu, Yinjiao Fei, Jinling Yuan, Jinling Yuan, Jinyan Luo, Yurong Li, Yuandong Cao, Minhong Pan, Shu Zhou

“…Pathological features were extracted using Pearson correlation, univariate Cox regression, and LASSO-Cox regression. Three models were developed: a Pathomics-based model, a clinical model, and a combined model integrating both.ResultsPathological and Clinical Features were used to build two models, leading to a predictive model with a C-index of 0.847 (train) and 0.739 (test). …”
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