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The nomogram of MRI-based radiomics with complementary visual features by machine learning improves stratification of glioblastoma patients
ZHENYU SHU1, YUYUN XU1, and YONG ZHANG2
1Zhejiang Provincial People’s Hospital, Hangzhou, China, 2MR Research, GE healthcare (China), SHANG HAI, China
The nomogram had a survival prediction accuracy of 0.878 and 0.875, a specificity of 0.875 and 0.583, and a sensitivity of 0.704 and 0.833, respectively, in the training and test set
(A)ROC curves for nomogram, radiomic signature, age and meninges predicting OS in validation set. (B) The stratification performance of the nomogram in validation set.
(A) and (B) show the ROC curves of the nomograms in the training and test sets; (C) and (D) show the calibration curves of the nomograms in the training and test sets; and (E) and (F) show the DCA curve of the nomograms in the training and test sets.