Fig. 4: Feature interaction and predictive performance analyses of the models.
From: Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer
Pearson correlation coefficients analyses for the features included in the multiomics model on validation set A, internal test set B, and external test set C. Receiver operating characteristic analyses and performance metrics for the models on validation set D, internal test set E, and external test set F. All statistical tests were two-sided, with p < 0.05 indicative of a statistically significant difference, and *** denotes p < 0.001; ** denotes p < 0.01; * denotes p < 0.05. Size, the radiological solid component size of pulmonary nodules; Radiomics, the deep learning-based radiomics model score; 6bp-5mC, the model score established by the 6-mer end motifs selected from 5mC-equencing data; 6bp-5hmC, the model score established by the 6-mer end motifs selected from 5hmC-sequencing data; clinical, the model established by the age and radiological solid component size of pulmonary nodule; clinic-Radiomics, the model established by combining clinical variables with the DL-radiomics model sore; clinic-mC, the model established by combining clinical variables with the 6bp-5mC model score; clinic-RadmC, the model established by combining clinical variables, DL-radiomics model score with the 6bp-5mC model score; clinic-Rad(h)mC, the model established by combining clinical variables, the DL-radiomics model score, the 6bp-5mC model score with the 6bp-5hmC model score. ROCs, receiver operating characteristics analyses; AUC area under the ROCs curve, Sens sensitivity, Spec specificity, PPV positive predictive value, NPV negative predictive value, Accur accuracy. Source data are provided as a Source Data file.
