Fig. 6: The performance comparisons for models after adjusting for clinical and radiological factors.
From: Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer

Predictive performance achieved by the models in subgroups stratified by age A, B radiological image C–E, and nodule size F–H. Pure-GGO comprised nodules with only GGO, and part-solid nodules consisted of GGOs and solid components, whereas pure-solid nodules had only solid components without GGOs. Subcentimeter pulmonary nodules were defined as the nodules with solid component size≤10 mm, and large nodules were defined as those with 15 mm≤solid component size≤30 mm, whereas pulmonary massed were defined as those with solid component size>30 mm. 6bp-5mC, the model established by the 6-mer end motifs selected from 5mC-sequencing data; DL-radiomics, the deep learning-based radiomic model score; clinic-mC, the model established by combining clinical variables with the 6bp-5mC model score; clinic-Radiomics, the model established by combining clinical variables with the DL-radiomics model sore; 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; clinic-RadmC, the model established by combining clinical variables, the DL-radiomics model score with the 6bp-5mC model score. AUCs areas under the receiver operating characteristics curves; GGO ground-glass opacity. Source data are provided as a Source Data file.