Purpose <p>We explored automated classification models of lung nodules into three morphological types [solid, part-solid, and pure ground-glass nodules (pGGN)] based on computed tomography (CT) using features related to the three-dimensional (3D) consolidation-to-tumor ratio (CTR), which is the maximum diameter ratio of consolidation (= solid component) to a whole tumor.</p> Materials and methods <p>This retrospective study included an internal dataset including 275 patients with non-small cell lung cancer (solid: 179; part-solid: 69; pGGN: 27) from our university hospital and an external test dataset comprising 60 nodules (20 nodules for each type) from The Cancer Imaging Archive. Twelve tumor contours for each patient were predicted by using three deep learning networks (U-net, V-net, and dense V-net) and nine fusion models. The CTR, consolidation-to-tumor-volume ratio (CTVR), and average voxel value of non-solid components, which directly associated with the solid and ground-glass components of nodules, were calculated as the interpretable features from 12 predicted contours. Six machine-learning and two ensemble-learning models (stacking and bagging) were constructed using CTR-related features to classify the nodules into the three morphological types. The classification performance was evaluated using the area under receiver operating characteristic curves (AUCs).</p> Results <p>The proposed interpretable model achieved the classification accuracies of 0.869 for the internal test dataset and 0.733 for the external test dataset. The sensitivity on the external test dataset for pGGNs was improved to 0.900 compared with conventional models.</p> Conclusions <p>This study suggests that the proposed interpretable classification model using CTR-related features could robustly predict morphological nodule types.</p>