CT Radiomic Nomogram Using Optimal Volume of Interest for Preoperatively Predicting Invasive Mucinous Adenocarcinomas...

This study evaluated the efficacy of radiomic analysis with optimal volumes of interest (VOIs) on computed tomography images to preoperatively differentiate invasive mucinous adenocarcinoma (IMA) from non-mucinous adenocarcinoma (non-IMA) in patients with incidental pulmonary nodules (IPNs).

This multicenter, large-scale retrospective study included 1383 patients with IPNs, 110 (8%) of whom were pathologically diagnosed with IMA postoperatively. Radiomic features were extracted from multi-scale VOI subgroups (VOI^{-2 mm}, VOI^entire, VOI ^{+ 2 mm}, and VOI ^{+ 4 mm}). Resampling methods, specifically, the synthetic minority oversampling technique, addressed the imbalance between the majority (IMA) and minority (non-IMA) groups. Radiomic features were identified using the least absolute shrinkage and selection operator algorithm. Radscores were calculated by linearly combining the selected features with their weights. A combined nomogram integrating the optimal VOI-based radiomic model with the image-finding classifier was constructed.

Bubble lucency and lower lobe predominance were significant in establishing an image-finding classifier to differentiate between IMA and non-IMA in IPNs, achieving an area under the curve (AUC) value of 0.684 (0.568-0.801). Across all radiomic models, IMA had a higher Radscore than did non-IMA. Specifically, the VOI + 2 mm-based radiomic model exhibited the highest performance, with an AUC of 0.832 (0.753-0.911). The combined nomogram outperformed the recognized image-finding classifier and radiomic models, achieving an AUC of 0.850 (0.776-0.925).

A nomogram that combines a recognized image-finding classifier with an optimal VOI-based radiomic model effectively predicts IMA in IPNs, aiding physicians in developing comprehensive treatment strategies.