Improving the prediction of lung adenocarcinoma invasive component on CT: Value of a vessel removal algorithm during software segmentation of subsolid nodules

This study assessed a vessel removal algorithm's effectiveness in segmenting subsolid nodules by comparing software measurements of the solid component on CT scans, before and after vessel removal, with invasive component measurements on pathology in lung adenocarcinomas. Seventy-three nodules with ≤10 mm invasive components on pathology were analyzed. Semi-automated segmentation was performed by two radiologists. Software-derived 3D longest, axial longest, and effective diameters of the solid component were obtained pre- and post-vessel removal. Measurements were compared to pathology using statistical tests. Results revealed a significant correlation between software and pathology measurements, with improved correlation after vessel removal. Measurement differences between CT and pathology were significantly reduced post-vessel removal. The smallest difference was observed with the 3D longest diameter after vessel removal, showing no significant difference from pathology. Incorporating the vessel removal algorithm in software segmentation enhanced the prediction of the invasive component in lung adenocarcinomas. AVIEW Lung Screen software from Coreline Soft was used for segmentation.