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

Abstract

Purpose: To evaluate the value of a vessel removal algorithm in segmentation of subsolid nodules by comparing the software solid component measurement on CT, before and after vessel removal, with the measurement of the invasive component on pathology in lung adenocarcinomas manifesting as subsolid nodules. Materials and methods: Between January 2014 and June 2015, 73 subsolid nodules with an invasive component of <= 10 mm on pathology were selected for analyses. For each nodule, semi-automated segmentation was performed by 2 radiologists and 3-dimensional (D) longest, axial longest and effective diameters of solid component were obtained from software, before and after using a vessel removal tool. These measurements were compared with the invasive component diameter on pathology using the paired t-test and Pearson's correlation test. Results: Sixty-eight successfully segmented subsolid nodules were included. The mean maximal diameter of the invasive component on pathology was 4.6mm (range, 0-10 mm). The correlation between software and pathology measurements was significant (p < 0.01) and the correlation after vessel removal (r = 0.49-0.54) was better than before vessel removal (r = 0.27-0.41). The mean measurement difference between solid component on CT and invasive tumor on pathology was significantly larger before vessel removal than after vessel removal in all measurements. The smallest mean measurement difference was obtained with 3D longest diameter of solid component after vessel removal in both readers (-0.26 mm to 0.10 mm), with no significant difference from pathology (p = 0.53-0.83). Conclusion: By adding a vessel removal algorithm in software segmentation of subsolid nodules, the prediction of invasive component in lung adenocarcinomas can be improved.