Lorenzo Garzelli1, Jin Mo Goo2, Su Yeon Ahn3, Kum Ju Chae3, Chang Min Park4, Julip Jung5, Helen Hong5. 1. Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea; Pitié-Salpêtrière Hospital, Pierre-and-Marie Curie University, Paris, France. 2. Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea; Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea. Electronic address: jmgoo@plaza.snu.ac.kr. 3. Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea. 4. Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea; Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea. 5. Department of Software Convergence, Seoul Women's University, Seoul, Republic of Korea.
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 4.6 mm (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.
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 4.6 mm (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.