Computer-aided Volumetry of Part-Solid Lung Cancers by Using CT: Solid Component Size Predicts Prognosis.

Purpose To investigate the relationship between the postoperative prognosis of patients with part-solid non-small cell lung cancer and the solid component size acquired by using three-dimensional (3D) volumetry software on multidetector computed tomographic (CT) images. Materials and Methods A retrospective study by using preoperative multidetector CT data with 0.5-mm section thickness, clinical records, and pathologic reports of 96 patients with primary subsolid non-small cell lung cancer (47 men and 49 women; mean age ± standard deviation, 66 years ± 8) were reviewed. Two radiologists measured the two-dimensional (2D) maximal solid size of each nodule on an axial image (hereafter, 2D MSSA), the 3D maximal solid size on multiplanar reconstructed images (hereafter, 3D MSSMPR), and the 3D solid volume of greater than 0 HU (hereafter, 3D SV) within each nodule. The correlations between the postoperative recurrence and the effects of clinical and pathologic characteristics, 2D MSSA, 3D MSSMPR, and 3D SV as prognostic imaging biomarkers were assessed by using a Cox proportional hazards model. Results For the prediction of postoperative recurrence, the area under the receiver operating characteristics curve was 0.796 (95% confidence interval: 0.692, 0.900) for 2D MSSA, 0.776 (95% confidence interval: 0.667, 0.886) for 3D MSSMPR, and 0.835 (95% confidence interval: 0.749, 0.922) for 3D SV. The optimal cutoff value for 3D SV for predicting tumor recurrence was 0.54 cm, with a sensitivity of 0.933 (95% confidence interval: 0.679, 0.998) and a specificity of 0.716 (95% confidence interval: 0.605, 0.811) for the recurrence. Significant predictive factors for disease-free survival were 3D SV greater than or equal to 0.54 cm (hazard ratio, 6.61; P = .001) and lymphatic and/or vascular invasion derived from histopathologic analysis (hazard ratio, 2.96; P = .040). Conclusion The measurement of 3D SV predicted the postoperative prognosis of patients with part-solid lung cancer more accurately than did 2D MSSA and 3D MSSMPR. RSNA, 2018.