Enhancing identification of early-stage lung adenocarcinomas through solid component analysis of three-dimensional computed tomography images.

Objectives: As the role of segmentectomy expands in managing early-stage lung adenocarcinoma, precise preoperative assessments of tumor invasiveness via computed tomography become crucial. This study aimed to evaluate the effectiveness of solid component analysis of three-dimensional (3D) computed tomography images and establish segmentectomy criteria for early-stage lung adenocarcinomas.

Methods: This retrospective study included 101 cases with adenocarcinoma diagnoses, with patients undergoing segmentectomy for clinical stage 0 or IA between 2012 and 2017. The solid component volume (3D-volume) and solid component ratio (3D-ratio) of tumors were calculated using 3D computed tomography. Additionally, based on two-dimensional (2D) computed tomography, the solid component diameter (2D-diameter) and solid component ratio (2D-ratio) were calculated. The area under the receiver-operating characteristic curve (AUC) was calculated for each method, facilitating predictions of mortality and recurrence within 5 years. The AUC of each measurement was compared with those of invasive component diameter (path-diameter) and invasive component ratio (path-ratio) obtained through pathology analysis.

Results: The predictive performance of 3D-volume did not differ significantly from that of path-diameter, whereas 2D-diameter exhibited less predictive accuracy (AUC: 3D-volume, 2D-diameter, and path-diameter: 0.772, 0.624, and 0.747, respectively; 3D-volume vs. path-diameter: p = 0.697; 2D-diameter vs. path-diameter: p = 0.048). Results were similar for the solid component ratio (AUC: 3D-ratio, 2D-ratio, path-ratio: 0.707, 0.534, and 0.698, respectively; 3D-ratio vs. path-ratio: p = 0.882; 2D-ratio vs. path-ratio: p = 0.038).

Conclusion: Solid component analysis using 3D computed tomography offers advantages in prognostic prediction for early-stage lung adenocarcinomas.