Can image analysis on high-resolution computed tomography predict non-invasive growth in adenocarcinoma of the lung?

Purpose: Preoperative radiological predictions of pathological invasiveness must be objective and reproducible in addition to being accurate when considering limited surgery for early lung cancer. Methods: Two cohorts were used for the analysis. Two independent observers traced lesion edges and measured areas and proportions of solid component on tumor images with the largest diameter by high resolution computed tomography images and “Image J” software. Results: The value of the intraclass correlation was 0.997 (95% confidence interval [CI], 0.996-0.998) for the area of solid component and 0.979 (95%CI, 0.958-0.986) for the proportion of solid component, suggesting such parameters were reliable in terms of reproducibility. Az value was 0.898 (95%CI, 0.842-0.953) for the area of solid component and 0.882 (95%CI, 0.816-0.949) for the proportion of solid component, demonstrating 2 parameters were both highly predictive of non-invasive adenocarcinoma. The optimal prediction of non-invasive adenocarcinoma with a cut-off value of 7.5 mm2 for the area of solid component resulted in a sensitivity of 85.3% and specificity of 86.2% in Cohort 1 and a sensitivity of 66.7% and specificity of 88.5% in Cohort 2. Conclusion: Image analysis using “Image J” software was promising for predicting non-invasive adenocarcinoma with its limited inter-observer variability and high predictive performance.