Radiomics features recommending surgical intervention among persistent subsolid lung nodules during health check-ups: A retrospective monocentric analysis

Abstract

Abstract Background Persistent subsolid nodules requiring follow-up are often detected during lung cancer screening; however, changes in their invasiveness can be overlooked owing to slow growth. The purpose of this exploratory study was to develop a method to automatically identify invasive tumors during multiple health check-ups. Methods We retrospectively reviewed patients who underwent screening using low-dose computed tomography (CT) between 2014 and 2019. Patients with lung adenocarcinomas manifesting as subsolid nodules resected after 5 years of follow-up were enrolled. The resected tumors were categorized into invasive or less-invasive groups. The annual growth or change rate (Δ) of the nodule voxel histogram on three-dimensional CT (e.g., tumor volume [cm3], solid volume percentage [%], mean CT value [HU], variance, kurtosis, skewness, and entropy) was assessed using radiomics. Multivariate regression modeling was employed to design a discriminant model. Results Forty-seven tumors (282 detectable lesions over 5 years) were included (23 and 24 in the invasive and less-invasive groups, respectively). The median tumor volumes at the initial screening were 130 and 106 mm3 in the less-invasive and invasive groups, respectively; the difference was not significant (P = 0.489). In the multivariate regression analysis to identify the invasive group, Δskewness was an independent predictor (adjusted odds ratio, 0.021; P = 0.043). When combined with Δvariance (odds ratio, 1.630; P = 0.037), the assessment method had high accuracy for detecting invasive lesions (true-positive rate, 88%; false-positive rate, 80%). Conclusions During check-ups, close investigation by surgery for subsolid nodules can be suggested with the application of radiomics, particularly focusing on skewness. Trial registration: Not applicable.