Radiomics-based prediction for tumour spread through air spaces in stage I lung adenocarcinoma using machine learning

Objectives: As evidence has proven that sublobar resection is oncologically contraindicated by tumour spread through air spaces (STAS), its preoperative recognition is vital in customizing surgical strategies. We aimed to assess the value of radiomics in predicting STAS in stage I lung adenocarcinoma.

Methods: We retrospectively reviewed the patients with stage I lung adenocarcinoma, who accepted curative resection in our institution between January 2011 and December 2013. Using 'PyRadiomics' package, 88 radiomics features were extracted from computed tomography (CT) images and a prediction model was consequently constructed using Naïve Bayes machine-learning approach. The accuracy of the model was assessed through receiver operating curve analysis, and the performance of the model was validated both internally and externally.

Results: A total of 233 patients were included as the training cohort with 69 (29.6%) patients being STAS (+). Patients with STAS had worse recurrence-free survival and overall survival (P < 0.001). After feature extraction, 5 most contributing radiomics features were selected out to develop a Naïve Bayes model. In the internal validation, the model exhibited good performance with an area under the curve value of 0.63 (0.55-0.71). External validation was conducted on a test cohort with 112 patients and produced an area under the curve value of 0.69.

Conclusions: CT-based radiomics is valuable in preoperatively predicting STAS in stage I lung adenocarcinoma, which may aid surgeons in determining the optimal surgical approach.