Radiomic Values from High-Grade Subtypes to Predict Spread Through Air Spaces in Lung Adenocarcinoma.

ABSTRACT

BACKGROUND: We aimed to establish a radiomic prediction model for tumor spread through air spaces (STAS) in lung adenocarcinoma using radiomic values from high-grade subtypes (solid and micropapillary). METHODS: We retrospectively reviewed 327 patients with lung adenocarcinoma from 2 institutions (cohort 1 227 patients; cohort 2 100 patients) between March 2017 and March 2019. STAS was identified in 113 (34.6%) patients. A high-grade likelihood prediction model was constructed based on a historical cohort of 82 patients with "near-pure" pathologic subtype. The STAS prediction model based on the patch-wise mechanism identified the high-grade likelihood area for each voxel within the internal border of the tumor. STAS presence was indirectly predicted by a volume percentage threshold of the high-grade likelihood area. Performance was evaluated by receiver operating curve analysis with 10-repetition, 3-fold cross-validation in cohort 1, and was individually tested in cohort 2. RESULTS: Overall, 227 patients (STAS-positive 77 [33.9%]) were enrolled for cross-validation (cohort 1) while 100 (STAS-positive 36 [36.0%]) underwent individual testing (cohort 2). The gray level cooccurrence matrix (variance) and histogram (75th percentile) features were selected to construct the high-grade likelihood prediction model, which was used as the STAS prediction model. The proposed model achieved good performance in cohort 1 with an area under the curve, sensitivity, and specificity, of 81.44%, 86.75%, and 62.60%, respectively, and correspondingly, in cohort 2, they were 83.16%, 83.33%, and 63.90%, respectively. CONCLUSIONS: The proposed computed tomography-based radiomic prediction model could help guide preoperative prediction of STAS in early-stage lung adenocarcinoma and relevant surgeries.