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- Title
Radiomics is feasible for prediction of spread through air spaces in patients with nonsmall cell lung cancer.
- Authors
Onozato, Yuki; Nakajima, Takahiro; Yokota, Hajime; Morimoto, Jyunichi; Nishiyama, Akira; Toyoda, Takahide; Inage, Terunaga; Tanaka, Kazuhisa; Sakairi, Yuichi; Suzuki, Hidemi; Uno, Takashi; Yoshino, Ichiro
- Abstract
Tumor spread through air spaces (STAS) in non-small-cell lung cancer (NSCLC) is known to influence a poor patient outcome, even in patients presenting with early-stage disease. However, the pre-operative diagnosis of STAS remains challenging. With the progress of radiomics-based analyses several attempts have been made to predict STAS based on radiological findings. In the present study, patients with NSCLC which is located peripherally and tumors ≤ 2 cm in size on computed tomography (CT) that were potential candidates for sublobar resection were enrolled in this study. The radiologic features of the targeted tumors on thin-section CT were extracted using the PyRadiomics v3.0 software package, and a predictive model for STAS was built using the t-test and XGBoost. Thirty-five out of 226 patients had a STAS histology. The predictive model of STAS indicated an area under the receiver-operator characteristic curve (AUC) of 0.77. There was no significant difference in the overall survival (OS) for lobectomy between the predicted-STAS (+) and (−) groups (p = 0.19), but an unfavorable OS for sublobar resection was indicated in the predicted-STAS (+) group (p < 0.01). These results suggest that radiomics with machine-learning helped to develop a favorable model of STAS (+) NSCLC, which might be useful for the proper selection of candidates who should undergo sublobar resection.
- Subjects
RADIOMICS; NON-small-cell lung carcinoma; COMPUTED tomography; OVERALL survival; RADIOLOGY
- Publication
Scientific Reports, 2021, Vol 11, Issue 1, p1
- ISSN
2045-2322
- Publication type
Article
- DOI
10.1038/s41598-021-93002-4