Peritumoral radiomics features on preoperative thin-slice CT images can predict the spread through air spaces of lung adenocarcinoma

• Published in: Scientific Reports Vol. 12; no. 1; pp. 10323 - 9
• Main Authors: Takehana, Keiichi, Sakamoto, Ryo, Fujimoto, Koji, Matsuo, Yukinori, Nakajima, Naoki, Yoshizawa, Akihiko, Menju, Toshi, Nakamura, Mitsuhiro, Yamada, Ryo, Mizowaki, Takashi, Nakamoto, Yuji
• Format: Journal Article
• Language: English
• Published: London Springer Science and Business Media LLC 20.06.2022; Nature Publishing Group UK; Nature Publishing Group; Nature Portfolio
• Subjects: 692/4028/67/1612; 692/4028/67/2321; Adenocarcinoma; Adenocarcinoma of Lung; Cancer; Cancer imaging; Computed tomography; Humanities and Social Sciences; Humans; Learning algorithms; Lung cancer; Lung Neoplasms; Machine learning; Medicine; multidisciplinary; Neoplasm Invasiveness; Prediction models; Prognosis; Q; R; Radiomics; Retrospective Studies; Risk groups; Science; Science (multidisciplinary); Tomography, X-Ray Computed; Tumors
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-14400-w

The spread through air spaces (STAS) is recognized as a negative prognostic factor in patients with early-stage lung adenocarcinoma. The present study aimed to develop a machine learning model for the prediction of STAS using peritumoral radiomics features extracted from preoperative CT imaging. A total of 339 patients who underwent lobectomy or limited resection for lung adenocarcinoma were included. The patients were randomly divided (3:2) into training and test cohorts. Two prediction models were created using the training cohort: a conventional model based on the tumor consolidation/tumor (C/T) ratio and a machine learning model based on peritumoral radiomics features. The areas under the curve for the two models in the testing cohort were 0.70 and 0.76, respectively ( P  = 0.045). The cumulative incidence of recurrence (CIR) was significantly higher in the STAS high-risk group when using the radiomics model than that in the low-risk group (44% vs. 4% at 5 years; P  = 0.002) in patients who underwent limited resection in the testing cohort. In contrast, the 5-year CIR was not significantly different among patients who underwent lobectomy (17% vs. 11%; P  = 0.469). In conclusion, the machine learning model for STAS prediction based on peritumoral radiomics features performed better than the C/T ratio model.