Scientific Reports (Jun 2021)

Radiomics is feasible for prediction of spread through air spaces in patients with nonsmall cell lung cancer

  • Yuki Onozato,
  • Takahiro Nakajima,
  • Hajime Yokota,
  • Jyunichi Morimoto,
  • Akira Nishiyama,
  • Takahide Toyoda,
  • Terunaga Inage,
  • Kazuhisa Tanaka,
  • Yuichi Sakairi,
  • Hidemi Suzuki,
  • Takashi Uno,
  • Ichiro Yoshino

DOI
https://doi.org/10.1038/s41598-021-93002-4
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 10

Abstract

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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.