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

Li-Wei Chen; Mong-Wei Lin; Min-Shu Hsieh; Shun-Mao Yang; Hao-Jen Wang; Yi-Chang Chen; Hsin-Yi Chen; Yu-Hsuan Hu; Chi-En Lee; Jin-Shing Chen; Yeun-Chung Chang; Chung-Ming Chen

doi:10.1016/j.athoracsur.2021.07.075

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

Ann Thorac Surg. 2022 Sep;114(3):999-1006. doi: 10.1016/j.athoracsur.2021.07.075. Epub 2021 Aug 27.

Authors

Affiliations

¹ Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei Taiwan.
² Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
³ Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
⁴ Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei Taiwan; Department of Surgery, National Taiwan University Hospital Biomedical Park Hospital, Zhubei City, Taiwan.
⁵ Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei Taiwan; Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
⁶ Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan; Department of Surgical Oncology, National Taiwan University Cancer Center, Taipei, Taiwan.
⁷ Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
⁸ Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei Taiwan. Electronic address: chung@ntu.edu.tw.

PMID: 34454902
DOI: 10.1016/j.athoracsur.2021.07.075

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.

MeSH terms

Adenocarcinoma of Lung* / surgery
Humans
Lung Neoplasms* / surgery
Neoplasm Invasiveness / pathology
Neoplasm Staging
Prognosis
Retrospective Studies