Artificial intelligence analysis of three-dimensional imaging data derives factors associated with postoperative recurrence in patients with radiologically solid-predominant small-sized lung cancers

Abstract OBJECTIVES Indications of limited resection, such as segmentectomy, have recently been reported for patients with solid-predominant lung cancers ≤2 cm. This study aims to identify unfavourable prognostic factors using three-dimensional imaging analysis with artificial intelligence (AI) tech...

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Published in:European journal of cardio-thoracic surgery 2022-03, Vol.61 (4), p.751-760
Main Authors: Kudo, Yujin, Shimada, Yoshihisa, Matsubayashi, Jun, Kitamura, Yoshiro, Makino, Yojiro, Maehara, Sachio, Hagiwara, Masaru, Park, Jinho, Yamada, Takafumi, Takeuchi, Susumu, Kakihana, Masatoshi, Nagao, Toshitaka, Ohira, Tatsuo, Masumoto, Jun, Ikeda, Norihiko
Format: Article
Language:English
Subjects:
Artificial Intelligence
Carcinoma, Non-Small-Cell Lung - surgery
Humans
Imaging, Three-Dimensional
Lung Neoplasms - diagnostic imaging
Lung Neoplasms - surgery
Neoplasm Staging
Pneumonectomy - methods
Prognosis
Retrospective Studies
Tomography, X-Ray Computed
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Summary:Abstract OBJECTIVES Indications of limited resection, such as segmentectomy, have recently been reported for patients with solid-predominant lung cancers ≤2 cm. This study aims to identify unfavourable prognostic factors using three-dimensional imaging analysis with artificial intelligence (AI) technology. METHODS A total of 157 patients who had clinical N0 non-small cell lung cancer with a radiological size ≤2 cm, and a consolidation tumour ratio > 0.5, who underwent anatomical lung resection between 2011 and 2017 were enrolled. To evaluate the three-dimensional structure, the ground-glass nodule/Solid Automatic Identification AI software Beta Version (AI software; Fujifilm Corporation, Japan) was used. RESULTS Maximum standardized uptake value (SUVmax) and solid-part volume measured by AI software (AI-SV) showed significant differences between the 139 patients with adenocarcinoma and the 18 patients with non-adenocarcinoma. Among the adenocarcinoma patients, 42 patients (30.2%) were found to be pathological upstaging. Multivariable analysis demonstrated that high SUVmax, high carcinoembryonic antigen level and high AI-SV were significant prognostic factors for recurrence-free survival (RFS; P 
ISSN:1010-7940
1873-734X
DOI:10.1093/ejcts/ezab541