Prognostic impact of artificial intelligence-based volumetric quantification of the solid part of the tumor in clinical stage 0-I adenocarcinoma. (August 2022)

• Record Type: Journal Article
• Authors: Kawaguchi, Yohei; Shimada, Yoshihisa; Murakami, Kotaro; Omori, Tomokazu; Kudo, Yujin; Makino, Yojiro; Maehara, Sachio; Hagiwara, Masaru; Kakihana, Masatoshi; Yamada, Takafumi; Park, Jinho; Matsubayashi, Jun; Ohira, Tatsuo; Ikeda, Norihiko
• Is Part Of: Lung cancer. Volume 170(2022)
• Page Start: 85
• Page End: 90
• Publication Date: 2022-08
• Subjects: HRCT high-resolution computed tomography -- GGO ground-glass opacity -- 3D-CT three-dimensional computed tomography -- HU Hounsfield unit -- AI artificial intelligence -- CNN convolutional neural network -- OS overall survival -- RFS recurrence free survival -- 3D-SV solid part volume based on three-dimensional computed tomography -- AI-SV solid part volume based on artificial intelligence -- ROC receiver operating characteristic -- AUC area under the receiver operating characteristic curve -- CI confidence interval -- ICC intraclass correlation coefficient -- HR hazard ratio -- CEA carcinoembryonic antigen; Artificial intelligence -- Three-dimensional convolutional network -- Part-solid ground grass nodule -- Adenocarcinoma -- Three-dimensional volumetric analysis; Lungs -- Cancer -- Periodicals; Lung Neoplasms -- Abstracts; Lung Neoplasms -- Periodicals; Poumons -- Cancer -- Périodiques; Lungs -- Cancer; Periodicals; Electronic journals; Electronic journals; 616.99424
• DOI: 10.1016/j.lungcan.2022.06.007
• Languages: English
• ISSNs: 0169-5002

Abstract

Highlights: Appropriate evaluation of solid component of early-stage lung cancer is crucial. A new volumetric analysis technique based on artificial intelligence was developed. Artificial intelligence was more powerful prognostic factor than conventional methods. Abstract: Introduction: The size of the solid part of a tumor, as measured using thin-section computed tomography, can help predict disease prognosis in patients with early-stage lung cancer. Although three-dimensional volumetric analysis may be more useful than two-dimensional evaluation, measuring the solid part of some lesions is difficult using this methods. We developed an artificial intelligence-based analysis software that can distinguish the solid and non-solid parts (ground-grass opacity). This software calculates the solid part volume in a totally automated and reproducible manner. The predictive performance of the artificial intelligence software was evaluated in terms of survival or recurrence-free survival. Methods: We analyzed the high-resolution computed tomography images of the primary lesion in 772 consecutive patients with clinical stage 0-I adenocarcinoma. We performed automated measurement of the solid part volume using an artificial intelligence-based algorithm in collaboration with FUJIFILM Corporation. The solid part size, the solid part volume based on traditional three-dimensional volumetric analysis, and the solid part volume based on artificial intelligence were compared. Results: Higher areas under the curve related to the solid part volume were provided by the artificial intelligence-based method (0.752) than by the solid part size (0.722) and traditional three-dimensional volumetric analysis-based method (0.723). Multivariate analysis demonstrated that the solid part volume based on artificial intelligence was independently correlated with overall survival ( P = 0.019) and recurrence-free survival ( P < 0.001). Conclusion: The solid part volume measured by artificial intelligence was superior to conventional methods in predicting the prognosis of clinical stage 0-I adenocarcinoma.