ORIGINAL RESEARCH article
Front. Oncol.
Sec. Thoracic Oncology
Short-term efficacy evaluation of artificial intelligence-based three-dimensional reconstruction of chest CT in segmentectomy: A propensity score-matched study
Provisionally accepted
- 1The First Clinical Medical College, Gansu University of Chinese Medicine, Lanzhou, China
- 2Department of Thoracic Surgery, Gansu Provincial Hospital, Lanzhou, China
- 3Department of Anorectal, Gansu Provincial People's Hospital, Lanzhou, China
- 4Gansu Provincial Clinical Medical Research Center for Anorectal Diseases, Lanzhou, China
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Artificial intelligence (AI)-based 3D reconstruction software is increasingly used in clinical practice. This study compared its clinical value with CT-guided Hook-wire localization in segmentectomy. A retrospective analysis was performed on 257 patients who underwent video-assisted thoracoscopic segmentectomy at Gansu Provincial People's Hospital from January 2023 to August 2024. Patients were divided into an AI-3D group (134 cases, receiving preoperative AI-assisted 3D reconstruction) and a Hook-wire group (123 cases, undergoing Hook-wire localization). Propensity score matching ensured comparability, with 97 pairs matched. Perioperative indicators, pulmonary function, and quality of life were evaluated. After matching, the Hook-wire group had 2 pneumothorax and 4 hemothorax cases, while the AI-3D group had none. The AI-3D group showed significantly shorter operative time, chest tube indwelling time, and hospital stay, along with less intraoperative blood loss and drainage volume. No significant differences were found in postoperative complications or lymph nodes dissected. Six months post-surgery, the AI-3D group had better pulmonary function and quality of life scores. Thus, AI-based 3D reconstruction enables safe, effective segmentectomy with minimal impact on pulmonary function, showing good clinical value.
Keywords: artificial intelligence, 3D Reconstruction, Hook-wire, Pulmonary Function, retrospective analysis, lung cancer
Received: 24 Sep 2025; Accepted: 19 Nov 2025.
Copyright: © 2025 Luo, Wang, Kang, Gou and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yunjiu Gou, gouyunjiu@163.com
Honglai Zhang, 1815633675@qq.com
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