A fully automated noncontrast CT 3‐D reconstruction algorithm enabled accurate anatomical demonstration for lung segmentectomy. Issue 6 (9th February 2022)

• Record Type: Journal Article
• Authors: Chen, Xiuyuan; Wang, Zhenfan; Qi, Qingyi; Zhang, Kai; Sui, Xizhao; Wang, Xun; Weng, Wenhan; Wang, Shaodong; Zhao, Heng; Sun, Chao; Wang, Dawei; Zhang, Huajie; Liu, Enyou; Zou, Tong; Hong, Nan; Yang, Fan
• Is Part Of: Thoracic cancer. Volume 13:Issue 6(2022)
• Page Start: 795
• Page End: 803
• Publication Date: 2022-02-09
• Subjects: 3D reconstruction -- artificial intelligence -- lung -- noncontrast CT -- segmentectomy; Chest -- Cancer -- Periodicals; Chest -- Cancer -- Treatment -- Periodicals; Chest -- Surgery -- Periodicals; 616.99494005
• DOI: 10.1111/1759-7714.14322
• Languages: English
• ISSNs: 1759-7706

Abstract

Abstract: Background: Three‐dimensional reconstruction of chest computerized tomography (CT) excels in intuitively demonstrating anatomical patterns for pulmonary segmentectomy. However, current methods are labor‐intensive and rely on contrast CT. We hereby present a novel fully automated reconstruction algorithm based on noncontrast CT and assess its performance both independently and in combination with surgeons. Methods: A retrospective pilot study was performed. Patients between May 2020 to August 2020 who underwent segmentectomy in our single institution were enrolled. Noncontrast CTs were used for reconstruction. In the first part of the study, the accuracy of the demonstration of anatomical variants by either automated or manual reconstruction algorithm were compared to surgical observation, respectively. In the second part of the study, we tested the accuracy of the identification of anatomical variants by four independent attendees who reviewed 3‐D reconstruction in combination with CT scans. Results: A total of 20 cases were enrolled in this study. All segments were represented in this study with two left S1‐3, two left S4 + 5, one left S6, five left basal segmentectomies, one right S1, three right S2, 1 right S2b + 3a, one right S3, two right S6 and two right basal segmentectomies. The median time consumption for the automated reconstruction was 280 (205–324) s. Accurate vessel and bronchial detection were achieved in 85% by the AI approach and 80% by Mimics, p = 1.00. The accuracy of vessel classification was 80 and 95% by AI and manual approaches, respectively, p = 0.34. In real‐world application, the accuracy of the identification of anatomical variant by thoracic surgeons was 85% by AI+CT, and the median time consumption was 2 (1–3) min. Conclusions: The AI reconstruction algorithm overcame defects of traditional methods and is valuable in surgical planning for segmentectomy. With the AI reconstruction, surgeons may achieve high identification accuracy of anatomical patterns in a short time frame. Abstract : Intraoperative observation of three example cases (Ac, Bc, Cc) and the 3D reconstruction by manual (Ab, Bb, Cb) and automated (Aa, Ba, Ca) approach. All structures were correctly reconstructed in Aa and Ba. The interlobular vein failed to be reconstructed in the automated reconstruction (Ba) and manual reconstruction (Bb). V7a was wrongly recognized as A7a in the automated reconstruction (Ca).