文章：

肺癌患者亚肺叶切除术后切缘量化：通过术前与术后CT图像对比——基于CT的三维重建算法应用

Quantification of Resection Margin following Sublobar Resection in Lung Cancer Patients through Pre- and Post-Operative CT Image Comparison: Utilizing a CT-Based 3D Reconstruction Algorithm

原文发布日期：10 June 2024

DOI: 10.3390/cancers16122181

类型: Article

开放获取: 是

英文摘要：

Sublobar resection has emerged as a standard treatment option for early-stage peripheral non-small cell lung cancer. Achieving an adequate resection margin is crucial to prevent local tumor recurrence. However, gross measurement of the resection margin may lack accuracy due to the elasticity of lung tissue and interobserver variability. Therefore, this study aimed to develop an objective measurement method, the CT-based 3D reconstruction algorithm, to quantify the resection margin following sublobar resection in lung cancer patients through pre- and post-operative CT image comparison. An automated subvascular matching technique was first developed to ensure accuracy and reproducibility in the matching process. Following the extraction of matched feature points, another key technique involves calculating the displacement field within the image. This is particularly important for mapping discontinuous deformation fields around the surgical resection area. A transformation based on thin-plate spline is used for medical image registration. Upon completing the final step of image registration, the distance at the resection margin was measured. After developing the CT-based 3D reconstruction algorithm, we included 12 cases for resection margin distance measurement, comprising 4 right middle lobectomies, 6 segmentectomies, and 2 wedge resections. The outcomes obtained with our method revealed that the target registration error for all cases was less than 2.5 mm. Our method demonstrated the feasibility of measuring the resection margin following sublobar resection in lung cancer patients through pre- and post-operative CT image comparison. Further validation with a multicenter, large cohort, and analysis of clinical outcome correlation is necessary in future studies.

摘要翻译： 

亚肺叶切除术已成为早期周围型非小细胞肺癌的标准治疗选择。实现足够的切除边缘对于预防局部肿瘤复发至关重要。然而，由于肺组织的弹性及观察者间差异，肉眼测量切除边缘可能缺乏准确性。因此，本研究旨在开发一种客观测量方法——基于CT的三维重建算法，通过术前术后CT图像对比来量化肺癌患者亚肺叶切除术后的切除边缘。首先开发了自动化血管分支匹配技术，以确保匹配过程的准确性和可重复性。在提取匹配特征点后，另一关键技术涉及计算图像内的位移场。这对于映射手术切除区域周围的不连续形变场尤为重要。采用基于薄板样条的变换进行医学图像配准。完成图像配准的最后步骤后，对切除边缘的距离进行测量。在开发基于CT的三维重建算法后，我们纳入12例病例进行切除边缘距离测量，包括4例右肺中叶切除术、6例肺段切除术和2例楔形切除术。采用本方法获得的结果显示，所有病例的目标配准误差均小于2.5毫米。我们的方法证明了通过术前术后CT图像对比测量肺癌患者亚肺叶切除术后切除边缘的可行性。未来研究需要通过多中心、大样本队列进一步验证，并进行临床结局相关性分析。

原文链接：

Quantification of Resection Margin following Sublobar Resection in Lung Cancer Patients through Pre- and Post-Operative CT Image Comparison: Utilizing a CT-Based 3D Reconstruction Algorithm