PDF(Pubmed)
Abstract：
OBJECTIVE: Surgical resection with complete tumor excision (R0) provides the best chance of long-term survival for patients with intrahepatic cholangiocarcinoma (iCCA). A non-invasive imaging technology, which could provide quick intraoperative assessment of resection margins, as an adjunct to histological examination, is optical coherence tomography (OCT). In this study, we investigated the ability of OCT combined with convolutional neural networks (CNN), to differentiate iCCA from normal liver parenchyma ex vivo.
METHODS: Consecutive adult patients undergoing elective liver resections for iCCA between June 2020 and April 2021 (n = 11) were included in this study. Areas of interest from resection specimens were scanned ex vivo, before formalin fixation, using a table-top OCT device at 1310 nm wavelength. Scanned areas were marked and histologically examined, providing a diagnosis for each scan. An Xception CNN was trained, validated, and tested in matching OCT scans to their corresponding histological diagnoses, through a 5 × 5 stratified cross-validation process.
RESULTS: Twenty-four three-dimensional scans (corresponding to approx. 85,603 individual) from ten patients were included in the analysis. In 5 × 5 cross-validation, the model achieved a mean F1-score, sensitivity, and specificity of 0.94, 0.94, and 0.93, respectively.
CONCLUSIONS: Optical coherence tomography combined with CNN can differentiate iCCA from liver parenchyma ex vivo. Further studies are necessary to expand on these results and lead to innovative in vivo OCT applications, such as intraoperative or endoscopic scanning.
METHODS: Consecutive adult patients undergoing elective liver resections for iCCA between June 2020 and April 2021 (n = 11) were included in this study. Areas of interest from resection specimens were scanned ex vivo, before formalin fixation, using a table-top OCT device at 1310 nm wavelength. Scanned areas were marked and histologically examined, providing a diagnosis for each scan. An Xception CNN was trained, validated, and tested in matching OCT scans to their corresponding histological diagnoses, through a 5 × 5 stratified cross-validation process.
RESULTS: Twenty-four three-dimensional scans (corresponding to approx. 85,603 individual) from ten patients were included in the analysis. In 5 × 5 cross-validation, the model achieved a mean F1-score, sensitivity, and specificity of 0.94, 0.94, and 0.93, respectively.
CONCLUSIONS: Optical coherence tomography combined with CNN can differentiate iCCA from liver parenchyma ex vivo. Further studies are necessary to expand on these results and lead to innovative in vivo OCT applications, such as intraoperative or endoscopic scanning.
摘要：
目的：手术切除并完全切除肿瘤（R0）为肝内胆管癌（iCCA）患者提供了长期生存的最佳机会。一种非侵入性成像技术，这可以提供手术切除边缘的快速术中评估，作为组织学检查的辅助手段，是光学相干断层扫描(OCT)。在这项研究中,我们研究了OCT与卷积神经网络(CNN)结合的能力，体外区分iCCA和正常肝实质。
方法：本研究包括2020年6月至2021年4月接受iCCA选择性肝切除的连续成年患者（n=11）。离体扫描切除标本的感兴趣区域，在福尔马林固定之前，使用1310nm波长的台式OCT装置。对扫描区域进行标记并进行组织学检查，为每次扫描提供诊断。XceptionCNN接受了训练，已验证，并在将OCT扫描与相应的组织学诊断相匹配时进行了测试，通过5×5分层交叉验证过程。
结果：24次三维扫描（对应于大约来自10名患者的85,603名)被纳入分析。在5×5交叉验证中，该模型获得了平均F1分数，灵敏度,特异性分别为0.94、0.94和0.93。
结论：光学相干断层扫描联合CNN可以在体外区分iCCA和肝实质。需要进一步的研究来扩展这些结果，并导致创新的体内OCT应用，如术中或内窥镜扫描。
方法：本研究包括2020年6月至2021年4月接受iCCA选择性肝切除的连续成年患者（n=11）。离体扫描切除标本的感兴趣区域，在福尔马林固定之前，使用1310nm波长的台式OCT装置。对扫描区域进行标记并进行组织学检查，为每次扫描提供诊断。XceptionCNN接受了训练，已验证，并在将OCT扫描与相应的组织学诊断相匹配时进行了测试，通过5×5分层交叉验证过程。
结果：24次三维扫描（对应于大约来自10名患者的85,603名)被纳入分析。在5×5交叉验证中，该模型获得了平均F1分数，灵敏度,特异性分别为0.94、0.94和0.93。
结论：光学相干断层扫描联合CNN可以在体外区分iCCA和肝实质。需要进一步的研究来扩展这些结果，并导致创新的体内OCT应用，如术中或内窥镜扫描。
