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改善通过气腔扩散的术中诊断:一种用于冷冻切片分析的冷冻包埋介质充气方法。

Improving intraoperative diagnosis of spread through air spaces: A cryo-embedding-medium inflation method for frozen section analysis.

作者信息

Eguchi Takashi, Matsuoka Shunichiro, Iwaya Mai, Kobayashi Shota, Seshimoto Maho, Mishima Shuji, Hara Daisuke, Kumeda Hirotaka, Miura Kentaro, Hamanaka Kazutoshi, Uehara Takeshi, Shimizu Kimihiro

机构信息

Division of General Thoracic Surgery, Department of Surgery, Shinshu University Hospital, Matsumoto, Japan.

Department of Laboratory Medicine, Shinshu University Hospital, Matsumoto, Japan.

出版信息

JTCVS Tech. 2024 Mar 1;25:170-176. doi: 10.1016/j.xjtc.2024.02.014. eCollection 2024 Jun.

DOI:10.1016/j.xjtc.2024.02.014
PMID:38899076
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11184482/
Abstract

OBJECTIVE

Accurate intraoperative diagnosis of spread through air spaces (STAS), a known poor prognostic factor in lung cancer, is crucial for guiding surgical decision-making during sublobar resections. This study aimed to evaluate the diagnostic sensitivity of STAS using frozen section (FS) slides prepared with the cryo-embedding medium inflation technique.

METHODS

In this prospective study at Shinshu University Hospital, 99 patients undergoing lung resection for tumors <3 cm in size were included, a total of 114 lesions. FS slides were prepared with injecting diluted cryo-embedding medium into the lung parenchyma of resected specimens. The diagnostic performance of these FS slides for STAS detection was evaluated by comparing FS-STAS results with the gold-standard STAS status.

RESULTS

The incidence of STAS, determined by the gold standard, was 43 (38%) of 114 lesions, including 31 (37%) of 84 primary lung cancers and 12 (40%) of 30 metastatic lung tumors. The sensitivity, specificity, positive and negative predictive values, and accuracy of FS slides for STAS detection were 81%, 89%, 81%, 89%, and 86%, respectively. Specifically, in primary lung cancers, these values were 90%, 89%, 82%, 94%, and 89%, respectively. Regarding metastatic lung tumors, the corresponding values were 58%, 89%, 78%, 76%, and 77%, respectively.

CONCLUSIONS

Our adapted cryo-embedding medium inflation method has demonstrated enhanced sensitivity in detecting STAS on FS slides, providing results similar to the gold-standard STAS detection. Compared with historical benchmarks, this technique could show excellent performance and be readily incorporated into clinical practice without requiring additional resources beyond those used for standard FS analysis.

摘要

目的

准确术中诊断气腔播散(STAS),这是肺癌中已知的不良预后因素,对于指导亚肺叶切除术中的手术决策至关重要。本研究旨在评估使用冷冻包埋介质充气技术制备的冰冻切片(FS)对STAS的诊断敏感性。

方法

在信州大学医院进行的这项前瞻性研究中,纳入了99例因肿瘤大小<3 cm而接受肺切除的患者,共114个病灶。通过将稀释的冷冻包埋介质注入切除标本的肺实质来制备FS切片。通过将FS-STAS结果与金标准STAS状态进行比较,评估这些FS切片对STAS检测的诊断性能。

结果

根据金标准确定的STAS发生率为114个病灶中的43个(38%),包括84例原发性肺癌中的31个(37%)和30例转移性肺肿瘤中的12个(40%)。FS切片对STAS检测的敏感性、特异性、阳性和阴性预测值以及准确性分别为81%、89%、81%、89%和86%。具体而言,在原发性肺癌中,这些值分别为90%、89%、82%、94%和89%。对于转移性肺肿瘤,相应的值分别为58%、89%、78%、76%和77%。

结论

我们改进的冷冻包埋介质充气方法在检测FS切片上的STAS方面表现出更高的敏感性,提供的结果与金标准STAS检测相似。与历史基准相比,该技术表现出色,无需超出标准FS分析所需的额外资源即可轻松纳入临床实践。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9500/11184482/5dbdc69862f1/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9500/11184482/4eddbace477d/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9500/11184482/894a2541e756/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9500/11184482/c598b3b295ff/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9500/11184482/eddfd4406200/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9500/11184482/5dbdc69862f1/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9500/11184482/4eddbace477d/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9500/11184482/894a2541e756/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9500/11184482/c598b3b295ff/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9500/11184482/eddfd4406200/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9500/11184482/5dbdc69862f1/gr3.jpg

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N Engl J Med. 2023 Feb 9;388(6):489-498. doi: 10.1056/NEJMoa2212083.
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3D convolutional neural network model from contrast-enhanced CT to predict spread through air spaces in non-small cell lung cancer.基于增强 CT 的三维卷积神经网络模型预测非小细胞肺癌气腔播散
Diagn Interv Imaging. 2022 Nov;103(11):535-544. doi: 10.1016/j.diii.2022.06.002. Epub 2022 Jun 27.
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Role of radiomics in predicting lung cancer spread through air spaces in a heterogeneous dataset.
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