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外周和深部切缘评估中荧光分子成像的应用前景

Perspective on the use of fluorescence molecular imaging for peripheral and deep margin assessment.

作者信息

Nguyen Hang M, Torres Veronica C, Levy Joshua, Chen Eunice Y, LeBoeuf Matthew, Samkoe Kimberley S

机构信息

Dartmouth College, Thayer School of Engineering, Hanover, New Hampshire, United States.

Cedars-Sinai Medical Center, Department of Pathology and Laboratory Medicine, Los Angeles, California, United States.

出版信息

J Biomed Opt. 2025 Jan;30(Suppl 1):S13711. doi: 10.1117/1.JBO.30.S1.S13711. Epub 2025 May 2.


DOI:10.1117/1.JBO.30.S1.S13711
PMID:40321301
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12048172/
Abstract

SIGNIFICANCE: Current standard practice for margin assessment in solid tumor resection often leads to suboptimal results due to the inability to assess margins completely in a time-efficient manner. On the other hand, for small skin cancers, peripheral and deep margin assessment (PDEMA) offers 100% assessment of margins while sparing the utmost amount of normal surrounding tissues. Nonetheless, PDEMA is limited in its use owing to its lengthy tissue processing and imaging time as well as its requirement for high-quality frozen sections and real-time histologic analysis. AIM: We aim to explore fluorescence molecular imaging (FMI) as a tool for resolving obstacles and integrating PDEMA into the surgeon-to-pathologist workflow for large solid tumors. APPROACH: A review of recent pre-clinical and clinical studies using FMI to assess surgical margins was conducted to highlight promising fluorescence imaging technologies utilized in the surgical suite and laboratory. RESULTS: FMI techniques that provide macroscopic resolution are efficient in time and have a notable ability to identify true negative tissue yet have limited capability in identifying true positive tissues. Moreover, meso- and microscopic FMI methods require additional time to attain a higher resolution but deliver an enhanced sensitivity in detecting true positive tissues. In both cases, experts are still required to learn to interpret the FMI signals, which prohibits a seamless clinical integration. CONCLUSIONS: Our proposed margin assessment platform (MAP) incorporates both macroscopic and, meso- or microscopic imaging with post-processing and machine learning for interpretation, to enable the application of PDEMA into solid tumor surgery. MAP leverages the advantages of each technique and thoroughly tackles the limitations of time and expertise to optimize the efficiency and accuracy of margin assessment and ultimately improve clinical outcomes.

摘要

意义:实体肿瘤切除术中切缘评估的当前标准做法往往导致结果不理想,因为无法以高效的方式完全评估切缘。另一方面,对于小皮肤癌,外周和深部切缘评估(PDEMA)可对切缘进行100%评估,同时最大限度地保留周围正常组织。然而,由于其冗长的组织处理和成像时间以及对高质量冰冻切片和实时组织学分析的要求,PDEMA的应用受到限制。 目的:我们旨在探索荧光分子成像(FMI)作为一种工具,以解决相关障碍,并将PDEMA整合到大型实体肿瘤的外科医生与病理学家工作流程中。 方法:对近期使用FMI评估手术切缘的临床前和临床研究进行综述,以突出手术科室和实验室中使用的有前景的荧光成像技术。 结果:提供宏观分辨率的FMI技术在时间上是高效的,并且具有识别真阴性组织的显著能力,但在识别真阳性组织方面能力有限。此外,中观和微观FMI方法需要额外的时间来获得更高的分辨率,但在检测真阳性组织方面具有更高的灵敏度。在这两种情况下,仍需要专家学习解读FMI信号,这阻碍了无缝的临床整合。 结论:我们提出的切缘评估平台(MAP)将宏观、中观或微观成像与后处理和机器学习相结合用于解读,以使PDEMA能够应用于实体肿瘤手术。MAP利用了每种技术的优势,全面解决了时间和专业知识方面的限制,以优化切缘评估的效率和准确性,并最终改善临床结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbe/12048172/50bdeaa90848/JBO-030-S13711-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbe/12048172/38b95a1658b9/JBO-030-S13711-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbe/12048172/06ea9ef88a1c/JBO-030-S13711-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbe/12048172/122b3f34470f/JBO-030-S13711-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbe/12048172/cbdebfc0cca8/JBO-030-S13711-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbe/12048172/7b5ced27fd18/JBO-030-S13711-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbe/12048172/91fa30b53081/JBO-030-S13711-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbe/12048172/e82eea59b1a9/JBO-030-S13711-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbe/12048172/55a448beddd6/JBO-030-S13711-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbe/12048172/50bdeaa90848/JBO-030-S13711-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbe/12048172/38b95a1658b9/JBO-030-S13711-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbe/12048172/06ea9ef88a1c/JBO-030-S13711-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbe/12048172/122b3f34470f/JBO-030-S13711-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbe/12048172/cbdebfc0cca8/JBO-030-S13711-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbe/12048172/7b5ced27fd18/JBO-030-S13711-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbe/12048172/91fa30b53081/JBO-030-S13711-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbe/12048172/e82eea59b1a9/JBO-030-S13711-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbe/12048172/55a448beddd6/JBO-030-S13711-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbe/12048172/50bdeaa90848/JBO-030-S13711-g009.jpg

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