Department of Surgery, Netherlands Cancer Institute, Amsterdam, the Netherlands.
Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Amsterdam, Netherlands.
J Biophotonics. 2019 Nov;12(11):e201900086. doi: 10.1002/jbio.201900086. Epub 2019 Jul 23.
Hyperspectral imaging is a promising technique for resection margin assessment during cancer surgery. Thereby, only a specific amount of the tissue below the resection surface, the clinically defined margin width, should be assessed. Since the imaging depth of hyperspectral imaging varies with wavelength and tissue composition, this can have consequences for the clinical use of hyperspectral imaging as margin assessment technique. In this study, a method was developed that allows for hyperspectral analysis of resection margins in breast cancer. This method uses the spectral slope of the diffuse reflectance spectrum at wavelength regions where the imaging depth in tumor and healthy tissue is equal. Thereby, tumor can be discriminated from healthy breast tissue while imaging up to a similar depth as the required tumor-free margin width of 2 mm. Applying this method to hyperspectral images acquired during surgery would allow for robust margin assessment of resected specimens. In this paper, we focused on breast cancer, but the same approach can be applied to develop a method for other types of cancer.
高光谱成像是一种很有前途的癌症手术中评估切缘的技术。因此,只应评估切除面下方的特定组织量,即临床定义的切缘宽度。由于高光谱成像的成像深度随波长和组织成分而变化,这可能会对高光谱成像作为切缘评估技术的临床应用产生影响。在这项研究中,开发了一种用于乳腺癌切缘高光谱分析的方法。该方法使用在肿瘤和健康组织的成像深度相等的波长区域内漫反射光谱的光谱斜率。由此,可以在与所需的 2 毫米无肿瘤切缘宽度相似的深度上对肿瘤与健康的乳腺组织进行区分。将该方法应用于术中采集的高光谱图像将允许对切除标本进行可靠的切缘评估。在本文中,我们专注于乳腺癌,但同样的方法也可以应用于开发其他类型癌症的方法。