Feasibility of Breast Cancer Metastasis Assessment of Ex Vivo Sentinel Lymph Nodes through a p-H&E Optical Coherence Microscopic Imaging System

SIMPLE SUMMARY: Pseudo-H&E (p-H&E) imaging using a fluorescence-in-built optical coherence microscopic imaging system (OCMIS) is a novel microstructural technique for assessing en face optical sections of ex vivo specimens. In this study, the comparison of p-H&E, frozen-sectioned H&E, and paraffin-sectioned H&E images was intraoperatively realized through ex vivo sentinel lymph node (SLN) biopsy for breast cancer metastasis assessment. The results revealed that OCMIS is a useful tool for intraoperative breast cancer metastasis assessment through SLN biopsy in patients requiring surgery. ABSTRACT: Frozen-sectioned hematoxylin–eosin (H&E) image evaluation is the current method for intraoperative breast cancer metastasis assessment through ex vivo sentinel lymph nodes (SLNs). After frozen sectioning, the sliced fatty region of the frozen-sectioned specimen is easily dropped because of different freezing points for fatty tissues and other tissues. Optical-sectioned H&E images provide a nondestructive method for obtaining the insight en face image near the attached surface of the dissected specimen, preventing the freezing problem of fatty tissue. Specimens from 29 patients at Wanfang Hospital were collected after excision and were analyzed at the pathology laboratory, and a fluorescence-in-built optical coherence microscopic imaging system (OCMIS) was then used to visualize the pseudo-H&E (p-H&E) images of the SLNs for intraoperative breast cancer metastasis assessment, and the specificity, sensitivity, and accuracy were 100%, 88.9%, and 98.8% (n = 83), respectively. Compared with gold-standard paraffin-sectioned H&E images, the specificity, sensitivity, and accuracy obtained with the frozen-sectioned H&E images (n = 85) of the specimens were the same as those obtained with the p-H&E images (n = 95). Thus, OCMIS is a useful noninvasive image-assisted tool for breast cancer metastasis assessment based on SLN images.