Multimodal confocal microscopy for surgical guidance of skin resections

摘要 莫氏显微手术（Mohs）是治疗非黑色素瘤皮肤癌最有效的方法。莫氏 通过术中使用冷冻切片评估手术切缘获得了较高的成功率（98%治愈率） 组织病理学不幸的是，复杂的基础设施和艰苦的过程需要执行冻结 切片组织病理学导致冗长、昂贵的手术，限制了获得治疗的机会，并导致护理的不平等。 我们建议开发“光学莫氏”作为一种快速，低基础设施的替代莫氏适应症的患者， 农村和其他未得到充分服务的人群目前不接受莫氏手术。我们的光学Mohs 方法将基于多模态共焦显微镜（MCM）与机器学习相结合，以提供 一种低基础设施的自动诊断工具，需要最少的组织处理。MCM组合 反射、荧光和拉曼共焦显微镜整合到一个台式平台上。MCM（使用 反射率和荧光）最近已经证明在产生未处理的， 病理学家可以与冷冻切片组织病理学相比准确地读取新鲜切除的皮肤。 然而，这种方法本身仍然需要病理学家阅读图像。机器学习正在探索中 自动诊断这些图像，但尚未产生足够的准确性。我们假设 拉曼光谱的增加将显著提高自动化 approach.拉曼是一种补充方法，它对皮肤的分子组成敏感， 在皮肤内的临床边缘检测研究中得到证实，灵敏度为92-100%，特异性为 84-93%;然而，其采用的关键障碍是其缓慢的收购速度。我们引入两 在拉曼采集（超像素和线扫描）方面的创新，能够快速采集拉曼 与手术引导兼容（速度为1cm 2/min）。我们对30名患者的初步模型表明， 根据结构反射共聚焦图像和生化信息训练的预测模型 从拉曼图像中提取的特征区分基底细胞癌与具有非常高的 准确性，这表明光学莫氏可以帮助皮肤科医生“保持切割”，以消除所需的 完整的肿瘤（100%的灵敏度），而不去除过量的健康组织（92%的特异性）。我们 将设计，制造和实验室测试的MCM仪器（目标1）。我们将设计一个决策支持系统 使用108例患者的术后数据集，基于MCM图像进行肿瘤边缘评估（目的2）。我们 将确定基于MCM的肿瘤边缘评估决策支持系统的准确性 在72例患者中进行术中成像（目的3）。潜在的临床结果将证明 光学莫氏引导手术可用于常规莫氏指示但目前不适用的地方 使用，扩大访问莫氏的准确性，目前没有得到他的照顾水平的人群。

Abstract Mohs micrographic surgery (Mohs) is the most effective method to treat nonmelanoma skin cancer. Mohs achieves high success (98% cure rates) by assessing surgical margins intraoperatively with frozen section histopathology. Unfortunately, the sophisticated infrastructure and laborious process needed to perform frozen section histopathology leads to lengthy, expensive surgeries that limit access and result in disparities of care. We propose to develop "optical Mohs" as a rapid, low-infrastructure alternative for Mohs-indicated patients in rural and other underserved populations who do not currently undergo Mohs surgery. Our optical Mohs approach will be based on multimodal confocal microscopy (MCM) combined with machine learning to provide a low infrastructure, automated diagnostic tool requiring minimal tissue processing. MCM combines reflectance, fluorescence, and Raman confocal microscopy into a single benchtop platform. MCM (using reflectance and fluorescence) has recently demonstrated success in producing H&E images of unprocessed, freshly excised skin that pathologist can read with accuracy comparable to frozen section histopathology. However, this approach alone still requires a pathologist to read the image. Machine learning is being explored to automate the diagnosis of these images, but has not yet yielded sufficient accuracy. We hypothesize that the addition of Raman spectroscopy will significantly increase the diagnostic accuracy of an automated approach. Raman is a complementary approach that is sensitive to the skin’s molecular composition and has been proven in clinical margin detection studies within the skin with sensitivities of 92-100% and specificities of 84-93%; however, a critical barrier to its adoption has been its slow acquisition speed. We introduce two innovations in Raman acquisition (superpixel and line scanning) that enable acquisition of Raman at speeds compatible with surgical guidance (speeds of 1cm2/min.). Our preliminary model in thirty patients demonstrates that a predictive model trained on both structural reflectance confocal images and biochemical information extracted from Raman images discriminates basal cell carcinoma from normal structures with very high accuracy, suggesting that optical Mohs could help dermatologists "keep cutting" as needed to remove the entire tumor (100% sensitivity) while not removing an excessive amount of healthy tissue (92% specificity). We will design, fabricate and bench-test an MCM instrument (Aim 1). We will design a decision-support system for tumor margin assessment based on MCM images using a post-surgery data set in 108 patients (Aim 2). We will determine the accuracy of the decision support system for tumor margin assessment based on MCM imaging in an intraoperative setting in 72 patients (Aim 3). The potential clinical outcome would demonstrate that an optical Mohs guided surgery could be used where conventional Mohs is indicated but not currently used, expanding access of Mohs’ accuracy to populations currently not receiving his level of care.