Machine Learning and Reflectance Confocal Microscopy for Biopsy-free Virtual Histology of Squamous Skin Neoplasms

尽管在非侵入性医学成像方面有所改进以帮助诊断内部恶性肿瘤， 非侵入性地对皮肤成像的改进一直较慢。皮肤镜，一种可以给人 皮肤的放大和偏振视图，是通常用于临床评估的唯一辅助工具， 皮肤科医生协助诊断。角化细胞癌、基底细胞癌和鳞状细胞癌 癌症是迄今为止在美国诊断出的最常见的癌症。由于太阳暴晒， 军事部署，我们国家的退伍军人有一个发展这些和其他皮肤的可能性增加 与普通人群相比，癌症。早期角化细胞癌通常很难区分 临床上来自受刺激/发炎的癌前或良性皮肤病变（光化性或脂溢性角化病）。一个非- 可以帮助皮肤科医生获得皮肤损伤诊断的侵入性技术可以防止不必要的 活检，导致疤痕较少，以及允许诊断和确定性治疗皮肤恶性肿瘤， 相同的诊所访问，改善临床工作流程和患者获得皮肤科诊所。最近批准的 皮肤成像技术，反射共聚焦显微镜（RCM），提供了最先进的细胞水平 皮肤的分辨率没有活检，但仍然有许多局限性，限制其效用，只有最熟练的 用户.我们最近开始使用基于软件的数字增强技术来增强未染色的冷冻组织的自发荧光。 显微镜载玻片的组织切片，以虚拟染色未固定的组织，并提供快速的组织学质量图像 而不需要实际处理所需的费力的组织处理。我们的首要假设是 我们可以应用我们的数字技术来克服RCM的许多技术限制， 皮肤科医生或病理学家通过RCM获得更准确的皮肤病变诊断的能力， 需要皮肤活检。我们的初步数据表明，我们的软件算法可以数字化增强 正常皮肤和基底细胞癌的RCM图像，产生组织学质量图像。在目标1中，我们 使用方法学和计算方法来完善组织处理和数据采集， 皮肤图像的最佳配准，以获得最高质量的数据集来训练机器学习 算法在目标2中，我们将纳入炎症和非炎症性脂溢性角化病，光化性角化病， 鳞状细胞癌皮肤病变，将这些病变的特征纳入我们的算法， 用于正常皮肤和基底细胞癌。在目标3中，我们将进行试点研究，以测试优化的 通过前瞻性地收集各种患者的连续皮肤病变的图像的虚拟组织学算法 样品我们将比较新手和专家RCM皮肤病学和病理学用户如何在获得 使用具有和不具有虚拟组织学算法的RCM进行诊断。如果成功，这些研究将提供 为退伍军人和平民的皮肤癌的非侵入性诊断迈出了第一步。

Despite improvements in non-invasive medical imaging to aid in the diagnosis of internal malignancy, improvements in imaging the skin non-invasively have been slower. The dermatoscope, a device that gives a magnified and polarized view of the skin, is the only ancillary tool commonly used for clinical assessment by dermatologists to assist in diagnosis. Keratinocyte carcinomas, basal cell carcinoma and squamous cell carcinoma, are by far the most common cancers diagnosed in the United States. Due to sun exposure during military deployment, our nation’s Veterans have an increased likelihood of developing these and other skin cancers compared to the general population. Early keratinocyte carcinomas are often difficult to distinguish clinically from irritated/inflamed precancerous or benign skin lesions (actinic or seborrheic keratoses). A non- invasive technology that can assist dermatologists obtain a diagnosis of skin lesions may prevent unnecessary biopsy, resulting in fewer scars, as well as allow diagnosis and definitive treatment of skin malignancies in the same clinic visit, improving clinical workflow and patient access to dermatology clinics. A recently approved skin imaging technology, reflectance confocal microscopy (RCM), provides state-of-the-art cellular level resolution of the skin without biopsy, but still has many limitations, limiting its utility to only the most skilled users. We recently began using software-based digital enhancements to autofluorescence of unstained frozen tissue sections of microscopic slides to virtually stain unfixed tissue and provide rapid histology quality images without requiring the laborious tissue processing required of actual processing. Our overarching hypothesis is that we can apply our digital technology to overcome many of the technical limitations of RCM, and improve the dermatologists’ or pathologist’s ability to obtain more accurate diagnosis of skin lesion by RCM without requiring skin biopsy. Our preliminary data demonstrates that our software algorithms can digitally enhance RCM images of normal skin and basal cell carcinoma, resulting in histologic quality images. In Aim 1, we will use methodological and computational approaches to refine tissue processing and data acquisition to provide optimal registration of skin images to obtain the highest quality data sets to train the machine learning algorithm. In Aim 2, we will incorporate inflamed and uninflamed seborrheic keratosis, actinic keratoses, and squamous cell carcinoma skin lesions to incorporate features of these lesions into our algorithms originally developed for normal skin and basal cell carcinoma. In Aim 3, we will perform a pilot study to test the optimized virtual histology algorithm by prospectively collecting images of consecutive skin lesions on a variety of patient samples. We will compare how novice and expert RCM dermatology and pathology users perform in obtaining diagnosis using RCM with and without the virtual histology algorithm. If successful, these studies will provide an initial step towards noninvasive diagnosis of skin cancer for Veterans and civilians.