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

机器学习和反射共焦显微镜用于鳞状皮肤肿瘤的免活检虚拟组织学

• 批准号: 10364550
• 负责人: PHILIP SCUMPIA
• 金额: --
• 依托单位: VA GREATER LOS ANGELES HEALTHCARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10364550
• 关键词: Acetic Acids; Adoption; Age; Algorithmic Software; Algorithms; Architecture; Arizona; Basal cell carcinoma; Benign; Biopsy; COVID-19 pandemic; Carcinoma; Caring; Cicatrix; Clinic; Clinic Visits; Clinical; Clinical assessments; Clinics and Hospitals; Color; Communities; Computer software; Data; Data Set; Dermatologic; Dermatologist; Dermatology; Development; Devices; Diagnosis; Early Diagnosis; Early treatment; Enhancement Technology; Epidermis; Exposure to; Freezing; Future; General Population; Goals; Growth; Healthcare; Histologic; Histology; Image; Imaging Device; Imaging technology; Individual; Junctional Nevus; Lesion; Libraries; Life; Machine Learning; Malignant - descriptor; Malignant Epithelial Cell; Malignant Neoplasms; Medical Care Costs; Medical Imaging; Methodology; Microscopic; Military Personnel; Neoplasms; Network-based; Nuclear; Optical Coherence Tomography; Output; Pathologist; Pathology; Patient Care; Patients; Pilot Projects; Procedures; Resolution; Risk; Sampling; Scanning; Seborrheic keratosis; Signal Transduction; Skin; Skin Cancer; Skin Neoplasms; Slide; Squamous cell carcinoma; Stains; Sun Exposure; Techniques; Technology; Testing; The Sun; Time; Tissue Stains; Tissues; Training; Triage; United States; Universities; Veterans; Visit; Wait Time; Work; accurate diagnosis; base; cancer diagnosis; cost; data acquisition; deep learning; deep learning algorithm; diagnosis evaluation; diagnostic accuracy; digital; digital imaging; generative adversarial network; histological image; histological stains; imaging modality; improved; in vivo; keratinocyte; machine learning algorithm; microscopic imaging; military service; multiphoton microscopy; noninvasive diagnosis; novel; portability; premalignant; prevent; prospective; reflectance confocal microscopy; service member; skin disorder; skin lesion; skin squamous cell carcinoma; sun protection; technology development; teledermatology; telehealth; tissue processing; tool; tumor; uptake; virtual

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.

尽管非侵入性医学成像有所改善以帮助诊断内部恶性肿瘤，但 对皮肤进行成像的改进，非侵入性的速度很慢。皮肤镜，一种给出的设备 皮肤的放大和极化视图是唯一用于临床评估的辅助工具 皮肤科医生协助诊断。角质形成细胞癌，碱性细胞癌和鳞状细胞 癌是迄今为止美国被诊断出的最常见的癌症。由于在阳光下暴露 军事部署，我们国家的退伍军人有可能发展这些皮肤和其他皮肤的可能性 癌症与普通人群相比。早期角质形成癌通常很难区分 临床上来自炎症/发炎的癌前或良性皮肤病变（活化或脂肪性角膜造成）。非 - 可以帮助皮肤科医生获得皮肤病变诊断的侵入性技术可能会阻止不必要 活检，导致疤痕减少，并允许对皮肤恶性肿瘤的诊断和确切治疗 同样的诊所就诊，改善了临床工作流程和患者进入皮肤病学诊所的机会。最近批准的 皮肤成像技术，反射率共聚焦显微镜（RCM）提供最先进的细胞水平 没有活检的皮肤分辨率，但仍然有许多局限性，仅限于最熟练的效用 用户。我们最近开始使用基于软件的数字增强功能来自动荧光未染色的冷冻 微观幻灯片的组织切片几乎染色未粘的组织并提供快速的组织学质量图像 不需要实际处理所需的实验室组织处理。我们的总体假设是 我们可以运用数字技术来克服RCM的许多技术局限性，并改善 皮肤科医生或病理学家能够通过RCM获得更准确的皮肤病变诊断 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 正常皮肤和基本细胞癌的RCM图像，导致组织学质量图像。在AIM 1中，我们将 使用方法和计算方法来完善组织处理和数据获取 皮肤图像的最佳注册以获取训练机器学习的最高质量数据集 算法。在AIM 2中，我们将融合发炎且未发炎的脂肪脂性角化病，光化性角膜结构和 鳞状细胞癌皮肤病变最初将这些病变的特征纳入我们的算法 为正常皮肤和基本细胞癌而开发。在AIM 3中，我们将进行一项试点研究以测试优化 虚拟组织学算法通过前瞻性收集各种患者的连续皮肤病变图像 样品。我们将比较用户在获取方面的新颖和专家RCM皮肤病学和病理学 使用有或没有虚拟组织学算法的RCM诊断。如果成功，这些研究将提供 朝着退伍军人和平民对皮肤癌无创诊断的第一步。