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， 在非侵入性皮肤成像方面的进步一直较慢。皮肤镜，一种能给人 放大和偏振的皮肤视图，是临床评估常用的唯一辅助工具，通过 皮肤科医生协助诊断。角质形成细胞癌、基底细胞癌和鳞状细胞癌 癌症是迄今为止在美国被诊断出的最常见的癌症。由于日光照射， 军事部署，我们国家的退伍军人患这些和其他皮肤的可能性增加 与普通人群相比，癌症的发病率更高。早期角质形成细胞癌通常很难区分。 临床上由刺激/炎症、癌前病变或良性皮肤病变(光化性或脂溢性角化病)引起。一个非- 可以帮助皮肤科医生获得皮肤损害诊断的侵入性技术可能会防止不必要的 活组织检查，从而减少疤痕，以及允许诊断和明确治疗皮肤恶性肿瘤 同样的诊所就诊，改善临床工作流程和患者前往皮肤科诊所的机会。最近批准的一项 皮肤成像技术，反射共焦显微镜(RCM)，提供最先进的细胞水平 没有活组织检查的皮肤分辨率，但仍然有许多局限性，仅限于最熟练的人使用 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冰冻的自体荧光 显微镜切片的组织切片，可对未固定的组织进行虚拟染色，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。我们最重要的假设是 我们可以应用我们的数字技术来克服RCM的许多技术限制，并改进 皮肤科医生或病理学家通过RCM获得更准确的皮肤损害诊断的能力 需要皮肤活组织检查。我们的初步数据表明，我们的软件算法可以在数字上增强 正常皮肤和基底细胞癌的RCM图像，产生组织学质量的图像。在目标1中，我们将 使用方法学和计算方法改进组织处理和数据采集，以提供 皮肤图像的最优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，我们将包括炎症和非炎症脂溢性角化病、光化性角化病，以及 鳞状细胞癌皮肤病变将这些病变的特征合并到我们的算法中 专为正常皮肤和基底细胞癌开发。在目标3中，我们将执行一项初步研究，以测试优化的 通过前瞻性收集不同患者的连续皮肤病变的图像的虚拟组织学算法 样本。我们将比较新手和专家RCM皮肤病和病理学用户在获得 使用带和不带虚拟组织学算法的RCM进行诊断。如果成功，这些研究将提供 向退伍军人和平民非侵入性皮肤癌诊断迈出的第一步。