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: 10569029
• 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/10569029
• 关键词: Acetic Acids; Actinic keratosis; 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 Systems; Histologic; Histology; Image; Imaging Device; Imaging technology; Individual; Junctional Nevus; Lesion; Libraries; Licensing; Life; Machine Learning; Malignant - descriptor; Malignant Epithelial Cell; Malignant Neoplasms; Medical Care Costs; Medical Imaging; Methodology; Microscopic; Military Personnel; Neoplasms; 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; Time; Tissue Stains; Tissues; Training; Triage; United States; Universities; Veterans; Visit; Wait Time; Work; accurate diagnosis; cancer diagnosis; cost; data acquisition; deep learning; deep learning algorithm; diagnosis evaluation; diagnostic accuracy; digital; digital imaging; digital technology; generative adversarial network; histological image; histological stains; imaging modality; improved; in vivo; irritation; keratinocyte; machine learning algorithm; microscopic imaging; military service; multiphoton microscopy; noninvasive diagnosis; novel; portability; premalignant; prevent; prospective; reflectance confocal microscopy; service member; skills; 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在帮助诊断内部恶性肿瘤方面有所进步，