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

基本信息

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
项目类别：
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

项目摘要

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技术的进步有助于诊断内部恶性肿瘤，皮肤镜（一种提供皮肤成像的设备）的进步速度较慢。皮肤的放大和偏光视图，是临床评估中唯一常用的辅助工具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因阳光照射而诊断出的最常见的癌症。军事部署，我们国家的退伍军人开发这些和其他皮肤的可能性增加与一般人群相比，早期角质形成细胞癌通常很难区分。临床上由刺激/发炎的癌前或良性皮肤病变（光化性或脂溢性角化病）引起。侵入性技术可以帮助皮肤科医生获得皮肤病变的诊断，可以防止不必要的皮肤损伤活检，减少疤痕，并允许诊断和明确治疗皮肤恶性肿瘤同一诊所就诊，改善了临床工作流程和患者进入皮肤科诊所的机会。皮肤成像技术，反射共焦显微镜 (RCM)，提供最先进的细胞水平无需活检即可解析皮肤，但仍然有许多局限性，仅限于最熟练的人使用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冷冻的自发荧光。显微载玻片的组织切片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不需要实际处理所需的费力的组织处理我们的总体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 获得更准确的皮肤病变诊断的能力，而无需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，我们将。使用方法论和计算方法来完善组织处理和数据采集，以提供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在目标 2 中，我们将合并发炎和未发炎的脂溢性角化病、光化性角化病和鳞状细胞皮肤病变，将这些癌病变的特征纳入我们最初的算法中在目标 3 中，我们将进行一项试点研究来测试优化后的效果。虚拟组织学算法，前瞻性地收集各种患者的连续皮肤病变图像我们将比较 RCM 皮肤病学和病理学新手和专家用户在获取样本方面的表现。如果成功，这些研究将提供使用带有或不带有虚拟组织学算法的 RCM 进行诊断。为退伍军人和平民实现皮肤癌无创诊断的第一步。