Noninvasive prediction of skin precancer severity using in vivo cellular imaging and deep learning algorithms.

使用体内细胞成像和深度学习算法无创预测皮肤癌前病变的严重程度。

• 批准号: 10761578
• 负责人: Gabriel Nestor Sanchez
• 金额: $ 129.19万
• 依托单位: ENSPECTRA HEALTH, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10761578
• 关键词: Acceleration; Actinic keratosis; Address; Affect; Algorithms; American; Atypia; Award; Base Sequence; Biopsy; Biopsy Specimen; Cancer Detection; Classification; Clinical; Clinical Trials; Color; Computer software; Coupled; Cream; Data; Data Set; Databases; Dermatologist; Dermatology; Detection; Diagnosis; Diagnostic; Enrollment; Epidermis; Evaluation; Excision; Fiber; Fluorouracil; Functional disorder; Generations; Goals; Health; Health Care Costs; Healthcare Systems; Histologic; Histology; Histopathology; Human; Human body; Image; Imaging technology; Intervention; Lasers; Learning; Lesion; Literature; Malignant - descriptor; Malignant Neoplasms; Measures; Medical History; Methods; Microscope; Mission; Modality; Modern Medicine; Morbidity - disease rate; Motivation; Multicenter Trials; Multimodal Imaging; Oncology; Operative Surgical Procedures; Optics; Outcome; Pain; Pathologic; Pathology; Patient-Focused Outcomes; Patients; Persons; Phase; Physiologic pulse; Preparation; Probability; Process; Recurrence; Resolution; Risk; Scanning; Screening for Skin Cancer; Screening for cancer; Severities; Silicon; Skin; Skin Carcinoma; Slide; Sorting; Specimen; Squamous cell carcinoma; Stains; System; Technology; Thick; Training; Validation; algorithm training; care burden; cellular imaging; cost; deep learning algorithm; digital; digital pathology; follow-up; high risk; imaging system; improved; in vivo; in vivo imaging; in vivo imaging system; innovation; melanoma; multidimensional data; multimodal data; multiphoton microscopy; non-invasive imaging; novel; payment; personalized predictions; photomultiplier; point of care; portability; predict responsiveness; premalignant; progression risk; reflectance confocal microscopy; response; spectrograph; therapy outcome

Nonmelanoma skin cancer (NMSC) represents the most common form of cancer in the human body and causes twice as many fatalities each year as melanoma. The method for diagnosing and treating NMSCs requires a skin biopsy that is processed and stained for analysis on a standard optical microscope. This process is painful for patients, and the invasiveness of biopsy introduces a delay into NMSC detection, which contributes to patient morbidity and adds substantial cost to the healthcare system. Enspectra Health’s mission is to bring digital oncology diagnostics to the point-of-care for earlier cancer detection where the healthcare cost and burden to patients is minimal. Cancer is an inherently cellular disfunction, and yet modern medicine still lacks the basic ability to view cellular histology without biopsy. This widespread clinical need is the core motivation for Enspectra and its innovations. Enspectra aims to address this unmet clinical need for a better method to detect NMSCs earlier. This direct to Phase II application builds on the progress of awarded Phase I, Phase II, and Phase IIB projects (R43CA221591, R44CA221591). In these projects, Enspectra has progressed from concept, through technical feasibility, and into clinical trials, recently completing enrollment in a pivotal trial for submission to the FDA for 510(k) approval (ClinicalTrials.gov: NCT05619471). Enspectra has created the first portable, fiber coupled, combined multiphoton microscopy (MPM) and reflectance confocal microscopy (RCM) system for in vivo imaging of NMSC. In this direct to Phase II proposal, Enspectra aims to leverage the analytic power of its multimodal data and extend its reach to Actinic Keratosis (AK), a precancerous lesion that can progress to NMSC. Enspectra will build a large-scale digital database of histopathology in AKs on patients before topical therapy. AKs that do not respond to therapy are more likely to progress to NMSC and are clinically of higher risk. Using the therapy outcome as an indicator of AK severity, Enspectra will train a deep learning algorithm to predict which AKs would be unresponsive solely on pathologic features in our noninvasive images. The ability to identify problematic AKs before they become malignant should improve surveillance of high-risk patients, hasten detection of NMSC, and lessen the burden of surgical intervention to low-risk patients.

非黑色素瘤皮肤癌（NMSC）是人类最常见的癌症形式， 它每年造成的死亡人数是黑色素瘤的两倍。用于诊断和 治疗NMSC需要对皮肤活检进行处理和染色， 光学显微镜这个过程对病人来说是痛苦的，活检的侵入性 在NMSC检测中引入延迟，这导致患者发病率增加， 医疗保健系统的巨大成本。Enspectra Health的使命是将数字肿瘤学 早期癌症检测的护理点诊断，其中医疗保健成本和 对患者的负担很小。癌症是一种固有的细胞功能障碍，但现代 医学仍然缺乏在没有活组织检查的情况下观察细胞组织学的基本能力。这种广泛 临床需求是Enspectra及其创新的核心动力。Enspectra旨在解决 这种未满足的临床需求是更早地检测NMSC的更好方法。这直接到第二阶段 应用程序建立在授予的第一阶段、第二阶段和第二B阶段项目的进展基础上 （R43CA221591，R44CA221591）。在这些项目中，Enspectra已经从概念， 通过技术可行性，并进入临床试验，最近完成了一个关键的招募， 提交给FDA进行510（k）批准的试验（ClinicalTrials.gov：NCT 05619471）。 Enspectra创建了第一个便携式光纤耦合组合多光子显微镜 (MPM)和反射共聚焦显微镜（RCM）系统用于NMSC的体内成像。在这 针对第二阶段的建议，Enspectra的目标是利用其多模态数据的分析能力 并将其延伸到光化性角化病（AK），这是一种可进展为NMSC的癌前病变。 Enspectrum将建立一个大规模的数字化数据库， 局部治疗对治疗无反应的AK更有可能进展为NMSC， 临床风险较高。使用治疗结果作为AK严重程度的指标，Enspectra将 训练深度学习算法，以预测哪些AK仅对病理性 在我们的非侵入性图像中。在问题AK步枪成为 恶性肿瘤应加强对高危患者的监测，加速NMSC的检测， 减轻低风险患者的手术干预负担。