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。

基本信息

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
项目类别：
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

项目摘要

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每年造成的死亡人数是黑色素瘤的两倍。治疗 NMSC 需要进行皮肤活检，并进行处理和染色，以便根据标准进行分析光学显微镜这个过程对患者来说是痛苦的，而且活检具有侵入性。引入了 NMSC 检测的延迟，这会导致患者发病率增加，并增加 Enspectra Health 的使命是带来数字肿瘤学。早期癌症检测的护理点诊断，其中医疗费用和癌症是一种固有的细胞功能障碍，但对患者的负担却很小。医学仍然缺乏在不进行活检的情况下观察细胞组织学的基本能力。临床需求是 Enspectra 的核心动机，其创新旨在解决这一问题。对更早检测 NMSC 的更好方法的未满足的临床需求将直接进入 II 期临床。申请建立在已获得的第一期、第二期和 IIB 期项目进展的基础上（R43CA221591、R44CA221591）在这些项目中，Enspectra 从概念、通过技术可行性并进入临床试验，最近完成了关键的注册向 FDA 提交 510(k) 批准的试验（ClinicalTrials.gov：NCT05619471）。 Enspectra 创造了第一台便携式光纤耦合组合多光子显微镜 (MPM) 和反射共焦显微镜 (RCM) 系统用于 NMSC 体内成像。直接进入第二阶段提案，Enspectra 旨在利用其多模式数据的分析能力并将其范围扩展到光化性角化病 (AK)，这是一种可以进展为 NMSC 的癌前病变。 Enspectra 将在之前为患者建立一个大规模 AK 组织病理学数字数据库对治疗无反应的 AK 更有可能进展为 NMSC。 Enspectra 将使用治疗结果作为 AK 严重程度的指标。训练深度学习算法来预测哪些 AK 仅在病理情况下才会无反应我们的非侵入性图像中的功能能够在有问题的 AK 出现之前识别它们。恶性应加强对高危患者的监测，加快NMSC的检测，减轻低风险患者的手术干预负担。