Leveraging artificial intelligence/machine learning-based technology to overcome specialized training and technology barriers for the diagnosis and prognostication of colorectal cancer in Africa

利用基于人工智能/机器学习的技术克服非洲结直肠癌诊断和预测的专业培训和技术障碍

• 批准号: 10712793
• 负责人: Ulysses Gregory John Balis
• 金额: $ 25万
• 依托单位: AGA KHAN UNIVERSITY (KENYA)
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10712793
• 关键词: Address; Adjuvant; Africa; African; Algorithmic Analysis; Algorithms; Architecture; Area; Artificial Intelligence; Biological; Breast; Cancer Detection; Cancer Etiology; Cancer Prognosis; Cells; Cellular Morphology; Cervical; Cessation of life; Classification; Clinical; Collaborations; Colorectal Cancer; Communities; Computer Vision Systems; Computer software; Computers; Country; Data; Data Science; Diagnosis; Diagnostic; Disease; E-learning; Early Diagnosis; Education; Eye; Funding; Future; Goals; Hematoxylin and Eosin Staining Method; Histologic; Histology; Histopathology; Hospitals; Human; Image; Image Analysis; Incidence; Income; Individual; Infrastructure; Intervention; Kenya; Machine Learning; Malignant Neoplasms; Measurement; Methods; Michigan; Oncologist; Online Systems; Outcome; Pathologist; Pathology; Pattern; Performance; Physical shape; Population; Prognosis; Prostate; Public Hospitals; Radiation Oncologist; Reagent; Recommendation; Reproducibility; Research; Research Project Grants; Resources; Retrospective cohort; Risk Assessment; Rural; Services; Site; Slide; Stains; Surgeon; System; Techniques; Technology; Texture; Therapeutic; Tissue Sample; Training; Treatment Effectiveness; Universities; University Hospitals; Work; accurate diagnosis; cancer care; cancer diagnosis; clinical care; clinically relevant; cohort; computational pipelines; cost; deep learning; diagnostic algorithm; digital; digital imaging; imaging Segmentation; innovation; machine learning method; machine vision; mortality; open source; prognostic; prognostication; programs; risk stratification; routine imaging; screening; supervised learning; support tools; survivorship; tertiary care; tool; treatment planning; tumor; unsupervised learning; vector

SUMMARY/ABSTRACT Colorectal cancer (CRC) is the third most commonly diagnosed cancer and the second leading cause of cancer related deaths worldwide. Rates in Africa are on the rise, but essential histopathology services critical for cancer care are scarce. To address this barrier, we developed an artificial intelligence (AI)/machine learning (ML)-based computational pipeline (SIVQ/VIPR) that performs automated pixel-level image segmentation and classification from digital images of routinely collected hematoxylin and eosin (H&E)-stained slides. SIVQ/VIPR is highly precise, reproducible, and outperforms subject matter experts. Once histologically distinct regions are identified, image analysis algorithms can then identify individual regions and aggregate them to predict diagnostic and prognostic features in conjunction with clinical outcomes to guide treatment. Our overall approach is to leverage our validated SIVQ/VIPR computational pipeline to develop and validate an AI-based diagnostic decision support (AI-DDS) tool for CRC diagnosis and prognosis in an existing Kenyan cohort. To carry out this work, the Aga Khan University (AKU)- East Africa and the University of Michigan have partnered with Tenwek Hospital, a non-academic community-based public hospital in rural Bomet, Kenya, to develop a unique collaboration of oncologists, pathologists, surgeons, statisticians, and informaticians, making us uniquely suited to develop population-relevant, affordable, and scalable data science solutions in Kenya – all priorities of the DS-I Africa Program. We will: Aim 1. Adapt and validate an existing ML-based diagnostic algorithm for CRC using digital fields of view from H&E-stained slides in a retrospective cohort of n=675 CRC cases from the AKU and Tenwek Hospitals. We will apply the CRC-trained SIVQ/VIPR computational pipeline for segmentation and classification for CRC features, followed by a confirmatory classifier step to achieve a case level, binary result of a cancer/no-cancer (i.e., diagnosis). Aim 2. Develop and refine an unsupervised ML method to identify histopathology image-derived measurements associated with CRC prognosis. We will use computer/machine vision approaches to identify image features (e.g., cellular morphology) discriminative of CRC prognosis and biological potential for disease aggressiveness. Combined use of AI-based morphological signatures of aggressive disease (e.g., high-grade tumor architecture) will be compiled with other clinically relevant features towards the goal of generating a multi-axial multiplexed AI-DDS tool that can maximally inform of the biological and metastatic potential of each CRC case. This project will lay the groundwork for an AI-DDS tool for clinicians (e.g., pathologists, oncologists) that facilitates prompt and accurate diagnosis, prognosis, and risk stratification for CRC care in Africa. Because this approach leverages open-source software and can be deployed as a turn-key system intended for web-based cloud deployment, it is well-suited for capacity building, integrating into educational programs, and expanding to other emergent or prevalent cancers (i.e., breast, cervical, prostate) as part of the DS-I Africa Consortium.

摘要/摘要 结直肠癌（CRC）是第三大常见的癌症，也是第二个主要原因 全世界与癌症有关的死亡。非洲的比率正在上升，但基本的组织病理学服务至关重要 因为癌症护理很少。为了解决这一障碍，我们开发了人工智能（AI）/机器学习 （ML）基于自动像素级图像分割和 从常规收集的苏木精和曙红（H＆E）染色的幻灯片的数字图像进行分类。 SIVQ/VIPR 高度精确，可重现且优于主题专家。一旦组织学不同的区域是 识别，图像分析算法可以识别单个区域并汇总以预测 诊断和预后特征与临床结果一起指导治疗。我们的整体 方法是利用我们经过验证的SIVQ/VIPR计算管道来开发和验证基于AI的 现有肯尼亚队列中CRC诊断和预后的诊断决策支持（AI-DDS）工具。到 进行这项工作，Aga Khan大学（AKU） - 东非和密歇根大学已经合作 在肯尼亚乡村的一家非学术社区公立医院Tenwek医院，开发 肿瘤学家，病理学家，外科医生，统计学家和信息能力的独特合作，使我们 独特地适合于肯尼亚开发与人口相关，负担得起和可扩展的数据科学解决方案 - DS-I非洲计划的优先事项。我们将：AIM 1。适应并验证现有的基于ML的诊断 在回顾性n = 675 CRC中，使用H＆E染色幻灯片的数字视野用于CRC算法 来自AKU和Tenwek医院的病例。我们将应用经CRC训练的SIVQ/VIPR计算管道 用于CRC特征的分割和分类，然后进行确认分类器步骤以实现 病例水平，癌症/无癌的二元结果（即诊断）。目标2。开发和完善无监督的ML 识别与CRC相关的组织病理学图像衍生测量的方法 预后。 我们将使用 计算机/机器视觉方法以识别图像特征（例如细胞形态）的歧视性 CRC预后和疾病侵略性的生物潜力。基于AI的形态的联合使用 侵略性疾病的签名（例如，高级肿瘤结构）将与其他诊所合并 相关功能朝着生成多轴多路复用AI-DDS工具的目标 告知每种CRC病例的生物学和转移潜力。这个项目将为 临床医生（例如病理学家，肿瘤学家）的AI-DDS工具，可促进及时准确的诊断， 非洲CRC护理的预后和风险分层。因为这种方法利用开源 软件可以作为用于基于Web的云部署的交钥匙系统部署，非常适合 用于能力建设，融入教育计划，并扩展到其他新兴或普遍的 作为DS-I非洲财团的一部分，癌症（即乳房，宫颈，前列腺）。