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

摘要/摘要 结直肠癌(CRC)是第三种最常见的癌症，也是导致 全球与癌症相关的死亡人数。非洲的发病率正在上升，但基本的组织病理学服务至关重要 对于癌症的治疗是稀缺的。为了解决这个障碍，我们开发了一种人工智能(AI)/机器学习 基于(ML)的计算流水线(SIVQ/VIPR)，执行自动像素级图像分割和 从常规收集的苏木精-伊红(H&E)染色切片的数字图像中进行分类。SIVQ/VIPR 精确度高，重现性好，表现优于主题专家。一旦组织学上不同的区域 识别后，图像分析算法可以识别单个区域并将其聚合以进行预测 结合临床结果的诊断和预后特征来指导治疗。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 诊断决策支持(AI-DDS)工具，用于现有肯尼亚队列中的CRC诊断和预后。至 为了开展这项工作，阿加汗大学(AKU)-东非和密歇根大学建立了合作伙伴关系 与Tenwek医院，肯尼亚博梅特农村的一家非学术社区公立医院，开发一种 肿瘤学家、病理学家、外科医生、统计学家和信息学家的独特合作，使我们 非常适合在肯尼亚开发与人口相关、经济实惠且可扩展的数据科学解决方案-所有 DS-I非洲方案的优先事项。我们将：目标1.调整和验证现有的基于ML的诊断 在n=675例CRC回顾队列中使用H&E染色切片的数字视野进行CRC的算法 来自AKU和Tenwek医院的病例。我们将应用CRC训练的SIVQ/VIPR计算流水线 用于CRC特征的分割和分类，然后是确认性分类器步骤，以实现 病例级别，癌症/非癌症(即诊断)的二元结果。目标2.开发和提炼无监督ML 确定与结直肠癌相关的组织病理学图像测量的方法 预后。 我们将使用 计算机/机器视觉方法来识别可区分的图像特征(例如，细胞形态 结直肠癌的预后和疾病侵袭性的生物学潜能。基于人工智能的形态学的组合应用 侵袭性疾病的征象(例如，高级别肿瘤结构)将与其他临床 相关特征，以实现生成多轴多路复用AI-DDS工具的目标，该工具最大限度地 告知每个结直肠癌病例的生物学和转移潜能。该项目将为 AI-DDS工具，用于临床医生(例如，病理学家、肿瘤学家)，促进快速和准确的诊断， 非洲结直肠癌治疗的预后和风险分层。因为这种方法利用了开源 软件，并且可以部署为旨在基于Web的云部署的交钥匙系统，非常适合 能力建设，融入教育项目，并扩展到其他新兴或流行的项目 癌症(即乳腺癌、宫颈癌、前列腺癌)作为DS-I非洲联盟的一部分。

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.