Improving Colorectal Cancer Screening and Risk Assessment through Deep Learning on Medical Images and Records

项目摘要/摘要 大多数结直肠癌病例始于结肠或直肠内壁上的一个小肿块，称为息肉。 虽然结直肠息肉是结直肠癌的先兆，但这些息肉需要几年的时间才能 有可能转化为癌症。如果及早发现大肠息肉，就可以在发现之前将其切除。 癌症的进展。玻片上大肠息肉染色组织的显微镜检查 组织病理学实践-是结直肠癌筛查的关键部分，并构成目前 预后和病人管理。息肉的组织病理学特征是诊断息肉的重要原则 确定结直肠癌的风险和患者未来的监测率；然而，现在是时候- 工作强度大，需要多年的专业训练，变异性大，准确率低。此外, 正如领域文献所证明的那样，其他健康因素，如病史和家族史，对 在结直肠癌风险中的重要作用；然而，它们没有在当前的标准指南中考虑 结直肠癌风险评估。因此，迫切需要能够 结合组织病理学和相关的临床/家族信息，帮助临床医生更好地描述 大肠息肉，并更准确地评估结直肠癌的风险。 为了解决这一关键需求，该应用程序建议构建一种新的、自动的图像分析方法，该方法 可以在全载玻片显微镜图像上准确地检测和分类不同类型的大肠息肉。这个 提出的方法将能够识别这些图像上的区分区域和特征 大肠息肉类型，这将为大肠息肉的自动检测提供支持和洞察 整张幻灯片图像。最后，本项目将提供一个准确的风险预测模型，以集成可视化 具有其他危险因素的显微图像的组织学特征和相关的临床信息 用于全面的结直肠癌风险评估的医疗记录。建议的图像分析和 该项目中的预测方法基于一种新的深度学习方法，并依赖于大量的 数据表示和分析的抽象级别。这项提案中开发的技术将是 在调查人员那里接受结直肠癌筛查的患者的数据得到了严格的验证 学术医学中心和新罕布夏州结肠镜检查数据登记处的记录。 在这个项目成功完成后，拟议的生物信息学方法预计将减少 增加病理医生的认知负担，提高他们在组织病理学中的准确性和效率 大肠息肉的特征以及随后的风险评估和后续建议。作为一名 结果：本项目对提高结直肠癌的疗效有显著的积极影响。 筛查计划、精准医疗和公共卫生。

PROJECT SUMMARY/ABSTRACT Most colorectal cancer cases start as a small growth, known as a polyp, on the lining of the colon or rectum. Although colorectal polyps are precursors to colorectal cancer, it takes several years for these polyps to potentially transform into cancer. If colorectal polyps are detected early, they can be removed before they can progress to cancer. The microscopic examination of stained tissue from colorectal polyps on glass slides—the practice of histopathology—is a key part of colorectal cancer screening and forms the current basis for prognosis and patient management. Histopathological characterization of polyps is an important principle for determining the risk of colorectal cancer and future rates of surveillance for patients; however, it is time- intensive, requires years of specialized training, and suffers from high variability and low accuracy. In addition, as is evident by the domain literature, other health factors, such as medical and family history, play an important role in colorectal cancer risk; however, they are not considered in current standard guidelines for colorectal cancer risk assessment. Therefore, there is a critical need for computational tools that can incorporate both histopathological and relevant clinical/familial information to help clinicians better characterize colorectal polyps and more accurately assess risk for colorectal cancer. To address this critical need, this application proposes to build a novel, automatic, image-analysis method that can accurately detect and classify different types of colorectal polyps on whole-slide microscopic images. The proposed approach will be able to identify discriminative regions and features on these images for each colorectal polyp type, which will provide support and insight into the automatic detection of colorectal polyps on whole-slide images. Finally, this project will provide an accurate risk prediction model to integrate visual histology features from microscopic images with other risk factors and relevant clinical information from medical records for a comprehensive colorectal cancer risk assessment. The proposed image analysis and prediction methods in this project are based on a novel deep-learning methodology and rely on numerous levels of abstraction for data representation and analysis. The technology developed in this proposal will be rigorously validated on data from patients undergoing colorectal cancer screening at the investigators’ academic medical center and on the records from the New Hampshire statewide colonoscopy data registry. Upon successful completion of this project, the proposed bioinformatics approach is expected to reduce the cognitive burden on pathologists and improve their accuracy and efficiency in the histopathological characterization of colorectal polyps and in subsequent risk assessment and follow-up recommendations. As a result, this project can have a significant, positive impact on improving the efficacy of colorectal cancer screening programs, precision medicine, and public health.