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

通过医学图像和记录的深度学习改进结直肠癌筛查和风险评估

• 批准号: 10316231
• 负责人: Saeed Hassanpour
• 金额: $ 35.67万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-12 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10316231
• 关键词: Academic Medical Centers; Address; Architecture; Bioinformatics; Biopsy; Biopsy Specimen; Cancer Etiology; Cessation of life; Characteristics; Colonoscopy; Colorectal; Colorectal Cancer; Colorectal Polyp; Complex; Computer Models; Computing Methodologies; Data; Data Analyses; Data Reporting; Detection; Development; Diagnosis; Diagnostic Errors; Effectiveness; Evaluation; Family; Foundations; Future; Glass; Goals; Growth; Guidelines; Health; Health Care Costs; Histologic; Histology; Histopathology; Hyperplastic Polyp; Image; Image Analysis; Learning; Literature; Malignant Neoplasms; Manuals; Medical; Medical Imaging; Medical Records; Methodology; Methods; Microscopic; New Hampshire; Outcome; Output; Pathologist; Patients; Play; Polyps; Prognosis; Public Health; Recommendation; Recording of previous events; Records; Research Personnel; Risk; Risk Assessment; Risk Factors; Role; Screening for cancer; Screening procedure; Slide; Source; Stress; Structure; Study Section; System; Technology; Testing; Text; Time; Tissue Stains; Training; United States; Visual; base; clinically relevant; cognitive load; colorectal cancer prevention; colorectal cancer progression; colorectal cancer risk; colorectal cancer screening; computerized tools; cost; data registry; deep learning; deep learning model; deep neural network; design; diagnostic technologies; follow-up; high risk; histopathological examination; imaging study; improved; insight; learning strategy; microscopic imaging; mortality; mortality risk; novel; patient health information; precision medicine; prevent; prognostic; risk prediction; risk prediction model; screening; screening program; translational impact; tv watching; whole slide imaging

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.

项目总结/摘要 大多数结直肠癌病例开始于结肠或直肠内壁上的小生长，称为息肉。 虽然结肠直肠息肉是结肠直肠癌的前兆，但这些息肉需要几年时间才能发展成结肠直肠癌。 可能会转化为癌症。如果结肠直肠息肉被早期发现， 发展到癌症。结直肠息肉染色组织的显微镜检查--玻璃片法 组织病理学的实践-是结直肠癌筛查的关键部分，并形成了目前的基础， 预后和患者管理。息肉的组织病理学特征是治疗的重要原则 确定结直肠癌的风险和未来对患者的监测率;然而，现在是时候- 密集的，需要多年的专门培训，并遭受高可变性和低准确性。此外，本发明还提供了一种方法， 正如领域文献所证明的，其他健康因素，如病史和家族史， 在结直肠癌风险中起重要作用;然而，在目前的标准指南中， 结直肠癌风险评估。因此，迫切需要计算工具， 结合组织病理学和相关临床/家族信息，以帮助临床医生更好地表征 结直肠息肉和更准确地评估结直肠癌的风险。 为了解决这一关键需求，本申请提出构建一种新颖的自动图像分析方法， 可以在全切片显微图像上准确检测和分类不同类型的大肠息肉。的 所提出的方法将能够识别这些图像上的区分区域和特征， 结肠直肠息肉类型，这将为结肠直肠息肉的自动检测提供支持和见解， 全切片图像。最后，本项目将提供一个准确的风险预测模型， 显微镜图像的组织学特征与其他风险因素和相关临床信息， 用于全面的结直肠癌风险评估的医疗记录。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， 数据表示和分析的抽象层次。本提案中开发的技术将 严格验证了在研究者的结肠直肠癌筛查患者的数据， 学术医学中心和新罕布什尔州全州范围的结肠镜检查数据登记处的记录。 在成功完成该项目后，拟议的生物信息学方法预计将减少 减轻病理学家的认知负担，提高他们在组织病理学方面的准确性和效率 结肠直肠息肉的特征以及随后的风险评估和随访建议。作为 因此，该项目可以对提高结直肠癌的疗效产生显著的积极影响 筛查项目、精准医疗和公共卫生。

项目成果

Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities.

• DOI: 10.1016/j.compbiomed.2020.104065
• 发表时间: 2020-12
• 期刊: Computers in biology and medicine
• 影响因子: 7.7
• 作者: Goyal M;Knackstedt T;Yan S;Hassanpour S
• 通讯作者: Hassanpour S

Detection of Colorectal Adenocarcinoma and Grading Dysplasia on Histopathologic Slides Using Deep Learning.

使用深度学习在组织病理学切片上检测结直肠腺癌和分级不典型增生。

• DOI: 10.1016/j.ajpath.2022.12.003
• 发表时间: 2023
• 期刊: The American journal of pathology
• 作者: Kim,Junhwi;Tomita,Naofumi;Suriawinata,AriefA;Hassanpour,Saeed
• 通讯作者: Hassanpour,Saeed

Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides.

• DOI: 10.1038/s41598-021-86540-4
• 发表时间: 2021-03-29
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Zhu M;Ren B;Richards R;Suriawinata M;Tomita N;Hassanpour S
• 通讯作者: Hassanpour S

Graph Convolutional Neural Networks for Histologic Classification of Pancreatic Cancer.

• DOI: 10.5858/arpa.2022-0035-oa
• 发表时间: 2023-11-01
• 期刊: Archives of pathology & laboratory medicine
• 影响因子: 4.6
• 作者: Wu W;Liu X;Hamilton RB;Suriawinata AA;Hassanpour S
• 通讯作者: Hassanpour S

Bladder cancer prognosis using deep neural networks and histopathology images.

• DOI: 10.1016/j.jpi.2022.100135
• 发表时间: 2022
• 期刊: Journal of pathology informatics
• 作者: Barrios, Wayner;Abdollahi, Behnaz;Goyal, Manu;Song, Qingyuan;Suriawinata, Matthew;Richards, Ryland;Ren, Bing;Schned, Alan;Seigne, John;Karagas, Margaret;Hassanpour, Saeed
• 通讯作者: Hassanpour, Saeed