Development and Clinical Implementation of an Artificial Intelligence Tool to Predict Risk of Upgrade of Ductal Carcinoma In Situ

预测导管原位癌升级风险的人工智能工具的开发和临床实施

• 批准号: 9974496
• 负责人: Manisha Bahl
• 金额: $ 26万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-08 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9974496
• 关键词: Adjuvant Therapy; Algorithms; Area; Artificial Intelligence; Assessment tool; Biopsy; Breast Cancer Detection; Cancer Research Project; Clinical; Clinical Data; Computer Vision Systems; Core Biopsy; Data; Data Science; Development; Development Plans; Diagnosis; Duct (organ) structure; Eligibility Determination; Future; Goals; Growth; Guidelines; Histopathology; Hormone Receptor; Image; Incidence; Indolent; Information Systems; Institution; Knowledge; Laboratories; Machine Learning; Malignant Epithelial Cell; Mammographic Density; Mammography; Medical Imaging; Medical center; Modeling; Morbidity - disease rate; Natural Language Processing; Noninfiltrating Intraductal Carcinoma; Operative Surgical Procedures; Pathology; Patients; Performance; Positioning Attribute; Radiation therapy; Radiology Specialty; Randomized Controlled Trials; Regimen; Reporting; Research; Research Project Grants; Retrospective cohort; Risk; Risk stratification; Safety; Slide; Surveillance Program; Training; Treatment Protocols; Validation; Woman; Work; aggressive therapy; base; breast imaging; cancer invasiveness; career; career development; clinical center; clinical implementation; clinical practice; complex data; computer science; convolutional neural network; cost; deep learning; diverse data; experience; hormone therapy; image guided; improved outcome; intelligent algorithm; malignant breast neoplasm; medical schools; overtreatment; patient health information; patient stratification; predictive modeling; predictive tools; professor; prognostic tool; prospective; radiologist; random forest; research clinical testing; skills; standard care; surgery outcome; surgical risk; tool; tumor

PROJECT SUMMARY/ABSTRACT This proposal presents a five-year career development plan focused on data science and artificial intelligence (AI) and the application of AI to improve outcomes in women with ductal carcinoma in situ (DCIS). The candidate is a Radiologist at MGH and an Assistant Professor of Radiology at Harvard Medical School. The proposal builds upon the candidate’s previous research and clinical experiences in breast imaging and also upon a strong ongoing research partnership between MGH and MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). The candidate’s long-term career goal is to become a leader in academic breast imaging by investigating and applying AI to critical areas in breast cancer detection, diagnosis, and treatment. The proposed research project and advanced didactic training at Harvard and MIT will position the candidate with a unique set of knowledge and skills in data science and AI that will enable her to develop an independent cancer research program that focuses on applications of AI to breast imaging. The incidence of DCIS has dramatically increased over the past 40 years, with an estimated 63,960 diagnoses in 2018. Current guidelines recommend that DCIS be treated with surgery, radiation, and endocrine therapy, but there remains considerable controversy over whether this regimen represents overtreatment for those women with indolent non-hazardous DCIS. Given concerns about overtreatment, there are currently three randomized controlled trials underway to evaluate the safety and efficacy of active surveillance versus standard treatment, and critical to the implementation of active surveillance programs is careful selection of eligible patients. The goal of the proposed project is to develop a robust AI tool that incorporates clinical data, mammographic imaging, and biopsy histopathology slides for pre-operatively predicting the risk of concurrent invasive cancer in women with DCIS. The tool will be built using machine learning, deep learning, and computer vision. Incorporation of mammographic imaging and histopathology slides into the AI tool will be supported by the MGH & BWH Center for Clinical Data Science (CCDS) and the MGH Department of Pathology. After development and validation of the AI tool based on a retrospective cohort of 1,400 women diagnosed with DCIS at MGH, the tool will then be integrated into MGH’s mammography information system and used to categorize new cases of DCIS. The specific aims are: (1) to develop a robust AI tool that predicts the risk of upgrade of DCIS diagnosed by image-guided core needle biopsy to invasive cancer at surgery and (2) to implement and evaluate the AI tool in clinical practice. Use of this tool could identify the subset of women who are appropriate candidates for active surveillance, decrease the morbidity and costs of overtreatment, and support more targeted and precise treatment options for women diagnosed with DCIS.

项目总结/摘要 该提案提出了一个专注于数据科学和人工智能的五年职业发展计划。 智能（AI）以及应用AI改善导管原位癌（DCIS）女性的预后。 候选人是MGH的放射科医生和哈佛医学院的放射学助理教授。 该提案建立在候选人以前在乳腺成像方面的研究和临床经验的基础上， 也基于MGH和麻省理工学院计算机科学和人工智能之间正在进行的强有力的研究伙伴关系 情报实验室。候选人的长期职业目标是成为学术界的领导者 通过研究并将人工智能应用于乳腺癌检测、诊断和诊断的关键领域， 治疗拟议的研究项目和哈佛和麻省理工学院的高级教学培训将定位于 候选人在数据科学和人工智能方面拥有独特的知识和技能，这将使她能够开发出一种 一个独立的癌症研究项目，专注于人工智能在乳腺成像中的应用。 DCIS的发病率在过去40年中急剧增加，估计有63，960人 2018年的诊断目前的指南建议DCIS通过手术、放射和内分泌治疗来治疗。 治疗，但仍有相当大的争议，是否这种方案代表过度治疗， 那些患有无痛性非危险性DCIS的女性。由于担心过度治疗，目前 三项随机对照试验正在进行中，以评估主动监测与 标准治疗，并积极监测方案的实施至关重要的是仔细选择 合格的患者。拟议项目的目标是开发一个强大的人工智能工具，结合临床数据， 乳腺摄影成像和活检组织病理学切片，用于术前预测并发症的风险 DCIS女性中的浸润性癌症。该工具将使用机器学习、深度学习和 计算机视觉将乳腺X线摄影成像和组织病理学幻灯片纳入AI工具， 由MGH & BWH临床数据科学中心（CCDS）和MGH部门支持， 病理在基于1，400名女性的回顾性队列开发和验证AI工具后， 在MGH诊断为DCIS，该工具将被集成到MGH的乳房X光检查信息系统中 并用于对DCIS的新病例进行分类。具体目标是：（1）开发一个强大的人工智能工具， 通过图像引导空芯针活检诊断的DCIS在手术中升级为浸润性癌症的风险， (2)在临床实践中实施和评估AI工具。使用这一工具可以确定妇女的子集 谁是积极监测的适当候选人，降低过度治疗的发病率和成本， 为诊断为DCIS的女性提供更有针对性和更精确的治疗方案。