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

项目摘要/摘要 这份提案提出了一个以数据科学和人工智能为重点的五年职业发展计划 智能(AI)和AI的应用改善女性导管原位癌(DCIS)的预后。 候选人是麻省理工学院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之前在乳房成像和临床方面的研究和临床经验的基础上。 同样基于麻省理工学院和麻省理工学院计算机科学和人工智能之间强大的持续研究伙伴关系 情报实验室(CSAIL)。候选人的长期职业目标是成为学术上的领军人物 通过研究和应用人工智能在乳腺癌检测、诊断和治疗中的关键领域进行乳房成像 治疗。哈佛大学和麻省理工学院提出的研究项目和高级教学培训将使 应聘者拥有一套独特的数据科学和人工智能知识和技能，将使她能够开发 专注于将人工智能应用于乳房成像的独立癌症研究计划。 在过去的40年里，DCIS的发病率急剧增加，估计有63,960人 2018年的诊断。目前的指南建议对DCIS进行手术、放疗和内分泌治疗 治疗，但对于这种疗法是否代表着过度治疗仍有相当大的争议 那些患有惰性非危险DCIS的女性。鉴于对过度治疗的担忧，目前有 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，对实施主动监测方案至关重要的是仔细选择 符合条件的患者。拟议项目的目标是开发一个强大的人工智能工具，其中包含临床数据， 钼靶X线成像和活检组织病理学切片用于术前预测并发乳腺癌的风险 女性DCIS患者的浸润性癌症。该工具将使用机器学习、深度学习和 计算机视觉。将乳房X光成像和组织病理学幻灯片整合到人工智能工具中将是 由MGH&BWH临床数据科学中心(CCDS)和MGH部门 病理学。在开发和验证基于1,400名妇女回顾队列的人工智能工具之后 在MGH被诊断为DCIS，该工具将被集成到MGH的乳房X光检查信息系统中 并用于对新发的DCIS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，可以预测 影像引导下核心针活检诊断的DCIS在手术和手术中升级为浸润性癌的风险 (2)在临床实践中实施和评估人工智能工具。使用该工具可以识别妇女的子集 谁是积极监测的合适对象，减少过度治疗的发病率和费用，以及 支持为被诊断为DCIS的妇女提供更有针对性和更精确的治疗选择。

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.