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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

基本信息

批准号：
10685615
负责人：
Manisha Bahl
金额：
$ 26.13万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-01 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10685615
关键词：
Adjuvant Therapy Algorithms Area Artificial Intelligence Assessment tool Biopsy Breast Cancer Detection Cancer Research Project Categories Classification 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 Incidence Indolent Information Systems Institution Knowledge Laboratories Machine Learning Malignant Epithelial Cell Mammographic Density Mammography Medical Imaging Medical center Morbidity - disease rate Natural Language Processing Newly Diagnosed Noninfiltrating Intraductal Carcinoma Operative Surgical Procedures Pathology Patients Performance Positioning Attribute Proliferating Radiation therapy Radiology Specialty Randomized, Controlled Trials Recommendation Regimen Reporting Research Research Project Grants Retrospective cohort Risk Risk Marker Safety Slide Surveillance Program Training Treatment Protocols Validation Woman Work aggressive therapy artificial intelligence algorithm 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 machine learning classifier machine learning model malignant breast neoplasm medical schools overtreatment patient health information patient stratification predictive tools professor prognostic model prognostic tool prospective radiologist random forest research clinical testing risk prediction risk stratification skills standard care surgery outcome tool tumor

项目摘要

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/文摘

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

The Quest to Reduce the Use of Gadolinium-based Contrast Agents: AI May Provide a Solution.

寻求减少钆造影剂的使用：人工智能可能提供解决方案。

DOI：
10.1148/radiol.230325
发表时间：
2023
期刊：
Radiology
影响因子：
19.7
作者：
Bahl,Manisha
通讯作者：
Bahl,Manisha

The unintended consequences of artificial intelligence and high-risk triaging.

人工智能和高风险分类的意外后果。

DOI：
10.1007/s00330-023-10553-y
发表时间：
2024
期刊：
European radiology
影响因子：
5.9
作者：
Bahl,Manisha
通讯作者：
Bahl,Manisha

Contrast-enhanced Mammography: An Emerging Modality in Breast Imaging.

对比增强乳房X线摄影：乳房成像的新兴方式。

DOI：
10.1148/radiol.212856
发表时间：
2022
期刊：
Radiology
影响因子：
19.7
作者：
Bahl,Manisha
通讯作者：
Bahl,Manisha

Evaluating the Use of ChatGPT to Accurately Simplify Patient-centered Information about Breast Cancer Prevention and Screening.

评估 ChatGPT 的使用，以准确简化有关乳腺癌预防和筛查的以患者为中心的信息。

DOI：
10.1148/rycan.230086
发表时间：
2024
期刊：
Radiology. Imaging cancer
影响因子：
0
作者：
Haver,HanaL;Gupta,AnujK;Ambinder,EmilyB;Bahl,Manisha;Oluyemi,EniolaT;Jeudy,Jean;Yi,PaulH
通讯作者：
Yi,PaulH

Invited Commentary: The Power and Promise of Artificial Intelligence for Digital Breast Tomosynthesis.

特邀评论：人工智能在数字乳腺断层合成中的力量和前景。