Training in Image Guidance, Precision Diagnosis and Therapy

图像引导、精准诊断和治疗培训

• 批准号: 10647810
• 负责人: Fiona Fennessy
• 金额: $ 14.11万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10647810
• 关键词: Diagnosis; Training; image guided

The proposed T32 training program provides image guided therapy (IGT) training. It is a logical extension of our National Center for Image Guided Therapy (NCIGT: P41 EB 015898) program at the Brigham and Women’s Hospital. This is a unique environment-namely the advanced multi-modal image-guided operating rooms (AMIGO)-where the infrastructure and expertise co-exist to provide training in IGT from basic biology and pathology, to translational studies and Phase 2 clinical trials. All our AMIGO clinical programs provide state of the art care for patients, supplemented with imaging and cutting-edge research opportunities. All the teams currently working in AMIGO are multidisciplinary (MDs & PhDs). Our training program represents a unique approach to IGT. There are no other genuinely multidisciplinary, training structures available elsewhere to learn techniques and clinical applications in an advanced multimodality interventional/intraoperative setting. One of the critical issues of advancing the field of IGT is the lack of specially trained investigators who can respond to both the medical and technological needs of progress. The significance of this program is that it provides both the technological and clinical fundamentals for both MD and PhD trainees. The training includes the relevant concepts, methods, technologies and treatments that are necessary to advance this field and provide fully trained investigators for future IGT- related applications. This training program will therefore provide an opportunity for physicians and scientists who wish to train with the complex advanced technology that can diagnose, localize, and treat many types of diseases. Through multidisciplinary collaboration, our training program will expand the pool of experts of new diagnostic and treatment modalities. We will implement a dynamic, personalized thematic-based training curriculum that will be updated as breakthrough technologies are discovered and brought to clinical translation. Our goal is for each IGT-trainee to follow a thematic personalized program that best matches their prior training and experience with their future career goals. As this training program is aimed at precision diagnostics and therapy through IGT applications, the candidate pool will be post-doctorates-MDs and PhDs who are highly motivated to learn advanced IGT technologies and innovative new clinical applications, who will be hired for 1 year, with a possible extension to 2 years. Over the course of the training, fellows are exposed to both specialized and didactic curriculum, while concentrating on a research project in their own thematic area of interest within the field of IGT. Required program coursework will include understanding the science of biomarkers, a wide variety of IGT-related training activities such as a comprehensive, clinically-relevant review and hands-on experience with the available and emerging technologies, participation in cutting-edge research projects, strategies for successful grant writing and effective communication of research. The intended outcome is that trainees will be future academic leaders in the technological developments and implementation of IGT-related diagnostics and interventions.

拟议的T32培训计划提供图像引导治疗（IGT）培训。的逻辑延伸

项目成果

Enrichment of lung cancer computed tomography collections with AI-derived annotations.

• DOI: 10.1038/s41597-023-02864-y
• 发表时间: 2024-01-04
• 期刊: SCIENTIFIC DATA
• 影响因子: 9.8
• 作者: Krishnaswamy, Deepa;Bontempi, Dennis;Thiriveedhi, Vamsi Krishna;Punzo, Davide;Clunie, David;Bridge, Christopher P.;Aerts, Hugo J. W. L.;Kikinis, Ron;Fedorov, Andrey
• 通讯作者: Fedorov, Andrey

Deep Learning on Multimodal Chemical and Whole Slide Imaging Data for Predicting Prostate Cancer Directly from Tissue Images.

• DOI: 10.1021/jasms.2c00254
• 发表时间: 2023-02-01
• 期刊: JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY
• 影响因子: 3.2
• 作者: Haque, Md Inzamam Ul;Mukherjee, Debangshu;Stopka, Sylwia A.;Agar, Nathalie Y. R.;Hinkle, Jacob;Ovchinnikova, Olga S.
• 通讯作者: Ovchinnikova, Olga S.

A uniformity correction method to reduce scan time for 7T sodium imaging of brain tumors.

• DOI: 10.1111/jon.13041
• 发表时间: 2022-11
• 期刊: JOURNAL OF NEUROIMAGING
• 影响因子: 2.4
• 作者: Kim, Sanghoon;Merugumala, Sai;Lin, Alexander Peter
• 通讯作者: Lin, Alexander Peter

Local Staging of Prostate Cancer at MRI: What the Urologist and Radiation Oncologist Want to Know.

MRI 前列腺癌的局部分期：泌尿科医生和放射肿瘤科医生想知道的。

• DOI: 10.1148/rg.220100
• 发表时间: 2023
• 期刊: Radiographics : a review publication of the Radiological Society of North America, Inc
• 作者: Gottumukkala,RaviV;Lee,LeslieK;Tu,Wendy;Tempany,ClareM;Fennessy,FionaM
• 通讯作者: Fennessy,FionaM