BLRD Research Career Scientist Award Application

BLRD 研究职业科学家奖申请

• 批准号: 10589239
• 负责人: Anant Madabhushi
• 金额: --
• 依托单位: VETERANS HEALTH ADMINISTRATION
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10589239
• 关键词: Address; Adjuvant Chemotherapy; Algorithms; Artificial Intelligence; Award; Benign; Bioinformatics; Biopsy; Breast; COVID-19 severity; Cancer Patient; Cardiovascular Diseases; Chemotherapy and/or radiation; Clinical; Collaborations; Communities; Computer Vision Systems; Computer-Assisted Diagnosis; Data; Development; Diagnosis; Diagnostic; Disease; Environment; Exposure to; Eye diseases; Funding; Genomics; Head and neck structure; Health; Image; Image Analysis; Immunotherapy; Journals; Kidney; Kidney Diseases; Legal patent; Life Style; Lung; Machine Learning; Magnetic Resonance Imaging; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of prostate; Manuscripts; Marketing; Medicine; Modernization; Multimodal Imaging; National Cancer Institute; Nodule; Oncologist; Ophthalmology; Oropharyngeal; Outcome; Paper; Pathologic; Pathology; Patients; Pattern; Pattern Recognition; Peer Review; Population; Populations at Risk; Prediction of Response to Therapy; Prognosis; Prostate; Proteomics; Publications; Publishing; Radiation therapy; Radiogenomics; Radiology Specialty; Recurrence; Research; Research Personnel; Resources; Risk; Risk Assessment; Risk Marker; San Francisco; Scanning; Scientist; Series; Site; Technology; Tissues; Toxic effect; Translating; Tumor-Infiltrating Lymphocytes; Urologist; Validation; Veterans; Work; X-Ray Computed Tomography; aggressive therapy; artificial intelligence algorithm; cancer diagnosis; cancer immunotherapy; cancer type; career; clinical decision support; clinical investigation; cohort; companion diagnostics; computer infrastructure; computerized; computerized tools; diagnostic tool; digital; digital pathology; disease diagnosis; disease phenotype; experience; head and neck cancer patient; high risk; imaging biomarker; lung basal segment; lung cancer screening; malignant breast neoplasm; malignant oropharynx neoplasm; military veteran; molecular phenotype; multimodality; novel; oncology program; oral HPV-positive head and neck cancers; pathology imaging; patient subsets; precision medicine; precision oncology; predicting response; prognostication; prostate cancer risk; racial disparity; radiological imaging; radiologist; radiomics; risk prediction; risk stratification; service programs; standard of care; support tools; symposium; tool; treatment response

Project Summary/Abstract Dr Madabhushi has emerged as a pioneer in the development and application of novel and interpretable Artificial Intelligence (AI) algorithms for disease diagnosis, prognosis and prediction of treatment response for a variety of diseases including several cancers, cardiovascular, kidney and eye disease. Veterans, in many cases on account of their exposure to wartime environments and particular lifestyle choices, engenders different disease phenotypes compared to the civilian population. Over the last three years he has been optimizing and tailoring AI tools to addressing problems in precision medicine for Veterans. While his primary focus has been on diagnosis, prognosis and prediction of treatment response of lung, oropharyngeal, breast and prostate cancers for the Veteran population, he is also focused on translating and deploying these clinical decision support tools across VA stations and VISNs so that Veterans can experience precision medicine across different diseases. Dr Madabhushi's research within the VA began in 2019 with a VA Merit award (I01BX004121) focused on AI based lung cancer screening for VA patients, specifically helping to discriminate malignant from benign nodules on routine CT scans. This work has led to development of AI driven imaging biomarkers for predicting response to immunotherapy for lung cancer patients. More recently in a paper just published in the J of Immunotherapy for Cancer1, Dr. Madabhushi's group demonstrated the utility of radiomics on CT scans to identify clinical outcome for Stage III lung cancer patients treated with chemo-radiation therapy and immunotherapy. Interestingly, the work showed that a subset of patients identified by his AI-based approach might be able to avoid chemo-radiation therapy and hence the associated toxicity. The study included a cohort of 15 patients from the Cleveland VA. Similarly, his team has been developing and applying AI tools both for digital pathology as well as on radiology scans for risk stratification of oropharyngeal cancers within the VA. This work was achieved through collaboration with Vlad Sandulache at the Houston VA and with Stephen Connelly at the San Francisco VA that has resulted in a series of high impact manuscripts (J of NCI, J of Clinical Investigation, Modern Pathology) and an NCI funded R01 (R01CA249992). In order to expand his work and footprint within the VA, he and his team have received funding support (in 2021) from the Cooperative Services Program to create a VA Hub for Computer Vision and Machine Learning in Precision Oncology (CoMPL). This new VA Hub will create computer vision and machine learning (CVML) tools for addressing cancer diagnosis, prognosis, risk stratification and prediction of treatment response in the VA population. The objectives of CoMPL are: 1) focus on building the computational infrastructure and tools to allow for expanding the scope and access to CVML resources within the VA, and building a community to enable VA researchers to take advantage of these tools to develop their own CVML applications; and 2) to develop new companion diagnostic tools for risk assessment, predicting response and need for more or less aggressive therapy in prostate and lung cancer. An initial demonstration project of CoMPL will focus on application of AI tools with CT scans and digital pathology images to identify benefit of adjuvant chemotherapy in early-stage Veteran lung cancer patients. Dr. Madabhushi's is also leading a new prostate cancer collaborative involving urologists, radiologists and oncologists from multiple different VA stations and VISNs to develop the use of AI with multimodal imaging (MRI and digital pathology) along with genomics for more accurate risk stratification of Veterans with high-risk prostate cancer. The CoMPL team is partnering with the National Artificial Intelligence Institute (NAII), Lung Cancer Precision Oncology (LPOP) and Precision Oncology Program for Cancer of the Prostate (POPCaP) centers to enable dissemination of the decision support tools and the deeply annotated digital pathology and radiology scans that result from CoMPL's activities.

项目摘要/摘要 Madabhushi博士已成为新颖且可解释的人造的开发和应用的先驱 智力（AI）疾病诊断，预后和治疗反应预测的算法 包括几种癌症，心血管，肾脏和眼病的疾病。退伍军人，在许多情况下 关于他们接触战时环境和特定生活方式的选择，会导致不同的疾病 与平民相比，表型。在过去的三年中，他一直在优化和裁缝 AI来解决退伍军人精确医学问题的工具。虽然他的主要重点一直在 肺，口咽，乳腺癌和前列腺癌的治疗反应的诊断，预后和预测 对于资深人口，他还专注于翻译和部署这些临床决策支持工具 跨VA站和VISN，使退伍军人可以在不同疾病中体验精确的药物。 Madabhushi博士在VA中的研究始于2019年，该奖项颁发了VA功绩奖（I01BX004121），重点介绍了AI 基于VA患者的基于肺癌筛查，特别有助于区分恶性肿瘤与良性结节 在常规的CT扫描中。这项工作导致了AI驱动的成像生物标志物的发展，以预测响应 进行肺癌患者的免疫疗法。最近在刚刚发表在《免疫疗法J》上的论文中 对于Cancer1，Madabhushi博士的小组展示了在CT扫描中放射线学的实用性以识别临床 接受化学放射治疗和免疫疗法治疗的III期肺癌患者的结果。 有趣的是，这项工作表明，通过其基于AI的方法确定的一部分患者可能能够 避免化学放射疗法，从而相关的毒性。该研究包括来自 克利夫兰弗吉尼亚州。同样，他的团队一直在开发和应用AI工具作为数字病理学 以及放射学扫描，以便在VA内进行口咽癌的风险分层。这项工作是实现的 通过与弗吉尼亚州休斯顿的弗拉德·桑德拉奇（Vlad Sandulache）合作，并与旧金山的斯蒂芬·康纳利（Stephen Connelly）合作 VA产生了一系列高影响手稿（NCI的J，临床研究J，现代 病理学）和NCI资助的R01（R01CA249992）。 为了在VA中扩大他的工作和足迹，他和他的团队获得了资金支持（2021年） 从合作服务计划创建用于计算机视觉和机器学习的VA中心 精度肿瘤学（compl）。这个新的VA中心将创建计算机视觉和机器学习（CVML）工具 用于解决癌症的诊断，预后，风险分层和VA治疗反应的预测 人口。辩护的目标是：1）专注于构建计算基础架构和工具以允许 为了扩大VA内的范围并访问CVML资源，并建立一个社区以启用VA 研究人员利用这些工具来开发自己的CVML应用程序； 2）开发新的 伴侣诊断工具用于风险评估，预测响应和或多或少具有积极性的需求 前列腺和肺癌的治疗。最初的辩护示范项目将重点放在AI的应用上 具有CT扫描和数字病理图像的工具，以确定早期辅助化疗的好处 老将肺癌患者。 Madabhushi博士还领导着一种新的前列腺癌协作 来自多个不同VA站和VISN的泌尿科医生，放射学家和肿瘤学家开发AI的使用 具有多模式成像（MRI和数字病理）以及基因组学，以更准确的风险分层 患有高风险前列腺癌的退伍军人。申诉团队正在与国家人工智能合作 研究所（NAII），肺癌精度肿瘤学（LPOP）和精确肿瘤学计划 前列腺（爆米花）中心，以使决策支持工具和深度注释的传播 数字病理学和放射学扫描申诉活动所产生的。