BLRD Research Career Scientist Award Application

项目概要/摘要 Madabhushi博士已经成为开发和应用新颖和可解释的人工智能的先驱。 智能（AI）算法用于疾病诊断、预后和各种治疗反应的预测 包括几种癌症、心血管疾病、肾病和眼病。退伍军人，在许多情况下 考虑到他们暴露于战时环境和特定的生活方式选择， 与平民相比。在过去的三年里，他一直在优化和剪裁 人工智能工具，以解决退伍军人的精准医疗问题。虽然他的主要重点是 肺癌、口咽癌、乳腺癌和前列腺癌的诊断、预后和治疗反应预测 对于退伍军人群体，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，以便退伍军人可以体验不同疾病的精准医疗。 Madabhushi博士在VA的研究始于2019年，当时他获得了VA Merit奖（I 01 BX 004121），专注于人工智能。 基于VA患者的肺癌筛查，特别有助于区分恶性结节和良性结节 做常规CT扫描这项工作导致了人工智能驱动的成像生物标志物的发展，用于预测反应 用于肺癌患者的免疫治疗。最近，在《免疫疗法杂志》上发表的一篇论文中， 对于癌症1，Madabhushi博士的研究小组证明了放射组学在CT扫描中的实用性， 用化学放射疗法和免疫疗法治疗的III期肺癌患者的结果。 有趣的是，这项工作表明，通过他基于人工智能的方法识别的一部分患者可能能够 避免化疗和放疗以及相关的毒性。该研究包括一组15名患者， 克利夫兰VA同样，他的团队一直在开发和应用AI工具，用于数字病理学， 以及放射学扫描，用于VA内口咽癌的风险分层。这项工作取得了 通过与休斯顿退伍军人事务部的弗拉德·桑杜拉切和旧金山弗朗西斯科的斯蒂芬·康奈利的合作， VA，产生了一系列高影响力的手稿（J of NCI，J of Clinical Investigation，Modern 病理学）和NCI资助的R 01（R 01 CA 249992）。 为了扩大他在VA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一个VA中心的计算机视觉和机器学习， 精准肿瘤学（CoMPL）。这个新的VA Hub将创建计算机视觉和机器学习（CVML）工具 用于解决VA中的癌症诊断、预后、风险分层和治疗反应预测 人口CoMPL的目标是：1）专注于构建计算基础设施和工具， 扩大VA内CVML资源的范围和访问，并建立一个社区， 研究人员利用这些工具开发自己的CVML应用程序; 2）开发新的 用于风险评估的伴随诊断工具，预测反应和需要更多或更少的积极治疗 前列腺癌和肺癌的治疗。CoMPL的初步示范项目将专注于AI的应用 使用CT扫描和数字病理学图像的工具，以确定早期辅助化疗的益处 有经验的肺癌患者。Madabhushi博士还领导了一个新的前列腺癌合作项目， 来自多个不同VA站和VISN的泌尿科医生、放射科医生和肿瘤科医生开发AI的使用 多模态成像（MRI和数字病理学）与基因组学一起沿着， 患有前列腺癌的退伍军人。CoMPL团队正在与国家人工智能 研究所（NAII），肺癌精确肿瘤学（LPOP）和癌症精确肿瘤学计划 前列腺（POPCaP）中心，以便能够传播决策支持工具和深入注释的 数字病理学和放射学扫描是CoMPL活动的结果。

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.