BLRD Research Career Scientist Award Application

项目摘要/摘要 Madabhushi博士已经成为开发和应用新型和可解释的人工智能的先驱 用于疾病诊断、预后和治疗反应预测的智能(AI)算法 这些疾病包括多种癌症、心血管疾病、肾脏疾病和眼科疾病。退伍军人，在许多情况下 由于他们暴露在战时环境和特定的生活方式选择中，会产生不同的疾病 表型与平民百姓相比。在过去的三年里，他一直在优化和调整 解决退伍军人精准医学问题的人工智能工具。虽然他的主要关注点是 肺癌、口咽癌、乳腺癌、前列腺癌的诊断、预后及疗效预测 对于资深人群，他还专注于翻译和部署这些临床决策支持工具 跨越退伍军人管理局和VISN，让退伍军人能够体验到跨越不同疾病的精准医疗。 Madabhushi博士在退伍军人管理局内的研究始于2019年退伍军人管理局的荣誉奖(I01BX004121)，重点是人工智能 基于VA患者的肺癌筛查，特别有助于区分良恶性结节 在常规CT扫描中。这项工作导致了用于预测反应的人工智能驱动的成像生物标记物的开发 到肺癌患者的免疫治疗。最近发表在《免疫疗法杂志》上的一篇论文 对于癌症1，Madabhushi博士的团队展示了放射组学在CT扫描上的实用性，以确定临床 接受放化疗和免疫治疗的III期肺癌患者的结局。 有趣的是，这项工作表明，通过他的基于人工智能的方法识别的患者子集可能能够 避免化疗和放射治疗，从而避免相关的毒性。这项研究包括来自中国的15名患者 克利夫兰退伍军人事务部。同样，他的团队一直在开发和应用人工智能工具，既用于数字病理，也用于 以及退伍军人事务部内口咽癌风险分层的放射学扫描。这项工作已经完成 通过与休斯顿退伍军人管理局的弗拉德·桑杜拉奇和旧金山的斯蒂芬·康奈利合作 VA，产生了一系列影响很大的手稿(NCI的J，临床研究的J，现代 病理)和NCI资助的R01(R01CA249992)。 为了扩大他在退伍军人事务部的工作和足迹，他和他的团队获得了资金支持(2021年) 从合作服务计划中创建用于计算机视觉和机器学习的VA中心 精确肿瘤学(COMPL)。这个新的VA Hub将创建计算机视觉和机器学习(CVML)工具 用于解决退伍军人管理局的癌症诊断、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，以允许 扩大退伍军人管理局内CVML资源的范围和访问权限，并建立社区以支持退伍军人管理局 研究人员利用这些工具开发他们自己的CVML应用程序；以及2)开发新的 用于风险评估、预测响应和或多或少激进需求的配套诊断工具 前列腺癌和肺癌的治疗。COMPL的初步示范项目将重点放在人工智能的应用上 结合CT扫描和数字病理图像的工具来确定早期辅助化疗的益处 资深肺癌患者。Madabhushi博士还领导了一个新的前列腺癌合作项目，该项目涉及 来自多个不同VA站和VISN的泌尿科医生、放射科医生和肿瘤学家开发人工智能的使用 通过多模式成像(MRI和数字病理学)以及基因组学，可更准确地进行风险分层 患有高危前列腺癌的退伍军人。COMPL团队正在与国家人工智能 (NAII)、肺癌精确肿瘤学(LPOP)和癌症精确肿瘤学计划 前列腺癌(PopCap)中心，支持传播决策支持工具和深度注释的 由COMPL的活动产生的数字病理和放射扫描。

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.