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VA-VA Seminar Series: Dr. Anant Madabhushi
VA-VA Seminar Series: Dr. Anant Madabhushi

Tue, Dec 12

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Teams: ID: 216 651 436 794 Passcode: 8giro9

VA-VA Seminar Series: Dr. Anant Madabhushi

It is our pleasure to welcome Dr. Madabhushi as the next speaker in our VA-VA seminar series. Dr. Madabhushi will provide a virtual presentation, which will be followed by several small group/discussion meetings.

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Time & Location

Dec 12, 2023, 1:00 PM – 1:50 PM EST

Teams: ID: 216 651 436 794 Passcode: 8giro9

About the event

It is our pleasure to welcome Dr. Madabhushi as the next speaker in our VA-VA seminar series. Dr. Madabhushi will provide a virtual presentation on December 12th from 1-1:50PM.

The presentation will be followed by several small group/discussion meetings.  If you are interested in meeting with Dr. Madabhushi please let Rebecca Mays and Jeff Dupree know ASAP.

Dr. Anant Madabhushi is a Research Health Scientist at the Atlanta VA with a joint appointment at Emory University in the departments of Biomedical Engineering, Biomedical Informatics, Radiology and Imaging Sciences, and Pathology. Dr. Madabhushi has authored more than 275 peer-reviewed journal articles in high impact journals such as Nature Rev Drug Discovery, Nature Rev Clin Oncology, and Lancet Oncology. Dr. Madabhushi has emerged as a pioneer in the development and application of novel and interpretable AI algorithms for disease diagnosis, prognosis, and prediction of treatment.

Dr. Madabhushi’ s work on the development of smart computers for identifying lung cancer patients who will benefit from chemotherapy was ranked as one of the top 10 medical breakthroughs of 2018. His research within the VA began in 2019 with a VA Merit award 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 the development of imaging biomarkers for predicting responses to immunotherapy for lung cancer patients.  Some of Dr. Madabhushi’ s recent work has demonstrated the utility of radionics on CT scans to identify clinical assays for Stage III lung cancer patients treated with chemo-radiation therapy and immunotherapy. 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.

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