Photo illustration of cancer cells

“AI and Imaging for Cancer Diagnostics”

The Adamczyk Lecture Series showcases the use of cutting-edge technology in the development of noninvasive diagnostics and novel therapies, especially in the context of cancer treatments. This year’s mini symposium is titled “AI and Imaging for Cancer Diagnostics” and will be presented by Janine Lupo.

Event Schedule

This event will take place April 19 from 3 p.m to 5:30 p.m. at the Linsalata Alumni Center located at 11310 Juniper Road. There will be a reception and hors d’oeuvres following the lecture from 5:30 to 6:30 p.m. 

  • 3-3:25 p.m. – Glioblastoma Imaging- can we detect tumor invasion?; Chaitra Badve
  • 3:30-3:55 p.m. – Theranostic Imaging of the Tumor Microenvironment; Susann Brady-Kalnay
  • 4-4:25 p.m. Computational Imaging & Radiomics: Implications for Precision Medicine; Satish E. Viswanath
  • 4:30-5:30 p.m. Adamcyzk Lecture – Towards Predicting Glioma Tumor Biology, Progression, and the Effects of Treatment with Multi-Parametric MRI and AI; Presented by Janine Lupo – Associate Professor, University of California

This event is open to all; registration is not required. The symposium and lecture will be available via live stream as well. 

Lecture Abstract

Artificial intelligence in the form of deep learning has shown great promise in expanding the capabilities of medical imaging over the last decade. To date, the vast majority of studies using deep learning in patients with brain tumors have focused on either lesion segmentation or predicting grade, molecular phenotype, or survival, typically using standard anatomical MRI. This talk will highlight the benefits of employing deep learning along with physiologic and metabolic MR imaging in patients with gliomas in order to identify image-based signatures of underlying tumor biology, track lesion progression, and distinguish the effects of treatment from tumor recurrence.

About Lupo

Janine Lupo is an associate professor in the Neuroimaging Research Interest Group and Center for Intelligent Imaging of the Department of Radiology and Biomedical Imaging and a member of the UCSF/UC Berkeley Graduate Group in Bioengineering, Helen Diller Family Comprehensive Cancer Center, Institute for Computational Health Sciences, and Quantitative Biosciences Institute. Lupo received her BSE in bioengineering at the University of Pennsylvania, School of Engineering and Applied Science in Philadelphia before completing her PhD at the UCSF/UCB PhD Joint Graduate Group in Bioengineering. 

Lupo’s research focuses on the development and application of novel MR imaging data acquisition, processing, and analysis techniques for the evaluation of patients with brain tumors and other neurological diseases using our research 3T and 7T whole body scanners. This includes the development of quantitative algorithms critical for processing data from advanced acquisitions for patient studies, implementation of statistical and machine learning models to relate imaging markers with biological characteristics from pathology and clinical outcome in order to both identify patients who would benefit most for a given therapy and monitor the effects of therapy over time on tumor control, normal brain tissue structure, and cognition.