AI in Precision Oncology, Jakob Kather

Speaker: Jakob N. Kather, PhD, Departments of Medicine and Computer Science, Technical University of Dresden, Germany

Date: Monday, February 12th, 2024

Time: 10:00 AM Central Time

Location:  Zoom

Title: AI in Precision Oncology

Abstract: Histopathology images are available for every single patient with a solid tumor. These images contain a large amount of information. In particular, they reflect underlying genetic changes which are present in the tumor cells. Recent work has shown that deep learning-based image analysis can predict genetic alterations just from  routine pathology slides. This talk will summarize the state of the art, show the limitations of this method and discuss how spatial intratumor heterogeneity can be evaluated from the tumor phenotype. Ultimately, these recent advances set the stage for multimodal artificial intelligence models which simultaneously use pathology slides and genomics as an input, yielding biomarkers for treatment response to immunotherapy and targeted therapy.

Bio: Professor Jakob Kather holds dual appointments in medicine and computer science at the Technical University Dresden, Germany, serves as a senior physician in medical oncology at the University Hospital Dresden and holds an additional affiliation with the National Center for Tumor Diseases in Heidelberg. His research is focused on applying artificial intelligence in precision oncology. Prof. Kather’s team is using deep learning techniques to analyze a spectrum of clinical data, including histopathology, radiology images, textual records, and multimodal datasets. Guided by the belief that medical and tech expertise needs to be combined, medical researchers in his team learn computer programming and data analysis, while computer scientists are immersed in cancer biology and oncology. Prof. Kather chairs the “Working group on Artificial Intelligence” at the German Society of Hematology and Oncology (DGHO) and is a member of the pathology task force of the American Association for Cancer Research (AACR). His work is supported by numerous European and national grants, which enable the team to develop new deep learning methods for medical data analysis techniques and to apply them in precision oncology.