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April 2019: Innovative use of machine learning techniques to aid with diagnosis, treatment and prognostication of cancer

Featuring Professor Anant Madabhushi

01 April, 2019


Dr.Anant Madabhushi, was the presenter for the April 2019,BrainX Community live event.He is currently, Director of the Center for Computational Imaging and Personalized Diagnostics (CCIPD) and the F. Alex Nason Professor II in the Departments of Biomedical Engineering, Pathology, Radiology, Radiation Oncology, Urology, General Medical Sciences, and Electrical Engineering and Computer Science at Case Western Reserve University.

His presentation was on innovative use of machine learning techniques to aid with diagnosis, treatment and prognostication of cancer.He and his team have developed unique cost-effective solutions and pathbreaking techniques to provide less invasive, personalized and precise care to  cancer patients.They are currently researching application of these techniques to healthcare areas other than cancer including cardiology.

The key aspect of his presentation was demonstration of feature engineering as a key ML technique in the field of pathomics and radiomics to generate new and actionable knowledge.This new knowledge is enhancing  decision making for management of patients and supporting clinicians by providing them with actionable and precise information.

It is truly remarkable to see how Dr.Madabhushi is changing the management of cancer patients by providing actionable information in near real time and with significant accuracy.His orientation to patient care and clinician engagement is an important reason for this collaborative success.

He also discussed challenges associated with applications of machine learning in healthcare including access to data,quality of data,reproducibility of results,legal and ethical dilemmas.

Abstract to his presentation, links to his center and publications are available below.




Title – “Artificial Intelligence in Radiology and Pathology: Implications for Precision Medicine”

Abstract – Traditional biology generally looks at only a few aspects of an organism at a time and attempts to molecularly dissect diseases and study them part by part with the hope that the sum of knowledge of parts would help explain the operation of the whole. Rarely has this been a successful strategy to understand the causes and cures for complex diseases. The motivation for a systems based approach to disease understanding aims to understand how large numbers of interrelated health variables, gene expression profiling, its cellular architecture and microenvironment, as seen in its histological image features, its 3 dimensional tissue architecture and vascularization, as seen in dynamic contrast enhanced (DCE) MRI, and its metabolic features, as seen by Magnetic Resonance Spectroscopy (MRS) or Positron Emission Tomography (PET), result in emergence of definable phenotypes. At the Center for Computational Imaging and Personalized Diagnostics (CCIPD) at Case Western Reserve University, we have been developing computerized knowledge alignment, representation, and fusion tools for integrating and correlating heterogeneous biological data spanning different spatial and temporal scales, modalities, and functionalities. These tools include computerized feature analysis methods for extracting subvisual attributes for characterizing disease appearance and behavior on radiographic (radiomics) and digitized pathology images (pathomics). In this talk I will discuss the development work in CCIPD on new radiomic and pathomic approaches for capturing intra-tumoral heterogeneity and modeling tumor appearance. I will also focus my talk on how these radiomic and pathomic approaches can be applied to predicting disease outcome, recurrence, progression and response to therapy in the context of prostate, brain, rectal, oropharyngeal, and lung cancers. Additionally I will also discuss some recent work on looking at use of pathomics in the context of racial health disparity and creation of more precise and tailored prognostic and response prediction models.

Learn more about CCIPD:

CCIPD publications: