Radio-Path-Omics Workshop

Radio-Path-Omics: Analytic Tools for Correlating, Co-registering and Combining Radiologic, Pathologic, and Molecular Data 

The goal of this MICCAI workshop is to discuss the latest advances, challenges, and opportunities in developing computational tools for spatially aligning, co-registering, fusing and combining radiographic and digital pathology images and molecular “omics” data streams. 

In the last decade there has been substantial interest in “radiogenomics” and “patho-genomic” approaches where the goal has been to correlate and associate radiological and pathological image features with gene mutations and genomic profiles of tumors. Additionally there is a great deal of recent interest in developing approaches for co-registering ex vivo pathology with in vivo imaging in order to spatially map disease extent from the ex vivo pathology onto the radiologic imaging. This allows for the establishment of “learning sets” for training and validation of radiographic image based classifiers. Finally there has also been substantial recent interest in finding more intelligent ways to combine measurements from the genomic, pathologic, and radiologic length scales to create fused predictors of disease outcome and aggressiveness.

The goal of this workshop is to introduce the key concepts behind these approaches as well as the state-of-the-art in the field to both computational imaging and clinical scientists. We will also make available software that will allow for co-registration of radiology and pathology images, which will allow for hands-on use by participants of the workshop.  

Program

1. Co-registration of ex vivo pathology with in vivo imaging for identifying radiomic markers of disease aggressiveness (Anant Madabhushi, Case Western Reserve University)
2. Registration of multiple stains (e.g. H&E - IHC, IHC1 - IHC2) in pathology, and evaluation methodologies (Metin Gurcan, The Ohio State University)
3. Combining histochemical, proteomic markers with histopathologic image features for improved prognosis prediction (George Lee, Case Western Reserve University)
4. Anatomy-Histology-‘Omic’s Integration: Ground Truth Data Fusion (Doyle, Tomaszewski, State University of New York at Buffalo)
5. Challenges of 3D histology reconstruction in the breast (Anne Martel, University of Toronto)
6. Histo-Genomic Profiling of Tumor Cells in Colorectal Cancer (Nasir Rajpoot, University of Warwick)
7. Radiology-Pathology correlation for improved breast cancer care: results from the VPH-PRISM project (Jeroen van der Laak – Radboud University)
8. Title TBA(Ulysses Bayis - University of Michigan)