{"id":153,"date":"2022-06-24T09:46:31","date_gmt":"2022-06-24T13:46:31","guid":{"rendered":"http:\/\/chi.jumphost.ca\/?post_type=project&p=153"},"modified":"2023-11-14T11:03:07","modified_gmt":"2023-11-14T16:03:07","slug":"development-and-validation-of-prognostic-radiomic-markers-of-response-and-recurrence-for-patients-with-colorectal-liver-metastases","status":"publish","type":"project","link":"https:\/\/www.queensu.ca\/health-innovation\/research\/development-and-validation-of-prognostic-radiomic-markers-of-response-and-recurrence-for-patients-with-colorectal-liver-metastases\/","title":{"rendered":"Development and Validation of Prognostic Radiomic Markers of Response and Recurrence for Patients with Colorectal Liver Metastases"},"content":{"rendered":"\n

A core tenet of the emerging field of radiomics is that modern, high-resolution CT imaging contains information that is invisible to the human eye, but that can be extracted and analyzed using image processing and computer vision techniques. In this project we are applying this insight to CT images of patients with metastatic liver tumors resulting from colorectal cancer.<\/p>\n\n\n\n

Colorectal cancer is the second leading cause of cancer-related mortality in the United States. More than 50% of patients with colorectal cancer will develop liver metastases in their lifetime with a dismal <10% surviving past three years. Our goal is to leverage all information contained in CT imaging of patients with colorectal liver metastases (CRLM) to better understand the disease course and treatment response as a necessary step toward improving patient outcomes.<\/p>\n\n\n\n

Our large interdisciplinary team of experts, combined with the largest clinical experience in CRLM in the western world makes this project a unique and unrivaled opportunity to define radiomics of CRLM. In the future, integration into existing clinical workflows means that small medical centers without highly specialized radiology groups would benefit from predictive algorithms developed at two high-volume centers via a low-cost software update. Successful completion of our aims will provide validated prognostic imaging markers with a pathway to routine clinical use, which are of paramount importance to improving patient survival of this deadly disease.<\/p>\n\n\n\n

\"CRLM<\/figure>\n\n\n\n

Project Aims<\/h2>\n\n\n\n

The objectives of this project are to develop and validate robust imaging features by standardizing image acquisition, to improve automated tools for clinical trial use, and to validate the predictive power of imaging features with external data. To pursue these goals, we have assembled an interdisciplinary team of experts in surgery, medical oncology, pathology, radiology, biostatistics, and image analysis, from Memorial Sloan Kettering Cancer Center (MSK), University of Texas MD Anderson Cancer Center (MDA), Rensselaer Polytechnic Institute, GE Research, and 91TV\u2019s University.<\/p>\n\n\n\n

The project can be broken down into three aims:<\/p>\n\n\n\n

    \n
  1. Development and validation of imaging features and models that are predictive of treatment response and recurrence of CRLM when undergoing chemotherapy or hepatic resection. These models will be developed and validated using 2450 retrospectively acquired CT images from both MSK and MDA, the two largest liver cancer centers in the United States.<\/li>\n\n\n\n
  2. Systematic analysis of the repeatability and reproducibility of radiomic imaging features across CT imaging protocols through the prospective collection of test-retest imaging data. CT images are being collected at multiple time points and reconstructed with a variety of parameters in order to determine which radiomic features are robust to variations in CT acquisition protocol, and thus suitable for use across centers.<\/li>\n\n\n\n
  3. Recapturing even more data from CRLM imaging by developing \u201crawdiomics\u201d models that fully utilize CT sinogram data. Reconstruction algorithms that transform the raw sinogram data collected by scanners into human-readable images lose information in the process. By operating on the raw data, rawdiomics models will be able to better leverage the complete data collected by CT scanners. Raw sinogram data is being prospectively collected at both MSK and 91TV\u2019s University.<\/li>\n<\/ol>\n\n\n\n
    \"Outside<\/figure>\n\n\n\n

    In support of these studies we are developing a number of software tools including an automated segmentation pipeline using state of the art machine learning, a tool for remote viewing and editing of model-produced segmentations, and a radiomic feature extraction pipeline.<\/p>\n\n\n\n

    \"Automated<\/figure>\n\n\n\n

    Publications<\/h2>\n\n\n\n