Keywords: Pelvis, Multimodal
Motivation: The consensus molecular subtype (CMS) is a novel classification system that reflects the genetic characteristics of the tumor. CMS4 is associated with the worst prognosis.
Goal(s): To investigate whether a radiomics-based machine learning approach could predict CMS4 status in CRC patients.
Approach: The sequencing data was input into the CMS classification system to generate CMS subtype outcomes. Radiomics features were extracted from baseline T2WI and contrast-enhanced MRI. Machine learning algorithms were applied to explore the best-performing and most robust model.
Results: The best performing model achieved AUCs of 0.855 and 0.759 in the test set and external validation set.
Impact: The genetic phenotype of CMS4 colorectal cancer may be potentially associated with morphological features. Multiparametric radiomics-based machine learning shows promising potential in distinguishing CMS4 from other subtypes of CRC.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords