MRI-based radiomics of sarcomas in the preclinical arm of a Co-clinical trial

Conference Paper

Radiomics provide an exciting approach to developing imaging biomarkers in the context of precision medicine. We focus on the preclinical arm of a co-clinical trial investigating synergy of immunotherapy combined with radiation therapy (RT) and surgical resection using a genetically engineered mouse model of sarcoma. Our protocol involves the acquisition of MRI data with T1, T2 and T1 with contrast agent. There are two MRI time points i.e. one day before RT (20Gy) and one week later. After the second MRI acquisition the primary tumor is surgically removed, and the mice are followed for up to 6 months to investigate for local recurrence or distant metastases. The tumor images are segmented using deep learning. We performed radiomics for the tumor, peritumoral rim and the combined tumor and peritumoral rim. Our first radiomics analysis was focused on determining features which are most indicative to the effects of RT. Our second analysis aimed to answer if radiomics features could predict primary tumor recurrence within this population. Top features were selected for training classifiers based on neural networks and support vector machines. Our results show that gray level radiomic features show that tumors often acquire more heterogeneous texture and that tumor volume increases one-week post RT. The results also suggest that radiomics features serve to indicate likelihood of primary tumor recurrence with the best predictive power in the combined tumor and peritumoral area in pre-RT data (AUC: 0.83). In conclusion, we have created a radiomics pipeline to serve in our current preclinical arm of the co-clinical trial.

Full Text

Duke Authors

Cited Authors

  • Holbrook, MD; Blocker, SJ; Mowery, Y; Badea, A; Qi, Y; Kirsch, DG; Badea, CT

Published Date

  • January 1, 2020

Published In

Volume / Issue

  • 11317 /

International Standard Serial Number (ISSN)

  • 1605-7422

International Standard Book Number 13 (ISBN-13)

  • 9781510634015

Digital Object Identifier (DOI)

  • 10.1117/12.2549628

Citation Source

  • Scopus