MRI-Based Deep Learning Segmentation and Radiomics of Sarcoma in Mice.

Published

Journal Article

Small-animal imaging is an essential tool that provides noninvasive, longitudinal insight into novel cancer therapies. However, considerable variability in image analysis techniques can lead to inconsistent results. We have developed quantitative imaging for application in the preclinical arm of a coclinical trial by using a genetically engineered mouse model of soft tissue sarcoma. Magnetic resonance imaging (MRI) images were acquired 1 day before and 1 week after radiation therapy. After the second MRI, the primary tumor was surgically removed by amputating the tumor-bearing hind limb, and mice were followed for up to 6 months. An automatic analysis pipeline was used for multicontrast MRI data using a convolutional neural network for tumor segmentation followed by radiomics analysis. We then calculated radiomics features for the tumor, the peritumoral area, and the 2 combined. The first radiomics analysis focused on features most indicative of radiation therapy effects; the second radiomics analysis looked for features that might predict primary tumor recurrence. The segmentation results indicated that Dice scores were similar when using multicontrast versus single T2-weighted data (0.863 vs 0.861). One week post RT, larger tumor volumes were measured, and radiomics analysis showed greater heterogeneity. In the tumor and peritumoral area, radiomics features were predictive of primary tumor recurrence (AUC: 0.79). We have created an image processing pipeline for high-throughput, reduced-bias segmentation of multiparametric tumor MRI data and radiomics analysis, to better our understanding of preclinical imaging and the insights it provides when studying new cancer therapies.

Full Text

Duke Authors

Cited Authors

  • Holbrook, MD; Blocker, SJ; Mowery, YM; Badea, A; Qi, Y; Xu, ES; Kirsch, DG; Johnson, GA; Badea, CT

Published Date

  • March 2020

Published In

  • Tomography

Volume / Issue

  • 6 / 1

Start / End Page

  • 23 - 33

PubMed ID

  • 32280747

Pubmed Central ID

  • 32280747

Electronic International Standard Serial Number (EISSN)

  • 2379-139X

Digital Object Identifier (DOI)

  • 10.18383/j.tom.2019.00021

Language

  • eng

Conference Location

  • United States