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

Holbrook, MD

Blocker, SJ

Mowery, YM

Badea, A

Qi, Y

Xu, ES

Kirsch, DG

Johnson, GA

Badea, CT

2022-01-27T18:04:36Z

2022-01-27T18:04:36Z

2020-03

2022-01-27T18:04:35Z

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.

TOMO.2019.00021.R1

2379-1381

2379-139X

https://hdl.handle.net/10161/24250

eng

MDPI AG

Tomography (Ann Arbor, Mich.)

10.18383/j.tom.2019.00021

Animals

Mice

Sarcoma

Soft Tissue Neoplasms

Neoplasm Recurrence, Local

Magnetic Resonance Imaging

Deep Learning

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

Journal article

Blocker, SJ|0000-0002-6665-7844

Mowery, YM|0000-0002-9839-2414

Badea, A|0000-0001-6621-4560

Johnson, GA|0000-0002-7606-5447

Badea, CT|0000-0002-1850-2522

23

33

1

Duke

Pratt School of Engineering

School of Medicine

Trinity College of Arts & Sciences

Basic Science Departments

Clinical Science Departments

Institutes and Centers

Pharmacology & Cancer Biology

Biomedical Engineering

Radiation Oncology

Radiology

Duke Cancer Institute

Physics

Duke-UNC Center for Brain Imaging and Analysis

Neurology

Neurology, Behavioral Neurology

Head and Neck Surgery & Communication Sciences

Published

6