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MRI-Based Deep Learning Segmentation and Radiomics of Sarcoma in Mice
M D Holbrook1, S J Blocker1, Y M Mowery2
1Departments of Radiology, Center for In Vivo Microscopy; and.
Tomography (Ann Arbor, Mich.)
|April 14, 2020
Summary
Quantitative imaging with magnetic resonance imaging (MRI) and radiomics analysis improves cancer therapy studies. This method accurately segments tumors and predicts recurrence, reducing variability in preclinical research.
Area of Science:
- Oncology
- Medical Imaging
- Radiomics
Background:
- Small-animal imaging offers noninvasive, longitudinal insights into new cancer therapies.
- Variability in image analysis techniques can lead to inconsistent results in preclinical studies.
- Quantitative imaging is crucial for reliable preclinical cancer research.
Purpose of the Study:
- To develop and validate a quantitative imaging analysis pipeline for preclinical cancer therapy studies.
- To assess the utility of radiomics features for predicting tumor recurrence after radiation therapy.
- To reduce bias and improve consistency in analyzing multiparametric MRI data.
Main Methods:
- A genetically engineered mouse model of soft tissue sarcoma was used.
- Magnetic resonance imaging (MRI) was performed before and after radiation therapy (RT).
- An automated pipeline with convolutional neural networks for segmentation and radiomics analysis was applied to multicontrast MRI data.
Main Results:
- The automated pipeline achieved high segmentation accuracy (Dice scores ~0.86).
- Radiation therapy led to increased tumor volumes and heterogeneity one week post-treatment.
- Radiomics features effectively predicted primary tumor recurrence (AUC: 0.79).
Conclusions:
- The developed image processing pipeline enables high-throughput, reduced-bias segmentation of multiparametric MRI data.
- Radiomics analysis of tumor and peritumoral areas can predict recurrence.
- This quantitative approach enhances the understanding of preclinical imaging for novel cancer therapies.
