(Monday, 7/13/2020) 4:30 PM - 5:30 PM [Eastern Time (GMT-4)]
Room: Track 2
Purpose: As a stand of care for brain metastases (BMs), stereotactic radiosurgery (SRS) enables the treatment of multiple (>4) BMs (mBMs) patients with excellent tumor control. In this study, we propose a deep learning driven systematic BMs follow-up evaluation framework to ensure the treatment quality and post-treatment quality of life (QoL).
Methods: The framework consists of a segmentation session and an evaluation session. A) In the segmentation session, BMs are auto-segmented in follow-up MRIs using our En-DeepMedic CNN-based segmentation platform. Deformable image registration is utilized to help excluding false-positives and identifying recurrences by mapping segmentations from planning MRI. BMs volumes are calculated for every follow-up. B) In the evaluation session, BMs post-treatment progression pattern is analyzed. First, polynomial curve fitting and dynamic time warping is conducted to obtain the volumes of each BMs changing over time. K-means clustering is then performed to cluster BMs into different progression categories and discover representative patterns, which can be future used for treatment outcome prediction. Thirteen mBMs (vary from 7-69) SRS patients with follow-up MRIs are collected to test this framework.
Results: For the segmentation session, our approach can detect mBMs on follow-up MRIs with averaged sensitivity of 0.97. Segmentation accuracy is evaluated by averaged center of mass shift as 1.41±0.32mm, Hausdorff distance as 2.75±0.57mm, the mean of surface-to-surface distance (SSD) as 1.01±0.28mm and the standard deviation of SSD as 0.80±0.19mm. For the evaluation session, our approach can differentiate BMs into different post-treatment progression trend categories, and reveal the representative patterns in each categories to aid treatment outcome prediction.
Conclusion: The developed deep learning based systematic follow-up framework can accurately identify mBMs and conduct tumor progression/regression analysis based on segmented volumes. It can assist BMs’ follow-up clinical workflow and aid BM SRS treatment outcome prediction, and is a promising tool for BM SRS management.
Funding Support, Disclosures, and Conflict of Interest: This work is supported by NIH R01 CA235723 and the seed grant from Department of Radiation Oncology at University of Texas Southwestern Medical Center.
Keywords
Segmentation, Gamma Knife, Tumor Control
Taxonomy
TH- Response Assessment: Modeling: Machine Learning
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