Supervised Machine-Learning Enables Segmentation and Evaluation of Heterogeneous Post-treatment Changes in Multi-Parametric MRI of Soft-Tissue Sarcoma

Supervised Machine-Learning Enables Segmentation and Evaluation of Heterogeneous Post-treatment Changes in Multi-Parametric MRI of Soft-Tissue Sarcoma

Background: Multi-parametric MRI provides non-invasive methods for response assessment of soft-tissue sarcoma (STS) from non-surgical treatments. However, evaluation of MRI parameters over the whole tumor volume may not reveal the full extent of post-treatment changes as STS tumors are often highly...

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Bibliographic Details
Main Authors: Matthew D. Blackledge, Jessica M. Winfield, Aisha Miah, Dirk Strauss, Khin Thway, Veronica A. Morgan, David J. Collins, Dow-Mu Koh, Martin O. Leach, Christina Messiou
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-10-01
Series:Frontiers in Oncology
Subjects:
magnetic resonance imaging
soft-tissue sarcoma
artificial intelligence
cancer heterogeneity
radiotherapy
imaging biomarkers
Online Access:https://www.frontiersin.org/article/10.3389/fonc.2019.00941/full

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https://www.frontiersin.org/article/10.3389/fonc.2019.00941/full

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