Abstract #0018

Automatic Infiltration Risk Prior Generation with Modified Triplet Loss for Pre-Operative Glioblastoma Infiltration Prediction

Walter Zhao¹, Sree Gongala², Eunate Alzaga Goñi¹, Xiaofeng Wang³, Shengwen Deng², Charit Tippareddy², Hamed Akbari⁴, Anahita Fathi Kazerooni⁵, Christos Davatzikos⁶, Marta Couce⁷, Andrew E. Sloan⁸, Chaitra Badve², and Dan Ma¹

¹Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, ²Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States, ³Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, United States, ⁴Department of Bioengineering, Santa Clara University, Santa Clara, OH, United States, ⁵Center for Data Driven Discovery in Biomedicine, Children's Hospital of Pennsylvania, Philadelphia, OH, United States, ⁶Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, OH, United States, ⁷Department of Pathology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States, ⁸Piedmont Physicians Neurosurgery Atlanta, Piedmont Healthcare, Atlanta, GA, United States

Synopsis

Keywords: Tumors (Pre-Treatment), Tumor, Glioblastoma

Motivation: Pre-operative glioblastoma (GBM) infiltration prediction models rely on manual infiltration risk (IR) prior segmentation which is tedious, requires expert input, and is highly variable.

Goal(s): Automation is needed for fast segmentation. A data-driven method would account for GBM heterogeneity and be independent of specific MRI input for applicability to clinical protocols.

Approach: IR priors are grown using modified triplet loss with inter-prior and intra-prior terms to ensure priors are distinct from each other and maintain similarity within individual priors.

Results: TripleSeq generated more consistent IR priors compared to manual segmentation. TripleSeq-trained models showed good classification (> 85% mean accuracy) of ground truth infiltration.

Impact: Glioblastoma (GBM) infiltration inevitably leads to tumor recurrence and progression. We introduce an automatic method to generate infiltration risk priors for improved GBM infiltration machine learning prediction, which applied pre-operatively can identify at-risk peritumoral regions for targeted neurosurgery and radiotherapy.

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