Predicting peritumoral glioblastoma infiltration and subsequent recurrence using deep-learning–based analysis of multi-parametric magnetic resonance imaging

Sunwoo Kwak; Hamed Akbari; Jose A. Garcia; Suyash Mohan; Yehuda Dicker; Chiharu Sako; Yuji Matsumoto; MacLean P. Nasrallah; Mahmoud Shalaby; Donald M. O’Rourke; Russel T. Shinohara; Fang Liu; Chaitra Badve; Jill S. Barnholtz-Sloan; Andrew E. Sloan; Matthew Lee; Rajan Jain; Santiago Cepeda; Arnab Chakravarti; Joshua D. Palmer; Adam P. Dicker; Gaurav Shukla; Adam E. Flanders; Wenyin Shi; Graeme F. Woodworth; Christos Davatzikos

doi:10.1117/1.JMI.11.5.054001

30 August 2024 Predicting peritumoral glioblastoma infiltration and subsequent recurrence using deep-learning–based analysis of multi-parametric magnetic resonance imaging

Sunwoo Kwak, Hamed Akbari, Jose A. Garcia, Suyash Mohan, Yehuda Dicker, Chiharu Sako, Yuji Matsumoto, MacLean P. Nasrallah, Mahmoud Shalaby, Donald M. O’Rourke, Russel T. Shinohara, Fang Liu, Chaitra Badve, Jill S. Barnholtz-Sloan, Andrew E. Sloan, Matthew Lee, Rajan Jain, Santiago Cepeda, Arnab Chakravarti, Joshua D. Palmer, Adam P. Dicker, Gaurav Shukla, Adam E. Flanders, Wenyin Shi, Graeme F. Woodworth, Christos Davatzikos

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue 5, 054001 (August 2024). https://doi.org/10.1117/1.JMI.11.5.054001

Abstract

Purpose

Glioblastoma (GBM) is the most common and aggressive primary adult brain tumor. The standard treatment approach is surgical resection to target the enhancing tumor mass, followed by adjuvant chemoradiotherapy. However, malignant cells often extend beyond the enhancing tumor boundaries and infiltrate the peritumoral edema. Traditional supervised machine learning techniques hold potential in predicting tumor infiltration extent but are hindered by the extensive resources needed to generate expertly delineated regions of interest (ROIs) for training models on tissue most and least likely to be infiltrated.

Approach

We developed a method combining expert knowledge and training-based data augmentation to automatically generate numerous training examples, enhancing the accuracy of our model for predicting tumor infiltration through predictive maps. Such maps can be used for targeted supra-total surgical resection and other therapies that might benefit from intensive yet well-targeted treatment of infiltrated tissue. We apply our method to preoperative multi-parametric magnetic resonance imaging (mpMRI) scans from a subset of 229 patients of a multi-institutional consortium (Radiomics Signatures for Precision Diagnostics) and test the model on subsequent scans with pathology-proven recurrence.

Results

Leave-one-site-out cross-validation was used to train and evaluate the tumor infiltration prediction model using initial pre-surgical scans, comparing the generated prediction maps with follow-up mpMRI scans confirming recurrence through post-resection tissue analysis. Performance was measured by voxel-wised odds ratios (ORs) across six institutions: University of Pennsylvania (OR: 9.97), Ohio State University (OR: 14.03), Case Western Reserve University (OR: 8.13), New York University (OR: 16.43), Thomas Jefferson University (OR: 8.22), and Rio Hortega (OR: 19.48).

Conclusions

The proposed model demonstrates that mpMRI analysis using deep learning can predict infiltration in the peri-tumoral brain region for GBM patients without needing to train a model using expert ROI drawings. Results for each institution demonstrate the model’s generalizability and reproducibility.

Citation Download Citation

Sunwoo Kwak, Hamed Akbari, Jose A. Garcia, Suyash Mohan, Yehuda Dicker, Chiharu Sako, Yuji Matsumoto, MacLean P. Nasrallah, Mahmoud Shalaby, Donald M. O’Rourke, Russel T. Shinohara, Fang Liu, Chaitra Badve, Jill S. Barnholtz-Sloan, Andrew E. Sloan, Matthew Lee, Rajan Jain, Santiago Cepeda, Arnab Chakravarti, Joshua D. Palmer, Adam P. Dicker, Gaurav Shukla, Adam E. Flanders, Wenyin Shi, Graeme F. Woodworth, and Christos Davatzikos "Predicting peritumoral glioblastoma infiltration and subsequent recurrence using deep-learning–based analysis of multi-parametric magnetic resonance imaging," Journal of Medical Imaging 11(5), 054001 (30 August 2024). https://doi.org/10.1117/1.JMI.11.5.054001

Received: 19 April 2024; Accepted: 6 August 2024; Published: 30 August 2024

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KEYWORDS

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Education and training

Deep learning

Voxels

Magnetic resonance imaging

Data modeling

Tissues

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