Dr. Matthew D. Lee Profile

Dr. Matthew D. Lee

at NYU Grossman School of Medicine

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Author

Publications (2)

SPIE Journal Paper | 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 Garcia, Suyash Mohan, Yehuda Dicker, Chiharu Sako, Yuji Matsumoto, MacLean Nasrallah, Mahmoud Shalaby, Donald O’Rourke, Russel Shinohara, Fang Liu, Chaitra Badve, Jill Barnholtz-Sloan, Andrew Sloan, Matthew Lee, Rajan Jain, Santiago Cepeda, Arnab Chakravarti, Joshua Palmer, Adam P. Dicker, Gaurav Shukla, Adam Flanders, Wenyin Shi, Graeme Woodworth, Christos Davatzikos

JMI, Vol. 11, Issue 05, 054001, (August 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.5.054001

KEYWORDS: Tumors, Education and training, Deep learning, Voxels, Magnetic resonance imaging, Data modeling, Tissues, Resection, Brain, Cross validation

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PurposeGlioblastoma (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.ApproachWe 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.ResultsLeave-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).ConclusionsThe 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.

Proceedings Article | 2 April 2024 Presentation + Paper

Synthesizing 3D multicontrast brain tumor MRIs using tumor mask conditioning

Nghi C. Truong, Chandan Ganesh Bangalore Yogananda, Benjamin Wagner, James Holcomb, Divya Reddy, Niloufar Saadat, Kimmo Hatanpaa, Toral Patel, Baowei Fei, Matthew Lee, Rajan Jain, Richard Bruce, Marco Pinho, Ananth Madhuranthakam, Joseph Maldjian

Proceedings Volume 12931, 129310M (2024) https://doi.org/10.1117/12.3009331

KEYWORDS: Tumors, Magnetic resonance imaging, 3D modeling, Brain, Diffusion

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