Dr. Graeme F. Woodworth Profile

Dr. Graeme F. Woodworth

at Univ. of Maryland School of Medicine

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Author

Publications (3)

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 | 24 May 2022 Presentation

Clinically relevant pure porphyrin nanoparticles for photodynamic therapy and priming applications

Collin Inglut, Jillian Stable, Brandon Gaitan, Wen-An Chiou, Baktiar Karim, Nina Connolly, Robert Robey, Graeme Woodworth, Michael Gottesman, Huang-Chiao Huang

Proceedings Volume PC12146, PC1214606 (2022) https://doi.org/10.1117/12.2618159

KEYWORDS: Photodynamic therapy, Nanoparticles, Tumors, Plasma display panels, Cancer, Brain, Photomedicine, Luminescence, Animal model studies, Tumor growth modeling

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Liposomes have revolutionized the field of photomedicine. Photodynamic therapy (PDT) using Visudyne®, a liposomal photosensitizer formulation, has helped many patients globally. Since the FDA approved Visudyne® in 2002, countless studies have examined strategies to further improve the therapeutic index of lipid-based photosensitizing nanoconstructs. While liposomes can improve the pharmacokinetics of hydrophobic photosensitizers, they could also modulate cellular uptake and singlet oxygen production. Furthermore, it is evident that there are other immunological and toxicological considerations for the design of liposomal drugs. Accordingly, there is now an emerging trend to engineer carrier-free nanodrugs. Here, we developed a pure-drug nanoparticle using the clinically used verteporfin photosensitizer (termed nanoVP) for photodynamic applications. We validated the effects of nanoVP in three contexts: 1) cytotoxic PDT, 2) subtherapeutic PDT, and 3) dark toxicity. Using a brain cancer murine model, we showed that light activation of nanoVP reduced tumor volume by up to 54% compared to liposomal VP. Fluorescence imaging revealed that nanoVP had a superior tumor-to-liver tissue ratio (~0.92) compared to liposomal VP (~0.4). We further studied nanoVP-mediated PDT at subtherapeutic doses to achieve photodynamic priming (PDP). PDP has been shown to enhance drug delivery, activate antitumor immunity, and sensitize tumors to chemotherapy. This approach is particularly relevant in the brain, where high doses of PDT can result in edema, neurotoxicity, and even animal death. Using a rat model, we demonstrated that nanoVP-assisted PDP improved blood-brain barrier permeability and accumulation of a model drug (Evans Blue dye) in rat brains by >5 fold. Minimal to no brain damage was observed. Lastly, under dark conditions, we validated that nanoVP significantly reduced viability while liposomal VP stimulated cancer cell growth. Results from this work demonstrate the utility of nanoVP for cancer treatment. The development of pure-drug photosensitizing nanoparticles for photodynamic applications could further revolutionize the field of photomedicine.

Proceedings Article | 14 August 2019 Presentation

Photosensitizing biomolecules and nanocrystals for anti-glioma photodynamic therapy (Conference Presentation)

Collin Inglut, Yan Baglo, Barry Liang, Jill Stabile, Yahya Cheema, Graeme Woodworth, Huang-Chiao Huang

Proceedings Volume 11070, 110701M (2019) https://doi.org/10.1117/12.2525904

KEYWORDS: Nanocrystals, Photodynamic therapy, Biomedical optics, Polymers, Macromolecules, Crystals, Photosensitizer targeting

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