Dr. Akbari Hamed Profile

Dr. Akbari Hamed

at Santa Clara Univ

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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 Poster + Paper

Fully automatic mpMRI analysis using deep learning predicts peritumoral glioblastoma infiltration and subsequent recurrence

Sunwoo Kwak, Akbari Hamed, Jose Garcia, Suyash Mohan, Christos Davatzikos

Proceedings Volume 12926, 129261N (2024) https://doi.org/10.1117/12.3001752

KEYWORDS: Tumors, Deep learning, Magnetic resonance imaging, Brain

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Glioblastoma (GBM) is most aggressive and common adult brain tumor. The standard treatments typically include maximal surgical resection, followed adjuvant radiotherapy and chemotherapy. However, the efficacy of these treatment is often limited, as tumor often infiltrate into the surrounding brain tissue, often extending beyond the radiologically defined margins. This infiltration contributes to the high recurrence rate and poor prognosis associated with GBM patients, necessitating advanced methods for early and accurate detection of tumor infiltration. Despite the great promise traditional supervised machine learning shows in predicting tumor infiltration beyond resectable margins, these methods are heavily reliant on expert-drawn Regions of Interest (ROIs), which are used to construct multi-variate models of different Magnetic Resonance (MR) signal characteristics associated with tumor infiltration. This process is both time consuming and resource intensive. Addressing this limitation, our study proposes a novel integration of fully automatic methods for generating ROIs with deep learning algorithms to create predictive maps of tumor infiltration. This approach uses pre-operative multi-parametric MRI (mpMRI) scans, encompassing T1, T1Gd, T2, T2-FLAIR, and ADC sequences, to fully leverage the knowledge from previously drawn ROIs. Subsequently, a patch based Convolutional Neural Network (CNN) model is trained on these automatically generated ROIs to predict areas of potential tumor infiltration. The performance of this model was evaluated using a leave-one-out cross-validation approach. Generated predictive maps binarized for comparison against post-recurrence mpMRI scans. The model demonstrates robust predictive capability, evidenced by the average cross-validated accuracy of 0.87, specificity of 0.88, and sensitivity of 0.90. Notably, the odds ratio of 8.62 indicates that regions identified as high-risk on the predictive map were significantly more likely to exhibit tumor recurrence than low-risk regions. The proposed method demonstrates that a fully automatic mpMRI analysis using deep learning can successfully predict tumor infiltration in peritumoral region for GBM patients while bypassing the intensive requirement for expert-drawn ROIs.

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