Dr. Marwa Ismail Profile

Dr. Marwa Ismail

at Case Western Reserve Univ

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Publications (4)

Proceedings Article | 4 April 2022 Poster + Paper

A radiomics approach to distinguish non-contrast enhancing tumor from vasogenic edema on multi-parametric pre-treatment MRI scans for glioblastoma tumors

Ipsa Yadav, Marwa Ismail, Volodymyr Statsevych, Virginia Hill, Ramon Correa, Manmeet Ahluwalia, Pallavi Tiwari

Proceedings Volume 12033, 120332Y (2022) https://doi.org/10.1117/12.2612627

KEYWORDS: Tumors, Magnetic resonance imaging, Feature extraction, Neuroimaging, Feature selection, Brain mapping, Brain, Tissues, Surgery, Radiology

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Glioblastoma (GBM) is a highly aggressive tumor with a heterogeneous tumor micro-environment that extends beyond the visible tumor margin and is known to play a substantial role in GBM recurrence. For instance, it is often difficult to distinguish infiltrating non-contrast enhancing tumor (nCET) from peritumoral edema due to their confounding appearances on T2w/FLAIR MRI scans. Thus, nCET is often left unresected and contributes to over 90% of recurrences in GBM tumors that occur within 2-cm of the resected tumor margin. Histopathologically, the infiltrative nCET has high cellularity compared to vasogenic edema. This work explores the hypothesis that these histopathological changes may be reflected on routine imaging as subtle micro-architectural texture differences, that can be captured via radiomic features, allowing for differentiating nCET from vasogenic edema on routine pre-treatment MRI scans (Gd-T1w, T2w, FLAIR). Our radiomic analysis involved registering the preoperative MRI sequences of GBM patients from two institutions to a healthy MNI atlas. In the absence of histopathological confirmation,‘ground truth’ for nCET region of interest (ROI) on pre-treatment scans was defined as the site of future recurrence (as established on post-treatment scan with histopathologically-confirmed recurrence). Similarly, the ROI for vasogenic edema was defined as a region far from the site of recurrence, within the FLAIR/T2w hyperintensity edema on pre-treatment MRI. For every nCET and vasogenic edema ROI, a total of 316 3D radiomic features (e.g., Haralick, Laws, Gabor) were extracted from every MRI sequence. Feature pruning was conducted on the features’ statistics (median, variance, skewness, kurtosis) and a sequential feed forward classification scheme that employed a support vector machine classifier was applied. The FLAIR sequence yielded the highest accuracy in distinguishing nCET from vasogenic edema ROIs with accuracy of 91.3% and 78.5% on training (n = 25) and test (n = 30) sets respectively. Additionally, combining radiomic features from all three MRI sequences yielded an accuracy of 92.3% and 89.3% on the training and test sets, respectively. These results show that our radiomic approach may allow for reliable distinction of nCET from vasogenic edema on routine MRI scans, and thus may aid in improving treatment management of GBM tumors.

Proceedings Article | 16 March 2020 Presentation + Paper

Combining deep and hand-crafted MRI features for identifying sex-specific differences in autism spectrum disorder versus controls

Yashas Hiremath, Marwa Ismail, Ruchika Verma, Jacob Antunes, Pallavi Tiwari

Proceedings Volume 11314, 113141W (2020) https://doi.org/10.1117/12.2551341

KEYWORDS: Control systems, Brain, Magnetic resonance imaging, Machine learning, Feature extraction, Neuroimaging, Data modeling, Feature selection, Process modeling, Visualization

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Autism spectrum disorder (ASD) is a collection of neuro-developmental disorders with many symptoms, most prominently social impairment. It is known that there is a significant prevalence (4:1) of ASD in males compared to females. This suggests that there will likely be distinct neuro-anatomical structures across males and females that contribute to the distinct disease etiologies across the two sexes. Hence, in this work, we seek to develop “sex- specific" machine learning models that attempt to capture neuroanatomical differences in brain morphometry across ASD versus normal controls using structural MRI scans. Specifically, we train two different machine- learning models (one for male and one for female cohorts) consisting of hand-crafted" morphometric features such as shape and surface area of brain parcellations from T1w MRI, with deep" features learned from a Dense Convolutional Network (DenseNet). Our methodology consists of first computing morphometric hand-crafted features (i.e. volume and surface features of different brain parcellations) from the training cohort obtained from the ABIDE-II dataset consisting of 210 males and 98 females from structural T1w MRI scans. We then employ feed-forward feature selection within a linear discriminant analysis classifier trained separately for the male and female cohorts, to distinguish ASD from normal controls. Additionally, we train a DenseNet model with 4 dense blocks (with 2 layers each) to extract deep features from T1w MRI scans to distinguish ASD versus controls, separately for males and females. The deep features allow for capturing complementary data-driven feature differences in brain morphometry specific to male and female ASD cohort (versus normal controls). Finally, we combine the top morphometric and DenseNet features obtained from the training model and test them on the ABIDE I dataset for the male (n=85) and female (n=19) cohort separately, to distinguish ASD from normal controls. Our results demonstrated training and testing accuracies of 78% and 79% using hand-crafted features alone, 68% and 57% using DenseNet features alone, and 87% and 79% respectively using integrated hand-crafted and deep features for the male cohort. For the female cohort, we obtained accuracies of 81% and 84% with hand-crafted features alone, 71% and 62% for DenseNet features alone, and 81% and 84% with integrated hand- crafted and deep features, on training and testing sets respectively. With further optimization of deep features along with inclusion of a large multi-site cohort, our presented sex-specific ML approach could allow for improved diagnosis of ASD from controls, across males and females, using structural MRI scans.

SPIE Journal Paper | 7 May 2019

Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging

Prateek Prasanna, Ayush Karnawat, Marwa Ismail, Anant Madabhushi, Pallavi Tiwari

JMI, Vol. 6, Issue 02, 024005, (May 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.2.024005

KEYWORDS: Image segmentation, Tumors, Brain, Magnetic resonance imaging, Convolutional neural networks, 3D modeling, Neuroimaging, Feature extraction, Magnetorheological finishing, Distance measurement

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Accurate segmentation of gliomas on routine magnetic resonance image (MRI) scans plays an important role in disease diagnosis, prognosis, and patient treatment planning. We present a fully automated approach, radiomics-based convolutional neural network (RadCNN), for segmenting both high- and low-grade gliomas using multimodal MRI volumes (T1c, T2w, and FLAIR). RadCNN incorporates radiomic texture features (i.e., Haralick, Gabor, and Laws) within DeepMedic [a deep 3-D convolutional neural network (CNN) segmentation framework that uses image intensities; a top performing method in the BraTS 2016 challenge] to further augment the performance of brain tumor subcompartment segmentation. We first identify textural radiomic representations that best separate the different subcompartments [enhancing tumor (ET), whole tumor (WT), and tumor core (TC)] on the training set, and then feed these representations as inputs to the CNN classifier for prediction of different subcompartments. We hypothesize that textural radiomic representations of lesion subcompartments will enhance the separation of subcompartment boundaries, and hence providing these features as inputs to the deep CNN, over and above raw intensity values alone, will improve the subcompartment segmentation. Using a training set of N = 241 patients, validation set of N = 44, and test set of N = 46 patients, RadCNN method achieved Dice similarity coefficient (DSC) scores of 0.71, 0.89, and 0.73 for ET, WT, and TC, respectively. Compared to the DeepMedic model, RadCNN showed improvement in DSC scores for both ET and WT and demonstrated comparable results in segmenting the TC. Similarly, smaller Hausdorff distance measures were obtained with RadCNN as compared to the DeepMedic model across all the subcompartments. Following the segmentation of the different subcompartments, we extracted a set of subcompartment specific radiomic descriptors that capture lesion disorder and assessed their ability in separating patients into different survival cohorts (short-, mid- and long-term survival) based on their overall survival from the date of baseline diagnosis. Using a multilinear regression approach, we achieved accuracies of 0.57, 0.63, and 0.45 for the training, validation, and test cases, respectively.

Proceedings Article | 13 March 2019 Paper

Deformation heterogeneity radiomics to predict molecular subtypes of pediatric Medulloblastoma on routine MRI

Sukanya Iyer, Marwa Ismail, Benita Tamrazi, Ashley Margol, Ruchika Verma, Ramon Correa, Prateek Prasanna, Niha Beig, Kaustav Bera, Volodymyr Statsevych, Alexander Judkins, Anant Madabhushi, Pallavi Tiwari

Proceedings Volume 10950, 109501E (2019) https://doi.org/10.1117/12.2513567

KEYWORDS: Tumors, Magnetic resonance imaging, Brain, Tissues, Gadolinium, Cancer, Medical research, Lung cancer, Radiotherapy, Genomics

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Medulloblastoma (MB) is the most common malignant brain tumor in children. Currently, "one-size-fits-all" radiation and chemotherapy treatment regimen is employed for treating MB patient, causing at least some children to undergo highly aggressive and in some cases, inadequate radiation therapy. Consequently, there is a need for prognostic and predictive tools for identifying disease aggressiveness and ultimately which patients with MB may be able to benefit from de-escalation of therapy. Genomic characterization of MB has recently identified 4 distinct molecular subgroups: Sonic Hedgehog (SHH) , Wingless (WNT) , Group 3, Group 4 each exhibiting different clinical behavior. The molecular sub-types have unique risk-profiles and outcomes, and patients could potentially benefit from sub-group specific treatments. However, the transition of these molecular MB subtypes into clinical practice has been limited due to challenges in availability of molecular profiling in most hospitals, as well as variability in clinical assessment. In this work, we present a radiomic feature that captures subtle tissue deformations caused due to the impact of tumor growth on the normal-appearing brain around tumor (BAT), to distinguish molecular sub-types of MB. First, we obtain voxel-wise deformation magnitude from the deformation orientations, after registering Gadolinium (Gd)-enhanced T1-w MRI scan for every study to a normal age-specific T1w MRI template. Deformation statistics are then computed within every 5mm annular BAT region, 0 < d < 60mm, where d is the distance from the tumor infiltrating edge, to capture subtle localized deformation changes around the tumor. Our results using multi-class comparison via one-way ANOVA and post-hoc comparison showed significant differences across deformation magnitudes obtained for Group 3, Group 4, and SHH molecular sub-types, observed up to 15-mm outside the infiltrating edge. Our feasibility results suggest that the subtle deformation features in BAT observed on routine Gd-T1w MRI may potentially serve as surrogate markers to non-invasively characterize molecular sub-types of pediatric MB.

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