Marwa Ismail

695 total citations
33 papers, 419 citations indexed

About

Marwa Ismail is a scholar working on Radiology, Nuclear Medicine and Imaging, Genetics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Marwa Ismail has authored 33 papers receiving a total of 419 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Radiology, Nuclear Medicine and Imaging, 10 papers in Genetics and 10 papers in Computer Vision and Pattern Recognition. Recurrent topics in Marwa Ismail's work include Radiomics and Machine Learning in Medical Imaging (12 papers), Glioma Diagnosis and Treatment (10 papers) and Medical Image Segmentation Techniques (8 papers). Marwa Ismail is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (12 papers), Glioma Diagnosis and Treatment (10 papers) and Medical Image Segmentation Techniques (8 papers). Marwa Ismail collaborates with scholars based in United States, New Zealand and Egypt. Marwa Ismail's co-authors include Ayman El‐Baz, Prateek Prasanna, Anant Madabhushi, Pallavi Tiwari, Andrew E. Switala, Georgy Gimel’farb, Virginia Hill, Ahmed Shalaby, Niha Beig and Kaustav Bera and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Clinical Cancer Research.

In The Last Decade

Marwa Ismail

27 papers receiving 408 citations

Peers

Marwa Ismail
Ahmed Alksas United States
Jay Jiyong Kwak South Korea
Ujjwal Baid India
Beiji Zou China
Neda Haj‐Hosseini Sweden
Colm Elliott Canada
Caroline Yu United States
J.-N. Vallée France
Peter Y. M. Woo China
Ahmed Alksas United States
Marwa Ismail
Citations per year, relative to Marwa Ismail Marwa Ismail (= 1×) peers Ahmed Alksas

Countries citing papers authored by Marwa Ismail

Since Specialization
Citations

This map shows the geographic impact of Marwa Ismail's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Marwa Ismail with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marwa Ismail more than expected).

Fields of papers citing papers by Marwa Ismail

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marwa Ismail. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Marwa Ismail. The network helps show where Marwa Ismail may publish in the future.

Co-authorship network of co-authors of Marwa Ismail

This figure shows the co-authorship network connecting the top 25 collaborators of Marwa Ismail. A scholar is included among the top collaborators of Marwa Ismail based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Marwa Ismail. Marwa Ismail is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Wei, Zhouping, Marwa Ismail, Joseph Willis, et al.. (2025). A novel structural modeling magnitude and orientation radiomic descriptor for evaluating response to neoadjuvant therapy in rectal cancers via MRI. npj Precision Oncology. 9(1). 215–215.
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Bareja, Rohan, Marwa Ismail, Douglas Martin, et al.. (2024). nnU-Net–based Segmentation of Tumor Subcompartments in Pediatric Medulloblastoma Using Multiparametric MRI: A Multi-institutional Study. Radiology Artificial Intelligence. 6(5). e230115–e230115. 4 indexed citations
4.
Battalapalli, Dheerendranath, Marwa Ismail, Virginia Hill, et al.. (2024). Graph-Radiomics Learning (GrRAiL): Application to Distinguishing Glioblastoma Recurrence from Pseudo-Progression on Structural MRI. 1–5. 1 indexed citations
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Alqub, Malik, et al.. (2024). Prenatal, perinatal and postnatal risk factors associated with autism spectrum disorder in Palestine: a case-control study. An-Najah University Journal for Research - B (Humanities). 38(3). 612–632.
7.
Ismail, Marwa, Stephen C. Craig, Raheel Ahmed, Peter de Blank, & Pallavi Tiwari. (2023). Opportunities and Advances in Radiomics and Radiogenomics for Pediatric Medulloblastoma Tumors. Diagnostics. 13(17). 2727–2727. 8 indexed citations
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Ismail, Marwa, Imran Hossain, Prateek Prasanna, et al.. (2022). RADIomic Spatial TexturAl Descriptor (RADISTAT): Quantifying Spatial Organization of Imaging Heterogeneity Associated With Tumor Response to Treatment. IEEE Journal of Biomedical and Health Informatics. 26(6). 2627–2636. 7 indexed citations
10.
Ismail, Marwa, Prateek Prasanna, Kaustav Bera, et al.. (2022). Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma. IEEE Transactions on Medical Imaging. 41(7). 1764–1777. 16 indexed citations
11.
Beig, Niha, Kaustav Bera, Prateek Prasanna, et al.. (2020). Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma. Clinical Cancer Research. 26(8). 1866–1876. 92 indexed citations
12.
Ismail, Marwa, Virginia Hill, Volodymyr Statsevych, et al.. (2020). Can Tumor Location on Pre-treatment MRI Predict Likelihood of Pseudo-Progression vs. Tumor Recurrence in Glioblastoma?—A Feasibility Study. Frontiers in Computational Neuroscience. 14. 563439–563439. 3 indexed citations
13.
Ismail, Marwa, Virginia Hill, Volodymyr Statsevych, et al.. (2018). Shape Features of the Lesion Habitat to Differentiate Brain Tumor Progression from Pseudoprogression on Routine Multiparametric MRI: A Multisite Study. American Journal of Neuroradiology. 39(12). 2187–2193. 74 indexed citations
14.
Ismail, Marwa, Ahmed Soliman, Mohammed Ghazal, et al.. (2017). A fast stochastic framework for automatic MR brain images segmentation. PLoS ONE. 12(11). e0187391–e0187391. 8 indexed citations
15.
Neyer, Thomas, Ahmed ElTanboly, Marwa Ismail, et al.. (2016). A Novel Automated Method for the Objective Quantification of Retinal Layers Based on Spectral Domain Optical Coherence Tomography (SD-OCT) Imaging Reveals Sequential Changes in the Normal Retina with Age. Investigative Ophthalmology & Visual Science. 57(12). 5943–5943. 1 indexed citations
16.
Schaal, Shlomit, Marwa Ismail, Ahmed ElTanboly, et al.. (2016). Subtle Early Changes in Diabetic Retinas Revealed by a Novel Method that Automatically Quantifies Spectral Domain Optical Coherence Tomography (SD-OCT) Images. Investigative Ophthalmology & Visual Science. 57(12). 6324–6324. 1 indexed citations
17.
Ismail, Marwa, Ahmed Soliman, Ahmed ElTanboly, et al.. (2016). Detection of white matter abnormalities in MR brain images for diagnosis of autism in children. 11. 6–9. 11 indexed citations
18.
ElTanboly, Ahmed, Marwa Ismail, Ahmed Shalaby, et al.. (2016). A computer‐aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images. Medical Physics. 44(3). 914–923. 80 indexed citations
19.
Alansary, Amir, Marwa Ismail, Ahmed Soliman, et al.. (2015). Infant Brain Extraction in T1-Weighted MR Images Using BET and Refinement Using LCDG and MGRF Models. IEEE Journal of Biomedical and Health Informatics. 20(3). 925–935. 24 indexed citations
20.
Ismail, Marwa, Aly A. Farag, Robert Falk, & Gerald W. Dryden. (2014). Enhanced automatic colon segmentation for better cancer diagnosis. 23. 91–94. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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