Peer-Reviewed Publications

Ismail, M.; Craig, S.; Ahmed, R.; de Blank, P.; Tiwari, P. Opportunities and Advances in Radiomics and Radiogenomics for Pediatric Medulloblastoma Tumors. Diagnostics 202313, 2727.

Bhatia A, Moreno R, Reiner AS, Nandakumar S, Walch HS, Thomas TM, Nicklin PJ, Choi Y, Skakodub A, Malani R, Prabhakaran V, Tiwari P, Diaz M, Panageas KS, Mellinghoff IK, Bale TA, Young RJ. Tumor Volume Growth Rates and Doubling Times during Active Surveillance of IDH-mutant Low-Grade Glioma. Clin Cancer Res. 2023 Nov 1. Epub ahead of print.

Verma R, Hill V, Statsevych V, Bera K, Correa R, Leo P, Ahluwalia M, Madabhushi A, Tiwari P, Stable-and-discriminatory radiomic features from the tumor and its habitat associated with progression-free survival in Glioblastoma: A multi-institutional study, Submitted to American Journal of Neuroradiology (in press).

Yadav I, Ismail M, Statsevych V, Correa R, Ahluwalia M, Tiwari P, “Distinguishing non-contrast enhancing tumor from vasogenic edema using radiomic analysis on pre-treatment MRI scans”, SPIE Medical Imaging, 2022.

Sandino A, Verma R, Becerra D, Chen Y, Romero E, Tiwari P, “A Hierarchical Deep Learning Approach for Segmentation of Glioblastoma Tumor Niches on Digital Histopathology”, SPIE Medical Imaging, 2022

Ismail M, Prasanna P, Bera K, Statsevych V, Hill V, Singh G, Partovi S, Beig N, McGarry S, Laviolette P, Ahluwalia M, Madabhushi A, Tiwari P, Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma, IEEE Transactions of Medical Imaging (in press).

Antunes, JT*, Ismail, M, Hossain, I, Wang, Z, Prasanna, P, Madabhushi, A, Tiwari, P, Viswanath, SE, “RADIomic Spatial TexturAl descripTor (RADISTAT): Quantifying spatial organization of textural heterogeneity on imaging associated with tumor response to treatment”, IEEE Journal of Biomedical and Health Informatics (in press).

Pinho, M. C., Bera, K., Beig, N., & Tiwari, P. (2021). MRI Morphometry in Brain Tumors: Challenges and Opportunities in Expert, Radiomic, and Deep-Learning-Based Analyses. In G. Seano (Ed.), Brain Tumors (Vol. 158, pp. 323–368). Springer US.

Tiwari, P., & Verma, R. (2021). The Pursuit of Generalizability to Enable Clinical Translation of Radiomics. Radiology: Artificial Intelligence, 3(1), e200227.

Ismail, M., Hill, V., Statsevych, V., Mason, E., Correa, R., Prasanna, P., Singh, G., Bera, K., Thawani, R., Ahluwalia, M., Madabhushi, A., & Tiwari, P. (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.

Beig, N., Bera, K., & Tiwari, P. (2020). Introduction to radiomics and radiogenomics in neuro-oncology: Implications and challenges. Neuro-Oncology Advances, 2(Supplement_4), iv3–iv14. 

Sadri, A. R., Janowczyk, A., Zhou, R., Verma, R., Beig, N., Antunes, J., Madabhushi, A., Tiwari, P., & Viswanath, S. E. (2020). Technical Note: MRQy — An open source tool for quality control of MR imaging data. Medical Physics, 47(12), 6029–6038.

Verma, R., Correa, R., Hill, V. B., Statsevych, V., Bera, K., Beig, N., Mahammedi, A., Madabhushi, A., Ahluwalia, M., & Tiwari, P. (2020). Tumor Habitat–derived Radiomic Features at Pretreatment MRI That Are Prognostic for Progression-free Survival in Glioblastoma Are Associated with Key Morphologic Attributes at Histopathologic Examination: A Feasibility Study. Radiology: Artificial Intelligence, 2(6), e190168.

Pati, S., Verma, R., Akbari, H., Bilello, M., Hill, V. B., Sako, C., Correa, R., Beig, N., Venet, L., Thakur, S., Serai, P., Ha, S. M., Blake, G. D., Shinohara, R. T., Tiwari, P., & Bakas, S. (2020). Reproducibility analysis of multi-institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset. Medical Physics, 47(12), 6039–6052.

Beig, N., Singh, S., Bera, K., Prasanna, P., Singh, G., Chen, J., Saeed Bamashmos, A., Barnett, A., Hunter, K., Statsevych, V., Hill, V. B., Varadan, V., Madabhushi, A., Ahluwalia, M. S., & Tiwari, P. (2020). Sexually dimorphic radiogenomic models identify distinct imaging and biological pathways that are prognostic of overall survival in glioblastoma. Neuro-Oncology, noaa231. 

Beig, N., Bera, K., Prasanna, P., Antunes, J., Correa, R., Singh, S., Saeed Bamashmos, A., Ismail, M., Braman, N., Verma, R., Hill, V. B., Statsevych, V., Ahluwalia, M. S., Varadan, V., Madabhushi, A., & Tiwari, P. (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. 

Ismail M, Correa R, Bera K, Verma R, Bamashmos AS, Beig N, Antunes J, Prasanna P, Statsevych V, Ahulwalia M, Tiwari P. “Spatial-And-Context aware (SpACe) “virtual biopsy” radiogenomic maps to target tumor mutational status on structural MRI.” MICCAI 2020.

Gupta A, Viswanath S, Tiwari P, Quality assessment of MRI using a dense neural network model, In Proceedings of SPIE Medical Imaging conference, Houston, USA, February, 2020.

Hiremath Y, Ismail M, Verma R, Gupta A, and Tiwari P, Combining Deep and Hand-crafted MRI features for identifying gender-specific differences in ASD versus controls, In Proceedings of SPIE Medical Imaging conference, Houston, USA, February, 2020.

Correa R, Chen J, Yu J, and Tiwari P, Distinguishing Treatment Effects of Radiation Therapy from Recurrent Tumor in Post-Treatment Imaging of Metastatic Tumor, In Proceedings of SPIE Medical Imaging conference, Houston, USA, February, 2020

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge, arXiv:1811.02629, 2019 (pre-print).

Prasanna, P., L. Rogers, T. C. Lam, M. Cohen, A. Siddalingappa, L. Wolansky, M. Pinho et al. “Disorder in pixel-level edge directions on T1WI is associated with the degree of radiation necrosis in primary and metastatic brain tumors: preliminary findings.” American Journal of Neuroradiology 40, no. 3 (2019): 412-417.

Prasanna, P., Karnawat, A., Ismail, M., Madabhushi, A., & Tiwari, P. (2019). Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging. Journal of Medical Imaging, 6(2), 024005.

Prateek Prasanna, Jhimli Mitra , Niha Beig , Ameya Nayate , Jay Patel , Soumya Ghose , Rajat Thawani , Sasan Partovi , Anant Madabhushi, Pallavi Tiwari, Mass Effect Deformation Heterogeneity (MEDH) on T1-weighted MRI is associated with decreased survival in patients with right cerebral hemisphere Glioblastoma: A feasibility study, Volume 9, no. 1, Article number: 1145 (2019).

Beig N, Prasanna P, Hill V, Verma R, Varadan V, Madabhushi A, Tiwari P, “Radiogenomic characterization of response to chemo-radiation therapy in Glioblastoma is associated with PI3K/AKT/mTOR and apoptosis signaling pathways.” SPIE Medical Imaging 2019, vol 10951

Iyer S, Ismail M, Tamrazi B, Correa R, Prasanna P, Beig N, Verma R,  Bera K, Statsevych V, Margol A, Judkins A, Madabhushi A, Tiwari P,  “Deformation heterogeneity radiomics to predict molecular subtypes of pediatric Medulloblastoma on routine MRI“, The International Society for Optics and Photonics (SPIE) Medical Imaging 2019, vol 10950.

Verma R, Correa R, Hill V, Beig N, Mahammedi A, Madabhushi A, Tiwari P, “Radiomics of the lesion habitat on pre-treatment MRI to predict response to chemo-radiation therapy in Glioblastoma“, SPIE Medical Imaging 2019, vol 10950.

Beig, N, Khorrami , M, Alilou, M, Prasanna, P, Braman, N, Orooji, M, Rakshit, S, Bera, K, Rajiah, P, Ginnesburg, J, Donatelli, C, Thawani, R, Yang, M, Jacono, F, Tiwari, P, Velcheti, V, Gilkeson, R, Linden, P, Madabhushi A. A Combination of Intranodular and perinodular radiomic features on non-contrast lung CT distinguishes NSCLC adenocarcinoma from granulomas, Radiology.​

Ismail, M, Hill, V, Statsevych, V, Huang, R, Prasanna, P, Correa, R, Singh, G, Bera, K, Beig, N, Thawani, R, Madabhushi, A, Aahluwalia, M, Tiwari, P, Shape features of the lesion habitat to differentiate brain tumor progression from pseudo-progression on routine multi-parametric MRI: A multi-site study, American Journal of Neuro-radiology, 2018.

Penzias G, Singanamalli A, Elliott R, Gollamudi J, Shih N, Feldman M, Stricker P, Delprado W, Tiwari S,  Bohm M, Haynes AM, Ponsky L, Fu P, Tiwari P, Viswanath S, Madabhushi A, Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary Findings, PlosOne, 2018.