Pallavi Tiwari

Assistant Professor, Biomedical Engineering
Develops neuroinformatics techniques for applications in brain tumors and neurological disorders
Office: 6131 Wolstein Phone Number: (216) 368-0177 Email: pxt130@case.edu

Education

Ph.D., Biomedical Engineering, Rutgers University, 2012
M.S., Biomedical Engineering, Rutgers University, 2008
B.E., Biomedical Engineering, S.G.S.I.T.S, 2006

Awards and Recognitions

2016, 100 Women Achievers Award for Science and Innovation, Government of India

Research Interests

Neuroimaging, Medical image analysis, multi-modal data fusion, clinical decision support
My lab focuses on developing neuroinformatics techniques using machine learning, statistical modeling, and pattern recognition for applications in brain tumors and neurological disorders. One of the primary focuses of BrIC lab is to identify computerized image-based (also known as radiomic) phenotypes, and their associations with genomics (radiogenomics) and histo-pathology (radio-pathomics) for disease characterization.

Teaching Interests

Pattern Recognition, Image processing

Publications

Eck, B., Chirra, P., Muchhala, A., Hall, S., Bera, K., Tiwari, P., Madabhushi, A., Seiberlich, N., & Viswanath, S. E. (2021). Prospective Evaluation of Repeatability and Robustness of Radiomic Descriptors in Healthy Brain Tissue Regions In Vivo Across Systematic Variations in T2-Weighted Magnetic Resonance Imaging Acquisition Parameters. Journal of Magnetic Resonance Imaging (JMRI).
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.
Sadri, A., 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.
Tiwari, P., Prasanna, P., Wolansky, L., Pinho, M., Cohen, M., Nayate, A., Gupta, A., Singh, G., Hatanpaa, K., Sloan, A., Rogers, L., & Madabhushi, A. (2016). Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study.. AJNR. American journal of neuroradiology, 37 (12), 2231-2236.
Prasanna, P., Tiwari, P., & Madabhushi, A. (2016). Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor.. Scientific reports, 6 , 37241.
Prasanna, P., Patel, J., Partovi, S., Madabhushi, A., & Tiwari, P. (2016). Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings.. European radiology.
Tiwari, P., Danish, S., Jiang, B., & Madabhushi, A. (2015). Association of computerized texture features on MRI with early treatment response following laser ablation for neuropathic cancer pain: preliminary findings.. Journal of medical imaging (Bellingham, Wash.), 2 (4), 041008.
Tiwari, P. (2015). Association of computerized texture features on MRI with early treatment response following laser ablation for neuropathic cancer pain: preliminary findings. Journal of Medical Imaging , 2 (4).
Tiwari, P., Danish, S., & Madabhushi, A. (2014). Identifying MRI markers associated with early response following laser ablation for neurological disorders: preliminary findings.. PloS one, 9 (12), e114293.
Tiwari, P., Danish, S., & Madabhushi, A. (2014). Identifying MRI markers to evaluate early treatment related changes post laser ablation for cancer pain management.. Proceedings of SPIE--the International Society for Optical Engineering, 9036 , 90362L.
Prasanna, P., Tiwari, P., & Madabhushi, A. (2014). Co-occurrence of local anisotropic gradient orientations (CoLIAGe): distinguishing tumor confounders and molecular subtypes on MRI.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 17 (Pt 3), 73-80.
Tiwari, P., Danish, S., Wongkasemjit, S., & Madabhushi, A. (2013). Quantitative evaluation of multi-parametric MR imaging marker changes post-laser interstitial ablation therapy (LITT) for epilepsy.. Proceedings of SPIE--the International Society for Optical Engineering, 8671 , 86711Y.
Madabhushi, A., & Tiwari, P. (2013). Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS. Medical Image Analysis, 17 (2), 219-35.
Tiwari, P., Kurhanewicz, J., & Madabhushi, A. (2013). Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS.. Medical image analysis, 17 (2), 219-35.
Tiwari, P., Viswanath, S. E., Kurhanewicz, J. E., Sridharan, A. E., & Madabhushi, A. E. (2012). Multimodal wavelet embedding representation for data combination (MaWERiC): integrating magnetic resonance imaging and spectroscopy for prostate cancer detection.. NMR in biomedicine, 25 (4), 607-19.
Toth, R., Tiwari, P., Rosen, M., Reed, G., Kurhanewicz, J., Kalyanpur, A., Pungavkar, S., & Madabhushi, A. (2011). A magnetic resonance spectroscopy driven initialization scheme for active shape model based prostate segmentation.. Medical image analysis, 15 (2), 214-25.
Viswanath, S. E., Tiwari, P. E., Chappelow, J. E., Toth, R. E., Kurhanewicz, J. E., & Madabhushi, A. E. (2011). CADOnc<sup>©</sup>: An Integrated Toolkit For Evaluating Radiation Therapy Related Changes In The Prostate Using Multiparametric MRI.. Proceedings. IEEE International Symposium on Biomedical Imaging, 2011 , 2095-2098.
Tiwari, P., Kurhanewicz, J., Rosen, M., & Madabhushi, A. (2010). Semi supervised multi kernel (SeSMiK) graph embedding: identifying aggressive prostate cancer via magnetic resonance imaging and spectroscopy.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 13 (Pt 3), 666-73.
Tiwari, P., Rosen, M., & Madabhushi, A. (2009). A hierarchical spectral clustering and nonlinear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS).. Medical physics, 36 (9), 3927-39.
Tiwari, P., Rosen, M., Reed, G., Kurhanewicz, J., & Madabhushi, A. (2009). Spectral embedding based probabilistic boosting tree (ScEPTre): classifying high dimensional heterogeneous biomedical data.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 12 (Pt 2), 844-51.
Tiwari, P., Rosen, M., & Madabhushi, A. (2008). Consensus-locally linear embedding (C-LLE): application to prostate cancer detection on magnetic resonance spectroscopy.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 11 (Pt 2), 330-8.
Tiwari, P., Madabhushi, A., & Rosen, M. (2007). A hierarchical unsupervised spectral clustering scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS).. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 10 (Pt 2), 278-86.