2022

Journal Publications

• Ding, R*, Prasanna, P*, Corredor, G*, Barrera, C, Zens, P, Lu, C, Velu, P, Leo, P, Beig, N, Li, H, Toro, P, Berezowska, S, Baxi, V, Balli, D, Belete, M, Rimm, D, Velcheti, V, Schalper, K, and Madabhushi, A. “Image analysis reveals molecularly distinct patterns of TILs in NSCLC associated with treatment outcome”. NPJ Precision Oncology, 2022. (*co-first authors)
• Braman, N, Prasanna, P, Bera, K, Alilou, M, Khorrami, M, Leo, P, Etesami, M, Vulchi, M, Turk, P, Gupta, A, Jain, P,  Fu, P, pennell, N, velcheti, V, Abraham, J, Plecha, D, and Madabhushi, A. “Novel Radiomic Measurements of Tumor-Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers”. Clinical Cancer Research, 2022.
• Antunes, J, Ismail, M, Hossain, I, Wang, Z, Prasanna, P, Madabushi, A, Tiwari, P, and Viswanath, S. “RADIomic Spatial TexturAl descripTor (RADISTAT): Quantifying spatial organization of imaging heterogeneity associated with tumor response to treatment”. IEEE Journal of Biomedical and Health Informatics, 2022.

Conference/Workshop Publications

• Konwer, A, Xu, X, Bae, J, Chen, C, and Prasanna, P, “Temporal Context Matters: Enhancing Single Image Prediction with Disease Progression Representations”. CVPR, 2022.
• Kapse, S, Torre-Healy, L, Moffitt, R, Gupta, R, and Prasanna, P, “Subtype-specific Spatial Descriptors of Tumor-immune Microenvironment are Prognostic of Survival in Lung Adenocarcinoma”. ISBI, 2022.
• Xu, X, and Prasanna, P, “Brain Cancer Survival Prediction on Treatment-naive MRI using Deep Anchor Attention Learning with Vision Transformer”. ISBI, 2022.
• Kapse, S, Gupta, R, and Prasanna, P, “Shape-based tumor microenvironment analysis to differentiate non-small cell lung cancer subtypes: a radio-pathomic study”. SPIE Medical Imaging, 2022.
• Suman, S, and Prasanna, P, “Muti-stage attention-based network for brain tumor subtype classification”.  SPIE Medical Imaging, 2022.
• Bae, J, Cattell, R, Zabrocka, E, Roberson, J, Payne, D, Mani, K, and Prasanna, P, “Pre-treatment radiomics from radiotherapy dose regions predict distant brain metastases in stereotactic radiosurgery”. SPIE Medical Imaging, 2022.
• Konwer, A, and Prasanna, P, “Clinical outcome prediction in COVID-19 using self-supervised vision transformer representations”. SPIE Medical Imaging, 2022.
• Salguero, J, Prasanna, P, Corredor, G, Cruz-Roa, A, Becerra, D, Romero, E, “Selecting training samples for ovarian cancer classification via a semi-supervised clustering approach”. SPIE Medical Imaging, 2022.

2021

Journal Publications

• Bae, J, Kapse, S, Singh, G, Gattu, R, Ali, S, Shah, Marshall, C, Pierce, J, Phatak T, Gupta A, Green, J, Madan, N, and Prasanna, P, “Predicting Mechanical Ventilation and Mortality in COVID-19 Using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study”. Diagnostics, 2021.
• Singh, G, Manjila, S, Sakla, N, True, A, Wardeh, A.H, Beig, N, Vaysberg, A, Matthews, J, Prasanna, P*, and Spektor, V*, “Radiomics and radiogenomics in gliomas: a contemporary update” British Journal of Cancer, 2021. (*co-senior authors)
• Singh, G, Kainth, T, Manjila, N, Jain, S, Naysberg, A, Spektor, V, Prasanna, P*, and  Manjila, S*. “Long-term solutions in neurosurgery using extended reality technologies”. Neurosurgical Focus (Editorial), 2021 (*co-senior authors).
• Lu, C, Koyuncu, C, Corredor, G, Prasanna, P, Leo, P, Wang, X, Janowczyk, A, Bera, K, Lewis, J, Velcheti, V, and Madabhushi, A, “Feature-driven Local Cell Graph (FLocK): New Computational Pathology-based Descriptors for Prognosis of Lung Cancer and HPV Status of Oropharyngeal Cancers” Medical Image Analysis, 2021.
• Alilou, M, Prasanna, P, Bera, K, Gupta, A, Rajiah, P, Yang, M, Jacono, F, Velcheti, V, Gilkeson, R, Linden, P, and Madabhushi, A, “A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans”. Cancers, 2021.

Conference/Workshop Publications

• Zhou, L., Bae, J., Liu, H., Singh, G., Green, J., Samaras, D. and Prasanna, P, “Chest Radiograph Disentanglement for COVID-19 Outcome Prediction”. MICCAI, 2021.
• Konwer, A., Bae, J., Singh, G., Gattu, R., Ali, S., Green, J., Phatak, T. and Prasanna, P., “Attention-Based Multi-scale Gated Recurrent Encoder with Novel Correlation Loss for COVID-19 Progression Prediction”. MICCAI, 2021.
• Suman, S., Singh, G., Sakla, N., Gattu, R., Green, J., Phatak, T., Samaras, D. and Prasanna, P., “Attention Based CNN-LSTM Network for Pulmonary Embolism Prediction on Chest Computed Tomography Pulmonary Angiograms”. MICCAI, 2021.
• Wang, F, Kapse, S, Liu, S, Prasanna, P, and Chen, C. “TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer.”  International Conference on Information Processing in Medical Imaging, 2021.
• Patel, D., Cowan, C. and Prasanna, P., “Predicting Mutation Status and Recurrence Free Survival in Non-Small Cell Lung Cancer: A Hierarchical CT Radiomics–Deep Learning Approach”. IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021.
• Cowan, C., Bae, J., Singh, G., Khullar, R., Shah, S., Madan, N. and Prasanna, P., “Evolution of chest radiograph radiomics and association with respiratory and inflammatory parameters in COVID-19 patients undergoing prone ventilation: preliminary findings”. SPIE Medical Imaging, 2021.

2020

Journal Publications

• Khullar, R, Shah, S, Singh, G, Bae, J, Gattu, R, Jain, S, Green, J, Anandarangam, T, Cohen, M, Madan, N, and Prasanna, P, “Effects of Prone Ventilation on Oxygenation, Inflammation, and Lung Infiltrates in COVID-19 Related Acute Respiratory Distress Syndrome: A Retrospective Cohort Study”, Journal of Clinical Medicine, 2020.
• Prasanna, P, Bobba, V, Figueiredo, N, Sevgi, D.D, Lu, C, Braman, N, Alilou, M, Sharma, S, Srivastava, S.K, Madabhushi, A, and Ehlers, J.P, “Radiomics-based assessment of ultra-widefield leakage patterns and vessel network architecture in the PERMEATE study: insights into treatment durability”, British Journal of Ophthalmology, 2020.
• Ismail, M, Hill, V, Statsevych, V, Mason, E, Correa, R, Prasanna, P, Singh, G, Bera, K, Thawani, R, Ahluwalia, M and Madabhushi, A, and Tiwari, P, “Can Tumor Location on Pre-treatment MRI Predict Likelihood of Pseudo-Progression vs. Tumor Recurrence in Glioblastoma?—A Feasibility Study”, Frontiers in Computational Neuroscience, 2020.
• Singh, G, True, A.J, Lui, C.C, Prasanna, P, Orleans, G, Partyka, L, and Phatak, T.D, “Normal anterior-posterior diameters of the spinal cord and spinal canal in healthy term newborns on sonography”, Pediatric Radiology, 2020.
• Alvarez-Jimenez, C, Sandino, A.A, Prasanna, P, Gupta, A, Viswanath, S.E, and Romero, E, “Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results”, Cancers, 2020.
• Ding, J, Chen, S, Sosa, MS, Cattell, R, Lei, L, Sun, J, Prasanna, P, Liu, C, Huang, C, “Optimizing the Peritumoral Region Size in Radiomics Analysis for Sentinel Lymph Node Status Prediction in Breast Cancer”, Academic Radiology, 2020.
• Beig, N, Singh, S, Bera, K,  Prasanna, P,  Singh, S, Chen, J, SaeedBamashmos, A Barnett, A, Hunter, K, Statsevych, V, Hill, V, Varadan, V, Madabhushi, A, Ahluwalia, M Tiwari, P, “Sexually dimorphic radiogenomic models identify distinct imaging and biological pathways that are prognostic of overall survival in Glioblastoma”, Neuro-Oncology, 2020.
• Moosavi, A, Figueiredo, N, Prasanna, P, Srivastava, S.K, Sharma, S, Madabhushi, A and Ehlers, J. “Imaging Features of Vessels and Leakage Patterns Predict Extended Interval Aflibercept Dosing Using Ultra-Widefield Angiography in Retinal Vascular Disease: Findings from the PERMEATE Study”, IEEE Transactions on Biomedical Engineering, 2020.
• Lu, C, Bera, K, Wang, X, Prasanna, P, Xu, J, Janowczyk, A, Beig, N, et al. “A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study.” The Lancet Digital Health, 2020.
• Hiremath, A, Shiradkar, R, Merisaari, H, Prasanna, P, Ettala, O, Taimen, P, Aronen, H, Bostrom, P, Jambor, I, Madabhushi, A, “Test-retest repeatability of a deep learning architecture in detecting and segmenting clinically significant prostate cancer on apparent diffusion coefficient (ADC) maps”, European Radiology, 2020
• Beig, N, Bera, K, Prasanna, P, Antunes, J, Correa, R, Singh, S, Saeed Bamashmos, A, Ismail, M, Braman, N, Verma, R, Hill, V, Statsevych, V, Ahluwalia, M, Varadan, V, Madabhushi, A, Tiwari, P, “Radiogenomic-based survival risk stratification of tumor habitat on Gd-T1w MRI is associated with biological processes in Glioblastoma”, Clinical Cancer Research, 2020
• Vaidya, P, Bera, K, Gupta, A, Wang, X, Corredor, G, Fu, P, Beig, N, Prasanna, P, Patil, P, Velu, P, Rajiah, P, Gilkeson, R, Feldman, M, Choi, H, Velcheti, V, Madabhushi, A, “CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multi-cohort study for outcome prediction”, The Lancet Digital Health, 2020

Conference/Workshop Publications

• 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.
• Paredes, D, Prasanna, P, Preece, C, Gupta, R, Fereidouni, F, Samaras, D, Kurc, T, Levenson, R.M, Thompson-Carino, P, Saltz, J, and Chen, C. “Automated Assessment of the Curliness of Collagen Fiber in Breast Cancer.”  European Conference on Computer Vision (Bioimage Computing workshop), 2020.
• *Prasanna, P, *Ding, R, Corredor, G, Lu, C, Velu, P, Le, K, Leo, P, Velcheti, V, Rimm, D, Schalper, K, Madabhushi, A, “Compactness Measures of Tumor Infiltrating Lymphocytes in Lung Adenocarcinoma are Associated with Overall Patient
  Survival and Immune Scores”, SPIE Medical Imaging 2020 (Runner-up Best paper award)
• Hiremath, A, Shiradkar, R, Merisaari, H, Braman, N, Prasanna, P, Ettala, O, Taimen, P, Aronen, HJ, Bostroc, P, Jambor, I, Purysko, A, Madabhushi, A, “A combination of intra- and peri-tumoral deep features from prostate bi-parametric MRI can distinguish clinically significant and insignificant prostate cancer”, SPIE Medical Imaging 2020

2019

Journal Publications

• Prasanna, P*, Khorrami, M*, Gupta, A, Patil, P, Velu, P, Thawani, R, Corredor, G, Alilou, M, Bera, K, Fu, P, Feldman, M, Velcheti, V, Madabhushi, A, “Changes in CT radiomic features associated with lymphocyte distribution predict overall survival and response to immunotherapy in non-small cell lung cancer”, Cancer Immunology Research, 2019 (Included in Research Highlights for NCI EGRP, 2019)
• Prasanna, P, Rogers, L, Lam, TC, Cohen, M, Siddalingappa, A, Wolansky, L, Pinho, M, Gupta, A, Hattanpaa, K, Madabhushi, A, Tiwari, P, “Disorder in pixel-level edge directions on T1w MRI is associated with degree of radiation necrosis in primary and metastatic brain tumors: Preliminary Findings”, American Journal of Neuroradiology, 2019
• Prasanna, P*, Mitra, J*, Beig, N, Nayate, A, Patel, J, Ghose, S, Thawani, R, Partovi, S, Madabhushi, A, Tiwari, P, “Mass Effect Deformation Heterogeneity (MEDH) on T1-weighted MRI is associated with decreased survival in patients with right cerebral hemisphere Glioblastoma Multiforme (GBM)- A preliminary analysis”, Nature Scientific Reports, 2019
• Braman, N, Prasanna, P, Whitney, J, Singh S, Beig, N, Etesami, M, Bates, D, Gallagher, K, Bloch, N, Vulchi, M, Turk, P, Bera, K, Abraham, J, Sikov, W, Somlo, G, Harris, L, Gilmore, H, Plecha, D, Varadan, V, Madabhushi, A, “Peri-Tumoral Radiomics Discriminate Intrinsic Tumor Biology and Predict Pathologic Response to Preoperative HER2-Targeted Therapy on Pre-treatment MRI”, JAMA Network Open, 2019
• Prasanna, P*, Karnawat, A*, Ismail, M, Madabhushi, A, Tiwari, P, “Radiomics-based Convolutional Neural Network (RadCNN) for Brain Tumor Segmentation on Multi-parametric MRI”, Journal of Medical Imaging, 2019

Conference/Workshop Publications

• Prasanna, P, Ehlers, J, Braman, N, Figueredo, N, Bobba, V, Sharma , S, Srivastava, S, Madabhushi, A, “Morphology of Vascular Network in Eyes with Diabetic Macular Edema Varies Based on Tolerance of Aflibercept Treatment Interval Length: Preliminary Findings”, SPIE Medical Imaging 2019
• Prasanna, P, Ehlers, J, Braman, N, Figueredo, N, Bobba, V, Sharma , S, Srivastava, S, Madabhushi, A, “Graph-based Attributes of Leakage Patterns in Eyes with
  Diabetic Macular Edema Varies Based on Tolerance of Aflibercept Treatment Interval Length: Preliminary Findings”, SPIE Medical Imaging 2019
• 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”, SPIE Medical Imaging 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

2018

Journal Publications

• Beig, N, Khorrami, M, Alilou, M, Braman, N, Prasanna, P, Orooji, M, Rakshit, S, Rajiah, P, Ginnesburg, J, Donatelli, C, Thawani, R, Yang, M, Jacono, F, Tiwari, P, Velcheti, V, Gilkeson, R, Linden, P, Madabhushi, A, “Peri-nodular and intranodular radiomic features on lung CT distinguishes adenocarcinomas from granulomas”, Radiology, 2018 (Featured in the journal’s editorial article)
• Alilou, M, Orooji, M, Beig, N, Prasanna, P, Rajiah, P, Donatelli, C, Velcheti, V, Rakshit, S, Yang, M, Jacono, F, Gilkeson, R, Linden, P, Madabhushi, A, “Quantitative vessel tortuosity: A CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas”, Nature Scientific Reports, 2018
• Ismail, M, Hill, V, Statsevych, V, Huang, R, Prasanna, P, Correa, R, Singh, G, Bera, K, Beig, N, Thawani, R, Madabhushi, A, Ahluwalia, 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 Neuroradiology, 2018 (Featured on the journal’s cover page)
• Beig, N, Patel, J, Prasanna, P, Hill, V, Gupta, A, Correa, R, Bera, K, Singh, S, Partovi, S, Varadan, V, Ahluwalia, M, Madabhushi, A, Tiwari, P, “Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma”, Nature Scientific Reports, 2018

Conference/Workshop Publications

• Prasanna, P*, Braman, N*, Alilou, M, Beig, N, Madabhushi, A, “Vascular Network Organization via Hough Transform (VaNgOGH): A Novel Radiomic Biomarker for Diagnosis and Treatment Response”, MICCAI 2018
• Lu, C, Wang, X, Prasanna, P, Corredor, G, Sedor, G, Bera, K, Velcheti, V, Madabhushi, A, “Nuclear Features Driven Local Cell Graph (FeDeG): Quantifying the Interactions between Self-organized Cell Sub-graphs”, MICCAI 2018

2017

Journal Publications

• Thawani, R, McLane, M, Beig, N, Ghose, S, Prasanna, P, Velcheti, V, Madabhushi, A, “Radiomics and Radiogenomics in Lung cancer : A review for the Clinician”, Lung Cancer, 2017
• Bektik, E, Dennis, A, Prasanna, P, Madabhushi, A, Fu, JD, “Single cell qPCR reveals that additional HAND2 and microRNA-1 facilitate the early reprogramming progress of seven-factor-induced human myocytes”, PLoS one, 2017
• Braman, N, Etesami, M, Prasanna, P, Dubchuk, C, Gilmore, H, Tiwari, P, Plecha, D, Madabhushi, A, “DCE-MRI intratumoral and peritumoral radiomics enable pre-treatment prediction of response to neo-adjuvant chemotherapy in breast cancer”, Breast Cancer Research, 2017

Conference/Workshop Publications

• Prasanna, P, Mitra, J, Beig, N, Partovi, S, Singh, G, Pinho, M, Madabhushi, A, Tiwari, P. “Radiographic-Deformation and Textural Heterogeneity (r-DepTH): An integrated descriptor for brain tumor prognosis”, MICCAI 2017
• Antunes, J, Prasanna, P, Madabhushi, A, Tiwari P, Viswanath S, “RADIomic Spatial TexturAl descripTor (RADISTAT): Characterizing intra-tumoral heterogeneity for response and outcome prediction”, MICCAI 2017​
• Beig, N, Patel, J, Prasanna, P, Partovi, S, Varadan, V, Madabhushi, A, Tiwari, P, “Radiogenomic analysis of hypoxia pathway reveals computerized MRI descriptors predictive of overall survival in Glioblastoma”, The International Society for Optics and Photonics (SPIE) Medical Imaging, 2017

Prior to 2016

Journal Publications

• Prasanna, P, Patel, J, Partovi, S, Madabhushi, A, Tiwari, P, “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 2016. Pubmed 
• Prasanna, P, Tiwari, P, Madabhushi, A, “Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor”, Nature Scientific Reports,  2016  Link
• Tiwari, P, Prasanna, P, Wolansky, L, Pinho, M, Cohen, M, Nayate, AP, Gupta, A, Singh, G, Hatanpaa, K, Sloan, A, Rogers, L, Madabhushi, A, Can Computer-extracted texture features distinguish Radiation Necrosis from Recurrent Brain Tumors on multi-parametric MRI? – A Feasibility Study, American Journal of Neuro Radiology, 2016  (Top 10 most read AJNR papers in 2016) (Nominated for the annual Lucien Levy Best Research Article Award). Link
• Prasanna, P, Dana, K, Gucunski, N, Basily, B, La, H, Lim, R, Parvardeh, H, “Application of Computer Vision Techniques in Surface Health Monitoring of Concrete Bridges”,  IEEE Trans. on Automated Science and Engineering, 2016 (link)

Conference/Workshop Publications

• Prasanna, P*, Tiwari, P*, Madabhushi, A, “Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe):  Distinguishing tumor confounders and molecular subtypes on MRI,”  MICCAI 2014. (*joint first authors) (Young Scientist Award, runners up, selected as oral presentation, Acceptance rate = 4%) Pubmed
• ​Tiwari, P, Prasanna, P, Rogers, L, Wolansky, L,  Badve, C, Cohen, M, Madabhushi, A, “Computerized image analysis of texture descriptors in multi-parametric MRI to distinguish recurrent brain tumor from radiation necrosis”, SPIE Medical Imaging, 2014 (Honorable Mention for Best Poster Presentation, Conference on Computer Aided Diagnosis) Pubmed
• Prasanna, P, Jain, S, Bhagat, N, Madabhushi, A, “Decision support system for detection of diabetic retinopathy using smartphones”, Pervasive Health 2013 (link)
• Prasanna, P, Dana, K, Gucunski, N, Basily, B, “Computer-vision based crack detection and analysis”, SPIE Smart Structures 2012 (link)

Abstracts/demos

• Hu, X, Zheng, S, Chen, C, Prasanna, P, “Vessel Topology as a Computational Imaging Biomarker to Differentiate Lung Adenocarcinoma and Squamous Cell Carcinoma.” Proceedings of the Radiologic Society of North America (RSNA) 2020
• Prasanna, P, Beig, N, Gupta, A, Rajiah, P, Gilkeson, R, Madabhushi, A, “Radiomic Machine Interpretations Can Improve Lung Nodule Diagnostic Sensitivity for Human Readers: Preliminary Findings in a Multi-site Multi-reader Study.” RSNA 2019
• Beig, N, Prasanna, P, Saeed Bamashmos, A, Hill, V, Statsevych, Bera, K, Ismail, M, Varadan, V, Ahluwalia, M, Madabhushi, A and Tiwari, P, “Radiogenomic analysis of Glioblastoma on pre-treatment Gd-T1w MRI reveals gender-specific imaging features and signaling pathways.” RSNA 2019
• Braman, N, Prasanna, P, Bera, K, Alilou, M, Vulchi, M, Etesami, M, Turk, P, Abraham, J, Plecha, D, Madabhushi, A, “Radiomic measurements of tumor-associated vasculature morphology and function on pretreatment dynamic MRI identifies responders to neoadjuvant chemotherapy.” San Antonio Breast Cancer Symposium (SABCS) 2019
• Prasanna, P, Kohrrami, M, Gupta, A, Patil, P, Khunger, M, Velu, P, Bera, K, Alilou, M, Velcheti, V, Madabhushi, A, “Intra and perinodular CT delta radiomic features associated with early response can predict overall survival (OS) in immunotherapy-treated non-small cell lung cancer (NSCLC): A multi-site multi-agent study.” ASCO, 2019 (Conquer Cancer Foundation Merit Award)
• Prasanna*, P, Ismail*, M, Huang, R, Singh, G, Thawani, R, Madabhushi, A, Tiwari, P, “Compactness of peritumoral edema on routine MRI appears to distinguish tumor recurrence from pseudo-progression in primary brain tumors: Preliminary findings.” Proceedings of the Radiologic Society of North America (RSNA) 2017.
• Ismail, M, Prasanna, P, Huang, R, Singh, G, Thawani, R, Madabhushi, A, Aahluwalia, M, Tiwari, P, “Shape attributes of enhancing lesion boundaries can differentiate tumor recurrence from pseudoprogression on routine brain MRI scans: Preliminary findings.” Society of Neuro-oncology (SNO) 2017.
• Beig, N , Correa, R , Prasanna, P , Mitra, J , Nayate, A , Madabhushi, A , and Tiwari, P, “Radiogenomic analysis of distinct tumor sub-compartments on T2 and FLAIR predict distinct molecular subtypes in Lower Grade Gliomas”, The International Society for Magnetic Resonance in Medicine (ISMRM) 25th Annual Meeting , 2017
• Prasanna, P, Nayate, A, Gupta, A, Rogers, L,Wolansky, L, Singh, G,  Pinho, M, Hatanpaa, K, Madabhushi, A, Tiwari, P, “Human-Machine Performance Comparison Study in Distinguishing Radiation Necrosis from Brain Tumor Recurrence on Routine MRI”, RSNA 2016.
• Prasanna, P, Rogers, L, Cohen, M, Singh, G, Badve, C, Wolansky, Madabhushi, A, Tiwari, P, “Computer extracted Texture Descriptors on MRI that Distinguish Radiation, Necrosis and Tumor Recurrence Post-Radiotherapy in Primary Neoplasms are Associated with Vascular, Necrotic and Demyelinating changes”, RSNA 2016.
• Beig, N, Orooji, M, Rajiah, P, Rakshit, S, Yang, M, Jacono, F, Prasanna, P, Tiwari, P, Velcheti, V, Gilkeson, R, Linden, P, Madabhushi, A, “Radiomic Features of the Perinodular Habitat on Non-contrast Lung CT Discriminates Adenocarcinoma from Granulomas”, RSNA 2016.
• Beig, N, Correa, R, Prasanna P, Mitra J, Nayate A, Madabhushi A, Tiwari, P, “Predicting IDH mutation status on routine treatment-naïve MRI using radiogenomic features from peritumoral brain parenchyma”, SNO 2016.
• Prasanna, P, Nayate, A, Gupta, A, Rogers, L, Singh, G, Wolansky, L, Pinho, M, Hatanpaa, K, Madabhushi, A, Tiwari, P, “Distinguishing radiation necrosis from brain tumor recurrence on routine MRI: A preliminary human-machine reader comparison study”, SNO 2016.
• Prasanna, P, Rogers, L, Cohen, M, Singh, G, Badve, C, Wolansky, Madabhushi, A, Tiwari, P, “Features of local gradient disorder on MRI that distinguish radiation necrosis and tumor recurrence post-radiotherapy are associated with zonal necrosis, vessel wall thickening, hyalinization and demyelination: A preliminary study in brain tumors”, SNO 2016.
• Prasanna, P, Braman, N, Singh, S, Plecha, D, Gilmore, H, Harris, L, Wan, T, Varadan, V, and Madabhushi, A , “Directional-gradient based radiogenomic descriptors on DCE-MRI appear to distinguish different PAM50-identified subtypes of HER2+ Breast Cancer”, ISMRM  2016
• Karnawat, A, Prasanna, P, Madabhushi, A, Tiwari, P, “Use of textural radiomic maps in a 3D convolutional neural network framework can augment glioma lesion segmentation”, SNO 2017.
• Beig, N , Correa, R , Thawani, R , Prasanna, P , Badve, C , Gold, D , DeBlank, P , Tiwari, P. “MRI textural features can differentiate pediatric posterior fossa tumors”, SNO Pediatric Neuro-Oncology Basic and Translational Research Conference, 2017.
• Prasanna, P, Rose, A, Singh, G, Huang, R, Madabhushi, A and Tiwari, P, “Radiomic features from the necrotic region on post-treatment Gadolinium T1w MRI appear to differentiate pseudo-progression from true tumor progression in primary brain tumors”, ISMRM 2016
• Prasanna, P , Braman, N, Singh, S, Plecha, D, Gilmore, H, Harris, L, Wan, T, Varadan, V, and Madabhushi, A , “Predicting TP53 mutational status of breast cancers on clinical DCE MRI using directional-gradient based radiogenomic descriptors”, ISMRM 2016 (top 3%).
• Tiwari, P, Partovi, S, Patel, J, Prasanna, P, Madabhushi, A, “Computer extracted texture descriptors from different tissue compartments within the tumor habitat on treatment-naïve MRI predict clinical survival in glioblastoma patients”, RSNA 2015
• Tiwari, P, Partovi, S, Patel, J, Prasanna, P, Madabhushi, A, “Computer extracted texture descriptors from different tissue compartments within the tumor habitat on treatment-naïve MRI predict clinical survival in glioblastoma patients”, RSNA 2015
• Prasanna, P, Tiwari, P, Wolansky, L, Rogers, R, Madabhushi, A, “Morphologic heterogeneity at a pixel-level captured via entropy of gradient orientations on T1-post contrast MRI enables discrimination of tumor recurrence from cerebral radiation necrosis”, SNO 2015
• Tiwari, P, Prasanna, P, Partovi, S, Patel, J, Madabhushi, A,” Computer extracted texture descriptors from different tissue compartments within the tumor habitat on treatment-naïve MRI predict clinical survival in glioblastoma patients”, SNO 2015
• Prasanna, P, Tiwari, P, Siddalingappa, A, Lam, T, Wolansky, L, Rogers, R, Madabhushi, A, “Morphologic Study of contrast-enhanced T1-w MRI markers of cerebral radiation necrosis manifested in head-and-neck cancers, primary, and metastatic brain tumors: Preliminary findings”, ISMRM 2015
• Algohary, A, Viswanath, S, Prasanna, P, Pahwa, S, Gulani, V, Ponsky, L, Stricker, P, Moses, D, Shnier, R, Madabhushi, A, “Quantitative assessment of T2-w MRI to better identify patients with prostate cancer in a screening population”, AUA Annual Meeting, 2015
• Tiwari, P, Prasanna, P,  Partovi, S, Patel, J, Madabhushi, A, ”Quantitative texture descriptors on baseline MRI can predict patient survival in newly diagnosed GBM patients”, SNO 2014
• Tiwari, P, Prasanna, P, Wolansky, L, Rogers, R, Madabhushi, A, “Computer-extracted oriented texture features on T1-Gadolinium MRI for distinguishing radiation necrosis from recurrent brain tumors”, SNO 2014
• Patel, J, Prasanna, P, Partovi, S, Tiwari, P, Madabhushi, A, ”Identifying MRI markers on newly diagnosed Glioblastoma Multiforme to distinguish patients with long and short term survival, BMES Annual Meeting, 2014
• Tiwari, P, Prasanna, P, Wolansky, L, Rogers, R, Madabhushi, A, “Computerized image analysis of texture descriptors in multi-parametric MRI to distinguish recurrent brain tumor from radiation necrosis”, SNO  2013
• Prasanna, P, Dana, K, Gucunski, N, Basily, B, “Computer Vision applications in civil engineering”, First Multimedia and Vision Meeting in Greater NY Area, 2012