Publications

Patents:

  • Co-Occurrence of Local Anisotropic Gradient Orientations
    Prasanna, P, Tiwari, P, Madabhushi, A
    USSN: 9,483,822 (November, 2016)
  • Entropy-Based Radiogenomic Descriptors on Magnetic Resonance Imaging (MRI) for Molecular Characterization of Breast Cancer
    Prasanna, P, Braman, N, Singh, S, Madabhushi, A, Harris, L, Varadan, V
    USSN: 10,055,842 (August, 2018)
  • Radiographic-Deformation and Textural Heterogeneity (r-Dep TH): An Integrated Descriptor for Brain Tumor Prognosis
    Prasanna, P, Tiwari, P, Madabhushi, A
    Provisional patent filed, US Patent Application No. 16/402,494, 2020

Journal publications:

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

2019

  • 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

2018

  • 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

2017

  • 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

2016

  • 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 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
  • 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
  • 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)

Peer-reviewed abstracts/posters/demos

  • 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