Evan Calabrese

Research Interests

As a physician scientist focused on artificial intelligence (AI) applications for neurologic disease, my ongoing career goal is to combine clinical excellence in neuroradiology with cutting-edge AI research. My primary research interest lies in the use of innovative AI techniques to help extract clinically useful information from multimodal health data with a focus on neuro-oncology. Modern neuroimaging studies, most notably multi-sequence MRI, are amongst the largest and most complex types of health data that are routinely acquired for patients with neurologic disorders. I believe that modern AI tools have enormous potential to help extract new, clinically useful information from complex neuroimaging studies, and through integration with other types of health data, will ultimately improve diagnosis, management, and treatment monitoring for patients with neurologic disease.

Bio

As a physician scientist focused on artificial intelligence (AI) applications for neurologic disease, my ongoing career goal is to combine clinical excellence in neuroradiology with cutting-edge AI research. My primary research interest lies in the use of innovative AI techniques to help extract clinically useful information from multimodal health data with a focus on neuroimaging. Modern neuroimaging studies, most notably multi-sequence MRI, are amongst the largest and most complex types of health data that are routinely acquired for patients with neurologic disorders. I believe that modern AI tools have enormous potential to help extract new, clinically useful information from complex neuroimaging studies, and through integration with other types of health data, will ultimately improve diagnosis, management, and treatment monitoring for patients with neurologic disease.

Education

• MD./PhD. Duke University, School of Medicine, 2016

Positions

• Assistant Professor of Radiology
• Assistant Professor of Biomedical Engineering

Publications

• Zhou L, Wiggins W, Zhang J, Colglazier R, Willhite J, Dixon A, et al. The Duke University Cervical Spine MRI Segmentation Dataset (CSpineSeg). Sci Data. 2025 Oct 27;12(1):1695.
• Singh K, Foster MW, Violette MJ, Corcoran AM, Hotchkiss KM, Railton CO, et al. A surgical window of opportunity trial evaluating the effect of the PCSK9 inhibitor evolocumab on tumoral MHC-I expression and CD8+ infiltration in glioma. Sci Rep. 2025 Oct 23;15(1):37112.
• Chisholm M, Jabal MS, He H, Wang Y, Kalisz K, Lafata KJ, et al. Investigating the Role of Area Deprivation Index in Observed Differences in CT-Based Body Composition by Race. J Am Coll Radiol. 2025 Oct;22(10):1182u201392.
• Zhao J, Vaios E, Calabrese E, Yang Z, Robertson S, Ginn J, et al. A Radiogenomic Deep Ensemble Learning Model for Identifying Radionecrosis Following Brain Metastases (BM) Stereotactic Radiosurgery in Patients With Non-small Cell Lung Cancer BM. Adv Radiat Oncol. 2025 Aug;10(8):101826.
• Zenk M, Baid U, Pati S, Linardos A, Edwards B, Sheller M, et al. Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge. Nat Commun. 2025 Jul 8;16(1):6274.
• Akbari H, Bakas S, Sako C, Fathi Kazerooni A, Villanueva-Meyer J, Garcia JA, et al. Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study. Neuro Oncol. 2025 May 15;27(4):1102u201315.
• Zhang J, LaBella D, Zhang D, Houk JL, Rudie JD, Zou H, et al. Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma. AJNR Am J Neuroradiol. 2025 May 2;46(5):990u20138.
• Jabal MS, Warman P, Zhang J, Gupta K, Jain A, Mazurowski M, et al. Open-Weight Language Models and Retrieval-Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports: Assessment of Approaches and Parameters. Radiol Artif Intell. 2025 May;7(3):e240551.
• Roth P, Capper D, Calabrese E, Halasz LM, Jakola AS. Role of the tumor board when prescribing mutant isocitrate dehydrogenase inhibitors to patients with isocitrate dehydrogenase-mutant glioma. Neurooncol Pract. 2025 Feb;12(Suppl 1):i29u201337.
• Walsh KM, Price M, Raleigh DR, Calabrese E, Kruchko C, Barnholtz-Sloan JS, et al. Grade-stratified meningioma risk among individuals who are non-Hispanic Black and interactions with male sex. J Natl Cancer Inst. 2025 Feb 1;117(2):366u201374.
• Isikbay M, Caton MT, Narvid J, Talbott J, Cha S, Calabrese E. Deep learning segmentation-based bone removal from computed tomography of the brain improves subdural hematoma detection. J Neuroradiol. 2025 Feb;52(1):101231.
• Pati S, Wagner S, Thakur S, Calabrese E, Shinohara R, Bakas S. An Unsupervised Brain Extraction Quality Control Approach for Efficient Neuro-Oncology Studies. Journal of Imaging Informatics in Medicine. 2025 Jan 1;
• Wang L, Yang Z, LaBella D, Reitman Z, Ginn J, Zhao J, et al. Uncertainty quantification in multi-parametric MRI-based meningioma radiotherapy target segmentation. Front Oncol. 2025;15:1474590.
• Otero M, Shih J, Zhou B, Ghandi S, Talbott J, Singhal NS, et al. Neurological Injury and Outcomes in Fentanyl-related Cardiac Arrest. Journal of Addiction Medicine. 2025 Jan 1;
• Villanueva-Meyer JE, Bakas S, Tiwari P, Lupo JM, Calabrese E, Davatzikos C, et al. Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements. Lancet Oncol. 2024 Nov;25(11):e581u20138.
• Bakas S, Vollmuth P, Galldiks N, Booth TC, Aerts HJWL, Bi WL, et al. Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice. Lancet Oncol. 2024 Nov;25(11):e589u2013601.
• Lew CO, Calabrese E, Chen JV, Tang F, Chaudhari G, Lee A, et al. Artificial Intelligence Outcome Prediction in Neonates with Encephalopathy (AI-OPiNE). Radiol Artif Intell. 2024 Sep;6(5):e240076.
• Tsui B, Calabrese E, Zaharchuk G, Rauschecker AM. Reducing Gadolinium Contrast With Artificial Intelligence. J Magn Reson Imaging. 2024 Sep;60(3):848u201359.
• Fields BKK, Calabrese E, Mongan J, Cha S, Hess CP, Sugrue LP, et al. The University of California San Francisco Adult Longitudinal Post-Treatment Diffuse Glioma MRI Dataset. Radiol Artif Intell. 2024 Jul;6(4):e230182.
• Walsh KM, Price M, Raleigh DR, Calabrese E, Kruchko C, Barnholtz-Sloan JS, et al. Elevated meningioma risk among individuals who are Non-Hispanic Black is strongest for grade 2-3 tumors and synergistically modified by male sex. 2024.
• Haskell-Mendoza AP, Reason EH, Gonzalez AT, Jackson JD, Sankey EW, Srinivasan ES, et al. Automated segmentation of ablated lesions using deep convolutional neural networks: A basis for response assessment following laser interstitial thermal therapy. Neuro Oncol. 2024 Jun 3;26(6):1152u201362.
• LaBella D, Khanna O, McBurney-Lin S, Mclean R, Nedelec P, Rashid AS, et al. A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation. Sci Data. 2024 May 15;11(1):496.
• Rudie JD, Saluja R, Weiss DA, Nedelec P, Calabrese E, Colby JB, et al. The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset. Radiol Artif Intell. 2024 Mar;6(2):e230126.
• Chen JV, Li Y, Tang F, Chaudhari G, Lew C, Lee A, et al. Automated neonatal nnU-Net brain MRI extractor trained on a large multi-institutional dataset. Sci Rep. 2024 Feb 26;14(1):4583.
• Lyngaas I, Meena MG, Calabrese E, Wahib M, Chen P, Igarashi J, et al. Efficient Distributed Sequence Parallelism for Transformer-based Image Segmentation. In: Is and T International Symposium on Electronic Imaging Science and Technology. 2024. p. 1991u20137.
• Lee MD, Patel SH, Mohan S, Akbari H, Bakas S, Nasrallah MP, et al. Association of partial T2-FLAIR mismatch sign and isocitrate dehydrogenase mutation in WHO grade 4 gliomas: results from the ReSPOND consortium. Neuroradiology. 2023 Sep;65(9):1343u201352.
• Calabrese E, Wu Y, Scheffler AW, Wisnowski JL, McKinstry RC, Mathur A, et al. Correlating Quantitative MRI-based Apparent Diffusion Coefficient Metrics with 24-month Neurodevelopmental Outcomes in Neonates from the HEAL Trial. Radiology. 2023 Sep;308(3):e223262.
• Isikbay M, Caton MT, Calabrese E. A Deep Learning Approach for Automated Bone Removal from Computed Tomography Angiography of the Brain. J Digit Imaging. 2023 Jun;36(3):964u201372.
• Calabrese E, Gandhi S, Shih J, Otero M, Randazzo D, Hemphill C, et al. Parieto-Occipital Injury on Diffusion MRI Correlates with Poor Neurologic Outcome following Cardiac Arrest. AJNR Am J Neuroradiol. 2023 Mar;44(3):254u201360.
• Chung M, Calabrese E, Mongan J, Ray KM, Hayward JH, Kelil T, et al. Deep Learning to Simulate Contrast-enhanced Breast MRI of Invasive Breast Cancer. Radiology. 2023 Mar;306(3):e213199.
• Chung M, Calabrese E, Mongan J, Ray KM, Hayward JH, Kelil T, et al. Deep Learning to Simulate Contrast-enhanced Breast MRI of Invasive Breast Cancer. Radiology. 2023 Mar;306(3):e239004.
• Pati S, Baid U, Edwards B, Sheller M, Wang S-H, Reina GA, et al. Author Correction: Federated learning enables big data for rare cancer boundary detection. Nat Commun. 2023 Jan 26;14(1):436.
• Pati S, Baid U, Edwards B, Sheller M, Wang S-H, Reina GA, et al. Federated learning enables big data for rare cancer boundary detection. Nat Commun. 2022 Dec 5;13(1):7346.
• Calabrese E, Villanueva-Meyer JE, Rudie JD, Rauschecker AM, Baid U, Bakas S, et al. The University of California San Francisco Preoperative Diffuse Glioma MRI Dataset. Radiol Artif Intell. 2022 Nov;4(6):e220058.
• Rudie JD, Calabrese E, Saluja R, Weiss D, Colby JB, Cha S, et al. Longitudinal Assessment of Posttreatment Diffuse Glioma Tissue Volumes with Three-dimensional Convolutional Neural Networks. Radiol Artif Intell. 2022 Sep;4(5):e210243.
• Rauschecker AM, Gleason TJ, Nedelec P, Duong MT, Weiss DA, Calabrese E, et al. Interinstitutional Portability of a Deep Learning Brain MRI Lesion Segmentation Algorithm. Radiol Artif Intell. 2022 Jan;4(1):e200152.
• Calabrese E, Rudie JD, Rauschecker AM, Villanueva-Meyer JE, Clarke JL, Solomon DA, et al. Combining radiomics and deep convolutional neural network features from preoperative MRI for predicting clinically relevant genetic biomarkers in glioblastoma. Neurooncol Adv. 2022;4(1):vdac060.
• Sitek KR, Calabrese E, Johnson GA, Ghosh SS, Chandrasekaran B. Structural Connectivity of Human Inferior Colliculus Subdivisions Using in vivo and post mortem Diffusion MRI Tractography. Front Neurosci. 2022;16:751595.
• Nicholls PJ, Pack TF, Urs NM, Kumar S, Zhou Y, Ichim G, et al. Measuring Nonapoptotic Caspase Activity with a Transgenic Reporter in Mice. eNeuro. 2022;9(5).
• Calabrese E, Rudie JD, Rauschecker AM, Villanueva-Meyer JE, Cha S. Feasibility of Simulated Postcontrast MRI of Glioblastomas and Lower-Grade Gliomas by Using Three-dimensional Fully Convolutional Neural Networks. Radiol Artif Intell. 2021 Sep;3(5):e200276.
• Calabrese E, Villanueva-Meyer JE, Rudie JD, Rauschecker AM, Baid U, Bakas S, et al. The University of California San Francisco Preoperative Diffuse Glioman MRI (UCSF-PDGM) Dataset. 2021.
• Adil SM, Calabrese E, Charalambous LT, Cook JJ, Rahimpour S, Atik AF, et al. A high-resolution interactive atlas of the human brainstem using magnetic resonance imaging. Neuroimage. 2021 Aug 15;237:118135.
• Callen AL, Dupont SM, Pyne J, Talbott J, Tien P, Calabrese E, et al. The regional pattern of abnormal cerebrovascular reactivity in HIV-infected, virally suppressed women. J Neurovirol. 2020 Oct;26(5):734u201342.
• Calabrese E, Villanueva-Meyer JE, Cha S. A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas. Sci Rep. 2020 Jul 16;10(1):11852.
• Trainee Research Prizes from the 2019 RSNA Scientific Assembly and Annual Meeting. In: Radiology. 2020. p. 1u20133.
• Anderson RJ, Long CM, Calabrese ED, Robertson SH, Johnson GA, Cofer GP, et al. Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions. Front Phys. 2020 Apr;8.
• Trinh B, Calabrese E, Vu T, Forman HP, Haas BM. Low-Volume and High-Volume Readers of Neurological and Musculoskeletal MRI: Achieving Subspecialization in Radiology. J Am Coll Radiol. 2020 Feb;17(2):314u201322.
• Rushmore RJ, Wilson-Braun P, Papadimitriou G, Ng I, Rathi Y, Zhang F, et al. 3D Exploration of the Brainstem in 50-Micron Resolution MRI. Front Neuroanat. 2020;14:40.
• Sitek KR, Gulban OF, Calabrese E, Johnson GA, Lage-Castellanos A, Moerel M, et al. Mapping the human subcortical auditory system using histology, postmortem MRI and in vivo MRI at 7T. Elife. 2019 Aug 1;8.
• Atik AF, Calabrese E, Gramer R, Adil SM, Rahimpour S, Pagadala P, et al. Structural mapping with fiber tractography of the human cuneate fasciculus at microscopic resolution in cervical region. Neuroimage. 2019 Aug 1;196:200u20136.
• Tange R, Ketcha M, Badea A, Calabrese ED, Margulies DS, Vogelstein JT, et al. Connectome Smoothing via Low-Rank Approximations. IEEE Trans Med Imaging. 2019 Jun;38(6):1446u201356.
• Calabrese E, Baghdanian AH, Zagoria RJ, Behr SC. Arteriovenous malformation of the ureter diagnosed by CT urogram. Urology Case Reports. 2018 Jul 1;19:20u20132.
• Perone CS, Calabrese E, Cohen-Adad J. Spinal cord gray matter segmentation using deep dilated convolutions. Sci Rep. 2018 Apr 13;8(1):5966.
• Badea A, Kamnaksh A, Anderson RJ, Calabrese E, Long JB, Agoston DV. Repeated mild blast exposure in young adult rats results in dynamic and persistent microstructural changes in the brain. Neuroimage Clin. 2018;18:60u201373.
• Calabrese E, Adil SM, Cofer G, Perone CS, Cohen-Adad J, Lad SP, et al. Postmortem diffusion MRI of the entire human spinal cord at microscopic resolution. Neuroimage Clin. 2018;18:963u201371.
• Tax CMW, Westin C-F, Dela Haije T, Fuster A, Viergever MA, Calabrese E, et al. Quantifying the brain's sheet structure with normalized convolution. Med Image Anal. 2017 Jul;39:162u201377.
• Calabrese E, Ceponis PJ, Derrick BJ, Moon RE. Successful treatment of pneumatosis intestinalis with associated pneumoperitoneum and ileus with hyperbaric oxygen therapy. BMJ Case Rep. 2017 May 30;2017.
• Peterson DE, Chen SD, Calabrese E, White LE, Provenzale JM. Automated segmentation of the canine corpus callosum for the measurement of diffusion tensor imaging. Neuroradiol J. 2016 Feb;29(1):4u201312.
• Calabrese E. Diffusion tractography in deep brain stimulation surgery: A review. Frontiers in Neuroanatomy. 2016 Jan 1;10(MAY).
• Choi J, Dickson P, Calabrese E, Chen S, White L, Ellingwood M, et al. Predicting degree of myelination based on diffusion tensor imagining of canines with mucopolysaccharidosis type I. Neuroradiol J. 2015 Dec;28(6):562u201373.
• Calabrese E, Badea A, Cofer G, Qi Y, Johnson GA. A Diffusion MRI Tractography Connectome of the Mouse Brain and Comparison with Neuronal Tracer Data. Cereb Cortex. 2015 Nov;25(11):4628u201337.
• Calabrese E, Badea A, Coe CL, Lubach GR, Shi Y, Styner MA, et al. A diffusion tensor MRI atlas of the postmortem rhesus macaque brain. Neuroimage. 2015 Aug 15;117:408u201316.
• Calabrese E, Hickey P, Hulette C, Zhang J, Parente B, Lad SP, et al. Postmortem diffusion MRI of the human brainstem and thalamus for deep brain stimulator electrode localization. Hum Brain Mapp. 2015 Aug;36(8):3167u201378.
• Paxinos G, Watson C, Calabrese E, Badea A, Johnson G. An MRI/DTI Atlas of the Rat Brain. Elsevier, Academic Press; 2015.
• Leong D, Calabrese E, White LE, Wei P, Chen S, Platt SR, et al. Correlation of diffusion tensor imaging parameters in the canine brain. Neuroradiol J. 2015 Feb;28(1):12u20138.
• Papp EA, Leergaard TB, Calabrese E, Johnson GA, Bjaalie JG. Addendum to u201cWaxholm Space atlas of the Sprague Dawley rat brainu201d [NeuroImage 97 (2014) 374-386]. Neuroimage. 2015 Jan 15;105:561u20132.
• Calabrese E, Badea A, Coe CL, Lubach GR, Styner MA, Johnson GA. Investigating the tradeoffs between spatial resolution and diffusion sampling for brain mapping with diffusion tractography: time well spent? Hum Brain Mapp. 2014 Nov;35(11):5667u201385.
• Angeli S, Befera N, Peyrat J-M, Calabrese E, Johnson GA, Constantinides C. A high-resolution cardiovascular magnetic resonance diffusion tensor map from ex-vivo C57BL/6 murine hearts. J Cardiovasc Magn Reson. 2014 Oct 16;16(1):77.
• Papp EA, Leergaard TB, Calabrese E, Johnson GA, Bjaalie JG. Waxholm Space atlas of the Sprague Dawley rat brain. Neuroimage. 2014 Aug 15;97:374u201386.
• Calabrese E, Du F, Garman RH, Johnson GA, Riccio C, Tong LC, et al. Diffusion tensor imaging reveals white matter injury in a rat model of repetitive blast-induced traumatic brain injury. J Neurotrauma. 2014 May 15;31(10):938u201350.
• Johnson GA, Calabrese E, Little PB, Hedlund L, Qi Y, Badea A. Quantitative mapping of trimethyltin injury in the rat brain using magnetic resonance histology. Neurotoxicology. 2014 May;42:12u201323.
• Gyengesi E, Calabrese E, Sherrier MC, Johnson GA, Paxinos G, Watson C. Semi-automated 3D segmentation of major tracts in the rat brain: comparing DTI with standard histological methods. Brain Struct Funct. 2014 Mar;219(2):539u201350.
• Pierce TT, Calabrese E, White LE, Chen SD, Platt SR, Provenzale JM. Segmentation of the canine corpus callosum using diffusion-tensor imaging tractography. AJR Am J Roentgenol. 2014 Jan;202(1):W19u201325.
• Calabrese E, Johnson GA. Diffusion tensor magnetic resonance histology reveals microstructural changes in the developing rat brain. Neuroimage. 2013 Oct 1;79:329u201339.
• Wei PT, Leong D, Calabrese E, White L, Pierce T, Platt S, et al. Diffusion tensor imaging of neural tissue organization: correlations between radiologic and histologic parameters. Neuroradiol J. 2013 Oct;26(5):501u201310.
• Calabrese E, Badea A, Watson C, Johnson GA. A quantitative magnetic resonance histology atlas of postnatal rat brain development with regional estimates of growth and variability. Neuroimage. 2013 May 1;71:196u2013206.
• Calabrese E, Johnson GA, Watson C. An ontology-based segmentation scheme for tracking postnatal changes in the developing rodent brain with MRI. Neuroimage. 2013 Feb 15;67:375u201384.
• Johnson GA, Calabrese E, Badea A, Paxinos G, Watson C. A multidimensional magnetic resonance histology atlas of the Wistar rat brain. Neuroimage. 2012 Sep;62(3):1848u201356.