| Dynamic contrast-enhanced and dynamic susceptibility contrast perfusion MR imaging for glioma grading: preliminary comparison of vessel compartment and permeability parameters … C Santarosa, A Castellano, GM Conte, M Cadioli, A Iadanza, MR Terreni, ... European journal of radiology 85 (6), 1147-1156, 2016 | 106 | 2016 |
| Generative adversarial networks to synthesize missing T1 and FLAIR MRI sequences for use in a multisequence brain tumor segmentation model GM Conte, AD Weston, DC Vogelsang, KA Philbrick, JC Cai, M Barbera, ... Radiology 299 (2), 313-323, 2021 | 100 | 2021 |
| Mitigating bias in radiology machine learning: 1. Data handling P Rouzrokh, B Khosravi, S Faghani, M Moassefi, DV Vera Garcia, Y Singh, ... Radiology: Artificial Intelligence 4 (5), e210290, 2022 | 98 | 2022 |
| Brain gliomas: multicenter standardized assessment of dynamic contrast-enhanced and dynamic susceptibility contrast MR images N Anzalone, A Castellano, M Cadioli, GM Conte, V Cuccarini, A Bizzi, ... Radiology 287 (3), 933-943, 2018 | 91 | 2018 |
| SOUP-GAN: Super-resolution MRI using generative adversarial networks K Zhang, H Hu, K Philbrick, GM Conte, JD Sobek, P Rouzrokh, ... Tomography 8 (2), 905-919, 2022 | 85 | 2022 |
| The brain tumor segmentation (BraTS) challenge 2023: focus on pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs) AF Kazerooni, N Khalili, X Liu, D Haldar, Z Jiang, SM Anwar, J Albrecht, ... ArXiv, arXiv: 2305.17033 v7, 2024 | 69 | 2024 |
| The brain tumor segmentation (brats) challenge 2023: Glioma segmentation in sub-saharan africa patient population (brats-africa) M Adewole, JD Rudie, A Gbdamosi, O Toyobo, C Raymond, D Zhang, ... ArXiv, arXiv: 2305.19369 v1, 2023 | 60 | 2023 |
| Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status CH Sudre, J Panovska-Griffiths, E Sanverdi, S Brandner, VK Katsaros, ... BMC medical informatics and decision making 20, 1-14, 2020 | 60 | 2020 |
| R-CHOP preceded by blood-brain barrier permeabilization with engineered tumor necrosis factor-α in primary CNS lymphoma AJM Ferreri, T Calimeri, GM Conte, D Cattaneo, F Fallanca, M Ponzoni, ... Blood, The Journal of the American Society of Hematology 134 (3), 252-262, 2019 | 55 | 2019 |
| The Brain Tumor Segmentation-Metastases (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI AW Moawad, A Janas, U Baid, D Ramakrishnan, R Saluja, N Ashraf, ... ArXiv, arXiv: 2306.00838 v2, 2024 | 49 | 2024 |
| Fully automated segmentation of head CT neuroanatomy using deep learning JC Cai, Z Akkus, KA Philbrick, A Boonrod, S Hoodeshenas, AD Weston, ... Radiology: Artificial Intelligence 2 (5), e190183, 2020 | 39 | 2020 |
| The asnr-miccai brain tumor segmentation (brats) challenge 2023: Intracranial meningioma D LaBella, M Adewole, M Alonso-Basanta, T Altes, SM Anwar, U Baid, ... arXiv preprint arXiv:2305.07642, 2023 | 37 | 2023 |
| Complete abdomen and pelvis segmentation using U‐net variant architecture AD Weston, P Korfiatis, KA Philbrick, GM Conte, P Kostandy, T Sakinis, ... Medical physics 47 (11), 5609-5618, 2020 | 32 | 2020 |
| Reproducibility of dynamic contrast-enhanced MRI and dynamic susceptibility contrast MRI in the study of brain gliomas: a comparison of data obtained using different commercial … GM Conte, A Castellano, L Altabella, A Iadanza, M Cadioli, A Falini, ... La radiologia medica 122, 294-302, 2017 | 30 | 2017 |
| A deep learning model for discriminating true progression from pseudoprogression in glioblastoma patients M Moassefi, S Faghani, GM Conte, RO Kowalchuk, S Vahdati, ... Journal of neuro-oncology 159 (2), 447-455, 2022 | 29 | 2022 |
| Comparison of T1 mapping and fixed T1 method for dynamic contrast-enhanced MRI perfusion in brain gliomas GM Conte, L Altabella, A Castellano, V Cuccarini, A Bizzi, M Grimaldi, ... European radiology 29, 3467-3479, 2019 | 29 | 2019 |
| Improving the antitumor activity of R-CHOP with NGR-hTNF in primary CNS lymphoma: final results of a phase 2 trial AJM Ferreri, T Calimeri, M Ponzoni, F Curnis, GM Conte, E Scarano, ... Blood Advances 4 (15), 3648-3658, 2020 | 28 | 2020 |
| A comparison of three different deep learning-based models to predict the MGMT promoter methylation status in glioblastoma using brain MRI S Faghani, B Khosravi, M Moassefi, GM Conte, BJ Erickson Journal of Digital Imaging 36 (3), 837-846, 2023 | 22 | 2023 |
| The 2024 Brain Tumor Segmentation (BraTS) challenge: glioma segmentation on post-treatment MRI MC de Verdier, R Saluja, L Gagnon, D LaBella, U Baid, NH Tahon, ... arXiv preprint arXiv:2405.18368, 2024 | 21 | 2024 |
| The brain tumor segmentation (brats) challenge 2023: Local synthesis of healthy brain tissue via inpainting F Kofler, F Meissen, F Steinbauer, R Graf, E Oswald, E de da Rosa, HB Li, ... arXiv preprint arXiv:2305.08992, 2023 | 20 | 2023 |
Bradley J EricksonProfessor of Radiology, Mayo ClinicAdresă de e-mail confirmată pe mayo.edu