| Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features S Bakas, H Akbari, A Sotiras, M Bilello, M Rozycki, JS Kirby, JB Freymann, ... Scientific data 4 (1), 1-13, 2017 | 2350 | 2017 |
| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 1793 | 2018 |
| Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection S Bakas, H Akbari, A Sotiras, M Bilello, M Rozycki, J Kirby, J Freymann, ... The cancer imaging archive 286, 2017 | 719 | 2017 |
| Radiomic MRI signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics, offering prognostic value beyond IDH1 S Rathore, H Akbari, M Rozycki, KG Abdullah, MLP Nasrallah, ZA Binder, ... Scientific reports 8 (1), 5087, 2018 | 163 | 2018 |
| Multisite machine learning analysis provides a robust structural imaging signature of schizophrenia detectable across diverse patient populations and within individuals M Rozycki, TD Satterthwaite, N Koutsouleris, G Erus, J Doshi, DH Wolf, ... Schizophrenia bulletin 44 (5), 1035-1044, 2018 | 140 | 2018 |
| Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning S Rathore, H Akbari, J Doshi, G Shukla, M Rozycki, M Bilello, R Lustig, ... Journal of Medical Imaging 5 (2), 021219-021219, 2018 | 129 | 2018 |
| Identifying the best machine learning algorithms for brain tumor segmentation S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... progression assessment, and overall survival prediction in the BRATS …, 2018 | 116 | 2018 |
| GLISTRboost: combining multimodal MRI segmentation, registration, and biophysical tumor growth modeling with gradient boosting machines for glioma segmentation S Bakas, K Zeng, A Sotiras, S Rathore, H Akbari, B Gaonkar, M Rozycki, ... Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2016 | 115 | 2016 |
| In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature H Akbari, S Bakas, JM Pisapia, MLP Nasrallah, M Rozycki, ... Neuro-oncology 20 (8), 1068-1079, 2018 | 109 | 2018 |
| In Vivo Detection of EGFRvIII in Glioblastoma via Perfusion Magnetic Resonance Imaging Signature Consistent with Deep Peritumoral Infiltration: The ϕ-Index S Bakas, H Akbari, J Pisapia, M Martinez-Lage, M Rozycki, S Rathore, ... Clinical Cancer Research 23 (16), 4724-4734, 2017 | 93 | 2017 |
| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv preprint … S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv, 1811 | 83 | 1811 |
| Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. The cancer imaging archive S Bakas, H Akbari, A Sotiras, M Bilello, M Rozycki, J Kirby, J Freymann, ... Nat. Sci. Data 4 (170117), 10.1038, 2017 | 81 | 2017 |
| Histopathology‐validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo‐progression in glioblastoma H Akbari, S Rathore, S Bakas, MLP Nasrallah, G Shukla, E Mamourian, ... Cancer 126 (11), 2625-2636, 2020 | 73 | 2020 |
| Brain cancer imaging phenomics toolkit (brain-CaPTk): an interactive platform for quantitative analysis of glioblastoma S Rathore, S Bakas, S Pati, H Akbari, R Kalarot, P Sridharan, M Rozycki, ... Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2018 | 61 | 2018 |
| Memory, executive, and multidomain subtle cognitive impairment: clinical and biomarker findings JB Toledo, M Bjerke, K Chen, M Rozycki, CR Jack Jr, MW Weiner, ... Neurology 85 (2), 144-153, 2015 | 57 | 2015 |
| Segmentation of gliomas in pre-operative and post-operative multimodal magnetic resonance imaging volumes based on a hybrid generative-discriminative framework K Zeng, S Bakas, A Sotiras, H Akbari, M Rozycki, S Rathore, S Pati, ... Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2016 | 43 | 2016 |
| Overall survival prediction in glioblastoma patients using structural magnetic resonance imaging (MRI): advanced radiomic features may compensate for lack of advanced MRI … S Bakas, G Shukla, H Akbari, G Erus, A Sotiras, S Rathore, C Sako, ... Journal of Medical Imaging 7 (3), 031505-031505, 2020 | 35 | 2020 |
| Use of machine learning techniques in the development and refinement of a predictive model for early diagnosis of ankylosing spondylitis A Deodhar, M Rozycki, C Garges, O Shukla, T Arndt, T Grabowsky, Y Park Clinical Rheumatology 39, 975-982, 2020 | 31 | 2020 |
| Application of machine learning in the diagnosis of axial spondyloarthritis JA Walsh, M Rozycki, E Yi, Y Park Current opinion in rheumatology 31 (4), 362-367, 2019 | 26 | 2019 |
| Use of fetal magnetic resonance image analysis and machine learning to predict the need for postnatal cerebrospinal fluid diversion in fetal ventriculomegaly JM Pisapia, H Akbari, M Rozycki, H Goldstein, S Bakas, S Rathore, ... JAMA pediatrics 172 (2), 128-135, 2018 | 25 | 2018 |
