| The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping A Zwanenburg, M Vallières, MA Abdalah, HJWL Aerts, V Andrearczyk, ... Radiology 295 (2), 328-338, 2020 | 2949 | 2020 |
| Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features O Gevaert, LA Mitchell, AS Achrol, J Xu, S Echegaray, GK Steinberg, ... Radiology 273 (1), 168-174, 2014 | 399 | 2014 |
| Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities H Itakura, AS Achrol, LA Mitchell, JJ Loya, T Liu, EM Westbroek, ... Science translational medicine 7 (303), 303ra138-303ra138, 2015 | 297 | 2015 |
| A radiogenomic dataset of non-small cell lung cancer S Bakr, O Gevaert, S Echegaray, K Ayers, M Zhou, M Shafiq, H Zheng, ... Scientific data 5 (1), 1-9, 2018 | 285 | 2018 |
| Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets H Yu, ES Barriga, C Agurto, S Echegaray, MS Pattichis, W Bauman, ... IEEE Transactions on information technology in biomedicine 16 (4), 644-657, 2012 | 238 | 2012 |
| Non–small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications M Zhou, A Leung, S Echegaray, A Gentles, JB Shrager, KC Jensen, ... Radiology 286 (1), 307-315, 2018 | 197 | 2018 |
| Predictive radiogenomics modeling of EGFR mutation status in lung cancer O Gevaert, S Echegaray, A Khuong, CD Hoang, JB Shrager, KC Jensen, ... Scientific reports 7 (1), 41674, 2017 | 159 | 2017 |
| Radiomics of lung nodules: a multi-institutional study of robustness and agreement of quantitative imaging features J Kalpathy-Cramer, A Mamomov, B Zhao, L Lu, D Cherezov, S Napel, ... Tomography 2 (4), 430, 2016 | 143 | 2016 |
| Data for NSCLC radiogenomics collection S Bakr, O Gevaert, S Echegaray, K Ayers, M Zhou, M Shafiq, H Zheng, ... The Cancer Imaging Archive 10, K9, 2017 | 85 | 2017 |
| Noninvasive radiomics signature based on quantitative analysis of computed tomography images as a surrogate for microvascular invasion in hepatocellular carcinoma: a pilot study S Bakr, S Echegaray, R Shah, A Kamaya, J Louie, S Napel, N Kothary, ... Journal of Medical Imaging 4 (4), 041303-041303, 2017 | 84 | 2017 |
| Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma S Echegaray, O Gevaert, R Shah, A Kamaya, J Louie, N Kothary, S Napel Journal of Medical Imaging 2 (4), 041011-041011, 2015 | 73 | 2015 |
| Automated analysis of optic nerve images for detection and staging of papilledema S Echegaray, G Zamora, H Yu, W Luo, P Soliz, R Kardon Investigative ophthalmology & visual science 52 (10), 7470-7478, 2011 | 67 | 2011 |
| Quantitative Image Feature Engine (QIFE): an open-source, modular engine for 3D quantitative feature extraction from volumetric medical images S Echegaray, S Bakr, DL Rubin, S Napel Journal of digital imaging 31, 403-414, 2018 | 63 | 2018 |
| Fast localization of optic disc and fovea in retinal images for eye disease screening H Yu, S Barriga, C Agurto, S Echegaray, M Pattichis, G Zamora, ... Medical Imaging 2011: Computer-Aided Diagnosis 7963, 373-384, 2011 | 53 | 2011 |
| Prediction of EGFR and KRAS mutation in non-small cell lung cancer using quantitative 18F FDG-PET/CT metrics R Minamimoto, M Jamali, O Gevaert, S Echegaray, A Khuong, CD Hoang, ... Oncotarget 8 (32), 52792, 2017 | 40 | 2017 |
| [18F] FDG positron emission tomography (PET) tumor and penumbra imaging features predict recurrence in non–small cell lung cancer SA Mattonen, GA Davidzon, S Bakr, S Echegaray, ANC Leung, ... Tomography 5 (1), 145, 2019 | 34 | 2019 |
| A rapid segmentation-insensitive “digital biopsy” method for Radiomic feature extraction: Method and pilot study using CT images of non–small cell lung cancer S Echegaray, V Nair, M Kadoch, A Leung, D Rubin, O Gevaert, S Napel Tomography 2 (4), 283, 2016 | 33 | 2016 |
| Quantitative imaging feature pipeline: a web-based tool for utilizing, sharing, and building image-processing pipelines SA Mattonen, D Gude, S Echegaray, S Bakr, DL Rubin, S Napel Journal of Medical Imaging 7 (4), 042803-042803, 2020 | 25 | 2020 |
| The modular design and implementation of an intelligent cruise control system S Echegaray, W Luo 2008 IEEE International Conference on System of Systems Engineering, 1-6, 2008 | 11 | 2008 |
| Correlations between P53 mutation status and texture features of CT images for hepatocellular carcinoma H Wu, X Chen, J Chen, Y Luo, X Jiang, X Wei, W Tang, Y Liu, Y Liang, ... Methods of Information in Medicine 58 (01), 042-049, 2019 | 7 | 2019 |
Sandy NapelProfessor of Radiology, Stanford UniversityVerified email at stanford.edu
