| The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis RTH Leijenaar, G Nalbantov, S Carvalho, WJ Van Elmpt, EGC Troost, ... Scientific reports 5 (1), 11075, 2015 | 483 | 2015 |
| Predicting outcomes in radiation oncology—multifactorial decision support systems P Lambin, RGPM Van Stiphout, MHW Starmans, E Rios-Velazquez, ... Nature reviews Clinical oncology 10 (1), 27-40, 2013 | 461 | 2013 |
| Rapid Learning health care in oncology’–an approach towards decision support systems enabling customised radiotherapy P Lambin, E Roelofs, B Reymen, ER Velazquez, J Buijsen, CML Zegers, ... Radiotherapy and Oncology 109 (1), 159-164, 2013 | 223 | 2013 |
| Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT TM Deist, A Jochems, J van Soest, G Nalbantov, C Oberije, S Walsh, ... Clinical and translational radiation oncology 4, 24-31, 2017 | 173 | 2017 |
| A prospective study comparing the predictions of doctors versus models for treatment outcome of lung cancer patients: a step toward individualized care and shared decision making C Oberije, G Nalbantov, A Dekker, L Boersma, J Borger, B Reymen, ... Radiotherapy and Oncology 112 (1), 37-43, 2014 | 112 | 2014 |
| Intravascular enhancement with identical iodine delivery rate using different iodine contrast media in a circulation phantom C Mihl, JE Wildberger, T Jurencak, MJ Yanniello, EC Nijssen, JF Kalafut, ... Investigative radiology 48 (11), 813-818, 2013 | 78 | 2013 |
| Cardiac comorbidity is an independent risk factor for radiation-induced lung toxicity in lung cancer patients G Nalbantov, B Kietselaer, K Vandecasteele, C Oberije, M Berbee, ... Radiotherapy and Oncology 109 (1), 100-106, 2013 | 73 | 2013 |
| Nearest convex hull classification G Nalbantov, P Groenen, C Bioch Report/Econometric Institute, Erasmus University Rotterdam, 2006 | 53 | 2006 |
| Complete pathological response to neoadjuvant treatment is associated with better survival outcomes in patients with soft tissue sarcoma: Results of a retrospective multicenter … S Bonvalot, J Wunder, A Gronchi, JM Broto, R Turcotte, M Rastrelli, ... European Journal of Surgical Oncology 47 (8), 2166-2172, 2021 | 45 | 2021 |
| Preclinical assessment of efficacy of radiation dose painting based on intratumoral FDG-PET uptake D Trani, A Yaromina, L Dubois, M Granzier, SGJA Peeters, R Biemans, ... Clinical Cancer Research 21 (24), 5511-5518, 2015 | 41 | 2015 |
| The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis. Sci Rep. 2015; 5: 11075 RTH Leijenaar, G Nalbantov, S Carvalho, WJ Van Elmpt, EG Troost, ... Biomed Res Int 2015, 387653, 2015 | 37 | 2015 |
| SVM-Maj: a majorization approach to linear support vector machines with different hinge errors PJF Groenen, G Nalbantov, JC Bioch Advances in data analysis and classification 2, 17-43, 2008 | 31 | 2008 |
| Equity style timing using support vector regressions G Nalbantov, R Bauer, I Sprinkhuizen-Kuyper Applied Financial Economics 16 (15), 1095-1111, 2006 | 29 | 2006 |
| Quantification of CT-assessed radiation-induced lung damage in lung cancer patients treated with or without chemotherapy and cetuximab H Sharifi, W Van Elmpt, C Oberije, G Nalbantov, M Das, M Öllers, ... Acta Oncologica 55 (2), 156-162, 2016 | 22 | 2016 |
| Nonlinear support vector machines through iterative majorization and I-splines PJF Groenen, G Nalbantov, JC Bioch Advances in Data Analysis: Proceedings of the 30 th Annual Conference of the …, 2007 | 16 | 2007 |
| Unanimous voting using support vector machines EN Smirnov, IG Sprinkhuizen-Kuyper, GI Nalbantov | 16 | 2005 |
| Version space support vector machines E Smirnov, I Sprinkhuizen-Kuyper, G Nalbantov, S Vanderlooy FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS 141, 809, 2006 | 14 | 2006 |
| What level of accuracy is achievable for preclinical dose painting studies on a clinical irradiation platform? D Trani, B Reniers, L Persoon, M Podesta, G Nalbantov, RTH Leijenaar, ... Radiation research 183 (5), 501-510, 2015 | 13 | 2015 |
| Application of machine learning for multicenter learning JPA van Soest, ALAJ Dekker, E Roelofs, G Nalbantov Machine Learning in Radiation Oncology: Theory and Applications, 71-97, 2015 | 11 | 2015 |
| Soft nearest convex hull classifier G Nalbantov, E Smirnov ECAI 2010, 841-846, 2010 | 11 | 2010 |
