| Machine learning algorithms for outcome prediction in (chemo) radiotherapy: An empirical comparison of classifiers TM Deist, FJWM Dankers, G Valdes, R Wijsman, IC Hsu, C Oberije, ... Medical physics 45 (7), 3449-3459, 2018 | 290 | 2018 |
| Artificial intelligence and machine learning for medical imaging: A technology review A Barragán-Montero, U Javaid, G Valdés, D Nguyen, P Desbordes, ... Physica Medica 83, 242-256, 2021 | 247 | 2021 |
| Artificial intelligence in radiation oncology: a specialty-wide disruptive transformation? RF Thompson, G Valdes, CD Fuller, CM Carpenter, O Morin, S Aneja, ... Radiotherapy and Oncology 129 (3), 421-426, 2018 | 236 | 2018 |
| A mathematical framework for virtual IMRT QA using machine learning G Valdes, R Scheuermann, CY Hung, A Olszanski, M Bellerive, ... Medical physics 43 (7), 4323-4334, 2016 | 187 | 2016 |
| IMRT QA using machine learning: a multi‐institutional validation G Valdes, MF Chan, SB Lim, R Scheuermann, JO Deasy, TD Solberg Journal of applied clinical medical physics 18 (5), 279-284, 2017 | 147 | 2017 |
| MediBoost: a patient stratification tool for interpretable decision making in the era of precision medicine G Valdes, JM Luna, E Eaton, CB Simone, LH Ungar, TD Solberg Scientific reports 6 (1), 37854, 2016 | 123 | 2016 |
| Machine learning in radiation oncology: opportunities, requirements, and needs M Feng, G Valdes, N Dixit, TD Solberg Frontiers in oncology 8, 110, 2018 | 119 | 2018 |
| Expert-augmented machine learning ED Gennatas, JH Friedman, LH Ungar, R Pirracchio, E Eaton, ... Proceedings of the National Academy of Sciences 117 (9), 4571-4577, 2020 | 115 | 2020 |
| Deep nets vs expert designed features in medical physics: an IMRT QA case study Y Interian, V Rideout, VP Kearney, E Gennatas, O Morin, J Cheung, ... Medical physics 45 (6), 2672-2680, 2018 | 110 | 2018 |
| Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival O Morin, WC Chen, F Nassiri, M Susko, ST Magill, HN Vasudevan, A Wu, ... Neuro-oncology advances 1 (1), vdz011, 2019 | 104 | 2019 |
| A deep look into the future of quantitative imaging in oncology: a statement of working principles and proposal for change O Morin, M Vallières, A Jochems, HC Woodruff, G Valdes, SE Braunstein, ... International Journal of Radiation Oncology* Biology* Physics 102 (4), 1074-1082, 2018 | 102 | 2018 |
| Clinical decision support of radiotherapy treatment planning: A data-driven machine learning strategy for patient-specific dosimetric decision making G Valdes, CB Simone II, J Chen, A Lin, SS Yom, AJ Pattison, ... Radiotherapy and Oncology 125 (3), 392-397, 2017 | 102 | 2017 |
| Machine learning and modeling: data, validation, communication challenges I El Naqa, D Ruan, G Valdes, A Dekker, T McNutt, Y Ge, QJ Wu, JH Oh, ... Medical physics 45 (10), e834-e840, 2018 | 99 | 2018 |
| Using machine learning to predict radiation pneumonitis in patients with stage I non-small cell lung cancer treated with stereotactic body radiation therapy G Valdes, TD Solberg, M Heskel, L Ungar, CB Simone Physics in Medicine & Biology 61 (16), 6105, 2016 | 96 | 2016 |
| Integration of AI and machine learning in radiotherapy QA MF Chan, A Witztum, G Valdes Frontiers in artificial intelligence 3, 577620, 2020 | 89 | 2020 |
| Predicting radiation pneumonitis in locally advanced stage II–III non-small cell lung cancer using machine learning JM Luna, HH Chao, ES Diffenderfer, G Valdes, C Chinniah, G Ma, ... Radiotherapy and Oncology 133, 106-112, 2019 | 86 | 2019 |
| Building more accurate decision trees with the additive tree JM Luna, ED Gennatas, LH Ungar, E Eaton, ES Diffenderfer, ST Jensen, ... Proceedings of the national academy of sciences 116 (40), 19887-19893, 2019 | 81 | 2019 |
| The application of artificial intelligence in the IMRT planning process for head and neck cancer V Kearney, JW Chan, G Valdes, TD Solberg, SS Yom Oral Oncology 87, 111-116, 2018 | 76 | 2018 |
| An unsupervised convolutional neural network-based algorithm for deformable image registration V Kearney, S Haaf, A Sudhyadhom, G Valdes, TD Solberg Physics in Medicine & Biology 63 (18), 185017, 2018 | 76 | 2018 |
| An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication O Morin, M Vallières, S Braunstein, JB Ginart, T Upadhaya, HC Woodruff, ... Nature Cancer 2 (7), 709-722, 2021 | 66 | 2021 |
