| Radiomics: the process and the challenges V Kumar, Y Gu, S Basu, A Berglund, SA Eschrich, MB Schabath, K Forster, ... Magnetic resonance imaging 30 (9), 1234-1248, 2012 | 2667 | 2012 |
| Artificial intelligence in cancer imaging: clinical challenges and applications WL Bi, A Hosny, MB Schabath, ML Giger, NJ Birkbak, A Mehrtash, ... CA: a cancer journal for clinicians 69 (2), 127-157, 2019 | 2223 | 2019 |
| Cancer progress and priorities: lung cancer MB Schabath, ML Cote Cancer epidemiology, biomarkers & prevention 28 (10), 1563-1579, 2019 | 1484 | 2019 |
| Meta-analyses of genome-wide association studies identify multiple loci associated with pulmonary function DB Hancock, M Eijgelsheim, JB Wilk, SA Gharib, LR Loehr, KD Marciante, ... Nature genetics 42 (1), 45-52, 2010 | 787 | 2010 |
| Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes JD McKay, RJ Hung, Y Han, X Zong, R Carreras-Torres, DC Christiani, ... Nature genetics 49 (7), 1126-1132, 2017 | 744 | 2017 |
| Lung cancer screening, version 3.2018, NCCN clinical practice guidelines in oncology DE Wood, EA Kazerooni, SL Baum, GA Eapen, DS Ettinger, L Hou, ... Journal of the National Comprehensive Cancer Network 16 (4), 412-441, 2018 | 672 | 2018 |
| Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels M Shafiq‐ul‐Hassan, GG Zhang, K Latifi, G Ullah, DC Hunt, ... Medical physics 44 (3), 1050-1062, 2017 | 654 | 2017 |
| Cancer and lesbian, gay, bisexual, transgender/transsexual, and queer/questioning (LGBTQ) populations GP Quinn, JA Sanchez, SK Sutton, ST Vadaparampil, GT Nguyen, ... CA: a cancer journal for clinicians 65 (5), 384-400, 2015 | 563 | 2015 |
| A risk model for prediction of lung cancer MR Spitz, WK Hong, CI Amos, X Wu, MB Schabath, Q Dong, S Shete, ... Journal of the National Cancer Institute 99 (9), 715-726, 2007 | 512 | 2007 |
| Differential association of STK11 and TP53 with KRAS mutation-associated gene expression, proliferation and immune surveillance in lung adenocarcinoma MB Schabath, EA Welsh, WJ Fulp, L Chen, JK Teer, ZJ Thompson, ... Oncogene 35 (24), 3209-3216, 2016 | 372 | 2016 |
| Predicting malignant nodules from screening CT scans S Hawkins, H Wang, Y Liu, A Garcia, O Stringfield, H Krewer, Q Li, ... Journal of Thoracic Oncology 11 (12), 2120-2128, 2016 | 351 | 2016 |
| Lung cancer screening DE Wood, GA Eapen, DS Ettinger, L Hou, D Jackman, E Kazerooni, ... Journal of the National Comprehensive Cancer Network 10 (2), 240-265, 2012 | 351 | 2012 |
| Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma O Grove, AE Berglund, MB Schabath, HJWL Aerts, A Dekker, H Wang, ... PloS one 10 (3), e0118261, 2015 | 326 | 2015 |
| History of atopy or autoimmunity increases risk of alopecia areata N Barahmani, MB Schabath, M Duvic, NAA Registry Journal of the American Academy of Dermatology 61 (4), 581-591, 2009 | 298 | 2009 |
| Cytokinesis-blocked micronucleus assay as a novel biomarker for lung cancer risk RA El-Zein, MB Schabath, CJ Etzel, MS Lopez, JD Franklin, MR Spitz Cancer research 66 (12), 6449-6456, 2006 | 274 | 2006 |
| Non-invasive decision support for NSCLC treatment using PET/CT radiomics W Mu, L Jiang, JY Zhang, Y Shi, JE Gray, I Tunali, C Gao, Y Sun, J Tian, ... Nature communications 11 (1), 5228, 2020 | 270 | 2020 |
| TGF-β–inducible microRNA-183 silences tumor-associated natural killer cells SS Donatelli, JM Zhou, DL Gilvary, EA Eksioglu, X Chen, WD Cress, ... Proceedings of the National Academy of Sciences 111 (11), 4203-4208, 2014 | 256 | 2014 |
| Genomic and evolutionary classification of lung cancer in never smokers T Zhang, P Joubert, N Ansari-Pour, W Zhao, PH Hoang, R Lokanga, ... Nature genetics 53 (9), 1348-1359, 2021 | 231 | 2021 |
| Hormone replacement therapy and lung cancer risk: a case-control analysis MB Schabath, X Wu, R Vassilopoulou-Sellin, AA Vaporciyan, MR Spitz Clinical cancer research 10 (1), 113-123, 2004 | 230 | 2004 |
| Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma R Paul, SH Hawkins, Y Balagurunathan, MB Schabath, RJ Gillies, LO Hall, ... Tomography 2 (4), 388, 2016 | 220 | 2016 |
Robert GilliesChair of Cancer Physiology, H. Lee Moffitt Cancer CenterVerified email at moffitt.org
