| Reproducibility of radiomics for deciphering tumor phenotype with imaging B Zhao, Y Tan, WY Tsai, J Qi, C Xie, L Lu, LH Schwartz Scientific reports 6 (1), 23428, 2016 | 520 | 2016 |
| Assessing agreement between radiomic features computed for multiple CT imaging settings L Lu, RC Ehmke, LH Schwartz, B Zhao PloS one 11 (12), e0166550, 2016 | 165 | 2016 |
| Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules FZ Mokrane, L Lu, A Vavasseur, P Otal, JM Peron, L Luk, H Yang, ... European radiology 30, 558-570, 2020 | 144 | 2020 |
| Exploring variability in CT characterization of tumors: a preliminary phantom study B Zhao, Y Tan, WY Tsai, LH Schwartz, L Lu Translational oncology 7 (1), 88-93, 2014 | 138 | 2014 |
| 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), 430-437, 2016 | 134 | 2016 |
| Identification of non–small cell lung cancer sensitive to systemic cancer therapies using radiomics L Dercle, M Fronheiser, L Lu, S Du, W Hayes, DK Leung, A Roy, ... Clinical Cancer Research 26 (9), 2151-2162, 2020 | 122 | 2020 |
| Radiomics response signature for identification of metastatic colorectal cancer sensitive to therapies targeting EGFR pathway L Dercle, L Lu, LH Schwartz, M Qian, S Tejpar, P Eggleton, B Zhao, ... JNCI: Journal of the National Cancer Institute 112 (9), 902-912, 2020 | 106 | 2020 |
| Standardization in quantitative imaging: a multicenter comparison of radiomic features from different software packages on digital reference objects and patient data sets M McNitt-Gray, S Napel, A Jaggi, SA Mattonen, L Hadjiiski, M Muzi, ... Tomography 6 (2), 118-128, 2020 | 73 | 2020 |
| Hybrid detection of lung nodules on CT scan images L Lu, Y Tan, LH Schwartz, B Zhao Medical physics 42 (9), 5042-5054, 2015 | 71 | 2015 |
| CT slice thickness and convolution kernel affect performance of a radiomic model for predicting EGFR status in non-small cell lung cancer: a preliminary study Y Li, L Lu, M Xiao, L Dercle, Y Huang, Z Zhang, LH Schwartz, D Li, B Zhao Scientific reports 8 (1), 17913, 2018 | 68 | 2018 |
| Radiomics for classification of lung cancer histological subtypes based on nonenhanced computed tomography E Linning, L Lu, L Li, H Yang, LH Schwartz, B Zhao Academic radiology 26 (9), 1245-1252, 2019 | 66 | 2019 |
| Radiomics for classifying histological subtypes of lung cancer based on multiphasic contrast-enhanced computed tomography E Linning, L Lu, L Li, H Yang, LH Schwartz, B Zhao Journal of computer assisted tomography 43 (2), 300-306, 2019 | 66 | 2019 |
| Analysis of gene expression profiles of lung cancer subtypes with machine learning algorithms F Yuan, L Lu, Q Zou Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease 1866 (8), 165822, 2020 | 58 | 2020 |
| Multiple classifier integration for the prediction of protein structural classes L Chen, L Lu, K Feng, W Li, J Song, L Zheng, Y Yuan, Z Zeng, K Feng, ... Journal of Computational Chemistry 30 (14), 2248-2254, 2009 | 56 | 2009 |
| Early readout on overall survival of patients with melanoma treated with immunotherapy using a novel imaging analysis L Dercle, B Zhao, M Gönen, CS Moskowitz, A Firas, V Beylergil, ... JAMA oncology 8 (3), 385-392, 2022 | 53 | 2022 |
| Identification of synthetic lethality based on a functional network by using machine learning algorithms JR Li, L Lu, YH Zhang, M Liu, L Chen, T Huang, YD Cai Journal of cellular biochemistry 120 (1), 405-416, 2019 | 52 | 2019 |
| Application of radiomics in predicting the malignancy of pulmonary nodules in different sizes Y Xu, L Lu, L E, W Lian, H Yang, LH Schwartz, Z Yang, B Zhao American Journal of Roentgenology 213 (6), 1213-1220, 2019 | 50 | 2019 |
| Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine JR Li, L Lu, YH Zhang, YC Xu, M Liu, KY Feng, L Chen, XY Kong, ... Cancer Gene Therapy 27 (1), 56-69, 2020 | 47 | 2020 |
| Prediction of compounds’ biological function (metabolic pathways) based on functional group composition YD Cai, Z Qian, L Lu, KY Feng, X Meng, B Niu, GD Zhao, WC Lu Molecular diversity 12, 131-137, 2008 | 47 | 2008 |
| Interobserver variability in tumor contouring affects the use of radiomics to predict mutational status Q Huang, L Lu, L Dercle, P Lichtenstein, Y Li, Q Yin, M Zong, L Schwartz, ... Journal of Medical Imaging 5 (1), 011005-011005, 2018 | 44 | 2018 |
Binsheng ZhaoProfessor of Radiology (Medical Physics), Columbia University Medical CenterVerified email at cumc.columbia.edu
