Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach HJWL Aerts, ER Velazquez, RTH Leijenaar, C Parmar, P Grossmann, ... Nature communications 5 (1), 4006, 2014 | 4814 | 2014 |
Computational radiomics system to decode the radiographic phenotype JJM Van Griethuysen, A Fedorov, C Parmar, A Hosny, N Aucoin, ... Cancer research 77 (21), e104-e107, 2017 | 4607 | 2017 |
Artificial intelligence in radiology A Hosny, C Parmar, J Quackenbush, LH Schwartz, HJWL Aerts Nature Reviews Cancer 18 (8), 500-510, 2018 | 2864 | 2018 |
Machine learning methods for quantitative radiomic biomarkers C Parmar, P Grossmann, J Bussink, P Lambin, HJWL Aerts Scientific reports 5 (1), 1-11, 2015 | 994 | 2015 |
Robust radiomics feature quantification using semiautomatic volumetric segmentation C Parmar, E Rios Velazquez, R Leijenaar, M Jermoumi, S Carvalho, ... PloS one 9 (7), e102107, 2014 | 633 | 2014 |
Deep learning predicts lung cancer treatment response from serial medical imaging Y Xu, A Hosny, R Zeleznik, C Parmar, T Coroller, I Franco, RH Mak, ... Clinical Cancer Research 25 (11), 3266-3275, 2019 | 496 | 2019 |
Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study A Hosny, C Parmar, TP Coroller, P Grossmann, R Zeleznik, A Kumar, ... PLoS medicine 15 (11), e1002711, 2018 | 488 | 2018 |
Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer C Parmar, RTH Leijenaar, P Grossmann, E Rios Velazquez, J Bussink, ... Scientific reports 5 (1), 11044, 2015 | 483 | 2015 |
Stability of FDG-PET radiomics features: an integrated analysis of test-retest and inter-observer variability RTH Leijenaar, S Carvalho, ER Velazquez, WJC Van Elmpt, C Parmar, ... Acta oncologica 52 (7), 1391-1397, 2013 | 467 | 2013 |
Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers S Trebeschi, SG Drago, NJ Birkbak, I Kurilova, AM Cǎlin, AD Pizzi, ... Annals of Oncology 30 (6), 998-1004, 2019 | 440 | 2019 |
Exploratory study to identify radiomics classifiers for lung cancer histology W Wu, C Parmar, P Grossmann, J Quackenbush, P Lambin, J Bussink, ... Frontiers in oncology 6, 71, 2016 | 389 | 2016 |
Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer C Parmar, P Grossmann, D Rietveld, MM Rietbergen, P Lambin, ... Frontiers in oncology 5, 272, 2015 | 388 | 2015 |
Somatic mutations drive distinct imaging phenotypes in lung cancer E Rios Velazquez, C Parmar, Y Liu, TP Coroller, G Cruz, O Stringfield, ... Cancer research 77 (14), 3922-3930, 2017 | 361 | 2017 |
Defining the biological basis of radiomic phenotypes in lung cancer P Grossmann, O Stringfield, N El-Hachem, MM Bui, E Rios Velazquez, ... elife 6, e23421, 2017 | 322 | 2017 |
Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR HJWLA Stefano Trebeschi, Joost JM van Griethuysen, Doenja MJ Lambregts, Max ... Scientific Reports 7 (1), 5301, 2017 | 268 | 2017 |
Volumetric CT-based segmentation of NSCLC using 3D-Slicer ER Velazquez, C Parmar, M Jermoumi, RH Mak, A Van Baardwijk, ... Scientific reports 3 (1), 3529, 2013 | 235 | 2013 |
Data from NSCLC-radiomics H Aerts, ER Velazquez, RT Leijenaar, C Parmar, P Grossmann, ... The cancer imaging archive, 2015 | 182 | 2015 |
Associations between somatic mutations and metabolic imaging phenotypes in non–small cell lung cancer SSF Yip, J Kim, TP Coroller, C Parmar, ER Velazquez, E Huynh, RH Mak, ... Journal of Nuclear Medicine 58 (4), 569-576, 2017 | 166 | 2017 |
Data analysis strategies in medical imaging C Parmar, JD Barry, A Hosny, J Quackenbush, HJWL Aerts Clinical cancer research 24 (15), 3492-3499, 2018 | 160 | 2018 |
Deep convolutional neural networks to predict cardiovascular risk from computed tomography R Zeleznik, B Foldyna, P Eslami, J Weiss, I Alexander, J Taron, C Parmar, ... Nature communications 12 (1), 715, 2021 | 148 | 2021 |