Hugo J.W.L. Aerts

Research interests

Publications

• Transparency and reproducibility in artificial intelligence

  Nature · 2020 · 10.1038/s41586-020-2766-y

• Artificial intelligence in radiation oncology

  Nature Reviews Clinical Oncology · 2020 · 10.1038/s41571-020-0417-8

• The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

  Radiology · 2020 · https://doi.org/10.1148/radiol.2020191145

• Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers

  Annals of Oncology · 2019 · 10.1093/annonc/mdz108

• Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging

  Clinical Cancer Research · 2019 · 10.1158/1078-0432.ccr-18-2495

• Artificial intelligence in cancer imaging: Clinical challenges and applications

  CA A Cancer Journal for Clinicians · 2019 · https://doi.org/10.3322/caac.21552

• Artificial intelligence in radiology

  Nature reviews. Cancer · 2018 · https://doi.org/10.1038/s41568-018-0016-5

• Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study

  PLoS Medicine · 2018 · 10.1371/journal.pmed.1002711

• Computational Radiomics System to Decode the Radiographic Phenotype

  Cancer Research · 2017 · https://doi.org/10.1158/0008-5472.can-17-0339

• Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution

  Nature · 2017 · https://doi.org/10.1038/nature22364

• Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

  Scientific Reports · 2017 · 10.1038/s41598-017-10371-5

• The Potential of Radiomic-Based Phenotyping in Precision Medicine

  JAMA Oncology · 2016 · 10.1001/jamaoncol.2016.2631

• Imaging biomarker roadmap for cancer studies

  Nature Reviews Clinical Oncology · 2016 · 10.1038/nrclinonc.2016.162

• Applications and limitations of radiomics

  Physics in Medicine and Biology · 2016 · 10.1088/0031-9155/61/13/r150

• CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma

  Radiotherapy and Oncology · 2015 · 10.1016/j.radonc.2015.02.015

• Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer

  Scientific Reports · 2015 · 10.1038/srep11044

• Machine Learning methods for Quantitative Radiomic Biomarkers

  Scientific Reports · 2015 · 10.1038/srep13087

• Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer

  Frontiers in Oncology · 2015 · 10.3389/fonc.2015.00272

• The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis

  Scientific Reports · 2015 · 10.1038/srep11075

• Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

  Nature Communications · 2014 · 10.1038/ncomms5006

• Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation

  PLoS ONE · 2014 · 10.1371/journal.pone.0102107

• Stability of FDG-PET Radiomics features: An integrated analysis of test-retest and inter-observer variability

  Acta Oncologica · 2013 · 10.3109/0284186x.2013.812798

• Inconsistency in large pharmacogenomic studies

  Nature · 2013 · 10.1038/nature12831

• Radiomics: Extracting more information from medical images using advanced feature analysis

  European Journal of Cancer · 2012 · 10.1016/j.ejca.2011.11.036

• Radiomics: the process and the challenges

  Magnetic Resonance Imaging · 2012 · 10.1016/j.mri.2012.06.010

Current projects

No projects listed.