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Medicine

D-Index
85
Citations
57720
World Ranking
14333
National Ranking
7246

Overview

Hugo J.W.L. Aerts is affiliated with Brigham and Women's Hospital in the United States. Their research primarily spans the field of Medicine, with 428 publications, and notably focuses on several subfields including Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Artificial Intelligence, Oncology, and Cardiology and Cardiovascular Medicine.

The main topics addressed in their work include Radiomics and Machine Learning in Medical Imaging, Lung Cancer Diagnosis and Treatment, Artificial Intelligence in Healthcare and Education, Medical Imaging Techniques and Applications, Cardiac Imaging and Diagnostics, AI in Cancer Detection, and Advanced X-ray and CT Imaging.

Their recent papers highlight a range of subjects relevant to medical imaging and artificial intelligence. Notable publications include:

  • The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping, 2020, Radiology
  • Transparency and reproducibility in artificial intelligence, 2020, Nature
  • Artificial intelligence in radiation oncology, 2020, Nature Reviews Clinical Oncology
  • Artificial intelligence for clinical oncology, 2021, Cancer Cell
  • Tracking early lung cancer metastatic dissemination in TRACERx using ctDNA, 2023, Nature

Aerts collaborates regularly with several coauthors who have contributed to multiple publications together, including Raymond H. Mak, Benjamin H. Kann, Danielle S. Bitterman, Michael T. Lu, and Zezhong Ye.

Their work is published frequently in several prominent venues, with the greatest number of papers appearing in:

  • arXiv (Cornell University)
  • International Journal of Radiation Oncology*Biology*Physics
  • bioRxiv (Cold Spring Harbor Laboratory)
  • The Lancet Digital Health
  • Cancer Research

Best Publications

  • Computational Radiomics System to Decode the Radiographic Phenotype

    Joost J.M. van Griethuysen;Joost J.M. van Griethuysen;Joost J.M. van Griethuysen;Andriy Fedorov;Chintan Parmar;Ahmed Hosny

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

    Philippe Lambin;Emmanuel Rios-Velazquez;Ralph Leijenaar;Sara Carvalho

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

    Hugo J W L Aerts;Emmanuel Rios Velazquez;Ralph T H Leijenaar;Chintan Parmar

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

    Alex Zwanenburg;Alex Zwanenburg;Martin Vallières;Mahmoud A. Abdalah;Hugo J. W. L. Aerts;Hugo J. W. L. Aerts

  • Artificial intelligence in radiology

    Ahmed Hosny;Chintan Parmar;John Quackenbush;Lawrence H. Schwartz;Lawrence H. Schwartz

  • Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution

    Christopher Abbosh;Nicolai J. Birkbak;Nicolai J. Birkbak;Gareth A. Wilson;Gareth A. Wilson;Mariam Jamal-Hanjani

  • Artificial intelligence in cancer imaging: Clinical challenges and applications.

    Wenya Linda Bi;Ahmed Hosny;Matthew B. Schabath;Maryellen L. Giger

  • Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution

    Nicholas McGranahan;Rachel Rosenthal;Crispin T. Hiley;Crispin T. Hiley;Andrew J. Rowan

  • Applications and limitations of radiomics

    Stephen S F Yip;Hugo J W L Aerts;Hugo J W L Aerts

  • Imaging biomarker roadmap for cancer studies.

    James P.B. O'Connor;Eric O. Aboagye;Judith E. Adams;Hugo J.W.L. Aerts;Hugo J.W.L. Aerts

  • Machine Learning methods for Quantitative Radiomic Biomarkers

    Chintan Parmar;Chintan Parmar;Patrick Grossmann;Johan Bussink;Philippe Lambin

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

    Thibaud Patrick Coroller;Thibaud Patrick Coroller;Patrick Grossmann;Patrick Grossmann;Ying Hou;Emmanuel Rios Velazquez

  • Robust Radiomics feature quantification using semiautomatic volumetric segmentation.

    Chintan Parmar;Chintan Parmar;Chintan Parmar;Emmanuel Rios Velazquez;Emmanuel Rios Velazquez;Ralph Leijenaar;Mohammed Jermoumi;Mohammed Jermoumi

  • The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review

    Hugo J. W. L. Aerts

  • Inconsistency in large pharmacogenomic studies

    Benjamin Haibe-Kains;Nehme El-Hachem;Nicolai Juul Birkbak;Andrew C. Jin

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

    Martin Vallières;Emily Kay-Rivest;Léo Jean Perrin;Xavier Liem

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

    Ahmed Hosny;Chintan Parmar;Thibaud P. Coroller;Patrick Grossmann

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

    Yiwen Xu;Ahmed Hosny;Ahmed Hosny;Roman Zeleznik;Roman Zeleznik;Chintan Parmar

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

    Chintan Parmar;Chintan Parmar;Ralph T. H. Leijenaar;Patrick Grossmann;Emmanuel Rios Velazquez

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

    Hugo J.W.L. Aerts;Emmanuel Rios Velazquez;Ralph T.H. Leijenaar;Chintan Parmar

Frequent Co-Authors

Philippe Lambin
Philippe Lambin Maastricht University
Dirk De Ruysscher
Dirk De Ruysscher Maastricht University
Benjamin Haibe-Kains
Benjamin Haibe-Kains Princess Margaret Cancer Centre
Udo Hoffmann
Udo Hoffmann Harvard University
Robert J. Gillies
Robert J. Gillies Moffitt Cancer Center
John Quackenbush
John Quackenbush Harvard University
Regina G. H. Beets-Tan
Regina G. H. Beets-Tan Antoni van Leeuwenhoek Hospital
Charles Swanton
Charles Swanton The Francis Crick Institute
Lawrence H. Schwartz
Lawrence H. Schwartz Columbia University
Leming Shi
Leming Shi Fudan University

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