Aerts, Hugo J. W. L.

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

Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R. G. P. M., Granton, P., Zegers, C. M. L., Gillies, R., Boellard, R., Dekker, A., et al. (2012) · European Journal of Cancer

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

Cited by 6,084

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., et al. (2014) · Nature Communications

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

Cited by 5,132

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

Zwanenburg, A., Vallières, M., Abdalah, M. A., Aerts, H. J. W. L., Andrearczyk, V., Apte, A., Ashrafinia, S., Bakas, S., Beukinga, R. J., Boellaard, R., et al. (2020) · Radiology

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

Cited by 4,177

Artificial intelligence in radiology

Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 3,803

Radiomics: the process and the challenges

Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S. A., Schabath, M. B., Forster, K., Aerts, H. J. W. L., Dekker, A., Fenstermacher, D., et al. (2012) · Magnetic Resonance Imaging

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

Cited by 2,258

Artificial intelligence in cancer imaging: Clinical challenges and applications

Bi, W. L., Hosny, A., Schabath, M. B., Giger, M. L., Birkbak, N. J., Mehrtash, A., Allison, T., Arnaout, O., Abbosh, C., Dunn, I. F., et al. (2019) · CA: A Cancer Journal for Clinicians

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 1,846

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

McGranahan, N., Rosenthal, R., Hiley, C. T., Rowan, A. J., Watkins, T. B. K., Wilson, G. A., Birkbak, N. J., Veeriah, S., Van Loo, P., Herrero, J., et al. (2017) · Cell

Cancer Immunotherapy and BiomarkersCancer Genomics and Diagnostics

Cited by 1,298

Imaging biomarker roadmap for cancer studies

O'Connor, J. P. B., Aboagye, E. O., Adams, J. E., Aerts, H. J. W. L., Barrington, S. F., Beer, A. J., Boellaard, R., Bohndiek, S. E., Brady, M., Brown, G., et al. (2017) · Nature Reviews Clinical Oncology

Medical Imaging Techniques and ApplicationsRadiomics and Machine Learning in Medical Imaging

Cited by 1,091

Lung adenocarcinoma promotion by air pollutants

Hill, W., Lim, E. L., Weeden, C. E., Lee, C., Augustine, M., Chen, K., Kuan, F.-C., Marongiu, F., Evans, E. J., Moore, D. A., et al. (2023) · Nature

Air Quality and Health ImpactsCarcinogens and Genotoxicity Assessment

Cited by 808

CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma

Coroller, T. P., Grossmann, P., Hou, Y., Rios Velazquez, E., Leijenaar, R. T. H., Hermann, G., Lambin, P., Haibe-Kains, B., Mak, R. H., & Aerts, H. J. W. L. (2015) · Radiotherapy and Oncology

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

Cited by 695

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

Aerts, H. J. W. L. (2016) · JAMA Oncology

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

Cited by 669

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

Hosny, A., Parmar, C., Coroller, T. P., Grossmann, P., Zeleznik, R., Kumar, A., Bussink, J., Gillies, R. J., Mak, R. H., & Aerts, H. J. W. L. (2018) · PLOS Medicine

Radiomics and Machine Learning in Medical ImagingAI in cancer detection

Cited by 579

Transparency and reproducibility in artificial intelligence

Haibe-Kains, B., Adam, G. A., Hosny, A., Khodakarami, F., Massive Analysis Quality Control (MAQC) Society Board of Directors, Shraddha, T., Kusko, R., Sansone, S.-A., Tong, W., Wolfinger, R. D., et al. (2020) · Nature

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 540

Inconsistency in large pharmacogenomic studies

Haibe-Kains, B., El-Hachem, N., Birkbak, N. J., Jin, A. C., Beck, A. H., Aerts, H. J. W. L., & Quackenbush, J. (2013) · Nature

Computational Drug Discovery MethodsCancer Genomics and Diagnostics

Cited by 499

Tracking early lung cancer metastatic dissemination in TRACERx using ctDNA

Abbosh, C., Frankell, A. M., Harrison, T., Kisistok, J., Garnett, A., Johnson, L., Veeriah, S., Moreau, M., Chesh, A., Chaunzwa, T. L., et al. (2023) · Nature

Cancer Genomics and DiagnosticsLung Cancer Diagnosis and Treatment

Cited by 382

Defining the biological basis of radiomic phenotypes in lung cancer

Grossmann, P., Stringfield, O., El-Hachem, N., Bui, M. M., Rios Velazquez, E., Parmar, C., Leijenaar, R. T., Haibe-Kains, B., Lambin, P., Gillies, R. J., et al. (2017) · eLife

Radiomics and Machine Learning in Medical ImagingCancer Immunotherapy and Biomarkers

Cited by 365

Large language models to identify social determinants of health in electronic health records

Guevara, M., Chen, S., Thomas, S., Chaunzwa, T. L., Franco, I., Kann, B. H., Moningi, S., Qian, J. M., Goldstein, M., Harper, S., et al. (2024) · npj Digital Medicine

Machine Learning in HealthcareTopic Modeling

Cited by 278

Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma

Grove, O., Berglund, A. E., Schabath, M. B., Aerts, H. J. W. L., Dekker, A., Wang, H., Velazquez, E. R., Lambin, P., Gu, Y., Balagurunathan, Y., et al. (2015) · PLOS ONE

Radiomics and Machine Learning in Medical ImagingAI in cancer detection

Cited by 263

Use of Artificial Intelligence Chatbots for Cancer Treatment Information

Chen, S., Kann, B. H., Foote, M. B., Aerts, H. J. W. L., Savova, G. K., Mak, R. H., & Bitterman, D. S. (2023) · JAMA Oncology

Artificial Intelligence in Healthcare and EducationClinical practice guidelines implementation

Cited by 251

Deep convolutional neural networks to predict cardiovascular risk from computed tomography

Zeleznik, R., Foldyna, B., Eslami, P., Weiss, J., Alexander, I., Taron, J., Parmar, C., Alvi, R. M., Banerji, D., Uno, M., et al. (2021) · Nature Communications

Cardiac Imaging and DiagnosticsArtificial Intelligence in Healthcare and Education

Cited by 240

Foundation Model for Cancer Imaging Biomarkers

Pai, S., Bontempi, D., Hadzic, I., Prudente, V., Sokač, M., Chaunzwa, T. L., Bernatz, S., Hosny, A., Mak, R. H., Birkbak, N. J., et al. (2024) · Nature Machine Intelligence

Radiomics and Machine Learning in Medical ImagingAI in cancer detection

Cited by 199

Artificial intelligence for global health

Hosny, A., & Aerts, H. J. W. L. (2019) · Science

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 177

The effect of using a large language model to respond to patient messages

Chen, S., Guevara, M., Moningi, S., Hoebers, F., Elhalawani, H., Kann, B. H., Chipidza, F. E., Leeman, J., Aerts, H. J. W. L., Miller, T., et al. (2024) · The Lancet Digital Health

Artificial Intelligence in Healthcare and EducationElectronic Health Records Systems

Cited by 147

Deep Learning to Assess Long-term Mortality From Chest Radiographs

Lu, M. T., Ivanov, A., Mayrhofer, T., Hosny, A., Aerts, H. J. W. L., & Hoffmann, U. (2019) · JAMA Network Open

Radiomics and Machine Learning in Medical ImagingAI in cancer detection

Cited by 140