Gillies, Robert J.

Radiomics: Images Are More than Pictures, They Are Data

Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016) · Radiology

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

Cited by 8,421

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

Why Do Cancers Have High Aerobic Glycolysis?

Gatenby, R. A., & Gillies, R. J. (2004) · Nature reviews. Cancer

Cancer, Hypoxia, and MetabolismMetabolism, Diabetes, and Cancer

Cited by 4,784

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

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

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

Acid-Mediated Tumor Invasion: a Multidisciplinary Study

Gatenby, R. A., Gawlinski, E. T., Gmitro, A. F., Kaylor, B., & Gillies, R. J. (2006) · Cancer Research

Cancer, Hypoxia, and MetabolismMathematical Biology Tumor Growth

Cited by 771

Neutralization of Tumor Acidity Improves Antitumor Responses to Immunotherapy

Pilon-Thomas, S., Kodumudi, K. N., El-Kenawi, A. E., Russell, S., Weber, A. M., Luddy, K., Damaghi, M., Wojtkowiak, J. W., Mulé, J. J., Ibrahim-Hashim, A., et al. (2016) · Cancer Research

Cancer Immunotherapy and BiomarkersCancer, Hypoxia, and Metabolism

Cited by 593

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

Quantitative Imaging in Cancer Evolution and Ecology

Gatenby, R. A., Grove, O., & Gillies, R. J. (2013) · Radiology

Radiomics and Machine Learning in Medical ImagingMathematical Biology Tumor Growth

Cited by 515

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

The Biological Meaning of Radiomic Features

Tomaszewski, M. R., & Gillies, R. J. (2021) · Radiology

Radiomics and Machine Learning in Medical ImagingMedical Imaging Techniques and Applications

Cited by 327

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

Hypoxia-Inducible Factor-1α and the Glycolytic Phenotype in Tumors

Robey, I. F., Lien, A. D., Welsh, S. J., Baggett, B. K., & Gillies, R. J. (2005) · Neoplasia

Cancer, Hypoxia, and MetabolismMetabolism, Diabetes, and Cancer

Cited by 251

Radiomics Improves Cancer Screening and Early Detection

Gillies, R. J., & Schabath, M. B. (2020) · Cancer Epidemiology, Biomarkers & Prevention

Radiomics and Machine Learning in Medical ImagingAI in cancer detection

Cited by 159

Therapeutic targeting of the Warburg effect in pancreatic cancer relies on an absence of p53 function

Rajeshkumar, N. V., Dutta, P., Yabuuchi, S., de Wilde, R. F., Martinez, G. V., Le, A., Kamphorst, J. J., Rabinowitz, J. D., Jain, S. K., Hidalgo, M., et al. (2015) · Cancer Research

Cancer, Hypoxia, and MetabolismPancreatic and Hepatic Oncology Research

Cited by 158

Combining radiomic features with a miRNA classifier may improve prediction of malignant pathology for pancreatic intraductal papillary mucinous neoplasms

Permuth, J. B., Collaborative, T. F. P., Choi, J., Balarunathan, Y., Kim, J., Chen, D.-T., Chen, L., Orcutt, S., Doepker, M. P., Gage, K., et al. (2016) · Oncotarget

Radiomics and Machine Learning in Medical ImagingPancreatic and Hepatic Oncology Research

Cited by 127

A semiautomatic CT-based ensemble segmentation of lung tumors: Comparison with oncologists’ delineations and with the surgical specimen

Rios Velazquez, E., Aerts, H. J. W. L., Gu, Y., Goldgof, D. B., De Ruysscher, D., Dekker, A., Korn, R., Gillies, R. J., & Lambin, P. (2012) · Radiotherapy and Oncology

Medical Image Segmentation TechniquesLung Cancer Diagnosis and Treatment

Cited by 120

Radiologically Defined Ecological Dynamics and Clinical Outcomes in Glioblastoma Multiforme: Preliminary Results

Zhou, M., Hall, L., Goldgof, D., Russo, R., Balagurunathan, Y., Gillies, R., & Gatenby, R. (2014) · Translational Oncology

Radiomics and Machine Learning in Medical ImagingGlioma Diagnosis and Treatment

Cited by 97

Variance of SUVs for FDG-PET/CT is Greater in Clinical Practice Than Under Ideal Study Settings

Kumar, V., Nath, K., Berman, C. G., Kim, J., Tanvetyanon, T., Chiappori, A. A., Gatenby, R. A., Gillies, R. J., & Eikman, E. A. (2013) · Clinical Nuclear Medicine

Medical Imaging Techniques and ApplicationsRadiomics and Machine Learning in Medical Imaging

Cited by 76