Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study von Hosny, Ahmed, Parmar, Chintan, Coroller, Thibaud P, Grossmann, Patrick, Zeleznik, Roman, Kumar, Avnish, Bussink, Johan, Gillies, Robert J, Mak, Raymond H, Aerts, Hugo J W L

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Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats von Napel, Sandy, Mu, Wei, Jardim‐Perassi, Bruna V., Aerts, Hugo J. W. L., Gillies, Robert J.

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Deep learning classification of lung cancer histology using CT images von Chaunzwa, Tafadzwa L., Hosny, Ahmed, Xu, Yiwen, Shafer, Andrea, Diao, Nancy, Lanuti, Michael, Christiani, David C., Mak, Raymond H., Aerts, Hugo J. W. L.

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Deep convolutional neural networks to predict cardiovascular risk from computed tomography von Zeleznik, Roman, Foldyna, Borek, Eslami, Parastou, Weiss, Jakob, Alexander, Ivanov, Taron, Jana, Parmar, Chintan, Alvi, Raza M., Banerji, Dahlia, Uno, Mio, Kikuchi, Yasuka, Karady, Julia, Zhang, Lili, Scholtz, Jan-Erik, Mayrhofer, Thomas, Lyass, Asya, Mahoney, Taylor F., Massaro, Joseph M., Vasan, Ramachandran S., Douglas, Pamela S., Hoffmann, Udo, Lu, Michael T., Aerts, Hugo J. W. L.

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Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR von Trebeschi, Stefano, van Griethuysen, Joost J. M., Lambregts, Doenja M. J., Lahaye, Max J., Parmar, Chintan, Bakers, Frans C. H., Peters, Nicky H. G. M., Beets-Tan, Regina G. H., Aerts, Hugo J. W. L.

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Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma von Grove, Olya, Berglund, Anders E, Schabath, Matthew B, Aerts, Hugo J W L, Dekker, Andre, Wang, Hua, Velazquez, Emmanuel Rios, Lambin, Philippe, Gu, Yuhua, Balagurunathan, Yoganand, Eikman, Edward, Gatenby, Robert A, Eschrich, Steven, Gillies, Robert J

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Machine Learning methods for Quantitative Radiomic Biomarkers von Parmar, Chintan, Grossmann, Patrick, Bussink, Johan, Lambin, Philippe, Aerts, Hugo J. W. L.

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Comparison of texture features derived from static and respiratory-gated PET images in non-small cell lung cancer von Yip, Stephen, McCall, Keisha, Aristophanous, Michalis, Chen, Aileen B, Aerts, Hugo J W L, Berbeco, Ross

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Deep Learning to Assess Long-term Mortality From Chest Radiographs von Lu, Michael T, Ivanov, Alexander, Mayrhofer, Thomas, Hosny, Ahmed, Aerts, Hugo J W L, Hoffmann, Udo

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Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer von Yip, Stephen S. F., Liu, Ying, Parmar, Chintan, Li, Qian, Liu, Shichang, Qu, Fangyuan, Ye, Zhaoxiang, Gillies, Robert J., Aerts, Hugo J. W. L.

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Clinical variables and magnetic resonance imaging‐based radiomics predict human papillomavirus status of oropharyngeal cancer von Bos, Paula, Brekel, Michiel W. M., Gouw, Zeno A. R., Al‐Mamgani, Abrahim, Waktola, Selam, Aerts, Hugo J. W. L., Beets‐Tan, Regina G. H., Castelijns, Jonas A., Jasperse, Bas

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Artificial intelligence in cancer imaging: Clinical challenges and applications von Bi, Wenya Linda, Hosny, Ahmed, Schabath, Matthew B., Giger, Maryellen L., Birkbak, Nicolai J., Mehrtash, Alireza, Allison, Tavis, Arnaout, Omar, Abbosh, Christopher, Dunn, Ian F., Mak, Raymond H., Tamimi, Rulla M., Tempany, Clare M., Swanton, Charles, Hoffmann, Udo, Schwartz, Lawrence H., Gillies, Robert J., Huang, Raymond Y., Aerts, Hugo J. W. L.

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Repeatability of Multiparametric Prostate MRI Radiomics Features von Schwier, Michael, van Griethuysen, Joost, Vangel, Mark G., Pieper, Steve, Peled, Sharon, Tempany, Clare, Aerts, Hugo J. W. L., Kikinis, Ron, Fennessy, Fiona M., Fedorov, Andriy

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Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer von Parmar, Chintan, Grossmann, Patrick, Rietveld, Derek, Rietbergen, Michelle M, Lambin, Philippe, Aerts, Hugo J W L

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Edge roughness quantifies impact of physician variation on training and performance of deep learning auto-segmentation models for the esophagus von Yan, Yujie, Kehayias, Christopher, He, John, Aerts, Hugo J. W. L., Fitzgerald, Kelly J., Kann, Benjamin H., Kozono, David E., Guthier, Christian V., Mak, Raymond H.

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Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer von Vallières, Martin, Kay-Rivest, Emily, Perrin, Léo Jean, Liem, Xavier, Furstoss, Christophe, Aerts, Hugo J. W. L., Khaouam, Nader, Nguyen-Tan, Phuc Felix, Wang, Chang-Shu, Sultanem, Khalil, Seuntjens, Jan, El Naqa, Issam

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Radiomics performs comparable to morphologic assessment by expert radiologists for prediction of response to neoadjuvant chemoradiotherapy on baseline staging MRI in rectal cancer von van Griethuysen, Joost J. M., Lambregts, Doenja M. J., Trebeschi, Stefano, Lahaye, Max J., Bakers, Frans C. H., Vliegen, Roy F. A., Beets, Geerard L., Aerts, Hugo J. W. L., Beets-Tan, Regina G. H.

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Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC von Aerts, Hugo J. W. L., Grossmann, Patrick, Tan, Yongqiang, Oxnard, Geoffrey R., Rizvi, Naiyer, Schwartz, Lawrence H., Zhao, Binsheng

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Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology von Wu, Weimiao, Parmar, Chintan, Grossmann, Patrick, Quackenbush, John, Lambin, Philippe, Bussink, Johan, Mak, Raymond, Aerts, Hugo J W L

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Volumetric CT-based segmentation of NSCLC using 3D-Slicer von Velazquez, Emmanuel Rios, Parmar, Chintan, Jermoumi, Mohammed, Mak, Raymond H., van Baardwijk, Angela, Fennessy, Fiona M., Lewis, John H., De Ruysscher, Dirk, Kikinis, Ron, Lambin, Philippe, Aerts, Hugo J. W. L.

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