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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Artificial intelligence in radiology von Hosny, Ahmed, Parmar, Chintan, Quackenbush, John, Schwartz, Lawrence H., Aerts, Hugo J. W. L.

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Robust Radiomics feature quantification using semiautomatic volumetric segmentation von Parmar, Chintan, Rios Velazquez, Emmanuel, Leijenaar, Ralph, Jermoumi, Mohammed, Carvalho, Sara, Mak, Raymond H, Mitra, Sushmita, Shankar, B Uma, Kikinis, Ron, Haibe-Kains, Benjamin, Lambin, Philippe, 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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Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT von Huynh, Elizabeth, Coroller, Thibaud P, Narayan, Vivek, Agrawal, Vishesh, Romano, John, Franco, Idalid, Parmar, Chintan, Hou, Ying, Mak, Raymond H, Aerts, Hugo J W L

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Radiographic prediction of meningioma grade by semantic and radiomic features von Coroller, Thibaud P, Bi, Wenya Linda, Huynh, Elizabeth, Abedalthagafi, Malak, Aizer, Ayal A, Greenwald, Noah F, Parmar, Chintan, Narayan, Vivek, Wu, Winona W, Miranda de Moura, Samuel, Gupta, Saksham, Beroukhim, Rameen, Wen, Patrick Y, Al-Mefty, Ossama, Dunn, Ian F, Santagata, Sandro, Alexander, Brian M, Huang, Raymond Y, Aerts, Hugo J W L

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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 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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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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Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC von Dou, Tai H, Coroller, Thibaud P, van Griethuysen, Joost J M, 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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Standardization and quality control studies of 'real-time' quantitative reverse transcriptase polymerase chain reaction of fusion gene transcripts for residual disease detection in... von GABERT, J, BEILLARD, E, PANE, F, AERTS, J. L. E, DE MICHELI, D, THIRION, X, PRADEL, V, GONZALEZ, M, VIEHMANN, S, MALEC, M, SAGLIO, G, VAN DONGEN, J. J. M, VAN DER VELDEN, V. H. J, BI, W, GRIMWADE, D, PALLISGAARD, N, BARBANY, G, CAZZANIGA, G, CAYUELA, J. M, CAVE, H

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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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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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Robustness and reproducibility for AI learning in biomedical sciences: RENOIR von Barberis, Alessandro, Aerts, Hugo J. W. L., Buffa, Francesca M.

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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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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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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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End-to-end reproducible AI pipelines in radiology using the cloud von Bontempi, Dennis, Nuernberg, Leonard, Pai, Suraj, Krishnaswamy, Deepa, Thiriveedhi, Vamsi, Hosny, Ahmed, Mak, Raymond H., Farahani, Keyvan, Kikinis, Ron, Fedorov, Andrey, Aerts, Hugo J. W. L.

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Image based prognosis in head and neck cancer using convolutional neural networks: a case study in reproducibility and optimization von Mateus, Pedro, Volmer, Leroy, Wee, Leonard, Aerts, Hugo J. W. L., Hoebers, Frank, Dekker, Andre, Bermejo, Inigo

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