Search Results - "Aerts, Hugo J W"

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

“…Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from…”
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Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study by 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

“…Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. This study explores deep…”
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Robust Radiomics feature quantification using semiautomatic volumetric segmentation by 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

“…Due to advances in the acquisition and analysis of medical imaging, it is currently possible to quantify the tumor phenotype. The emerging field of Radiomics…”
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Deep learning classification of lung cancer histology using CT images by Chaunzwa, Tafadzwa L., Hosny, Ahmed, Xu, Yiwen, Shafer, Andrea, Diao, Nancy, Lanuti, Michael, Christiani, David C., Mak, Raymond H., Aerts, Hugo J. W. L.

“…Tumor histology is an important predictor of therapeutic response and outcomes in lung cancer. Tissue sampling for pathologist review is the most reliable…”
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Radiographic prediction of meningioma grade by semantic and radiomic features by 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

“…The clinical management of meningioma is guided by tumor grade and biological behavior. Currently, the assessment of tumor grade follows surgical resection and…”
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Deep convolutional neural networks to predict cardiovascular risk from computed tomography by 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.

“…Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this…”
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Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats by Napel, Sandy, Mu, Wei, Jardim‐Perassi, Bruna V., Aerts, Hugo J. W. L., Gillies, Robert J.

“…Although cancer often is referred to as “a disease of the genes,” it is indisputable that the (epi)genetic properties of individual cancer cells are highly…”
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Comparison of texture features derived from static and respiratory-gated PET images in non-small cell lung cancer by Yip, Stephen, McCall, Keisha, Aristophanous, Michalis, Chen, Aileen B, Aerts, Hugo J W L, Berbeco, Ross

“…PET-based texture features have been used to quantify tumor heterogeneity due to their predictive power in treatment outcome. We investigated the sensitivity…”
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Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma by 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

“…Two CT features were developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity (feature 1: convexity) and intratumor density…”
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Robustness and reproducibility for AI learning in biomedical sciences: RENOIR by Barberis, Alessandro, Aerts, Hugo J. W. L., Buffa, Francesca M.

“…Artificial intelligence (AI) techniques are increasingly applied across various domains, favoured by the growing acquisition and public availability of large,…”
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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 by 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

“…Radiomics aims to quantitatively capture the complex tumor phenotype contained in medical images to associate them with clinical outcomes. This study…”
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Deep Learning to Assess Long-term Mortality From Chest Radiographs by Lu, Michael T, Ivanov, Alexander, Mayrhofer, Thomas, Hosny, Ahmed, Aerts, Hugo J W L, Hoffmann, Udo

“…Chest radiography is the most common diagnostic imaging test in medicine and may also provide information about longevity and prognosis. To develop and test a…”
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End-to-end reproducible AI pipelines in radiology using the cloud by 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.

“…Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a significant portion of the published literature lacks…”
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Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR by 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.

“…Multiparametric Magnetic Resonance Imaging (MRI) can provide detailed information of the physical characteristics of rectum tumours. Several investigations…”
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Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC by Dou, Tai H, Coroller, Thibaud P, van Griethuysen, Joost J M, Mak, Raymond H, Aerts, Hugo J W L

“…Radiomics provides quantitative tissue heterogeneity profiling and is an exciting approach to developing imaging biomarkers in the context of precision…”
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Image based prognosis in head and neck cancer using convolutional neural networks: a case study in reproducibility and optimization by Mateus, Pedro, Volmer, Leroy, Wee, Leonard, Aerts, Hugo J. W. L., Hoebers, Frank, Dekker, Andre, Bermejo, Inigo

“…In the past decade, there has been a sharp increase in publications describing applications of convolutional neural networks (CNNs) in medical image analysis…”
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Edge roughness quantifies impact of physician variation on training and performance of deep learning auto-segmentation models for the esophagus by 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.

“…Manual segmentation of tumors and organs-at-risk (OAR) in 3D imaging for radiation-therapy planning is time-consuming and subject to variation between…”
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Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer by Yip, Stephen S. F., Liu, Ying, Parmar, Chintan, Li, Qian, Liu, Shichang, Qu, Fangyuan, Ye, Zhaoxiang, Gillies, Robert J., Aerts, Hugo J. W. L.

“…Tumor phenotypes captured in computed tomography (CT) images can be described qualitatively and quantitatively using radiologist-defined “semantic” and…”
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Radiologists can visually predict mortality risk based on the gestalt of chest radiographs comparable to a deep learning network by Weiss, Jakob, Taron, Jana, Jin, Zexi, Mayrhofer, Thomas, Aerts, Hugo J. W. L., Lu, Michael T., Hoffmann, Udo

“…Deep learning convolutional neural network (CNN) can predict mortality from chest radiographs, yet, it is unknown whether radiologists can perform the same…”
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Application of the 3D slicer chest imaging platform segmentation algorithm for large lung nodule delineation by Yip, Stephen S F, Parmar, Chintan, Blezek, Daniel, Estepar, Raul San Jose, Pieper, Steve, Kim, John, Aerts, Hugo J W L

“…Accurate segmentation of lung nodules is crucial in the development of imaging biomarkers for predicting malignancy of the nodules. Manual segmentation is time…”
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