Search Results - "Aerts, L."

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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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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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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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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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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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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MRNACalc: An accurate RNA quantification tool in the era of modified nucleosides by den Roover, Sabine, Aerts, Joeri L.

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

“…Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic characteristics. Highly accurate and reliable…”
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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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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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Sexual functioning in women after mastectomy versus breast conserving therapy for early-stage breast cancer: A prospective controlled study by Aerts, L, Christiaens, M.R, Enzlin, P, Neven, P, Amant, F

“…Abstract Introduction Breast cancer (BC) and/or its treatments may affect sexual functioning based on physiological and psychosocial mechanisms. The aim of…”
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Clinical variables and magnetic resonance imaging‐based radiomics predict human papillomavirus status of oropharyngeal cancer by 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

“…Background Human papillomavirus (HPV)‐positive oropharyngeal squamous cell carcinoma (OPSCC) have better prognosis and treatment response compared to…”
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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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Unveiling the intricacies of gene delivery: Caveolae-mediated endocytosis induces efficient mRNA delivery in slow-dividing cells by den Roover, Sabine, Aerts, Joeri L.

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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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