Search Results - "Aerts, Hugo J.W.L"

CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma by Coroller, Thibaud P, Grossmann, Patrick, Hou, Ying, Rios Velazquez, Emmanuel, Leijenaar, Ralph T.H, Hermann, Gretchen, Lambin, Philippe, Haibe-Kains, Benjamin, Mak, Raymond H, Aerts, Hugo J.W.L

“…Abstract Background and purpose Radiomics provides opportunities to quantify the tumor phenotype non-invasively by applying a large number of quantitative…”
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Radiomics: Extracting more information from medical images using advanced feature analysis by Lambin, Philippe, Rios-Velazquez, Emmanuel, Leijenaar, Ralph, Carvalho, Sara, van Stiphout, Ruud G.P.M, Granton, Patrick, Zegers, Catharina M.L, Gillies, Robert, Boellard, Ronald, Dekker, André, Aerts, Hugo J.W.L

“…Abstract Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for…”
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The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis by Leijenaar, Ralph T.H., Nalbantov, Georgi, Carvalho, Sara, van Elmpt, Wouter J.C., Troost, Esther G.C., Boellaard, Ronald, Aerts, Hugo J.W.L, Gillies, Robert J., Lambin, Philippe

“…FDG-PET-derived textural features describing intra-tumor heterogeneity are increasingly investigated as imaging biomarkers. As part of the process of…”
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Deep learning to estimate lung disease mortality from chest radiographs by Weiss, Jakob, Raghu, Vineet K., Bontempi, Dennis, Christiani, David C., Mak, Raymond H., Lu, Michael T., Aerts, Hugo J.W.L.

“…Prevention and management of chronic lung diseases (asthma, lung cancer, etc.) are of great importance. While tests are available for reliable diagnosis,…”
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Automated temporalis muscle quantification and growth charts for children through adulthood by Zapaishchykova, Anna, Liu, Kevin X., Saraf, Anurag, Ye, Zezhong, Catalano, Paul J., Benitez, Viviana, Ravipati, Yashwanth, Jain, Arnav, Huang, Julia, Hayat, Hasaan, Likitlersuang, Jirapat, Vajapeyam, Sridhar, Chopra, Rishi B., Familiar, Ariana M., Nabavidazeh, Ali, Mak, Raymond H., Resnick, Adam C., Mueller, Sabine, Cooney, Tabitha M., Haas-Kogan, Daphne A., Poussaint, Tina Y., Aerts, Hugo J.W.L., Kann, Benjamin H.

“…Lean muscle mass (LMM) is an important aspect of human health. Temporalis muscle thickness is a promising LMM marker but has had limited utility due to its…”
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Revisiting inconsistency in large pharmacogenomic studies [version 3; peer review: 2 approved, 1 approved with reservations] by Safikhani, Zhaleh, Smirnov, Petr, Freeman, Mark, El-Hachem, Nehme, She, Adrian, Rene, Quevedo, Goldenberg, Anna, Birkbak, Nicolai J, Hatzis, Christos, Shi, Leming, Beck, Andrew H, Aerts, Hugo J.W.L, Quackenbush, John, Haibe-Kains, Benjamin

“…In 2013, we published a comparative analysis of mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471…”
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Outcomes by Tumor Histology and KRAS Mutation Status After Lung Stereotactic Body Radiation Therapy for Early-Stage Non–Small-Cell Lung Cancer by Mak, Raymond H, Hermann, Gretchen, Lewis, John H, Aerts, Hugo J.W.L, Baldini, Elizabeth H, Chen, Aileen B, Colson, Yolonda L, Hacker, Fred H, Kozono, David, Wee, Jon O, Chen, Yu-Hui, Catalano, Paul J, Wong, Kwok-Kin, Sher, David J

“…Micro-Abstract We analyzed outcomes after lung stereotactic body radiotherapy (SBRT) for early-stage non–small-cell lung carcinoma in patients by histology and…”
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Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging by Xu, Yiwen, Hosny, Ahmed, Zeleznik, Roman, Parmar, Chintan, Coroller, Thibaud, Franco, Idalid, Mak, Raymond H, Aerts, Hugo J W L

“…Tumors are continuously evolving biological systems, and medical imaging is uniquely positioned to monitor changes throughout treatment. Although qualitatively…”
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Computational Radiomics System to Decode the Radiographic Phenotype by van Griethuysen, Joost J M, Fedorov, Andriy, Parmar, Chintan, Hosny, Ahmed, Aucoin, Nicole, Narayan, Vivek, Beets-Tan, Regina G H, Fillion-Robin, Jean-Christophe, Pieper, Steve, Aerts, Hugo J W L

“…Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI)…”
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Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC by Coroller, Thibaud P, Agrawal, Vishesh, Huynh, Elizabeth, Narayan, Vivek, Lee, Stephanie W, Mak, Raymond H, Aerts, Hugo J W L

“…Noninvasive biomarkers that capture the total tumor burden could provide important complementary information for precision medicine to aid clinical decision…”
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Vulnerabilities of radiomic signature development: The need for safeguards by Welch, Mattea L., McIntosh, Chris, Haibe-Kains, Benjamin, Milosevic, Michael F., Wee, Leonard, Dekker, Andre, Huang, Shao Hui, Purdie, Thomas G., O'Sullivan, Brian, Aerts, Hugo J.W.L., Jaffray, David A.

“…•Presented Safeguards ensure productive progress of the radiomic field.•Radiomic models and features should be tested to determine added prognostic and…”
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CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer by Huynh, Elizabeth, Coroller, Thibaud P, Narayan, Vivek, Agrawal, Vishesh, Hou, Ying, Romano, John, Franco, Idalid, Mak, Raymond H, Aerts, Hugo J.W.L

“…Abstract Background Radiomics uses a large number of quantitative imaging features that describe the tumor phenotype to develop imaging biomarkers for clinical…”
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Radiomic phenotype features predict pathological response in non-small cell lung cancer by Coroller, Thibaud P, Agrawal, Vishesh, Narayan, Vivek, Hou, Ying, Grossmann, Patrick, Lee, Stephanie W, Mak, Raymond H, Aerts, Hugo J.W.L

“…Abstract Background and purpose Radiomics can quantify tumor phenotype characteristics non-invasively by applying advanced imaging feature algorithms. In this…”
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Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer by Rios Velazquez, Emmanuel, Parmar, Chintan, Liu, Ying, Coroller, Thibaud P, Cruz, Gisele, Stringfield, Olya, Ye, Zhaoxiang, Makrigiorgos, Mike, Fennessy, Fiona, Mak, Raymond H, Gillies, Robert, Quackenbush, John, Aerts, Hugo J W L

“…Tumors are characterized by somatic mutations that drive biological processes ultimately reflected in tumor phenotype. With regard to radiographic phenotypes,…”
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Radiomics: the process and the challenges by Kumar, Virendra, Gu, Yuhua, Basu, Satrajit, Berglund, Anders, Eschrich, Steven A, Schabath, Matthew B, Forster, Kenneth, Aerts, Hugo J.W.L, Dekker, Andre, Fenstermacher, David, Goldgof, Dmitry B, Hall, Lawrence O, Lambin, Philippe, Balagurunathan, Yoganand, Gatenby, Robert A, Gillies, Robert J

“…Abstract “Radiomics” refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images…”
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Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer by Parmar, Chintan, Leijenaar, Ralph T. H., Grossmann, Patrick, Rios Velazquez, Emmanuel, Bussink, Johan, Rietveld, Derek, Rietbergen, Michelle M., Haibe-Kains, Benjamin, Lambin, Philippe, Aerts, Hugo J.W.L.

“…Radiomics provides a comprehensive quantification of tumor phenotypes by extracting and mining large number of quantitative image features. To reduce the…”
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Data Analysis Strategies in Medical Imaging by Parmar, Chintan, Barry, Joseph D, Hosny, Ahmed, Quackenbush, John, Aerts, Hugo J W L

“…Radiographic imaging continues to be one of the most effective and clinically useful tools within oncology. Sophistication of artificial intelligence has…”
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Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer by Parmar, Chintan, Grossmann, Patrick, Rietveld, Derek, Rietbergen, Michelle M, Lambin, Philippe, Aerts, Hugo J W L

“…"Radiomics" extracts and mines a large number of medical imaging features in a non-invasive and cost-effective way. The underlying assumption of radiomics is…”
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Deep Learning to Estimate Biological Age From Chest Radiographs by Raghu, Vineet K., Weiss, Jakob, Hoffmann, Udo, Aerts, Hugo J.W.L., Lu, Michael T.

“…The goal of this study was to assess whether a deep learning estimate of age from a chest radiograph image (CXR-Age) can predict longevity beyond chronological…”
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Predicting outcomes in radiation oncology—multifactorial decision support systems by Lambin, Philippe, van Stiphout, Ruud G. P. M., Starmans, Maud H. W., Rios-Velazquez, Emmanuel, Nalbantov, Georgi, Aerts, Hugo J. W. L., Roelofs, Erik, van Elmpt, Wouter, Boutros, Paul C., Granone, Pierluigi, Valentini, Vincenzo, Begg, Adrian C., De Ruysscher, Dirk, Dekker, Andre

“…The emergence of individualized medicine has spurred the need for the development of clinical decision-support systems (CDSSs) based on prediction models of…”
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