ScholarIQanswers from OpenAlex
Radiomics and Machine Learning in Medical Imaging
TopicLeading institutions, researchers & key papers
Radiomics and Machine Learning in Medical Imaging is a topic indexed in ScholarIQ from OpenAlex.
What is known about Radiomics and Machine Learning in Medical Imaging?
ScholarIQrecord summary
This cluster of papers focuses on the application of radiomics, a quantitative analysis of medical images, particularly in the context of cancer imaging and precision medicine. It explores the extraction of advanced features from medical images, the use of machine learning for predictive modeling, and the assessment of tumor heterogeneity through texture analysis.
How many works does Radiomics and Machine Learning in Medical Imaging have?
ScholarIQindexed works
Radiomics and Machine Learning in Medical Imaging has 613 works in the ScholarIQ index. The count is the OpenAlex total, not the number of papers listed on this page.
What is the OpenAlex record for Radiomics and Machine Learning in Medical Imaging?
ScholarIQopenalex
The OpenAlex for Radiomics and Machine Learning in Medical Imaging is on the source record.
What are the most-cited papers on Radiomics and Machine Learning in Medical Imaging?
ScholarIQmost cited works
Radiomics: Extracting more information from medical images using advanced feature analysis
Philippe Lambin, Emmanuel Rios-Velazquez, Ralph T. H. Leijenaar, Sara Carvalho, Ruud G.P.M. van Stiphout, Patrick V. Granton, Catharina M.L. Zegers, Robert J. Gillies, Ronald Boellard, André Dekker, Hugo J.W.L. Aerts
Radiomics: the bridge between medical imaging and personalized medicine
Philippe Lambin, Ralph T. H. Leijenaar, Timo M. Deist, Jurgen Peerlings, Evelyn E.C. de Jong, Janita E. van Timmeren, Sebastian Sanduleanu, Ruben T. H. M. Larue, Aniek J.G. Even, Arthur Jochems, Yvonka van Wijk, Henry C. Woodruff, Johan van Soest, Tim Lustberg, Erik Roelofs, Wouter van Elmpt, André Dekker, Felix M. Mottaghy, Joachim E. Wildberger, Seán Walsh
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Hugo J.W.L. Aerts, Emmanuel Rios Velazquez, Ralph T. H. Leijenaar, Chintan Parmar, Patrick Großmann, Sara Carvalho, Johan Bussink, René Monshouwer, Benjamin Haibe‐Kains, D. Rietveld, Frank Hoebers, Michelle M. Rietbergen, C. René Leemans, André Dekker, John Quackenbush, Robert J. Gillies, Philippe Lambin
An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics
Jianfang Liu, Tara M. Lichtenberg, Katherine A. Hoadley, Laila Poisson, Alexander J. Lazar, Andrew D. Cherniack, Albert J. Kovatich, Christopher C. Benz, Douglas A. Levine, Adrian V. Lee, Larsson Omberg, Denise M. Wolf, Craig D. Shriver, Vésteinn Thórsson, Rory Johnson, John A. Demchok, Ina Felau, Melpomeni Kasapi, Martin L. Ferguson, Carolyn M. Hutter, Heidi J. Sofia, Roy Tarnuzzer, Zhining Wang, Liming Yang, Jean C. Zenklusen, Jiashan Zhang, Sudha Chudamani, Jia Liu, Laxmi Lolla, Rashi Naresh, Todd Pihl, Qiang Sun, Yunhu Wan, Ye Wu, Juok Cho, Timothy Defreitas, Scott Frazer, Nils Gehlenborg, Gad Getz, David I. Heiman, Jaegil Kim, Michael S. Lawrence, Pei Lin, Sam Meier, Michael S. Noble, Gordon Saksena, Doug Voet, Hailei Zhang, Brady Bernard, Nyasha Chambwe, Varsha Dhankani, Theo Knijnenburg, Roger Kramer, Kalle Leinonen, Yuexin Liu, Michael B. Miller, Sheila M. Reynolds, Ilya Shmulevich, Vesteinn Thorsson, Wei Zhang, Rehan Akbani, Bradley M. Broom, Apurva M. Hegde, Zhenlin Ju, Rupa S. Kanchi, Anil Korkut, Jun Li, Han Liang, Shiyun Ling, Wenbin Liu, Yiling Lu, Gordon B. Mills, Kwok-Shing Ng, Arvind Rao, Michael Ryan, Jing Wang, John N. Weinstein, Jiexin Zhang, Adam Abeshouse, Joshua Armenia, Debyani Chakravarty, Walid K. Chatila, Ino de Bruijn, Galen F. Gao, Benjamin E. Gross, Zachary Heins, Ritika Kundra, Konnor La, Marc Ladanyi, Augustin Luna, Moriah G. Nissan, Angelica Ochoa, Sarah Phillips, Ed Reznik, Francisco Sánchez-Vega, Chris Sander, Nikolaus Schultz, Robert E. Sheridan, S. Onur Sumer, Yichao Sun
Radiomics: the process and the challenges
Virendra Kumar, Yuhua Gu, Satrajit Basu, Anders Berglund, Steven A. Eschrich, Matthew B. Schabath, Kenneth Forster, Hugo J.W.L. Aerts, André Dekker, David Fenstermacher, Dmitry B. Goldgof, Lawrence Hall, Philippe Lambin, Yoganand Balagurunathan, Robert A. Gatenby, Robert J. Gillies
Where is Radiomics and Machine Learning in Medical Imaging research published, and who funds it?
ScholarIQvenues & funding sources
TOP JOURNALS
TOP FUNDERS
National Science Foundation—
NIH—
Wellcome Trust—
European Research Council—
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How much of the research on Radiomics and Machine Learning in Medical Imaging is open access?
ScholarIQopen access share
87%OPEN ACCESS