FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4365444328> ?p ?o ?g. }

• W4365444328 abstract "Journal of Magnetic Resonance ImagingEarly View Editorial Editorial for “MRI-Based Breast Cancer Classification and Localization by Multiparametric Feature Extraction and Combination Using Deep Learning” Folk W. Narongrit MS, Folk W. Narongrit MS orcid.org/0000-0001-5731-7074 Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USASearch for more papers by this authorJoseph V. Rispoli PhD, Corresponding Author Joseph V. Rispoli PhD [email protected] orcid.org/0000-0003-4514-3390 Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA Purdue Institute for Cancer Research, West Lafayette, Indiana, USA Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA[email protected]Search for more papers by this author Folk W. Narongrit MS, Folk W. Narongrit MS orcid.org/0000-0001-5731-7074 Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USASearch for more papers by this authorJoseph V. Rispoli PhD, Corresponding Author Joseph V. Rispoli PhD [email protected] orcid.org/0000-0003-4514-3390 Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA Purdue Institute for Cancer Research, West Lafayette, Indiana, USA Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA[email protected]Search for more papers by this author First published: 13 April 2023 https://doi.org/10.1002/jmri.28733 Evidence Level: 5 Technical Efficacy: 2 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. References 1Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71: 209-249. https://doi.org/10.3322/caac.21660. 2Pinker K, Helbich TH, Morris EA. The potential of multiparametric MRI of the breast. Br J Radiol 2017; 90:20160715. https://doi.org/10.1259/bjr.20160715. 3Pinker K, Baltzer P, Bogner W, et al. Multiparametric MR imaging with high-resolution dynamic contrast-enhanced and diffusion-weighted imaging at 7 T improves the assessment of breast tumors: A feasibility study. Radiology 2015; 276: 360-370. https://doi.org/10.1148/radiol.15141905. 4Nie K, Chen J-H, Yu HJ, Chu Y, Nalcioglu O, Su M-Y. Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI. Acad Radiol 2008; 15: 1513-1525. https://doi.org/10.1016/j.acra.2008.06.005. 5Bolan PJ. Magnetic resonance spectroscopy of the breast. Magn Reson Imaging Clin N Am 2013; 21: 625-639. https://doi.org/10.1016/j.mric.2013.04.008. 6Pinker K, Bickel H, Helbich TH, et al. Combined contrast-enhanced magnetic resonance and diffusion-weighted imaging reading adapted to the “breast imaging reporting and data system” for multiparametric 3-T imaging of breast lesions. Eur Radiol 2013; 23: 1791-1802. https://doi.org/10.1007/s00330-013-2771-8. 7Reig B, Heacock L, Geras KJ, Moy L. Machine learning in breast MRI. J Magn Reson Imaging 2020; 52: 998-1018. https://doi.org/10.1002/jmri.26852. 8Truhn D, Schrading S, Haarburger C, Schneider H, Merhof D, Kuhl C. Radiomic versus convolutional neural networks analysis for classification of contrast-enhancing lesions at multiparametric breast MRI. Radiology 2019; 290: 290-297. https://doi.org/10.1148/radiol.2018181352. 9Ren T, Lin S, Huang P, Duong TQ. Convolutional neural network of multiparametric MRI accurately detects axillary lymph node metastasis in breast cancer patients with pre neoadjuvant chemotherapy. Clin Breast Cancer 2022; 22: 170-177. https://doi.org/10.1016/j.clbc.2021.07.002. 10Cong C, Li X, Zhang C, et al. MRI-based breast cancer classification and localization by multiparametric feature extraction and combination using deep learning. J Magn Reason Imaging 2023; Epub ahead of print. Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation" @default.
• W4365444328 created "2023-04-15" @default.
• W4365444328 creator A5001485999 @default.
• W4365444328 creator A5007999565 @default.
• W4365444328 date "2023-04-13" @default.
• W4365444328 modified "2023-10-17" @default.
• W4365444328 title "Editorial for “<scp>MRI</scp>‐Based Breast Cancer Classification and Localization by Multiparametric Feature Extraction and Combination Using Deep Learning”" @default.
• W4365444328 cites W1979226915 @default.
• W4365444328 cites W1980483305 @default.
• W4365444328 cites W2076175584 @default.
• W4365444328 cites W2548436343 @default.
• W4365444328 cites W2900955936 @default.
• W4365444328 cites W2954836639 @default.
• W4365444328 cites W3128646645 @default.
• W4365444328 cites W3179615595 @default.
• W4365444328 cites W4362523590 @default.
• W4365444328 cites W87155539 @default.
• W4365444328 doi "https://doi.org/10.1002/jmri.28733" @default.
• W4365444328 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37052873" @default.
• W4365444328 hasPublicationYear "2023" @default.
• W4365444328 type Work @default.
• W4365444328 citedByCount "0" @default.
• W4365444328 crossrefType "journal-article" @default.
• W4365444328 hasAuthorship W4365444328A5001485999 @default.
• W4365444328 hasAuthorship W4365444328A5007999565 @default.
• W4365444328 hasConcept C161191863 @default.
• W4365444328 hasConcept C2993838110 @default.
• W4365444328 hasConcept C41008148 @default.
• W4365444328 hasConcept C509550671 @default.
• W4365444328 hasConcept C71924100 @default.
• W4365444328 hasConceptScore W4365444328C161191863 @default.
• W4365444328 hasConceptScore W4365444328C2993838110 @default.
• W4365444328 hasConceptScore W4365444328C41008148 @default.
• W4365444328 hasConceptScore W4365444328C509550671 @default.
• W4365444328 hasConceptScore W4365444328C71924100 @default.
• W4365444328 hasLocation W43654443281 @default.
• W4365444328 hasLocation W43654443282 @default.
• W4365444328 hasOpenAccess W4365444328 @default.
• W4365444328 hasPrimaryLocation W43654443281 @default.
• W4365444328 hasRelatedWork W1995515455 @default.
• W4365444328 hasRelatedWork W2039318446 @default.
• W4365444328 hasRelatedWork W2080531066 @default.
• W4365444328 hasRelatedWork W2148965644 @default.
• W4365444328 hasRelatedWork W2318742798 @default.
• W4365444328 hasRelatedWork W2748952813 @default.
• W4365444328 hasRelatedWork W2899084033 @default.
• W4365444328 hasRelatedWork W3031052312 @default.
• W4365444328 hasRelatedWork W3032375762 @default.
• W4365444328 hasRelatedWork W3108674512 @default.
• W4365444328 isParatext "false" @default.
• W4365444328 isRetracted "false" @default.
• W4365444328 workType "article" @default.