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dc.contributor.authorXie, C-Y
dc.contributor.authorPang, Chun-Lap
dc.contributor.authorChan, B
dc.contributor.authorYuen-yuen Wong, E
dc.contributor.authorDu, Q
dc.contributor.authorVardanabhuti, V
dc.date.accessioned2021-07-19T10:28:51Z
dc.date.available2021-07-19T10:28:51Z
dc.date.issued2021en
dc.identifier.citationXie C-Y, Pang C-L, Chan B, Wong EY-Y, Dou Q, Vardhanabhuti V. Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods—A Critical Review of Literature. Cancers. 2021 May 19;13(10):2469.en
dc.identifier.pmid34069367en
dc.identifier.doi10.3390/cancers13102469en
dc.identifier.urihttp://hdl.handle.net/10541/624118
dc.description.abstractEsophageal cancer (EC) is of public health significance as one of the leading causes of cancer death worldwide. Accurate staging, treatment planning and prognostication in EC patients are of vital importance. Recent advances in machine learning (ML) techniques demonstrate their potential to provide novel quantitative imaging markers in medical imaging. Radiomics approaches that could quantify medical images into high-dimensional data have been shown to improve the imaging-based classification system in characterizing the heterogeneity of primary tumors and lymph nodes in EC patients. In this review, we aim to provide a comprehensive summary of the evidence of the most recent developments in ML application in imaging pertinent to EC patient care. According to the published results, ML models evaluating treatment response and lymph node metastasis achieve reliable predictions, ranging from acceptable to outstanding in their validation groups. Patients stratified by ML models in different risk groups have a significant or borderline significant difference in survival outcomes. Prospective large multi-center studies are suggested to improve the generalizability of ML techniques with standardized imaging protocols and harmonization between different centers.en
dc.language.isoenen
dc.relation.urlhttps://dx.doi.org/10.3390/cancers13102469en
dc.titleMachine learning and radiomics applications in esophageal cancers using non-invasive imaging methods—a critical review of literature.en
dc.typeOtheren
dc.contributor.departmentDepartment of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.en
dc.identifier.journalCancersen
dc.description.noteen]
refterms.dateFOA2021-07-26T12:40:39Z


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