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Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastases.

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Title: Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastases.
Authors: Peoples JJ; School of Computing, Queen's University, Kingston, ON, Canada., Hamghalam M; School of Computing, Queen's University, Kingston, ON, Canada.; Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran., James I; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Wasim M; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Gangai N; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Kang HC; Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA., Rong XJ; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA., Chun YS; Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA., Do RKG; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Simpson AL; School of Computing, Queen's University, Kingston, ON, Canada.; Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada.
Source: The journal of machine learning for biomedical imaging [J Mach Learn Biomed Imaging] 2025; Vol. 2 (UNSURE2023), pp. 2326-2357. Date of Electronic Publication: 2025 Jan 15.
Publication Type: Journal Article
Journal Info: Publisher: MELBA Country of Publication: United States NLM ID: 9918400085306676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2766-905X (Electronic) Linking ISSN: 2766905X NLM ISO Abbreviation: J Mach Learn Biomed Imaging Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
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  Data: <searchLink fieldCode="AU" term="%22Peoples+JJ%22">Peoples JJ</searchLink>; School of Computing, Queen's University, Kingston, ON, Canada.<br /><searchLink fieldCode="AU" term="%22Hamghalam+M%22">Hamghalam M</searchLink>; School of Computing, Queen's University, Kingston, ON, Canada.; Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.<br /><searchLink fieldCode="AU" term="%22James+I%22">James I</searchLink>; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.<br /><searchLink fieldCode="AU" term="%22Wasim+M%22">Wasim M</searchLink>; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.<br /><searchLink fieldCode="AU" term="%22Gangai+N%22">Gangai N</searchLink>; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.<br /><searchLink fieldCode="AU" term="%22Kang+HC%22">Kang HC</searchLink>; Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.<br /><searchLink fieldCode="AU" term="%22Rong+XJ%22">Rong XJ</searchLink>; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.<br /><searchLink fieldCode="AU" term="%22Chun+YS%22">Chun YS</searchLink>; Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.<br /><searchLink fieldCode="AU" term="%22Do+RKG%22">Do RKG</searchLink>; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.<br /><searchLink fieldCode="AU" term="%22Simpson+AL%22">Simpson AL</searchLink>; School of Computing, Queen's University, Kingston, ON, Canada.; Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22MELBA%22">MELBA </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>9918400085306676 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2766-905X (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%222766905X%22">2766905X </searchLink><i>NLM ISO Abbreviation: </i>J Mach Learn Biomed Imaging <i>Subsets: </i>PubMed not MEDLINE
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        Value: 10.59275/j.melba.2024-24gc
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              Text: 2025
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