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Ultrasound-based deep learning radiomics for enhanced axillary lymph node metastasis assessment: a multicenter study.

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Title: Ultrasound-based deep learning radiomics for enhanced axillary lymph node metastasis assessment: a multicenter study.
Authors: Zhang D; Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China., Zhou W; Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China., Lu WW; Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China., Qin XC; Department of Medical Ultrasound, Chengdu Second People's Hospital, Chengdu, China., Zhang XY; Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China., Luo YH; Department of Ultrasound, The Third Affiliated Hospital of Anhui Medical University, Hefei First People's Hospital, Hefei, China., Wu J; Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Hefei, China., Wang JL; Department of Ultrasound, WuHu Hospital, East China Normal University (The Second People's Hospital, WuHu), Wuhu, China., Zhao JJ; Department of Medical Ultrasound, Fuyang Cancer Hospital, Fuyang, China., Zhang CX; Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Source: The oncologist [Oncologist] 2025 May 08; Vol. 30 (5).
Publication Type: Journal Article; Multicenter Study
Journal Info: Publisher: Oxford University Press Country of Publication: England NLM ID: 9607837 Publication Model: Print Cited Medium: Internet ISSN: 1549-490X (Electronic) Linking ISSN: 10837159 NLM ISO Abbreviation: Oncologist Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: Ultrasound-based deep learning radiomics for enhanced axillary lymph node metastasis assessment: a multicenter study.
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  Data: <searchLink fieldCode="AU" term="%22Zhang+D%22">Zhang D</searchLink>; Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China.<br /><searchLink fieldCode="AU" term="%22Zhou+W%22">Zhou W</searchLink>; Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China.<br /><searchLink fieldCode="AU" term="%22Lu+WW%22">Lu WW</searchLink>; Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China.<br /><searchLink fieldCode="AU" term="%22Qin+XC%22">Qin XC</searchLink>; Department of Medical Ultrasound, Chengdu Second People's Hospital, Chengdu, China.<br /><searchLink fieldCode="AU" term="%22Zhang+XY%22">Zhang XY</searchLink>; Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.<br /><searchLink fieldCode="AU" term="%22Luo+YH%22">Luo YH</searchLink>; Department of Ultrasound, The Third Affiliated Hospital of Anhui Medical University, Hefei First People's Hospital, Hefei, China.<br /><searchLink fieldCode="AU" term="%22Wu+J%22">Wu J</searchLink>; Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.<br /><searchLink fieldCode="AU" term="%22Wang+JL%22">Wang JL</searchLink>; Department of Ultrasound, WuHu Hospital, East China Normal University (The Second People's Hospital, WuHu), Wuhu, China.<br /><searchLink fieldCode="AU" term="%22Zhao+JJ%22">Zhao JJ</searchLink>; Department of Medical Ultrasound, Fuyang Cancer Hospital, Fuyang, China.<br /><searchLink fieldCode="AU" term="%22Zhang+CX%22">Zhang CX</searchLink>; Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Oxford+University+Press%22">Oxford University Press </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>9607837 <i>Publication Model: </i>Print <i>Cited Medium: </i>Internet <i>ISSN: </i>1549-490X (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2210837159%22">10837159 </searchLink><i>NLM ISO Abbreviation: </i>Oncologist <i>Subsets: </i>MEDLINE
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        Value: 10.1093/oncolo/oyaf090
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        Text: English
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      – TitleFull: Ultrasound-based deep learning radiomics for enhanced axillary lymph node metastasis assessment: a multicenter study.
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          Dates:
            – D: 08
              M: 05
              Text: 2025 May 08
              Type: published
              Y: 2025
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              Value: 30
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