Deep learning PET/CT-based radiomics integrates clinical data: A feasibility study to distinguish between tuberculosis nodules and lung cancer.

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作者： Xiaolei,Zhang ^[1] ; Xianling,Dong ^[2] ; M Iqbal Bin,Saripan ^[2] ; Dongyang,Du ^[3] ; Yanjun,Wu ^[1] ; Zhongxiao,Wang ^[4] ; Zhendong,Cao ^[2] ; Dong,Wen ^[2] ; Yanli,Liu ^[5] ; Mohammad Hamiruce,Marhaban ^[6]

作者单位： Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia. ^[1] Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei, China. ^[2] Hebei International Research Center of Medical Engineering and Hebei Provincial Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde, Hebei, China. ^[3] School of Biomedical Engineering and Guangdong Province Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China. ^[4] Department of Radiology, the Affiliated Hospital of Chengde Medical University, Chengde, China. ^[5] Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China. ^[6]

关键词 clinical data deep learning lung cancer radiomics tuberculosis nodules

主题词人类(Humans);可行性研究(Feasibility Studies);结果可重复性(Reproducibility of Results);肺肿瘤(Lung Neoplasms);结核(Tuberculosis)

DOI 10.1111/1759-7714.14924

PMID 37183577

发布时间 2023-07-05

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Thoracic cancer

2023年14卷19期

1802-1811页

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Deep learning PET/CT-based radiomics integrates clinical data: A feasibility study to distinguish between tuberculosis nodules and lung cancer.

Thoracic cancer

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Thoracic cancer

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Deep learning PET/CT-based radiomics integrates clinical data: A feasibility study to distinguish between tuberculosis nodules and lung cancer.

Thoracic cancer

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Thoracic cancer

相关期刊

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