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Novel CT based clinical nomogram comparable to radiomics model for identification of occult peritoneal metastasis in advanced gastric cancer

  • Author Footnotes
    1 These co-first authors contributed equally to this manuscript.
    Lili Wang
    Footnotes
    1 These co-first authors contributed equally to this manuscript.
    Affiliations
    Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China

    Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors (Fujian Medical University), China

    Clinical Research Center for Radiology and Radiotherapy of Fujian Province (Digestive, Hematological and Breast Malignancies), China
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  • Author Footnotes
    1 These co-first authors contributed equally to this manuscript.
    Peng Lv
    Footnotes
    1 These co-first authors contributed equally to this manuscript.
    Affiliations
    Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
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  • Author Footnotes
    1 These co-first authors contributed equally to this manuscript.
    Zhen Xue
    Footnotes
    1 These co-first authors contributed equally to this manuscript.
    Affiliations
    Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
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  • Author Footnotes
    1 These co-first authors contributed equally to this manuscript.
    Lihong Chen
    Footnotes
    1 These co-first authors contributed equally to this manuscript.
    Affiliations
    Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
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  • Bin Zheng
    Affiliations
    School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA
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  • Guifang Lin
    Affiliations
    Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
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  • Weiwen Lin
    Affiliations
    Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
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  • Jingming Chen
    Affiliations
    Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
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  • Jiangao Xie
    Affiliations
    Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
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  • Qing Duan
    Correspondence
    Corresponding author. Fujian Union Hospital, Fujian, 350001, China.
    Affiliations
    Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
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  • Jun Lu
    Correspondence
    Corresponding author. Fujian Union Hospital, Fujian, 350001, China.
    Affiliations
    Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
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  • Author Footnotes
    1 These co-first authors contributed equally to this manuscript.

      Abstract

      Background

      Occult peritoneal metastasis (OPM) in advanced gastric cancer (AGC) patients remains a major diagnostic challenge. The aim of this study was to develop novel predictive models for identification of OPM in AGCs.

      Method

      A total of 810 patients with primary AGCs from two hospitals were retrospectively selected and divided into training (n = 393), internal validation (n = 215) and external validation cohorts (n = 202). CT based machine learning models were built and tested to predict the OPM status in AGCs., which are 1) Radiomic signatures: using venous CT imaging features, 2) Clinical models: integrating tumor location, differentiation and extent of serosal exposure, and 3) Radiomics models: combining of radiomic signature, tumor location and tumor differentiation.

      Result

      Total incidence of OPM was 8.27% (67/810). Clinical models yielded comparable classification accuracy with the corresponding radiomics models with similar AUCs (0.902–0.969 vs. 0.896–0.975) while the radiomic signatures showed relatively low AUCs of 0.863–0.976. In the case where the specificity is higher than 90%, the overall sensitivity of clinical model and radiomics model for OPM positive cases was 76.1% (51/67) and 82.1% (55/67). A nomogram based on the logistic clinical model was drawn to facilitate the usage and verification of the clinical model.

      Conclusion

      Both the novel CT based clinical nomogram and radiomics model provide promising method to yield high accuracy in identification of OPM in AGC patients.

      Keywords

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