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A CT-Based Radiomics Model for Evaluating Peritoneal Cancer Index in Peritoneal Metastasis Cases: A Preliminary Study

  • Author Footnotes
    # Qianwen Zhang, Yuan Yuan and Sijie Li have contributed equally to this work.
    Qianwen Zhang
    Footnotes
    # Qianwen Zhang, Yuan Yuan and Sijie Li have contributed equally to this work.
    Affiliations
    Department of Radiology, Changhai Hospital, Shanghai, China
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  • Author Footnotes
    # Qianwen Zhang, Yuan Yuan and Sijie Li have contributed equally to this work.
    Yuan Yuan
    Footnotes
    # Qianwen Zhang, Yuan Yuan and Sijie Li have contributed equally to this work.
    Affiliations
    Department of Radiology, Changhai Hospital, Shanghai, China
    Search for articles by this author
  • Author Footnotes
    # Qianwen Zhang, Yuan Yuan and Sijie Li have contributed equally to this work.
    Sijie Li
    Footnotes
    # Qianwen Zhang, Yuan Yuan and Sijie Li have contributed equally to this work.
    Affiliations
    Department of Radiology, Changhai Hospital, Shanghai, China
    Search for articles by this author
  • Zhihui Li
    Affiliations
    Department of Radiology, Ruijin Hospital Luwan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China
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  • Guodong Jing
    Affiliations
    Department of Radiology, Changhai Hospital, Shanghai, China
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  • Jianping Lu
    Affiliations
    Department of Radiology, Changhai Hospital, Shanghai, China
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  • Chengwei Shao
    Affiliations
    Department of Radiology, Changhai Hospital, Shanghai, China
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  • Author Footnotes
    $ Qiang Hao, Yong Lu and Fu Shen have contributed equally to this work.
    Qiang Hao
    Footnotes
    $ Qiang Hao, Yong Lu and Fu Shen have contributed equally to this work.
    Affiliations
    Department of Radiology, Changhai Hospital, Shanghai, China
    Search for articles by this author
  • Author Footnotes
    $ Qiang Hao, Yong Lu and Fu Shen have contributed equally to this work.
    Yong Lu
    Footnotes
    $ Qiang Hao, Yong Lu and Fu Shen have contributed equally to this work.
    Affiliations
    Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
    Search for articles by this author
  • Author Footnotes
    $ Qiang Hao, Yong Lu and Fu Shen have contributed equally to this work.
    Fu Shen
    Correspondence
    Address correspondence to: Fu Shen, Department of Radiology, Changhai Hospital, 168 Changhai Road, Shanghai 200433, China.
    Footnotes
    $ Qiang Hao, Yong Lu and Fu Shen have contributed equally to this work.
    Affiliations
    Department of Radiology, Changhai Hospital, Shanghai, China
    Search for articles by this author
  • Author Footnotes
    # Qianwen Zhang, Yuan Yuan and Sijie Li have contributed equally to this work.
    $ Qiang Hao, Yong Lu and Fu Shen have contributed equally to this work.
Published:September 28, 2022DOI:https://doi.org/10.1016/j.acra.2022.09.001

      Rationale and Objectives

      The present work aimed to develop and validate a radiomics model for evaluating peritoneal cancer index (PCI) in peritoneal metastasis (PM) cases based on preoperative CT scans.

      Materials and Methods

      Pathologically confirmed pancreatic, colon, rectal, and gastric cancer cases with PM administered exploratory laparotomy in 2 different cohorts were retrospectively analyzed. Surgical PCIs (sPCIs) were confirmed by the surgery team, and CT-PCI scores were assessed by radiologists. Totally 63 and 27 cases in cohort 1 were assigned to the training and test groups, respectively. Then, 73 cases in cohort 2 were enrolled as an external validation set. Radiomics features were derived from the portal venous phase of preoperative abdominopelvic CT scans. Nineteen optimal features related to sPCI were finally selected. Support vector machine (SVM) was adopted for radiomics model generation. The associations of CT-PCI, radiomics PCI and sPCI were analyzed. The performances in distinguishing between low-sPCI (≤ 20) and high-sPCI (> 20) cases were also assessed by receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).

      Results

      Both CT-PCI and radiomics PCI scores had positive associations with sPCI. The radiomics approach had higher agreement for detecting sPCI than CT-PCI. In addition, the radiomics model had enhanced diagnostic performance than CT-PCI (AUCs were 0.894, 0.822 and 0.810 in training, test and validation sets, respectively, vs 0.749, 0.678 and 0.693, respectively). The net reclassification index was 0.266. The usefulness of the proposed model was confirmed by DCA in an external validation set.

      Conclusion

      The present pilot study showed that the radiomics model based on preoperative abdominopelvic CT has increased agreement and diagnostic performance in detecting sPCI than CT-PCI in patients with PM, which could be used to optimize individualized treatment strategies.

      Keywords

      Abbreviations:

      PM (peritoneal metastasis), CRS (cytoreductive surgery), HIPEC (hyperthermic intraperitoneal chemotherapy), PCI (peritoneal cancer index), CT (computed tomography), ROI (region of interest), ROC (receiver operating characteristic), AUC (area under the ROC curve), ML (machine learning), DCA (decision curve analysis), LASSO (least absolute shrinkage and selection operator)
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