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Preoperative prediction of postsurgical outcomes in mass-forming intrahepatic cholangiocarcinoma based on clinical, radiologic, and radiomics features

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dc.contributor.author박미숙-
dc.contributor.author이형진-
dc.contributor.author조은석-
dc.date.accessioned2021-12-28T17:22:12Z-
dc.date.available2021-12-28T17:22:12Z-
dc.date.issued2021-11-
dc.identifier.issn0938-7994-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/187070-
dc.description.abstractObjectives: Current prognostic systems for intrahepatic cholangiocarcinoma (IHCC) rely on surgical pathology data and are not applicable to a preoperative setting. We aimed to develop and validate preoperative models to predict postsurgical outcomes in mass-forming IHCC patients based on clinical, radiologic, and radiomics features. Methods: This multicenter retrospective cohort study included patients who underwent curative-intent resection for mass-forming IHCC. In the development cohort (single institution data), three preoperative multivariable Cox models for predicting recurrence-free survival (RFS) were constructed, including the clinical-radiologic, radiomics, and clinical-radiologic-radiomics (CRR) models based on clinical and CT findings, CT-radiomics features, and a combination of both, respectively. Model performance was evaluated in the test cohort (data from five institutions) using Harrell's C-index and compared with postoperative prognostic systems. Results: A total of 345 patients (233, development cohort; 112, test cohort) were evaluated. The clinical-radiologic model included five independent CT predictors (infiltrative contour, multiplicity, periductal infiltration, extrahepatic organ invasion, and suspicious metastatic lymph node) and showed similar performance in predicting RFS to the radiomics model (C-index, 0.65 vs. 0.68; p = 0.43 in the test cohort). The CRR model showed significantly improved performance (C-index, 0.71; p = 0.01) than the clinical-radiologic model and demonstrated similar performance to the postoperative prognostic systems in predicting RFS (C-index, 0.71-0.73 vs. 0.70-0.73; p ≥ 0.40) and overall survival (C-index, 0.68-0.71 vs. 0.64-0.74; p ≥ 0.27) in the test cohort. Conclusions: A model integrating clinical, CT, and radiomics information may be useful for the preoperative assessment of postsurgical outcomes in patients with mass-forming IHCC. Key points: • The radiomics analysis had incremental value in predicting recurrence-free survival of patients with intrahepatic mass-forming cholangiocarcinoma. • The clinical-radiologic-radiomics model demonstrated similar performance to the postoperatively available prognostic systems (including 8th AJCC system) in predicting recurrence-free survival and overall survival. • The clinical-radiologic-radiomics model may be useful for the preoperative assessment of postsurgical outcomes in patients with mass-forming intrahepatic cholangiocarcinoma.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherSpringer International-
dc.relation.isPartOfEUROPEAN RADIOLOGY-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHBile Duct Neoplasms* / diagnostic imaging-
dc.subject.MESHBile Duct Neoplasms* / surgery-
dc.subject.MESHBile Ducts, Intrahepatic-
dc.subject.MESHCholangiocarcinoma* / diagnostic imaging-
dc.subject.MESHCholangiocarcinoma* / surgery-
dc.subject.MESHHumans-
dc.subject.MESHPrognosis-
dc.subject.MESHRetrospective Studies-
dc.subject.MESHTomography, X-Ray Computed-
dc.titlePreoperative prediction of postsurgical outcomes in mass-forming intrahepatic cholangiocarcinoma based on clinical, radiologic, and radiomics features-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorHyo Jung Park-
dc.contributor.googleauthorBumwoo Park-
dc.contributor.googleauthorSeo Young Park-
dc.contributor.googleauthorSang Hyun Choi-
dc.contributor.googleauthorHyungjin Rhee-
dc.contributor.googleauthorJi Hoon Park-
dc.contributor.googleauthorEun-Suk Cho-
dc.contributor.googleauthorSuk-Keu Yeom-
dc.contributor.googleauthorSumi Park-
dc.contributor.googleauthorMi-Suk Park-
dc.contributor.googleauthorSeung Soo Lee-
dc.identifier.doi10.1007/s00330-021-07926-6-
dc.contributor.localIdA01463-
dc.contributor.localIdA05171-
dc.contributor.localIdA03881-
dc.relation.journalcodeJ00851-
dc.identifier.eissn1432-1084-
dc.identifier.pmid33890153-
dc.identifier.urlhttps://link.springer.com/article/10.1007%2Fs00330-021-07926-6-
dc.subject.keywordCholangiocarcinoma-
dc.subject.keywordImage processing, computer-assisted-
dc.subject.keywordMultidetector computed tomography-
dc.subject.keywordPrecision medicine-
dc.subject.keywordPrognosis-
dc.contributor.alternativeNamePark, Mi Sook-
dc.contributor.affiliatedAuthor박미숙-
dc.contributor.affiliatedAuthor이형진-
dc.contributor.affiliatedAuthor조은석-
dc.citation.volume31-
dc.citation.number11-
dc.citation.startPage8638-
dc.citation.endPage8648-
dc.identifier.bibliographicCitationEUROPEAN RADIOLOGY, Vol.31(11) : 8638-8648, 2021-11-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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