Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

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

Full metadata record
DC Field Value Language
dc.contributor.authorPark, Hyo Jung-
dc.contributor.authorPark, Bumwoo-
dc.contributor.authorPark, Seo Young-
dc.contributor.authorChoi, Sang Hyun-
dc.contributor.authorRhee, Hyungjin-
dc.contributor.authorPark, Ji Hoon-
dc.contributor.authorCho, Eun-Suk-
dc.contributor.authorYeom, Suk-Keu-
dc.contributor.authorPark, Sumi-
dc.contributor.authorPark, Mi-Suk-
dc.contributor.authorLee, Seung Soo-
dc.date.accessioned2022-02-15T06:42:31Z-
dc.date.available2022-02-15T06:42:31Z-
dc.date.created2022-02-09-
dc.date.issued2021-11-
dc.identifier.issn0938-7994-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/135833-
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.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherSPRINGER-
dc.titlePreoperative prediction of postsurgical outcomes in mass-forming intrahepatic cholangiocarcinoma based on clinical, radiologic, and radiomics features-
dc.typeArticle-
dc.contributor.affiliatedAuthorYeom, Suk-Keu-
dc.identifier.doi10.1007/s00330-021-07926-6-
dc.identifier.scopusid2-s2.0-85105020031-
dc.identifier.wosid000642387300002-
dc.identifier.bibliographicCitationEUROPEAN RADIOLOGY, v.31, no.11, pp.8638 - 8648-
dc.relation.isPartOfEUROPEAN RADIOLOGY-
dc.citation.titleEUROPEAN RADIOLOGY-
dc.citation.volume31-
dc.citation.number11-
dc.citation.startPage8638-
dc.citation.endPage8648-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaRadiology, Nuclear Medicine & Medical Imaging-
dc.relation.journalWebOfScienceCategoryRadiology, Nuclear Medicine & Medical Imaging-
dc.subject.keywordAuthorCholangiocarcinoma-
dc.subject.keywordAuthorImage processing-
dc.subject.keywordAuthorMultidetector computed tomography-
dc.subject.keywordAuthorPrecision medicine-
dc.subject.keywordAuthorPrognosis-
dc.subject.keywordAuthorcomputer-assisted-
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Medicine > Department of Medical Science > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Yeom, Suk Keu photo

Yeom, Suk Keu
College of Medicine (Department of Medical Science)
Read more

Altmetrics

Total Views & Downloads

BROWSE