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A novel and validated prognostic nomogram based on liver fibrosis and tumor burden for patients with hepatocellular carcinoma after curative resection
Author(s) -
Huang JinLong,
Fu YiPeng,
Jing ChuYu,
Yi Yong,
Sun Jian,
Gan Wei,
Lu ZhuFeng,
Zhou Jian,
Fan Jia,
Qiu ShuangJian
Publication year - 2018
Publication title -
journal of surgical oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.201
H-Index - 111
eISSN - 1096-9098
pISSN - 0022-4790
DOI - 10.1002/jso.24895
Subject(s) - nomogram , medicine , hepatocellular carcinoma , multivariate analysis , oncology , fibrosis , stage (stratigraphy) , concordance , urology , biology , paleontology
Background and Objectives Most conventional staging systems were formulated concerning the tumor burden rather than the severity of liver fibrosis, which plays a central role in tumor promotion. The aim of this study was to formulate a prognostic nomogram comprehensively considering these two aspects for HCC after hepatectomy. Methods The prognostic significances of the four indicators namely laminin, hyaluronic acid, human procollagen type‐III, and collagen type‐IV that reflect liver fibrosis were explored in two independent cohorts. A nomogram was established based on the results of multivariate analysis. The predictive accuracy of the nomogram was measured by concordance index (C‐index) and calibration. The decision curve analysis (DCA) was used to evaluate the clinical benefit of the nomogram. Results Preoperative serum laminin level is an independent prognostic factor for overall survival in HCC patients after resection. The C‐indices of the nomogram in the training and validation cohorts were 0.779 and 0.719, respectively. The calibration showed optimal agreement between the prediction by nomogram and actual observation. Moreover, the C‐indices and DCA revealed that the nomogram provided better clinical benefit compared with the BCLC stage, CLIP score, and AJCC 7th edition. Conclusions The prognostic nomogram constructed on laminin represents a superior predictive model.
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