Abstract
Hepatic alveolar echinococcosis (HAE) and liver cancer had similaritiesin imaging results, clinical characteristics, and so on. And it is difficult forclinicians to distinguish them before operation. The aim of our study was to build adifferential diagnosis nomogram based on platelet (PLT) score model and use internalvalidation to check the model. The predicting model was constructed by theretrospective database that included in 153 patients with HAE (66 cases) or livercancer (87 cases), and all cases was confirmed by clinicopathology and collectedfrom November 2011 to December 2018. Lasso regression analysis model was used toconstruct data dimensionality reduction, elements selection, and building predictionmodel based on the 9 PLT-based scores. A multi-factor regression analysis wasperformed to construct a simplified prediction model, and we added the selectedPLT-based scores and relevant clinicopathologic features into the nomogram.Identification capability, calibration, and clinical serviceability of thesimplified model were evaluated by the Harrell’s concordance index (C-index),calibration plot, receiver operating characteristic curve (ROC), and decision curve.An internal validation was also evaluated by the bootstrap resampling. Thesimplified model, including in 4 selected factors, was significantly associated withdifferential diagnosis of HAE and liver cancer. Predictors of the simplifieddiagnosis nomogram consisted of the API index, the FIB-4 index, fibro-quotent(FibroQ), and fibrosis index constructed by King’s College Hospital (King’s score).The model presented a perfect identification capability, with a high C-index of0.929 (0.919 through internal validation), and good calibration. The area under thecurve (AUC) values of this simplified prediction nomogram was 0.929, and the resultof ROC indicated that this nomogram had a good predictive value. Decision curveanalysis showed that our differential diagnosis nomogram had clinicallyidentification capability. In conclusion, the differential diagnosis nomogram couldbe feasibly performed to verify the preoperative individualized diagnosis of HAE andliver cancer.
Highlights
T Hepatic alveolar echinococcosis (HAE) and liver cancer had similarities in imaging results, clinical characteristics, and so on
A database that included in 153 patients with HAE (66 cases) or liver cancer (87 cases), and all cases was confirmed by clinicopathology and collected from November 2011 to December 2018
The univariate analysis shows that the PLT (P < 0.001), API (P < 0.001), CDS (P < 0.001), FIB-4 (P < 0.001), FibroQ (P < 0.001), GUCI (Goteburg University Cirrhosis Index) (P < 0.001), King’s
Summary
Thaptlehaypetpaeditclieiaactgl-albnilvvaoeessroeticdlcaasrnnceococemrhreoinmgorocadomecIclosCoffsoiLsr aEnd QianchengDu1,6,YanyanWang2,6,ShihaoGuan3,6,ChenliangHu4,6,MengxuanLi4, Ling Zhou[1], Mengzhao Zhang5,Yichong Chen[5], Xuepeng Mei[5], Jian Sun 1,7* &Ying Zhou 4,7*. Of PLT-based score models, 9 factors are reduced to four possible indicators based on 153 patients in the primary cohort (2:1 ratio; Fig. 2A,B), and these factors characterized by nonzero coefficients in the TLASSO regression analysis model These factors included API, FIB-4, FibroQ, and King’s score. API is a model based on age and platelet count When this score reached 5, liver biopsy could be avoided because of its high predictive value between HAE and liver cancer. The AUC of ROC curve after combining King’s score, API, FIB-4, and FibroQ is 0.932, which is significantly higher than that of each independent factor (Fig. 1H) This indicates that the independent risk factor combined with the other three factors has more diagnostic value. The model that incorporates the above possible predictive factors is constructed and showed as the visualization nomogram (Fig. 4)
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