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ORIGINAL RESEARCH

A Novel Nomogram for the Preoperative Prediction of Edmondson-Steiner Grade III-IV in Hepatocellular Carcinoma Patients

ORCID Icon, ORCID Icon, ORCID Icon, , , , , , & show all
Pages 1399-1409 | Received 12 May 2023, Accepted 17 Aug 2023, Published online: 23 Aug 2023
 

Abstract

Background

Edmondson-Steiner (E-S) grade is a pathological indicator of the degree of hepatocellular carcinoma (HCC) differentiation, and E-S grade III–IV is a poor prognostic factor for HCC patients. Predicting poorly differentiated HCC has essential significance for clinical decision-making. Although some studies have developed predictive models based on magnetic resonance imaging (MRI) and radiomics, radiomic features that require specific software for analysis are impractical for clinical work. This study aims to develop a novel and user-friendly nomogram model to predict E-S grade III–IV.

Patients and Methods

Medical data on patients meeting the inclusion criteria were obtained from the Nanjing Drum Tower Hospital HCC database (January 2020 to December 2022). Univariate analysis was used to screen for risk factors associated with E-S grade III–IV. A novel nomogram was established based on the subsequent multivariate logistic regression analysis. The performance of the established model was evaluated through diagnostic ability, calibration, and clinical benefits.

Results

Overall, 240 HCC patients were included in this study. Among them, 103 were highly differentiated (E-S grade I–II) HCC and 137 were poorly differentiated (E-S grade III–IV) HCC. A nomogram model that integrated alpha-fetoprotein (AFP), des-γ-carboxy prothrombin (DCP), hepatitis B virus surface antigen (HBsAg), hepatitis C virus antibodies (HCVAb), aspartate aminotransferase to lymphocyte ratio index (ALRI), and macrovascular invasion was established. The novel model had a good diagnostic performance with an area under the curve (AUC) value of 0.763. Meanwhile, the model had a diagnostic accuracy of 72.5%, a sensitivity of 78.1%, and a specificity of 65.1%. The calibration curve showed good calibration of the nomogram model (mean absolute error = 0.043), and the decision curve analysis (DCA) demonstrated that the clinical benefit was provided.

Conclusion

Our developed nomogram model could successfully predict E-S grade III–IV in HCC patients, which may be helpful in clinical decision-making.

Data Sharing Statement

The dataset analyzed during the current study is available from corresponding authors upon reasonable request.

Ethics Approval

The institutional review board of The Affiliated Drum Tower Hospital of Nanjing University Medical School approved this retrospective study, and the requirement for written informed consent was waived due to its retrospective study nature. All included patients’ personal information is strictly confidential. This study followed the 1964 Declaration of Helsinki and its later amendments.

Disclosure

The authors declare no conflicts of interest in this work.

Additional information

Funding

This research was funded by the Anhui Provincial Key Research and Development Project (202204295107020032), the National Natural Science Youth Foundation of China (81902415, 82103135, and 82101850), and the Natural Science Youth Foundation of Jiangsu Province (BK20190116).