Development and validation of a CT-based nomogram for accurate hepatocellular carcinoma detection in high risk patients

TL;DR

This study created a CT-based tool to accurately detect liver cancer in high-risk patients using clinical and imaging features.

Contribution

A novel CT-based nomogram combining clinical and radiological features for HCC detection in high-risk patients.

Findings

The nomogram achieved high accuracy with AUCs of 0.961 in training and 0.979 in validation cohorts.

Gender, AFP levels, and specific CT features were identified as independent predictors of HCC.

The nomogram outperformed separate clinical and radiological models in both cohorts.

Abstract

To establish and validate a CT-based nomogram for accurately detecting HCC in patients at high risk for the disease.

A total of 223 patients were divided into training (n=161) and validation (n=62) cohorts between January of 2017 and May of 2022. Logistic analysis was performed, and clinical model and radiological model were developed separately. Finally, a nomogram was established based on clinical and radiological features. All models were evaluated using the area under the curve (AUC). DeLong’s test was used to evaluate the differences among these models.

In the multivariate analysis, gender (p = 0.014), increased Alpha-fetoprotein (AFP) (p = 0.017), non-rim arterial phase hyperenhancement (APHE) (p = 0.011), washout (p = 0.011), and enhancing capsule (p = 0.001) were the independent differential predictors of HCC. A nomogram was formed with well-fitted calibration curves based on these five factors. The area under the curve (AUC) of the nomogram in the training and validation cohorts was 0.961(95%CI: 0.935~0.986) and 0.979 (95% CI: 0.949~1), respectively. The nomogram outperformed the clinical and the radiological models in training and validation cohorts.

The nomogram incorporating clinical and CT features can be a simple and reliable tool for detecting HCC and achieving risk stratification in patients at high risk for HCC.

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