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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1374373
This article is part of the Research Topic Quantitative Imaging: Revolutionizing Cancer Management with biological sensitivity, specificity, and AI integration View all 10 articles

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

Provisionally accepted
Yingying Liang Yingying Liang Hongzhen Wu Hongzhen Wu Xinhua Wei Xinhua Wei *
  • Guangzhou First People's Hospital, Guangzhou, Guangdong Province, China

The final, formatted version of the article will be published soon.

    Purpose: To establish and validate a CT-based nomogram for accurately detecting HCC in patients at high risk for the disease. Methods: 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. Results: 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 0.971 (95% CI: 0.949~1), respectively. The nomogram (AUC: 0.929) outperformed the clinical (AUC: 0.878) and the radiological models (AUC: 0.849). Conclusions: 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.

    Keywords: Hepatocellular Carcinoma, diagnosis, nomogram, CT, Model

    Received: 22 Jan 2024; Accepted: 18 Jul 2024.

    Copyright: © 2024 Liang, Wu and Wei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Xinhua Wei, Guangzhou First People's Hospital, Guangzhou, 510180, Guangdong Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.