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<p>We aimed to develop a convenient nomogram to predict preoperative MVI in patients with hepatocellular carcinoma (HCC). Patients who underwent surgical resection due to HCC from June 2018 to June 2023 at the Third Affiliated Hospital of Sun Yat-sen University were retrospectively reviewed. Univariate and multivariable logistic linear regression analyses were used to investigate potential risk factors for MVI. A nomogram was plotted based on these risk factors. The tumor diameter (≥ 5&#xa0;cm), BCLC stage, PLT (&gt;127.50 × 10<sup>9</sup>/L), AST (&gt;29.50 U/L) and AFP (&gt;10.07 ng/ml) were identified as independent preoperative risk factors for MVI by univariate and multivariable logistic analysis. The nomogram demonstrated decent accuracy in estimating the presence of MVI, with an AUC of 0.69 (95%CI: 0.64–0.73). The calibration curves exhibited a close match between the predicted probabilities and the actual estimates of MVI in the nomogram (<i>p</i> = 0.947). Decision curve analysis (DCA) revealed that the prediction model had a high net benefit if the threshold probability&gt;20%. High platelet counts were strongly associated with the presence of MVI in HCC patients. Our convenient nomogram demonstrated decent accuracy in estimating the presence of MVI and had notable clinical application.</p>

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Development and clinical validation of a novel platelet count-based nomogram for predicting microvascular invasion in HCC

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月18日

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  • Wenjie Zheng,
  • Haoqi Chen,
  • Jianfeng Zhang,
  • Kaiming He,
  • Wenfeng Zhu,
  • Xiaolong Chen,
  • Xijing Yan,
  • Zexin Lin,
  • Yang Yang,
  • Xiaowen Wang,
  • Hua Li,
  • Shuguang Zhu

摘要

We aimed to develop a convenient nomogram to predict preoperative MVI in patients with hepatocellular carcinoma (HCC). Patients who underwent surgical resection due to HCC from June 2018 to June 2023 at the Third Affiliated Hospital of Sun Yat-sen University were retrospectively reviewed. Univariate and multivariable logistic linear regression analyses were used to investigate potential risk factors for MVI. A nomogram was plotted based on these risk factors. The tumor diameter (≥ 5 cm), BCLC stage, PLT (>127.50 × 109/L), AST (>29.50 U/L) and AFP (>10.07 ng/ml) were identified as independent preoperative risk factors for MVI by univariate and multivariable logistic analysis. The nomogram demonstrated decent accuracy in estimating the presence of MVI, with an AUC of 0.69 (95%CI: 0.64–0.73). The calibration curves exhibited a close match between the predicted probabilities and the actual estimates of MVI in the nomogram (p = 0.947). Decision curve analysis (DCA) revealed that the prediction model had a high net benefit if the threshold probability>20%. High platelet counts were strongly associated with the presence of MVI in HCC patients. Our convenient nomogram demonstrated decent accuracy in estimating the presence of MVI and had notable clinical application.