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Objective <p>A risk-stratification strategy can improve the effectiveness of intensive hepatocellular carcinoma (HCC) surveillance with an alternative modality. However, such strategies and prediction models incorporating ultrasound features remain undeveloped for a suitable population. Therefore, we developed and validated an HCC risk prediction model using ultrasound features in patients with viral hepatitis who are potentially eligible for intensive surveillance.</p> Materials and methods <p>This retrospective multicenter study included 17,557 HCC-naïve patients with viral hepatitis who underwent US surveillance between 2005 and 2015. In the development dataset (<i>n</i> = 7918), clinical and US features were analyzed to establish the prediction model. Factors associated with HCC were identified by multivariable Cox regression analysis. Model performance was compared to existing prediction models in internal (<i>n</i> = 3393) and external (<i>n</i> = 6246) validation datasets.</p> Results <p>The SELECT model included age, male sex, diabetes, serum albumin and alanine aminotransferase levels, platelet count, and ultrasound-detected cirrhosis and multiple cirrhotic nodules. In the external validation dataset, the low-, intermediate-, and high-risk groups had 0.8%, 6.9%, and 16.1% 5-year cumulative HCC incidence, respectively. In those with an estimated annual HCC incidence ≥ 2.5% (SELECT score &gt; −2.04), the 5-year cumulative HCC incidence was 15.5%. The SELECT model had better discrimination capability than aMAP, THRI, ADRESS-HCC, the Velazquez score, and mPAGE-B (Uno C-index, 0.791 vs. 0.740, 0.668, 0.658, 0.650, and 0.740, respectively; all adjusted <i>p</i> &lt; 0.001).</p> Conclusion <p>The SELECT model better estimated HCC risk than other models in viral hepatitis patients. Intensive surveillance with alternative modalities may be considered based on this model.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Ultrasound features have not been incorporated into hepatocellular carcinoma risk prediction models, despite ultrasound being the primary surveillance modality.</i></p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>The SELECT model, incorporating demographics, laboratory findings and ultrasound features (cirrhosis and multiple cirrhotic nodules), demonstrated superior performance compared to existing models.</i></p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>The SELECT model effectively identifies viral hepatitis patients with ≥ 2.5% annual HCC risk who would benefit from intensive surveillance using alternative imaging modalities, optimizing resource allocation and achieving higher diagnostic yield.</i></p> Graphical Abstract <p></p>

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Hepatocellular carcinoma risk stratification to identify patients suitable for intensive surveillance in viral hepatitis: the SELECT score

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月25日

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  • Yeun-Yoon Kim,
  • Won Chang,
  • Jeong Min Lee,
  • Se Woo Kim,
  • Jae Seok Bae,
  • Jeongin Yoo,
  • Sun Kyung Jeon,
  • HeeSoo Kim,
  • Young Hoon Kim,
  • Jin-Young Choi,
  • Eun Ju Cho,
  • Yun Bin Lee,
  • Sook-Hyang Jeong,
  • Do Young Kim,
  • Yunhee Choi,
  • Jeong Hee Yoon

摘要

Objective

A risk-stratification strategy can improve the effectiveness of intensive hepatocellular carcinoma (HCC) surveillance with an alternative modality. However, such strategies and prediction models incorporating ultrasound features remain undeveloped for a suitable population. Therefore, we developed and validated an HCC risk prediction model using ultrasound features in patients with viral hepatitis who are potentially eligible for intensive surveillance.

Materials and methods

This retrospective multicenter study included 17,557 HCC-naïve patients with viral hepatitis who underwent US surveillance between 2005 and 2015. In the development dataset (n = 7918), clinical and US features were analyzed to establish the prediction model. Factors associated with HCC were identified by multivariable Cox regression analysis. Model performance was compared to existing prediction models in internal (n = 3393) and external (n = 6246) validation datasets.

Results

The SELECT model included age, male sex, diabetes, serum albumin and alanine aminotransferase levels, platelet count, and ultrasound-detected cirrhosis and multiple cirrhotic nodules. In the external validation dataset, the low-, intermediate-, and high-risk groups had 0.8%, 6.9%, and 16.1% 5-year cumulative HCC incidence, respectively. In those with an estimated annual HCC incidence ≥ 2.5% (SELECT score > −2.04), the 5-year cumulative HCC incidence was 15.5%. The SELECT model had better discrimination capability than aMAP, THRI, ADRESS-HCC, the Velazquez score, and mPAGE-B (Uno C-index, 0.791 vs. 0.740, 0.668, 0.658, 0.650, and 0.740, respectively; all adjusted p < 0.001).

Conclusion

The SELECT model better estimated HCC risk than other models in viral hepatitis patients. Intensive surveillance with alternative modalities may be considered based on this model.

Key Points

Question Ultrasound features have not been incorporated into hepatocellular carcinoma risk prediction models, despite ultrasound being the primary surveillance modality.

Findings The SELECT model, incorporating demographics, laboratory findings and ultrasound features (cirrhosis and multiple cirrhotic nodules), demonstrated superior performance compared to existing models.

Clinical relevance The SELECT model effectively identifies viral hepatitis patients with ≥ 2.5% annual HCC risk who would benefit from intensive surveillance using alternative imaging modalities, optimizing resource allocation and achieving higher diagnostic yield.

Graphical Abstract