基于评分的女性隐性乙肝表面抗原携带者肝细胞癌监测预测模型:中国多中心队列研究

IF 9.4 Q1 ONCOLOGY
Mengdi Cao , Maomao Cao , Changfa Xia , Fan Yang , Xinxin Yan , Siyi He , Shaoli Zhang , Yi Teng , Qianru Li , Nuopei Tan , Jiachen Wang , Chunfeng Qu , Wanqing Chen
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引用次数: 0

摘要

现有的肝细胞癌(HCC)预测模型在应用于女性人群时缺乏可转移性和通用性,导致其性能下降,并且在女性中准确进行HCC风险分层的工具不足。本研究旨在建立并验证一种基于评分的预测模型,用于女性乙型肝炎表面抗原(HBsAg)携带者HCC的早期检测。方法从中国从事肝癌筛查的多中心前瞻性队列中招募参与者,包括7个高危农村地区和1个额外的高危农村地区。本研究纳入7080名女性作为衍生队列,2069名女性作为验证队列,所有参与者年龄为35-70岁,HBsAg阳性。进行了实验室检测和流行病学调查。通过LASSO回归分析确定关键预测变量,并基于Cox比例风险模型建立评分预测模型。评估了模型的性能,包括判别和校准,并比较了现有的预测模型和筛选策略。结果中位随访时间分别为3.69年和5.42年,在衍生组和验证组中分别发现147例和45例HCC病例。纳入年龄、α-胎蛋白(AFP)、白蛋白、丙氨酸转氨酶和血小板5个自变量的女性HCC (HCCF)模型表现出色,受者工作特征曲线下面积(AUC)为0.82 (95% CI: 0.78-0.86)。包含肝硬化的hccf增强模型的AUC为0.85 (95% CI: 0.81-0.89)。两种模型均表现出优于现有模型的预测性能,在验证队列中具有较强的预测准确性:auc分别为0.83 (95% CI: 0.77-0.89)和0.88 (95% CI: 0.83 - 0.92)。HCCF模型在得分阈值为7时获得最大的约登指数,识别出32.80%的高危个体。当与超声检查(US)相结合时,该模型检测出37例额外病例,与传统的AFP + US策略相比,显着提高了筛查的灵敏度和准确性。结论建立的HCCF模型对hbsag阳性女性HCC预测效果较好,可显著提高筛查效率,为HCC的监测提供有效工具,最终有助于优化HCC的预防和管理策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Score-based prediction model for female hepatocellular carcinoma surveillance in asymptotic HBsAg carriers: a multicenter cohort study in China

Background

Existing hepatocellular carcinoma (HCC) prediction models lack transferability and generalizability when applied to female populations, resulting in diminished performance and inadequate tools for accurate HCC risk stratification among females. This study aims to develop and validate a score-based prediction model for early detection of HCC in female hepatitis B surface antigen (HBsAg) carriers.

Methods

Participants were recruited from a multicenter prospective cohort engaged in liver cancer screening across China including seven high-risk rural areas and one additional high-risk rural area. The study involved 7080 females as the derivation cohort and 2069 as the validation cohort, with all participants aged 35–70 years and HBsAg positive. Laboratory tests and epidemiological surveys were conducted. Key predictor variables were identified through LASSO regression analysis, and score-based prediction models were developed based on Cox proportional hazards model. Model performance including discrimination and calibration was evaluated, and compared to existing prediction models and screening strategies.

Results

After a median follow-up of 3.69 and 5.42 years, 147 and 45 HCC cases were identified in the derivation and validation cohorts, respectively. The female HCC (HCCF) model incorporating five independent variables: age, α-fetoprotein (AFP), albumin, alanine aminotransferase, and platelet, showed excellent performance with an area under the receiver operating characteristic curve (AUC) of 0.82 (95 % CI: 0.78–0.86). The HCCF-Enhanced model which included cirrhosis, achieved an AUC of 0.85 (95 % CI: 0.81–0.89). Both models demonstrated superior predictive performance than existing models, with strong predictive accuracy in the validation cohort: AUCs of 0.83 (95 % CI: 0.77–0.89) and 0.88 (95 % CI: 0.83–0.92), respectively. The HCCF model, at a score threshold of 7, achieved the largest Youden’s index and identified 32.80 % of high-risk individuals. When combined with ultrasonography (US), the model detected 37 additional cases, significantly improved screening sensitivity and accuracy compared to the traditional AFP plus US strategy.

Conclusions

The developed HCCF models with good performance for HCC prediction in HBsAg-positive females significantly improve screening efficiency and provide an effective tool for surveillance, ultimately helping to optimize prevention and management strategies for HCC.
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