Lin Yu, Wang Qianyi, Feng Minxuan, Lao Jize, Wu Changmeng, Luo Houlong, Ji Ling, Xia Yong
Department of Laboratory Medicine, Peking University Shenzhen Hospital, Shenzhen, Guangdong Province, China.
Department of Laboratory Medicine, JingNing People's Hospital, Pingliang, Gansu Province, China.
PeerJ. 2025 Mar 26;13:e19150. doi: 10.7717/peerj.19150. eCollection 2025.
Identifying alpha-fetal protein (AFP)-negative focal hepatic lesions presents a significant challenge, particularly in China. We sought to develop an economically portable tool for the diagnosis of benign and malignant liver lesions with AFP-negative status, and explore its clinical diagnostic efficiency.
A retrospective study was conducted at Peking University Shenzhen Hospital from January 2017 to February 2023, including a total of 348 inpatients with AFP-negative liver space-occupying lesions. The study used a training set of 252 inpatients from January 2017 to September 2021 to establish a diagnostic model for differentiating benign and malignant AFP-negative liver space-occupying lesions. Additionally, a validation cohort of 96 inpatients from October 2021 to February 2023 was used to confirm the diagnostic performance of the model. From January 2017 to February 2023, patients at JingNing People's Hospital, Gansu Province were assigned to the external cohort ( = 78).
A predictive tool was established by screening age, gender, hepatitis B virus (HBV)/hepatitis C virus (HCV) infected, single lesion, alanine amino transferase (ALT), and lymphocyte-to-monocyte ratio (LMR) using multivariate logistic regression analysis and clinical practice. The area under the curve (AUC) of the model was 0.911 (95% CI [0.873-0.949]) in the training set and 0.882 (95% CI [0.815-0.949]) in the validation cohort. In addition, the model achieved an area under the curve of 0.811 (95% CI [0.687-0.935]) in the external validation cohort.
Our results demonstrated that the predictive tool has the characteristics of good diagnostic efficiency, economy and convenience, which is helpful for the clinical triage and decision-making of AFP-negative liver space-occupying lesions.
识别甲胎蛋白(AFP)阴性的肝脏局灶性病变是一项重大挑战,在中国尤其如此。我们试图开发一种经济便携的工具,用于诊断AFP阴性状态的肝脏良恶性病变,并探索其临床诊断效率。
2017年1月至2023年2月在北京大学深圳医院进行了一项回顾性研究,共纳入348例AFP阴性肝脏占位性病变的住院患者。该研究使用2017年1月至2021年9月的252例住院患者作为训练集,建立区分AFP阴性肝脏占位性病变良恶性的诊断模型。此外,使用2021年10月至2023年2月的96例住院患者作为验证队列,以确认该模型的诊断性能。2017年1月至2023年2月,甘肃省静宁县人民医院的患者被纳入外部队列(n = 78)。
通过多因素逻辑回归分析和临床实践,筛选年龄、性别、乙肝病毒(HBV)/丙肝病毒(HCV)感染情况、单发病变、谷丙转氨酶(ALT)和淋巴细胞与单核细胞比值(LMR),建立了一个预测工具。该模型在训练集中的曲线下面积(AUC)为0.911(95%可信区间[0.873 - 0.949]),在验证队列中为0.882(95%可信区间[0.815 - 0.949])。此外,该模型在外部验证队列中的曲线下面积为0.811(95%可信区间[0.687 - 0.935])。
我们的结果表明,该预测工具具有诊断效率高、经济便捷的特点,有助于AFP阴性肝脏占位性病变的临床分诊和决策。