Wu Hesen, Chen Xingyi, Feng Xiaoming, He Wei, Divisi Duilio, Efird Jimmy T, Franco José, Guo Xuemin
Center for Clinical Medicine Diagnosis, Meizhou People's Hospital, Meizhou, China.
Guangdong Engineering Technological Research Center of Clinical Molecular Diagnosis and Antibody Drugs, Meizhou, China.
Transl Cancer Res. 2025 Apr 30;14(4):2410-2420. doi: 10.21037/tcr-2025-468. Epub 2025 Apr 27.
The predictive models for malignant lung nodules have been developed, but need further validation and optimization for broader clinical use. This study aimed to compare the diagnostic efficacy of the Mayo model, Peking University People's Hospital (PKUPH) model, and the lung cancer biomarker panel (LCBP) model in distinguishing between benign and malignant pulmonary nodules, providing valuable clinical research data for the early diagnosis of lung cancer.
Clinical and imaging data of patients diagnosed with pulmonary nodules at Meizhou People's Hospital from March 2021 through January 2023 were collected. Data from patients with benign pulmonary nodules during the same period, who served as negative referents, were also gathered. The Mayo model, PKUPH model, and LCBP model were used to clinically validate lung cancer prediction rates. The receiver operating characteristic (ROC) curves and statistical significance comparing the areas under the curve (AUCs) for each model were evaluated.
A total of 428 patients were included: 160 females and 268 males. The noncancer group included 218 cases (50.93%), and the cancer group included 210 cases (49.07%). The AUC values of the three models were as follows: Mayo model, 0.783; PKUPH model, 0.726; and LCBP model, 0.759. (I) For the Mayo model, at the maximum Youden index, the concordance rate was 74.3%, the sensitivity 85.71%, the specificity 63.30%, the positive predictive value 69.23%, the negative predictive value 82.14%, the positive likelihood ratio 2.335, and the negative likelihood ratio 0.226. (II) For the PKUPH model, at the maximum Youden index, the concordance rate was 70.3%, the sensitivity 84.29%, the specificity 56.88%, the positive predictive value 65.31%, the negative predictive value 78.98%, the positive likelihood ratio 1.955, and the negative likelihood ratio 0.276. (III) For the LCBP model, at the maximum Youden index, the concordance rate was 75.0%, the sensitivity 72.38%, the specificity 77.52%, the positive predictive value 75.62%, the negative predictive value 74.45%, the positive likelihood ratio 3.220, and the negative likelihood ratio 0.356.
All three predictive models exhibit clinical applicability, with minimal differences in diagnostic efficacy. The LCBP model outperformed both the Mayo and PKUPH models in diagnostic performance, showing greater diagnostic value for the Chinese population. However, there is still room for optimization in each model.
恶性肺结节的预测模型已经建立，但为了更广泛的临床应用，仍需进一步验证和优化。本研究旨在比较梅奥模型、北京大学人民医院（PKUPH）模型和肺癌生物标志物 panel（LCBP）模型在鉴别良性和恶性肺结节方面的诊断效能，为肺癌的早期诊断提供有价值的临床研究数据。
收集了2021年3月至2023年1月在梅州人民医院被诊断为肺结节的患者的临床和影像数据。同时收集了同期作为阴性对照的良性肺结节患者的数据。使用梅奥模型、PKUPH模型和LCBP模型对肺癌预测率进行临床验证。评估了每个模型的受试者操作特征（ROC）曲线以及比较曲线下面积（AUC）的统计学意义。
共纳入428例患者，其中女性160例，男性268例。非癌症组218例（50.93%），癌症组210例（49.07%）。三种模型的AUC值如下：梅奥模型为0.783；PKUPH模型为0.726；LCBP模型为0.759。（I）对于梅奥模型，在最大约登指数时，一致性率为74.3%，灵敏度为85.71%，特异度为63.30%，阳性预测值为69.23%，阴性预测值为82.14%，阳性似然比为2.335，阴性似然比为0.226。（II）对于PKUPH模型，在最大约登指数时，一致性率为70.3%，灵敏度为84.29%，特异度为56.88%，阳性预测值为65.31%，阴性预测值为78.98%，阳性似然比为1.955，阴性似然比为0.276。（III）对于LCBP模型，在最大约登指数时，一致性率为75.0%，灵敏度为72.38%，特异度为77.52%，阳性预测值为75.62%，阴性预测值为74.45%，阳性似然比为3.220，阴性似然比为0.356。
所有三种预测模型均具有临床适用性，诊断效能差异最小。LCBP模型在诊断性能上优于梅奥模型和PKUPH模型，对中国人群显示出更大的诊断价值。然而，每个模型仍有优化空间。
