Academic radiology · 2026
Development and Internal Validation of a Nomogram Model Discriminating Liver Metastases of Melanoma From Other Common Liver Metastases Based on the Combination of Conventional Ultrasonography and Contrast-enhanced Ultrasonography.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Xu Yuanyuan, Wu Hao, Tian Haijing, Tang Hewen, Cong Yue, Yin Shanshan, Yang Wei, Yan Kun, Wu Wei
Аннотация
RATIONALE AND OBJECTIVES: By comparing conventional ultrasonography (US) and contrast-enhanced ultrasonography (CEUS) features of liver metastases of melanoma (LMM) with those of other common liver metastases from non-melanoma malignancies (non-LMM), a differential diagnosis nomogram was developed and internally validated to explore its clinical application value.
MATERIALS AND METHODS: This single-center, retrospective, case-control study enrolled 108 patients with LMM (109 lesions) and 95 patients with non-LMM (109 lesions) at our institution from January 2017 to April 2025. All cases were confirmed by pathological diagnosis. Univariate analysis was performed to compare clinical characteristics and US/CEUS features between groups. Variables with P < 0.05 were entered into a stepwise bidirectional regression model (based on the Akaike information criterion (AIC)) to identify independent risk factors and assess collinearity (using variance inflation factor (VIF)). A nomogram was developed using logistic regression and generalized estimating equations (GEE) and evaluated by the area under the receiver operating characteristic curve (AUC-ROC), bootstrapping (1000 resamples), calibration curves, and decision curve analysis (DCA).
RESULTS: The independent risk factors included in the nomogram model were morphology, posterior acoustic enhancement, number, enhancement pattern and wash-out time. The model demonstrated an AUC of 0.892 (95% CI: 0.849-0.935) and a bias-corrected AUC of 0.873 (95% CI: 0.805-0.933) by bootstrapping. Both calibration curves and DCA demonstrated the nomogram's favorable calibration and clinical utility (Hosmer-Lemeshow test, P > 0.05).
CONCLUSION: The LMM differential diagnosis nomogram based on US/CEUS features complements clinical screening and facilitates precise diagnosis while aiding in subsequent treatment evaluation and follow-up.
MATERIALS AND METHODS: This single-center, retrospective, case-control study enrolled 108 patients with LMM (109 lesions) and 95 patients with non-LMM (109 lesions) at our institution from January 2017 to April 2025. All cases were confirmed by pathological diagnosis. Univariate analysis was performed to compare clinical characteristics and US/CEUS features between groups. Variables with P < 0.05 were entered into a stepwise bidirectional regression model (based on the Akaike information criterion (AIC)) to identify independent risk factors and assess collinearity (using variance inflation factor (VIF)). A nomogram was developed using logistic regression and generalized estimating equations (GEE) and evaluated by the area under the receiver operating characteristic curve (AUC-ROC), bootstrapping (1000 resamples), calibration curves, and decision curve analysis (DCA).
RESULTS: The independent risk factors included in the nomogram model were morphology, posterior acoustic enhancement, number, enhancement pattern and wash-out time. The model demonstrated an AUC of 0.892 (95% CI: 0.849-0.935) and a bias-corrected AUC of 0.873 (95% CI: 0.805-0.933) by bootstrapping. Both calibration curves and DCA demonstrated the nomogram's favorable calibration and clinical utility (Hosmer-Lemeshow test, P > 0.05).
CONCLUSION: The LMM differential diagnosis nomogram based on US/CEUS features complements clinical screening and facilitates precise diagnosis while aiding in subsequent treatment evaluation and follow-up.