Diagnostic value of a superb microvascular imaging vascular index-based nomogram for assessing regional lymph nodes in acral melanoma

doi:10.3877/cma.j.issn.1672-6448.2026.04.003

Abstract

Abstract:

Objective

To develop a nomogram model based on a Superb Microvascular Imaging (SMI) vascular index for the preoperative diagnosis of regional lymph node metastasis in patients with acral melanoma (AM).

Methods

Seventy-seven patients with pathologically confirmed AM at Sichuan Cancer Hospital from September 2022 to September 2025 were enrolled and divided into either a metastasis group or a non-metastasis group based on lymph node pathology. Baseline characteristics, conventional ultrasound features, and SMI vascular indices were compared between the two groups by the t-test or χ² test. Independent diagnostic indicators for metastatic lymph nodes were identified using multivariate logistic regression analysis. Two diagnostic models were constructed: one incorporating the SMI vascular index and one without it. Receiver operating characteristic (ROC) curves were plotted for both models, and the DeLong test was used to compare their area under the curve (AUC) values. The model with superior performance was selected to build a nomogram model, and the model's efficacy was evaluated using calibration curves and decision curve analysis (DCA).

Results

Univariate analysis showed statistically significant differences between the two groups in the lymph node long-to-short axis ratio (2.33±0.85 vs 3.30±0.99; t=-4.09, P＜0.001), cortical thickness [(3.71±2.03) mm vs (2.26±0.80) mm; t=4.17, P＜0.001], vascular pattern (10/27 vs 34/6; χ²=-25.46, P＜0.001), resistive index (0.75±0.05 vs 0.61±0.10; t=7.48, P＜0.001), and SMI vascular index (24.11±9.03 vs 13.70±5.22; t=6.25, P＜0.001). Further construction of diagnostic models revealed that the model incorporating the SMI vascular index had significantly superior diagnostic performance compared to the model without it (AUC: 0.906 vs 0.809, DeLong test P=0.012). The calibration curve demonstrated good consistency between predicted and actual outcomes. The DCA curve further confirmed that this model provided a significant positive net benefit for clinical application.

Conclusion

The nomogram model constructed based on the SMI vascular index can effectively diagnose regional lymph node status in AM patients, providing a valuable quantitative tool for the preoperative non-invasive diagnosis and clinical decision-making regarding regional lymph nodes in AM.

Key words: Superb microvascular imaging, Acral melanoma, Lymph nodes, Nomogram

Mingyuan Wu, Tingting Li, Xinxin Xian, Chaoyang Luo, Qing Yang, Man Lu. Diagnostic value of a superb microvascular imaging vascular index-based nomogram for assessing regional lymph nodes in acral melanoma[J]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2026, 23(04): 276-282.

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URL: https://chaosheng.cma-cmc.com.cn/EN/10.3877/cma.j.issn.1672-6448.2026.04.003

https://chaosheng.cma-cmc.com.cn/EN/Y2026/V23/I04/276

Figures/Tables 5

因素	非转移组（40例）	转移组（37例）	统计值	P值
年龄（岁， $x ¯$ ±s）	62.12±10.80	65.00±12.17	t=1.10	0.276
性别[例（%）]			χ²=-1.61	0.205
男	22（55.0）	15（40.5）
女	18（45.0）	22（55.0）
原发灶位置[例（%）]			χ²=-0.99	0.318
足底	16（40.0）	19（51.4）
指/趾甲床	24（60.0）	18（48.6）
淋巴结位置[例（%）]			χ²=-0.34	0.843
腹股沟	33（82.5）	29（78.4）
腋窝	5（12.5）	5（13.5）
腘窝	2（5.0）	3（8.1）
淋巴结长短径比值（ $x ¯$ ±s）	3.30±0.99	2.33±0.85	t=-4.09	＜0.001
淋巴结皮质厚度（mm， $x ¯$ ±s）	2.26±0.80	3.71±2.03	t=4.17	＜0.001
血流分型[例（%）]			χ²=-25.46	＜0.001
门型	34（85.0）	10（27.0）
非门型	6（15.0）	27（73.0）
PSV（cm/s， $x ¯$ ±s）	19.95±12.09	23.97±11.84	t=1.45	0.153
阻力指数（ $x ¯$ ±s）	0.61±0.10	0.75±0.05	t=7.48	＜0.001
SMI血管指数（ $x ¯$ ±s）	13.70±5.22	24.11±9.03	t=6.25	＜0.001

因素	非转移组（40例）	转移组（37例）	统计值	P值
年龄（岁， $x ¯$ ±s）	62.12±10.80	65.00±12.17	t=1.10	0.276
性别[例（%）]			χ²=-1.61	0.205
男	22（55.0）	15（40.5）
女	18（45.0）	22（55.0）
原发灶位置[例（%）]			χ²=-0.99	0.318
足底	16（40.0）	19（51.4）
指/趾甲床	24（60.0）	18（48.6）
淋巴结位置[例（%）]			χ²=-0.34	0.843
腹股沟	33（82.5）	29（78.4）
腋窝	5（12.5）	5（13.5）
腘窝	2（5.0）	3（8.1）
淋巴结长短径比值（ $x ¯$ ±s）	3.30±0.99	2.33±0.85	t=-4.09	＜0.001
淋巴结皮质厚度（mm， $x ¯$ ±s）	2.26±0.80	3.71±2.03	t=4.17	＜0.001
血流分型[例（%）]			χ²=-25.46	＜0.001
门型	34（85.0）	10（27.0）
非门型	6（15.0）	27（73.0）
PSV（cm/s， $x ¯$ ±s）	19.95±12.09	23.97±11.84	t=1.45	0.153
阻力指数（ $x ¯$ ±s）	0.61±0.10	0.75±0.05	t=7.48	＜0.001
SMI血管指数（ $x ¯$ ±s）	13.70±5.22	24.11±9.03	t=6.25	＜0.001

因素	不包含SMI血管指数指标的诊断模型
因素	β值	SE值	Wald值	P值	OR值	95%CI	方差膨胀因子
长短径比值	-0.739	0.321	5.278	0.022	0.478	0.254~0.897	1.228
皮质厚度	0.548	0.267	4.807	0.028	1.794	1.064~3.025	1.131
血流分型	1.228	0.622	3.907	0.048	3.416	1.010~11.549	1.076
因素	包含SMI血管指数指标的诊断模型
因素	β值	SE值	Wald值	P值	OR值	95%CI	方差膨胀因子
长短径比值	-1.207	0.460	6.888	0.009	0.299	0.121~0.737	1.196
皮质厚度	0.728	0.367	3.941	0.047	2.070	1.009~4.247	1.167
血流分型	1.565	0.617	6.420	0.011	4.781	1.457~16.036	1.051
SMI血管指数	0.211	0.061	11.819	0.001	1.234	1.095~1.392	1.431

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