Ultrasound in medicine & biology · 2026
Multimodal Deep Fusion of Ultrasound Images and Clinical Factors for Pre-operative Prediction of Lateral Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Wu Suqing, Kuang Junchang, Zhu Hui, Xu Jielan, Wang Yingyu, Shi Zizhen, Ouyang Jiajing, Pan Xipeng, Pei Shufang
Аннотация
OBJECTIVE: Lateral cervical lymph node metastasis (LLNM) is missed in up to 30% of papillary thyroid carcinoma (PTC) patients pre-operatively, leading to incomplete surgery or unnecessary two-stage lateral neck dissection. Here we aimed to develop and externally validate a multimodal deep learning network (M-DLN) that fuses ultrasound images with clinical factors for the pre-operative prediction of LLNM.
METHODS: In this retrospective, multicentre study, 1019 consecutive PTC patients (2014-2024) from two tertiary centres were split into training (n = 710), internal validation (n = 87), internal test (n = 87) and external validation (n = 135) cohorts. Three ResNet-50 backbones extracted features from type B, real-time tissue elastography and monochrome superb microvascular imaging ultrasound; a three-layer, fully connected network processed 15 clinical variables. Decision-level soft-voting fused image and clinical probabilities. Model explainability was provided by gradient-weighted class activation mapping and Shapley additive explanations. Discrimination, calibration and clinical utility were assessed by area under the curve and decision curve analysis.
RESULTS: In both the internal test cohort and external validation cohort, the M-DLN model-comprising a deep learning network based on multimodal ultrasound images integrated with clinical information-demonstrated a highly efficient and robust performance in predicting LLNM in PTC, with areas under the curve of 0.901 (95% confidence interval: 0.874-0.928) and 0.853 (95% confidence interval: 0.825-0.876), respectively. Decision curve analysis indicated that M-DLN provided a substantial net clinical benefit. In the external validation cohort, our M-DLN model yielded a high sensitivity of 0.946, a moderate specificity of 0.652 and an overall accuracy of 0.874. The high sensitivity effectively reduced the rate of missed LLNM diagnosis, which is essential to avoid under-treatment, while the moderate specificity was acceptable given the clinical priorities.
CONCLUSION: The open-source M-DLN system, integrating routinely acquired multimodal ultrasound images with clinical data, provides accurate, interpretable and externally validated pre-operative identification of LLNM in PTC, and could guide initial thyroid surgery precision.
METHODS: In this retrospective, multicentre study, 1019 consecutive PTC patients (2014-2024) from two tertiary centres were split into training (n = 710), internal validation (n = 87), internal test (n = 87) and external validation (n = 135) cohorts. Three ResNet-50 backbones extracted features from type B, real-time tissue elastography and monochrome superb microvascular imaging ultrasound; a three-layer, fully connected network processed 15 clinical variables. Decision-level soft-voting fused image and clinical probabilities. Model explainability was provided by gradient-weighted class activation mapping and Shapley additive explanations. Discrimination, calibration and clinical utility were assessed by area under the curve and decision curve analysis.
RESULTS: In both the internal test cohort and external validation cohort, the M-DLN model-comprising a deep learning network based on multimodal ultrasound images integrated with clinical information-demonstrated a highly efficient and robust performance in predicting LLNM in PTC, with areas under the curve of 0.901 (95% confidence interval: 0.874-0.928) and 0.853 (95% confidence interval: 0.825-0.876), respectively. Decision curve analysis indicated that M-DLN provided a substantial net clinical benefit. In the external validation cohort, our M-DLN model yielded a high sensitivity of 0.946, a moderate specificity of 0.652 and an overall accuracy of 0.874. The high sensitivity effectively reduced the rate of missed LLNM diagnosis, which is essential to avoid under-treatment, while the moderate specificity was acceptable given the clinical priorities.
CONCLUSION: The open-source M-DLN system, integrating routinely acquired multimodal ultrasound images with clinical data, provides accurate, interpretable and externally validated pre-operative identification of LLNM in PTC, and could guide initial thyroid surgery precision.