Feng Jia-Wei, Liu Shui-Qing, Yang Yu-Xin, Qi Gao-Feng, Ye Xin, Ye Jing, Jiang Yong, Lin Hui
Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China (J-W.F., H.L.); Department of thyroid surgery, The Third Affiliated Hospital of Soochow University, Changzhou First People's Hospital, Changzhou, Jiangsu, China (J-W.F., Y-X.Y., J.Y., Y.J.).
Department of Ultrasound, The Third Affiliated Hospital of Soochow University, Changzhou First People's Hospital, Changzhou, Jiangsu, China (S-Q.L.).
Acad Radiol. 2025 Apr;32(4):1918-1933. doi: 10.1016/j.acra.2024.12.037. Epub 2025 Jan 4.
Papillary thyroid carcinoma (PTC) often metastasizes to lateral cervical lymph nodes, especially in level II. This study aims to develop predictive models to identify level II lymph node metastasis (LNM), guiding selective neck dissection (SND) to minimize unnecessary surgery and morbidity in low-risk patients.
A retrospective cohort of 313 PTC patients who underwent modified radical neck dissection (MRND) between October 2020 and January 2023 was analyzed. The patients were randomly assigned to a training cohort (70%) and a validation cohort (30%). Five predictive models were developed using neural networks (NNET) and logistic regression (LR) based on ultrasound radiomic features, clinical-pathological data, or a combination of both. Each model's performance was evaluated based on accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity in predicting occult level II LNM. SHapley Additive exPlanations and nomogram were used to interpret the most important features in the models.
The occurrence rate of level II LNM was 28% in the cohort. Among the five predictive models developed, the LR-radiomics signature model demonstrated the highest performance, achieving an accuracy of 96.8% and an AUC of 0.989 in the validation set. In comparison, the NNET-radiomic + clinical feature model achieved an AUC of 0.935, while other models exhibited moderate to low accuracy and AUCs ranging from 0.699 to 0.785. The decision curve analysis demonstrated that the LR-radiomics signature model provided the greatest clinical utility, offering the highest net benefit across a range of decision thresholds for identifying occult level II LNM.
Our study developed predictive models using ultrasound-derived radiomic features and clinical-pathological data to assess the risk of occult level II LNM in PTC. The LR-radiomics signature model demonstrated high accuracy, making it a valuable tool for guiding personalized treatment decisions, by informing MRND for high-risk patients and supporting SND for low-risk patients to minimize unnecessary surgical interventions and optimize clinical outcomes.
甲状腺乳头状癌（PTC）常转移至颈部侧方淋巴结，尤其是Ⅱ区。本研究旨在建立预测模型以识别Ⅱ区淋巴结转移（LNM），指导选择性颈清扫术（SND），从而在低风险患者中尽量减少不必要的手术及并发症。
分析了2020年10月至2023年1月期间接受改良根治性颈清扫术（MRND）的313例PTC患者的回顾性队列。患者被随机分为训练队列（70%）和验证队列（30%）。基于超声影像组学特征、临床病理数据或两者结合，使用神经网络（NNET）和逻辑回归（LR）建立了五个预测模型。根据预测隐匿性Ⅱ区LNM的准确性、受试者操作特征曲线下面积（AUC）、敏感性和特异性评估每个模型的性能。使用SHapley加性解释和列线图来解释模型中最重要的特征。
该队列中Ⅱ区LNM的发生率为28%。在建立的五个预测模型中，LR-影像组学特征模型表现最佳，在验证集中准确率达到96.8%，AUC为0.989。相比之下，NNET-影像组学+临床特征模型的AUC为0.935，而其他模型的准确率和AUC为中等至低水平，范围在0.699至0.785之间。决策曲线分析表明，LR-影像组学特征模型具有最大的临床实用性，在识别隐匿性Ⅱ区LNM的一系列决策阈值范围内提供了最高的净效益。
我们的研究利用超声衍生的影像组学特征和临床病理数据建立了预测模型，以评估PTC中隐匿性Ⅱ区LNM的风险。LR-影像组学特征模型显示出高准确性，通过为高风险患者告知MRND并支持低风险患者进行SND，以尽量减少不必要的手术干预并优化临床结果，使其成为指导个性化治疗决策的有价值工具。
