Li L, Yang D, Wu Y, Sun R, Qin Y, Kang M, Deng X, Bu M, Li Z, Zeng Z, Zeng X, Jiang M, Chen B T
Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, PR China.
Department of Radiology, Guizhou Provincial People Hospital, No.83, East Zhongshan Road, Nanming District, Guizhou Province, 550000, Guiyang, PR China; Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, No. 2870, Huaxi Avenue South, Guiyang, 550025, Guizhou, PR China.
Radiography (Lond). 2025 Jul;31(4):102989. doi: 10.1016/j.radi.2025.102989. Epub 2025 May 26.
To develop and validate a machine learning model based on dual-energy computed tomography (DECT) for predicting cervical lymph node metastases (CLNM) in patients diagnosed with nasopharyngeal carcinoma (NPC).
This prospective single-center study enrolled patients with NPC and the study assessment included both DECT and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT). Radiomics features were extracted from each region of interest (ROI) for cervical lymph nodes using arterial and venous phase images at 100 keV and 150 keV, either individually as non-fusion models or combined as fusion models on the DECT images. The performance of the random forest (RF) models, combined with radiomics features, was evaluated by area under the receiver operating characteristic curve (AUC) analysis. DeLong's test was employed to compare model performances, while decision curve analysis (DCA) assessed the clinical utility of the predictive models.
Sixty-six patients with NPC were included for analysis, which was divided into a training set (n = 42) and a validation set (n = 22). A total of 13 radiomic models were constructed (4 non-fusion models and 9 fusion models). In the non-fusion models, when the threshold value exceeded 0.4, the venous phase at 100 keV (V100) (AUC, 0.9667; 95 % confidence interval [95 % CI], 0.9363-0.9901) model exhibited a higher net benefit than other non-fusion models. The V100 + V150 fusion model achieved the best performance, with an AUC of 0.9697 (95 % CI, 0.9393-0.9907).
DECT-based radiomics effectively diagnosed CLNM in patients with NPC and may potentially be a valuable tool for clinical decision-making.
This study improved pre-operative evaluation, treatment strategy selection, and prognostic evaluation for patients with nasopharyngeal carcinoma by combining DECT and radiomics to predict cervical lymph node status prior to treatment.
开发并验证一种基于双能计算机断层扫描（DECT）的机器学习模型，用于预测鼻咽癌（NPC）患者的颈部淋巴结转移（CLNM）。
这项前瞻性单中心研究纳入了NPC患者，研究评估包括DECT和18F-氟脱氧葡萄糖正电子发射断层扫描/计算机断层扫描（18F-FDG PET/CT）。使用100 keV和150 keV的动脉期和静脉期图像，从颈部淋巴结的每个感兴趣区域（ROI）提取放射组学特征，这些特征在DECT图像上既可以单独作为非融合模型，也可以组合作为融合模型。通过受试者操作特征曲线（AUC）分析下的面积评估结合放射组学特征的随机森林（RF）模型的性能。采用DeLong检验比较模型性能，而决策曲线分析（DCA）评估预测模型的临床实用性。
纳入66例NPC患者进行分析，分为训练集（n = 42）和验证集（n = 22）。共构建了13个放射组学模型（4个非融合模型和9个融合模型）。在非融合模型中，当阈值超过0.4时，100 keV静脉期（V100）（AUC，0.9667；95%置信区间[95%CI]，0.9363 - 0.9901）模型比其他非融合模型表现出更高的净效益。V100 + V150融合模型表现最佳，AUC为0.9697（95%CI，0.9393 - 0.9907）。
基于DECT的放射组学能够有效诊断NPC患者的CLNM，可能是临床决策的有价值工具。
本研究通过结合DECT和放射组学来预测治疗前颈部淋巴结状态，改善了鼻咽癌患者的术前评估、治疗策略选择和预后评估。
