Jiang Zhenzhen, Yuan Fang, Zhang Qi, Zhu Jianbo, Xu Meina, Hu Yanfeng, Hou Chuanling, Liu Xiatian
Department of Ultrasound, Shaoxing People's Hospital, Shaoxing, China.
Department of Ultrasound, Xiamen Hospital, Beijing University of Chinese Medicine, Xiamen, China.
Quant Imaging Med Surg. 2024 Feb 1;14(2):1507-1525. doi: 10.21037/qims-23-1182. Epub 2024 Jan 10.
Accurate determination of the types of lymphadenopathy is of great importance in disease diagnosis and treatment and is usually confirmed by pathological findings. Radiomics is a non-invasive tool that can extract quantitative information from medical images. Our study was designed to develop a non-invasive radiomic approach based on multiphase contrast-enhanced ultrasound (CEUS) images for the classification of different types of lymphadenopathy.
A total of 426 patients with superficial suspected lymph nodes (LNs) from three centres were grouped into a training cohort (n=190), an internal testing cohort (n=127), and an external testing cohort (n=109). The radiomic features were extracted from the prevascular phase, vascular phase, and postvascular phase of the CEUS images. Model 1 (the conventional feature model), model 2 (the multiphase radiomics model), and model 3 (the combined feature model) were established for lymphadenopathy classification. The area under the curve (AUC) and confusion matrix were used to evaluate the performance of the three models. The usefulness of the models was assessed in different threshold probabilities by decision curve analysis.
There were 139 patients (32.6%) with benign LNs, 110 patients (25.8%) with lymphoma, and 177 patients (41.5%) with metastatic LNs in our population. Finally, twenty features were selected to construct the radiomics models for these three types of lymphadenopathy. Model 2 integrating multiphase images of the CEUS yielded the AUCs of 0.838, 0.739, and 0.733 in the training cohort, internal testing cohort, and external testing cohort, respectively. After the combination of conventional features and radiomic features, the AUCs of model 3 improved to 0.943, 0.823 and 0.785 in the training cohort, internal testing cohort, and external testing cohort. Besides, model 3 had an accuracy of 81.05%, sensitivity of 80%, and specificity of 90.43% in the training cohort. Model performance was further confirmed in the internal testing cohort and external testing cohort.
We constructed a combined feature model using a series of CEUS images for the classification of the lymphadenopathies. For patients with superficial suspected LNs, this model can help clinicians make a decision on the LN type noninvasively and choose appropriate treatments.
准确确定淋巴结病的类型在疾病诊断和治疗中至关重要，通常通过病理检查结果来确诊。放射组学是一种可从医学图像中提取定量信息的非侵入性工具。我们的研究旨在基于多期超声造影（CEUS）图像开发一种非侵入性放射组学方法，用于不同类型淋巴结病的分类。
来自三个中心的426例疑似浅表淋巴结（LN）患者被分为训练队列（n = 190）、内部测试队列（n = 127）和外部测试队列（n = 109）。从CEUS图像的血管前期、血管期和血管后期提取放射组学特征。建立模型1（传统特征模型）、模型2（多期放射组学模型）和模型3（联合特征模型）用于淋巴结病分类。采用曲线下面积（AUC）和混淆矩阵评估三种模型的性能。通过决策曲线分析在不同阈值概率下评估模型的实用性。
在我们的研究人群中，有139例（32.6%）良性LN患者、110例（25.8%）淋巴瘤患者和177例（41.5%）转移性LN患者。最后，选择了20个特征来构建这三种类型淋巴结病的放射组学模型。整合CEUS多期图像的模型2在训练队列、内部测试队列和外部测试队列中的AUC分别为0.838、0.739和0.733。在将传统特征和放射组学特征相结合后，模型3在训练队列、内部测试队列和外部测试队列中的AUC分别提高到0.943、0.823和0.785。此外，模型3在训练队列中的准确率为81.05%，灵敏度为80%，特异性为90.43%。模型性能在内部测试队列和外部测试队列中得到进一步验证。
我们使用一系列CEUS图像构建了一个联合特征模型用于淋巴结病的分类。对于疑似浅表LN的患者，该模型可以帮助临床医生非侵入性地确定LN类型并选择合适的治疗方法。
