Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, No. 651 Dongfeng Road East, Guangzhou, 510060, Guangdong, People's Republic of China.
Eur Radiol. 2023 Feb;33(2):774-783. doi: 10.1007/s00330-022-09122-6. Epub 2022 Sep 7.
This study aimed to explore the clinical value of ultrasound radiomics analysis in the diagnosis of cervical lymph node metastasis (CLNM) in patients with nasopharyngeal carcinoma (NPC).
A total of 205 cases of NPC CLNM and 284 cases of benign lymphadenopathy with pathologic diagnosis were retrospectively included. Grayscale ultrasound (US) images of the largest section of every lymph node underwent feature extraction. Feature selection was done by maximum relevance minimum redundancy (mRMR) algorithm and multivariate logistic least absolute shrinkage and selection operator (LASSO) regression. Logistic regression models were developed based on clinical features, radiomics features, and the combination of those features. The AUCs of models were analyzed by DeLong's test.
In the clinical model, lymph nodes in the upper neck, larger long axis, and unclear hilus were significant factors for CLNM (p < 0.001). MRMR and LASSO regression selected 7 significant features for the radiomics model from the 386 radiomics features extracted. In the validation dataset, the AUC value was 0.838 (0.776-0.901) in the clinical model, 0.810 (0.739-0.881) in the radiomics model, and 0.880 (0.826-0.933) in the combined model. There was not a significant difference between the AUCs of clinical models and radiomics models in both datasets. DeLong's test revealed a significantly larger AUC in the combined model than in the clinical model in both training (p = 0.049) and validation datasets (p = 0.027).
Ultrasound radiomics analysis has potential value in screening meaningful ultrasound features and improving the diagnostic efficiency of ultrasound in CLNM of patients with NPC.
• Radiomics analysis of gray-scale ultrasound images can be used to develop an effective radiomics model for the diagnosis of cervical lymph node metastasis in nasopharyngeal carcinoma patients. • Radiomics model combined with general ultrasound features performed better than the clinical model in differentiating cervical lymph node metastases from benign lymphadenopathy.
本研究旨在探讨超声放射组学分析在诊断鼻咽癌(NPC)患者颈部淋巴结转移(CLNM)中的临床价值。
回顾性纳入 205 例 NPC-CLMN 和 284 例经病理诊断为良性淋巴结病变的患者。对每个淋巴结最大截面的灰阶超声(US)图像进行特征提取。采用最大相关性最小冗余度(mRMR)算法和多元逻辑最小绝对值收缩和选择算子(LASSO)回归进行特征选择。基于临床特征、放射组学特征和两者的组合建立逻辑回归模型。采用 DeLong 检验分析模型的 AUC。
在临床模型中,颈部上区、较长的长轴和模糊的门结构是 CLNM 的显著因素(p<0.001)。MRMR 和 LASSO 回归从提取的 386 个放射组学特征中选择了 7 个用于放射组学模型的显著特征。在验证数据集,临床模型的 AUC 值为 0.838(0.776-0.901),放射组学模型为 0.810(0.739-0.881),联合模型为 0.880(0.826-0.933)。在两个数据集,临床模型和放射组学模型的 AUC 值无显著差异。Delong 检验显示,在训练(p=0.049)和验证数据集(p=0.027)中,联合模型的 AUC 均显著大于临床模型。
超声放射组学分析具有筛选有意义的超声特征的潜力,并提高了超声在 NPC 患者 CLNM 诊断中的效率。
灰阶超声图像的放射组学分析可用于开发用于诊断鼻咽癌患者颈部淋巴结转移的有效放射组学模型。
放射组学模型与一般超声特征相结合,在区分颈部淋巴结转移与良性淋巴结病变方面优于临床模型。