Radiomics analysis of [18F]-fluoro-2-deoxyglucose positron emission tomography for the prediction of cervical lymph node metastasis in tongue squamous cell carcinoma
Radiomics analysis of [18F]-fluoro-2-deoxyglucose positron emission tomography for the prediction of cervical lymph node metastasis in tongue squamous cell carcinoma
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DOI：
10.1007/s11282-022-00600-7
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发表时间：
2022-03-07
期刊：
影响因子：
2.2
通讯作者：
Miyamoto, Youji
中科院分区：
文献类型：
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作者：
Kudoh, Takaharu;Haga, Akihiro;Miyamoto, Youji
目的建立舌鳞癌患者颈淋巴结转移的正电子发射断层扫描（PET）模型，并对舌鳞癌患者颈淋巴结转移的预测进行研究。方法40例舌鳞状细胞癌患者在首次体检时均行F-18-FDG PET显像。在随访期间（平均28个月），20例患者有CLNM，包括6例晚期CLNM，而其余20例患者没有CLNM。从所有患者的F-18-FDG PET图像中提取放射组学特征，无论是否存在金属伪影，并从病历中获得临床病理因素。晚期CLNM定义为主要治疗后发生的CLNM。最小绝对收缩和选择算子（LASSO）模型用于放射组学特征选择和序列数据拟合。采用受试者工作特征曲线分析评价F-18-FDG PET模型和临床病理因素模型（CFM）对CLNM的预测性能。结果从LASSO分析中筛选出6个放射组学特征。从F-18-FDG PET图像预测CLNM的放射组学分析的曲线下面积（AUC）、准确性、灵敏度和特异性的平均值分别为0.79、0.68、0.65和0.70。相反，CFM的那些分别为0.54、0.60、0.60和0.60。基于F-18-FDG PET的模型显示出显著高于CFM的AUC。结论F-18-FDG PET模型较CFM模型更能诊断舌鳞癌患者的慢性淋巴结转移和预测晚期慢性淋巴结转移。
Objectives This study aimed to create a predictive model for cervical lymph node metastasis (CLNM) in patients with tongue squamous cell carcinoma (SCC) based on radiomics features detected by [F-18]-fluoro-2-deoxyglucose (F-18-FDG) positron emission tomography (PET). Methods A total of 40 patients with tongue SCC who underwent F-18-FDG PET imaging during their first medical examination were enrolled. During the follow-up period (mean 28 months), 20 patients had CLNM, including six with late CLNM, whereas the remaining 20 patients did not have CLNM. Radiomics features were extracted from F-18-FDG PET images of all patients irrespective of metal artifact, and clinicopathological factors were obtained from the medical records. Late CLNM was defined as the CLNM that occurred after major treatment. The least absolute shrinkage and selection operator (LASSO) model was used for radiomics feature selection and sequential data fitting. The receiver operating characteristic curve analysis was used to assess the predictive performance of the F-18-FDG PET-based model and clinicopathological factors model (CFM) for CLNM. Results Six radiomics features were selected from LASSO analysis. The average values of the area under the curve (AUC), accuracy, sensitivity, and specificity of radiomics analysis for predicting CLNM from F-18-FDG PET images were 0.79, 0.68, 0.65, and 0.70, respectively. In contrast, those of the CFM were 0.54, 0.60, 0.60, and 0.60, respectively. The F-18-FDG PET-based model showed significantly higher AUC than that of the CFM. Conclusions The F-18-FDG PET-based model has better potential for diagnosing CLNM and predicting late CLNM in patients with tongue SCC than the CFM.