| 延涵,钟奕伟,王凌霄,伍文杰,张杰,张建国,黄明伟.基于18F-FDG PET/CT影像组学治疗前预测唾液腺癌颈部淋巴结转移[J].中华放射医学与防护杂志,2022,42(5):361-366 |
| 基于18F-FDG PET/CT影像组学治疗前预测唾液腺癌颈部淋巴结转移 |
| Pretreatment prediction of cervical lymph node metastasis in salivary gland carcinoma based on 18F-FDG PET/CT radiomics |
| 投稿时间：2022-01-27 |
| DOI：10.3760/cma.j.cn112271-20220127-00038 |
| 中文关键词: 唾液腺肿瘤 影像组学 PET/CT 淋巴结转移 |
| 英文关键词:Salivary gland neoplasms Radiomics PET/CT Lymph node metastasis |
| 基金项目:北京大学口腔医院临床新技术新疗法(PKUSSNCT-19A07) |
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| 中文摘要: |
| 目的 研究氟代脱氧葡萄糖正电子发射计算机断层显像(18F-FDG PET/CT)影像组学在预测唾液腺癌颈部淋巴结转移中的价值。方法 对北京大学口腔医学68例唾液腺癌患者进行回顾性研究,随机分为训练组(40例)、验证组(14例)和测试组(14例)。从PET图像中半自动勾画肿瘤原发病灶并提取影像组学特征。经过特征筛选和降维,构建人工神经网络(ANN)预测模型。采用受试者操作特征(ROC)曲线、ROC曲线下面积(AUC)、准确度、灵敏度、特异度对模型预测性能进行评价,采用Delong检验对各模型性能进行比较。结果 基于影像组学特征构建的影像组学模型AUC为0.88(95%CI:0.78~0.95),灵敏度为75%,特异度为92.3%,准确度为88.2%。结合PET/CT报告的淋巴结状态(cN)和影像组学特征构建的联合模型的AUC为0.97(95%CI:0.89~0.99),灵敏度为87.5%,特异度为100%,准确度为97.1%。Delong检验显示联合 模型与cN的差异具有统计学意义(Z=2.27,P<0.05),影像组学模型与cN差异无统计学意义( >0.05)。 结论 将原发肿瘤18F-FDG PET/CT影像组学与PET/CT报告的淋巴结状态相结合,构建的基于人工神经网络的模型,能够更准确地预测唾液腺癌患者的颈部淋巴结转移。 |
| 英文摘要: |
| Objective To explore the value of18F-FDG PET/CT radiomics in predicting the cervical lymph node metastasis in salivary gland cancer.Methods Sixty-eight patients with salivary gland carcinoma treated in the Peking University School and Hospital of Stomatology were retrospectively studied. They were randomly divided into training group (n=40), validation group (n=14), and test group (n=14). The primary tumor lesions were semi-automatically delineated on PET images as regions of interest (ROIs) and the radiomic features were extracted from ROIs. After feature selection and dimension reduction, an artificial neural network (ANN) prediction model was constructed. The prediction performance of the model was assessed using receiver operating characteristic (ROC) curves, the area under ROC curves (AUC), accuracy, sensitivity, and specificity. Moreover, the performance of various models was compared using the Delong test.Results The radiomic model yielded an AUC of 0.88 (95%CI: 0.78-0.95), a sensitivity of 75%, specificity of 92.3%, and accuracy of 88.2%. By contrast, the combined model constructed based on the clinical node status (cN) reported by PET/CT and radiomic features yielded an AUC of 0.97 (95%CI: 0.89-0.99), a sensitivity of 87.5%, specificity of 100%, and accuracy of 97.1%. The Delong test showed that there was a statistically significant difference between the combined model and cN (Z=2.27,P<0.05), but there was no statistically significant difference between the radiomic model and cN (P>0.05).Conclusions The ANN model based on18F-FDG PET/CT radiomics combined with cN reported by PET/CT can more accurately predict cervical lymph node metastasis in patients with salivary gland carcinoma. |
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