PDF(Pubmed)
Abstract：
Accurate differentiation of benign and malignant cervical lymph nodes is important for prognosis and treatment planning in patients with head and neck squamous cell carcinoma. We evaluated the diagnostic performance of magnetic resonance image (MRI) texture analysis and traditional 18F-deoxyglucose positron emission tomography (FDG-PET) features. This retrospective study included 21 patients with head and neck squamous cell carcinoma. We used texture analysis of MRI and FDG-PET features to evaluate 109 histologically confirmed cervical lymph nodes (41 metastatic, 68 benign). Predictive models were evaluated using area under the curve (AUC). Significant differences were observed between benign and malignant cervical lymph nodes for 36 of 41 texture features (p < 0.05). A combination of 22 MRI texture features discriminated benign and malignant nodal disease with AUC, sensitivity, and specificity of 0.952, 92.7%, and 86.7%, which was comparable to maximum short-axis diameter, lymph node morphology, and maximum standard uptake value (SUVmax). The addition of MRI texture features to traditional FDG-PET features differentiated these groups with the greatest AUC, sensitivity, and specificity (0.989, 97.5%, and 94.1%). The addition of the MRI texture feature to lymph node morphology improved nodal assessment specificity from 70.6% to 88.2% among FDG-PET indeterminate lymph nodes. Texture features are useful for differentiating benign and malignant cervical lymph nodes in patients with head and neck squamous cell carcinoma. Lymph node morphology and SUVmax remain accurate tools. Specificity is improved by the addition of MRI texture features among FDG-PET indeterminate lymph nodes. This approach is useful for differentiating benign and malignant cervical lymph nodes.
摘要：
准确鉴别颈淋巴结的良恶性对头颈部鳞状细胞癌患者的预后和治疗计划具有重要意义。我们评估了磁共振图像（MRI）纹理分析和传统18F-脱氧葡萄糖正电子发射断层扫描（FDG-PET）特征的诊断性能。这项回顾性研究包括21例头颈部鳞状细胞癌患者。我们使用MRI和FDG-PET特征的纹理分析来评估109个组织学证实的颈部淋巴结（41个转移性，68良性）。使用曲线下面积(AUC)评估预测模型。41个纹理特征中的36个在良性和恶性颈部淋巴结之间观察到显着差异（p<0.05）。结合22种MRI纹理特征，以AUC区分良性和恶性淋巴结疾病，灵敏度,特异性为0.952，92.7%，和86.7%，相当于最大短轴直径，淋巴结形态学,和最大标准摄取值(SUVmax)。将MRI纹理特征添加到传统的FDG-PET特征中，以最大的AUC区分这些组，灵敏度,和特异性(0.989，97.5%，和94.1%)。在FDG-PET不确定的淋巴结中，将MRI纹理特征添加到淋巴结形态将淋巴结评估特异性从70.6％提高到88.2％。纹理特征可用于区分头颈部鳞状细胞癌患者的良性和恶性颈部淋巴结。淋巴结形态和SUVmax仍然是准确的工具。通过在FDG-PET不确定的淋巴结中添加MRI纹理特征来提高特异性。这种方法对于区分良性和恶性颈部淋巴结很有用。
