18F-氟脱氧葡萄糖（18F-FDG）正电子发射计算机断层扫描（PET/CT）在评估临床无淋巴结转移（cN0）口腔鳞状细胞癌（OSCC）患者颈部状况的疗效仍不令人满意。我们尝试利用代谢和病理变量为cN0 OSCC患者开发淋巴结转移预测模型。被纳入的是行术前18F-FDG PET/CT、随后行原发肿瘤手术切除和颈部淋巴结清扫的连续cN0 OSCC患者。在上海第九人民医院接受PET/CT扫描的95名患者被确定为训练队列，而在上海通用医学影像诊断中心成像的另外46名患者则被选择作为验证队列。训练队列的淋巴结状态相关变量经过最小绝对收缩和选择算子（LASSO）多变量回归选择。使用显著变量构建了一个预测淋巴结转移风险的评分卡。最后，通过对其区分度、校准性和临床实用性的检验来确定评分卡的性能。淋巴结最大标准摄取值（nodal SUVmax）和病理T分期被选择为显著变量。采用这两个变量构建预测模型绘制出评分卡。在训练队列中，曲线下面积为0.871（标准误差[SE]，0.035；95％置信区间[CI]，0.787-0.931），在验证队列中为0.809（SE，0.069；95％ CI，0.666-0.910），具有良好的校准性能。一个结合了代谢和病理变量的预测模型对于预测cN0 OSCC患者的淋巴结转移具有良好的性能。然而，需要进一步研究大规模的人群来验证我们的发现。© 2023年。作者（们）。 Note: Since the original text includes specialized medical terminology, some terms may not have an equivalent in Simplified Chinese, and the translation may differ depending on the chosen terminology.

The efficacy of 18F-fluorodeoxyglucose (18F-FDG) Positron Emission Tomography/Computed Tomography(PET/CT) in evaluating the neck status in clinically node-negative (cN0) oral squamous cell carcinoma(OSCC) patients was still unsatisfying. We tried to develop a prediction model for nodal metastasis in cN0 OSCC patients by using metabolic and pathological variables.Consecutive cN0 OSCC patients with preoperative 18F-FDG PET/CT, subsequent surgical resection of primary tumor and neck dissection were included. Ninety-five patients who underwent PET/CT scanning in Shanghai ninth people's hospital were identified as training cohort, and another 46 patients who imaged in Shanghai Universal Medical Imaging Diagnostic Center were selected as validation cohort. Nodal-status-related variables in the training cohort were selected by multivariable regression after using the least absolute shrinkage and selection operator (LASSO). A nomogram was constructed with significant variables for the risk prediction of nodal metastasis. Finally, nomogram performance was determined by its discrimination, calibration, and clinical usefulness.Nodal maximum standardized uptake value(nodal SUVmax) and pathological T stage were selected as significant variables. A prediction model incorporating the two variables was used to plot a nomogram. The area under the curve was 0.871(Standard Error [SE], 0.035; 95% Confidence Interval [CI], 0.787-0.931) in the training cohort, and 0.809(SE, 0.069; 95% CI, 0.666-0.910) in the validation cohort, with good calibration demonstrated.A prediction model incorporates metabolic and pathological variables has good performance for predicting nodal metastasis in cN0 OSCC patients. However, further studies with large populations are needed to verify our findings.© 2023. The Author(s).