Liu Ying, Liu Limin, He Yining, Jiang Wen, Fang Tianyi, Huang Yingying, Zhou Xinyu, Zhu Dongwang, Li Jiang, Zhong Laiping
Department of Oral & Maxillofacial Head & Neck Oncology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Otorhinolaryngology-Head and Neck Surgery, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA.
Oral Dis. 2025 Mar;31(3):718-728. doi: 10.1111/odi.15141. Epub 2024 Oct 6.
This study aimed to develop and internally validate a prognostic nomogram for predicting nodal recurrence-free survival (NRFS) in patients with early-stage oral squamous cell carcinoma (OSCC) with clinically negative neck lymph nodes.
The management of early-stage oral cancer patients with clinically negative neck lymph nodes (cN0) remains controversial, especially concerning the need for elective neck dissection. Data from a single institution spanning 2010 to 2020 were utilized to develop and evaluate the nomogram. The nomogram was constructed using multivariable Cox regression and LASSO regression analyses to identify independent risk factors for lymph node metastasis. Internal validation was performed using bootstrap resampling to assess the nomogram's predictive accuracy.
A total of 930 cN0 patients with T1 and T2 stage OSCC were randomly divided into training and validation cohorts (8:2 ratio). Independent risk factors for lymph node metastasis included tumor pathological grade (well: reference, moderate/poor: OR 1.69), cT (cT1: reference, cT2: OR 2.01), history of drinking (never: reference, current/former: OR 1.72), and depth of invasion (0 mm < DOI ≤ 5 mm: reference, 5 mm < DOI ≤ 10 mm: OR 1.31). The nomogram, incorporating these variables, demonstrated good predictive accuracy with a C-index of 0.67 (95% CI: 0.58-0.76) in the validation set. In both training and validation groups, the nomogram effectively stratified patients into low-risk and high-risk groups for occult cervical nodal metastases (p < 0.05).
The nomogram enables risk stratification and improved identification of occult cervical nodal metastases in clinically node-negative OSCC patients by incorporating tumor-specific and patient-specific risk factors.
本研究旨在开发并内部验证一种预测模型，用于预测临床颈部淋巴结阴性的早期口腔鳞状细胞癌（OSCC）患者的无淋巴结复发生存期（NRFS）。
临床颈部淋巴结阴性（cN0）的早期口腔癌患者的治疗仍存在争议，尤其是关于选择性颈清扫术的必要性。利用单个机构2010年至2020年的数据来开发和评估该预测模型。通过多变量Cox回归和LASSO回归分析构建预测模型，以识别淋巴结转移的独立危险因素。使用自助重采样进行内部验证，以评估预测模型的预测准确性。
总共930例T1和T2期OSCC的cN0患者被随机分为训练组和验证组（比例为8:2）。淋巴结转移的独立危险因素包括肿瘤病理分级（高分化：参照，中/低分化：OR 1.69）、cT（cT1：参照，cT2：OR 2.01）、饮酒史（从不：参照，目前/既往：OR 1.72）和浸润深度（0 mm＜DOI≤5 mm：参照，5 mm＜DOI≤10 mm：OR 1.31）。纳入这些变量的预测模型在验证集中显示出良好的预测准确性，C指数为0.67（95% CI：0.58 - 0.76）。在训练组和验证组中，该预测模型均有效地将患者分为隐匿性颈部淋巴结转移的低风险和高风险组（p＜0.05）。
该预测模型通过纳入肿瘤特异性和患者特异性危险因素，能够对临床淋巴结阴性的OSCC患者进行风险分层，并更好地识别隐匿性颈部淋巴结转移。
