Management of clinically node-negative glottic squamous cell carcinoma patients according to risk-scoring model for occult lymph node metastases

Laryngoscope Investigative Otolaryngology(2022)

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摘要
Background Glottic squamous cell carcinoma (GSCC) is the most prevalent type of laryngeal carcinoma. The value of prophylactic lymph node dissection (LND) in resected GSCC remains controversial. This study aims to quantitatively assess the probability of occult lymph node metastasis (LNM) for GSCC patients and devise individualized postoperative radiotherapy strategies. Methods A total of 1319 patients with GSCC were retrospectively analyzed. Results GSCC patients with T1-T2 stages showed significantly lower LNM rate than those with T3-T4 stages. For patients with T3-T4 GSCC, multivariate logistic analyses indicated that three factors-maximum tumor diameter (MTD) of more than 2.0 cm, relatively low differentiation, and tumor invasive depth of no less than 1.0 cm-were independent risk factors for the existence of LNM. A predictive nomogram was established based on these factors. The accuracy and validity of our model were verified by 0.716 and remained at 0.717 after 1000 bootstrapping. The calibration curve was also plotted and showed a favorable agreement. The patients were stratified into two groups based on their individual LNM risk points. Possible LNM rates for low-risk and high-risk subgroups were 4.7% and 25.2%, respectively. Conclusions A new post-operative strategy selection flow chart was established based on our newly created nomogram which can effectively predict the individualized possibility of occult LNM for GSCC patients. For clinical T3-4N0 patients in the high-risk subgroup, prophylactic dose post-operative radiation therapy is recommended. However, for all those clinically diagnosed as T1-2N0 stage, regular follow-up is sufficient in view of the low occult LNM rate. Level of Evidence: 2a
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关键词
Glottic squamous cell carcinoma, occult contralateral lymph node metastasis, postoperative adjuvant radiotherapy, risk prediction model, treatment choice
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