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Recurrent laryngeal nerve lymph nodes status prediction after neoadjuvant therapy for thoracic esophageal squamous
Fengze Wu1, Chao Luo1, Shumin Zhou1,2
1Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Esophageal Cancer Institute, Guangzhou, Guangdong, China.
Objectives:
Accurately predicting recurrent laryngeal nerve lymph nodes (RLNs) status after neoadjuvant therapy is challenging but essential for efficient dissection of RLNs and for lowering local recurrence rates in patients with esophageal cancer.
Materials And Methods:
In this retrospective study, 403 patients diagnosed with esophageal squamous cell cancer between 2010 and 2021 were included and randomly divided into training and test cohorts at a ratio of 2:1. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression analyses were conducted to identify significant factors associated with residual RLNs. Multivariable logistic regression analysis was used to develop the final nomogram by integrating clinical factors and pre- and post-treatment CT imaging features. The discriminatory ability of the nomogram was assessed using the area under the receiver operating characteristic (ROC) curve.
Results:
The RLN metastatic rate after neoadjuvant therapy was 13.2%. Significant predictors of RLN (+) status post-neoadjuvant therapy identified by LASSO regression analysis included neoadjuvant therapy plan, serum albumin level, long diameter of the primary lesion, baseline necrosis of RLNs, baseline long diameter of RLNs, and short diameter of RLNs post-neoadjuvant treatment. The nomogram demonstrated good discriminative ability, with an area under the ROC curve of 0.856 in the test set. Neoadjuvant chemoradiotherapy was associated with a 70% reduction in the rate of RLN residues compared to neoadjuvant chemotherapy alone.
Conclusion:
The nomogram, based on clinical factors and pre- and post-treatment CT imaging features, provides a superior discriminatory ability for predicting the pathological status of RLNs after neoadjuvant chemoradiotherapy.
Critical Relevance Statement:
The nomogram presents a convenient and noninvasive method for evaluating the residual risk of RLNs after treatment for clinicians. It has the potential to provide guidance for a more precise lymph node dissection in patients with low residual risk.
Key Points:
Accurate RLNs status prediction can lower local recurrence rates in esophageal cancer. The nomogram can be used for RLN pathological status prediction. The nomogram is convenient and noninvasive for the assessment of residual risk of RLNs.
