Occult lymph node metastasis in patients with laryngeal cancer and relevant predicting factors: a single-center experience

Radiologic findings of occult nodal metastasis during clinically-N0 salvage total laryngectomy

INTRODUCTION

Occult nodal disease (OND) during clinically-N0 salvage total laryngectomy (TL) can be detected with the Neck-Imaging-Reporting-and-Data-Systems (NI-RADS). However, some patients will still have OND revealed on final pathology.

METHODS

A retrospective study on all patients who had OND during salvage TL with elective neck dissection (END) between 2009 and 2021 was performed. Repeat CT and PET scan interpretation was performed to evaluate their preoperative imaging for suspicious features.

RESULTS

Among 81 salvage TL patients undergoing END, 12 (16%) had OND and a total of 26 occult nodes were identified. On pathology, the average node length [SD] was 0.6 cm [0.3]. On CT, 31% (8 of 26) had rounded morphology. On PET, most had SUVmax below blood pool. One patient scored NI-RADS 2; the rest scored 1.

CONCLUSIONS

On re-review of preoperative imaging, occult nodes were subtle and challenging to identify. Despite no clear impact on survival, performing an END may provide prognostic information.

Nodal metastasis in surgically treated laryngeal squamous cell carcinoma

BACKGROUND

Management of the neck in laryngeal squamous cell carcinoma (LSCC) is essential to oncologic control and survival. We aim to describe patterns and rates of clinical/pathologic lymph node disease, elective neck dissection (END), and occult lymph node metastasis (LNM) in patients with surgically-managed LSCC.

METHODS

Retrospective cohort study of patients in the National Cancer Database (NCDB) diagnosed with LSCC between January 2004 and December 2016 who underwent primary surgery.

RESULTS

Seven thousand eight hundred and seventy-six patients met inclusion criteria. For cN0 patients, the rates of END and occult LNM both increased with tumor stage and were highest for supraglottic tumors. Predictors of occult LNM included supraglottic site, pathologic T3 and T4 stage, positive margins, and presence of lymphovascular invasion (p < 0.05).

CONCLUSIONS

The propensity for cervical LNM in surgically-managed LSCC varies based on primary tumor site and stage, and a variety of disease factors increase risk of occult LNM.

Application of an Interpretable Machine Learning Model to Predict Lymph Node Metastasis in Patients with Laryngeal Carcinoma

Objectives

A more accurate preoperative prediction of lymph node metastasis (LNM) plays a decisive role in the selection of treatment in patients with laryngeal carcinoma (LC). This study aimed to develop a machine learning (ML) prediction model for predicting LNM in patients with LC.

Methods

We collected and retrospectively analysed 4887 LC patients with detailed demographical characteristics including age at diagnosis, race, sex, primary site, histology, number of tumours, T-stage, grade, and tumour size in the National Institutes of Health (NIH) Surveillance, Epidemiology, and End Results (SEER) database from 2005 to 2015. A correlation analysis of all variables was evaluated by the Pearson correlation. Independent risk factors for LC patients with LNM were identified by univariate and multivariate logistic regression analyses. Afterward, patients were randomly divided into training and test sets in a ratio of 8 to 2. On this basis, we established logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and light gradient boosting machine (LightGBM) algorithm models based on ML. The area under the receiver operating characteristic curve (AUC) value, accuracy, precision, recall rate, F1-score, specificity, and Brier score was adopted to evaluate and compare the prediction performance of the models. Finally, the Shapley additive explanation (SHAP) method was used to interpret the association between each feature variable and target variables based on the best model.

Results

Of the 4887 total LC patients, 3409 were without LNM (69.76%), and 1478 had LNM (30.24%). The result of the Pearson correlation showed that variables were weakly correlated with each other. The independent risk factors for LC patients with LNM were age at diagnosis, race, primary site, number of tumours, tumour size, grade, and T-stage. Among six models, XGBoost displayed a better performance for predicting LNM, with five performance metrics outperforming other models in the training set (AUC: 0.791 (95% CI: 0.776–0.806), accuracy: 0.739, recall rate: 0.638, F1-score: 0.663, and Brier score: 0.165), and similar results were observed in the test set. Moreover, the SHAP value of XGBoost was calculated, and the result showed that the three features, T-stage, primary site, and grade, had the greatest impact on predicting the outcomes.

Conclusions

The XGBoost model performed better and can be applied to forecast the LNM of LC, offering a valuable and significant reference for clinicians in advanced decision-making.

Increased expression of Myosin X contributes to the metastasis in patients with laryngeal squamous cell carcinoma

Laryngeal Squamous Cell Carcinoma (LSCC) is one of the most common malignancy in Head and neck cancer for which the mechanism underlying its metastasis is poorly understood. Myosin X, a molecular motor in cells has been demonstrated to play an important role in cell migration. However, whether Myosin X is involved in the metastasis of LSCC remains unclear. To investigate the expression of Myosin X and its implication in the metastasis of LSCC, we recruited 30 patients with LSCC and 6 patients with vocal cord polyp range from October 2016 to October 2018. Tissue samples were obtained during surgery and the expression of Myosin X, Cortactin, MMP2, MMP9, E-cadherin, and β-catenin in tissue samples were evaluated by RT-PCR, Western blot, immunohistochemistry or ELISA. Patients with LSCC were further followed-up 2 year after surgery for metastasis analysis. We found that the level of Myosin X, Cortactin, MMP2, and MMP9 was much higher in poorly differentiated LSCC compared to that in moderately and highly LSCC, as well as the control tissues. In contrast, the expression of epithelial-mesenchymal transition related marker, E-cadherin, and β-catenin, were much lower in poorly differentiated LSCC tissues compared to that in moderately and highly differentiated LSCC tissues, as well as the control tissues. Moreover, the expression of Myosin X was positively correlated with Cortactin, MMP2, and MMP9 levels. Increased expression of Myosin X in LSCC tissues was related to higher risk of metastasis. In conclusion, our findings showed that. Myosin X augments the expression of Cortactin, MMP2 and MMP9, which could upregulate the cell migration and the matrix degradation, and consequently reduce the expression of E-cadherin and β-catenin, thereby activating epithelial-mesenchymal transformation and promoting the metastasis of LSCC. Targeting Myosin X may have potential therapeutic effect in the metastasis of LSCC.

A Predictive Nomogram for Lymph Node Metastasis in Supraglottic Laryngeal Squamous Cell Carcinoma

Purpose

Lymph node metastasis (LNM) has a negative impact on the survival of patients with laryngeal squamous cell carcinoma (LSCC). Supraglottic LSCC is the most common cause of cervical lymph node metastases due to the extensive submucosal lymphatic plexus. The accurate evaluation of LNM before surgery can inform improved decisions in the clinic. In this study, we aimed to construct a nomogram to predict LNM in primary supraglottic LSCC patients.

Methods

The data from 314 patients with clinico-pathological confirmed supraglottic LSCC who underwent partial or total laryngectomy in our department from 2016 to 2020 were retrospectively analyzed (243 cases in the training set and 71 cases in the validation set). A multivariate logistic regression model was used to screen out independent risk factors and a nomogram was established. The accuracy and discrimination ability of the nomogram was evaluated using a consistency index and calibration curves.

Results

Tumor size, tumor differentiation degree and LMR (lymphocyte-monocyte ratio) were selected to construct the nomogram. The C-index was 0.731 in the training set and 0.707 in the validation set. The calibration curves of the training and validation group both exhibited close agreement between the predicted and the actual presence of LNM.

Conclusions

A nomogram was established based on routinely measured pretreatment variables and the predicted results improved the management of patients with LNM.