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

Bayır, Ömer; Toptaş, Gökhan; Saylam, Güleser; İzgi, Turan Canmurat; Han, Ünsal; Keseroğlu, Kemal; Akyıldız, İlker; Korkmaz, Mehmet

doi:10.1177/03008916211026977

Cited by 12 publications

(9 citation statements)

References 28 publications

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“…found a very low incidence of LNM in T1-T2 stage and well-differentiated tumors. These data suggested that a watchful waiting strategy can be used in T1-T2 and selected T3 cases with well-differentiated tumors ( 6 ).…”

Section: Discussionmentioning

confidence: 94%

“…Several studies have identified indicators that may be independent factors for LNM such as the tumor depth, the degree of tumor differentiation, T-stage, thyroid cartilage invasion, and extra laryngeal extension. Traditional methods for determining the factors related to LNM are largely qualitative and there remains a need to develop quantitative measures to assess the factors associated with the risk of LNM ( 6 , 7 ). The accurate preoperative evaluation of LNM risk may guide the use of optimized treatment strategies in patients with supraglottic LSCC and provide important prognostic information.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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

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Section: Discussionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…Currently, the presence of LNM is mainly determined by cervical palpation and preoperative imaging, which largely depend on the clinical experience of the doctor and the ability of the human eye to identify imaging [21,22]. As reported, the presence of occult metastasis was confrmed by postsurgery histological examination in 4%-40% of cN0 LC patients [23][24][25]. Although prophylactic cervical LND can decrease the risk of LNM [26], it puts LC patients at a higher risk for operations, such as major bleeds, lymphatic fstulas, or impairments in the vagus, brachial plexus, and recurrent laryngeal nerve [27].…”

Section: Discussionmentioning

confidence: 97%

“…However, a study by Sessions et al found that patients with N0 disease may be safely observed with no loss of survival advantage [ 44 ]. Furthermore, Ömer et al showed that a watchful waiting strategy can be applied to T1-T2 and selected T3 cases with well-differentiated tumours [ 23 ]. These data further suggested that the LNM of LC is influenced by several factors, and that it is not accurate to judge LNM solely by T-stage and primary site.…”

Section: Discussionmentioning

confidence: 99%

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

Feng

Zhang

Zhou

et al. 2022

Journal of Oncology

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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.

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“…LSCC, especially poorly differentiated LSCC, is associated with high degree of malignancy, showing high potential of metastasis, leading to the low postoperative survival rate. Lymph node metastasis has been previously shown closely related to the prognosis of LSCC (Bayir et al 2021 ). Therefore, understanding the pathogenesis of LSCC, especially the molecular mechanism contributing to the metastasis, is beneficial to the early diagnosis and treatment of this disease (Olsen 2010 ; Rudolph et al 2011 ).…”

Section: Discussionmentioning

confidence: 99%

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

Deng¹,

Tiejun

Liu

2022

Mol Genet Genomics

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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.

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

Cited by 12 publications

References 28 publications

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

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

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

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

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