Development of a prediction model for the risk of recurrent laryngeal nerve lymph node metastasis in thoracolaparoscopic esophagectomy with cervical anastomosis

Long-term outcomes of intrathoracic versus cervical anastomosis after esophagectomy: A large-scale propensity score matching analysis

BACKGROUND

Esophageal squamous cell carcinoma is a prevalent and aggressive gastrointestinal tumor, particularly in East Asia. However, there is a lack of consensus on the long-term survival outcomes of intrathoracic anastomosis and cervical anastomosis following esophagectomy. This study aims to provide a comprehensive summary of the long-term survival outcomes of these 2 anastomosis techniques.

METHODS

We employed data drawn from the Sichuan Cancer Hospital and Institute Esophageal Cancer Case Management Database from January 2010 to December 2017. Patients were stratified into 2 distinct groups according to the anatomical location of anastomosis following esophagectomy: those who underwent intrathoracic anastomosis (IA) (IA group) and those who underwent cervical anastomosis (CA) (CA group). To account for potential confounding factors and baseline imbalances between the 2 groups, propensity score matching was employed.

RESULTS

The CA group exhibited longer overall survival compared with the IA group, with a median overall survival of 49.10 months versus 35.87 months (hazard ratio, 1.118; 95% CI, 1.118-1.412; P < .001). Additionally, survival rates at 1, 3, and 5 years were higher in the CA group (87%, 59%, and 48%, respectively) compared with the IA group (86%, 50%, and 39%, respectively). The significance persisted even after propensity score matching (hazard ratio, 1.164; 95% CI, 1.013-1.336; P < .001), inverse probability of treatment weighting, and overlap weighting were applied. The survival difference between CA and IA was attributed to varying extents of lymph node dissection, particularly in the upper mediastinal zone (P < .001).

CONCLUSIONS

Our study suggests that there could be the potential survival advantage of CA over IA in patients undergoing esophagectomy for esophageal squamous cell carcinoma.

Immediate Ansa cervicalis-to-recurrent laryngeal nerve low-tension anastomosis: A new technique for phonation recovery and bilateral anastomoses to avoid tracheotomy

OBJECTIVE

This case series study investigated the outcomes of an innovative approach, ansa cervicalis nerve (ACN)-to-recurrent laryngeal nerve (RLN) low-tension anastomosis.

METHODS

Patients who received laryngeal nerve anastomosis between May 2015 and September 2021 at the facility were enrolled. The inclusion criteria were patients with RLN dissection and anastomosis immediately during thyroid surgery. Exclusion criteria were cases with anastomosis other than cervical loop-RLN anastomosis or pronunciation recovery time > 6 months. Patients admitted before January 2020 were assigned to group A which underwent the conventional tension-free anastomosis, and patients admitted after January 2020 were group B and underwent the innovative low-tension anastomosis (Dong's method).

RESULTS

A total of 13 patients were included, 11 patients received unilateral surgery, and 2 underwent bilateral surgery. For patients who underwent unilateral anastomosis, group B had a significantly higher percentage of normal pronunciation via GRBAS scale (83.3 % vs. 0 %, p = 0.015) and voice handicap index (66.7 % vs. 0 %, p = 0.002), and shorter recovery time in pronunciation (median: 1-day vs. 4 months, p = 0.001) than those in group A after surgery.

CONCLUSIONS

ACNs-to-RLN low-tension anastomosis with a laryngeal segment ≤1 cm (Dong's method) significantly improves postoperative pronunciation and recovery time. The results provide clinicians with a new strategy for ACN -to-RLN anastomosis during thyroid surgery.

Machine learning to predict occult metastatic lymph nodes along the recurrent laryngeal nerves in thoracic esophageal squamous cell carcinoma

PURPOSE

Esophageal squamous cell carcinoma (ESCC) metastasizes in an unpredictable fashion to adjacent lymph nodes, including those along the recurrent laryngeal nerves (RLNs). This study is to apply machine learning (ML) for prediction of RLN node metastasis in ESCC.

METHODS

The dataset contained 3352 surgically treated ESCC patients whose RLN lymph nodes were removed and pathologically evaluated. Using their baseline and pathological features, ML models were established to predict RLN node metastasis on each side with or without the node status of the contralateral side. Models were trained to achieve at least 90% negative predictive value (NPV) in fivefold cross-validation. The importance of each feature was measured by the permutation score.

RESULTS

Tumor metastases were found in 17.0% RLN lymph nodes on the right and 10.8% on the left. In both tasks, the performance of each model was comparable, with a mean area under the curve ranging from 0.731 to 0.739 (without contralateral RLN node status) and from 0.744 to 0.748 (with contralateral status). All models showed approximately 90% NPV scores, suggesting proper generalizability. The pathology status of chest paraesophgeal nodes and tumor depth had the highest impacts on the risk of RLN node metastasis in both models.

CONCLUSION

This study demonstrated the feasibility of ML in predicting RLN node metastasis in ESCC. These models may potentially be used intraoperatively to spare RLN node dissection in low-risk patients, thereby minimizing adverse events associated with RLN injuries.

Development and validation of a nomogram model for the prediction of 4L lymph node metastasis in thoracic esophageal squamous cell carcinoma

Objectives

The left tracheobronchial (4L) lymph nodes (LNs) are considered as regional LNs for esophageal squamous cell carcinoma (ESCC), but there is a controversy about routine prophylactic 4L LN dissection for all resectable ESCCs. This study aimed to develop a nomogram for preoperative prediction of station 4L lymph node metastases (LNMs).

Methods

A total of 522 EC patients in the training cohort and 370 in the external validation cohort were included. The prognostic impact of station 4L LNM was evaluated, and multivariable logistic regression analyses were performed to identify independent risk factors of station 4L LNM. A nomogram model was developed based on multivariable logistic regression analysis. Model performance was evaluated in both cohorts in terms of calibration, discrimination, and clinical usefulness.

Results

The incidence of station 4L LNM was 7.9% (41/522) in the training cohort. Patients with station 4L LNM exhibited a poorer 5-year overall survival rate than those without (43.2% vs. 71.6%, p < 0.001). In multivariate logistic regression analyses, six variables were confirmed as independent 4L LNM risk factors: sex (p = 0.039), depth of invasion (p = 0.002), tumor differentiation (p = 0.016), short axis of the largest 4L LNs (p = 0.001), 4L conglomeration (p = 0.006), and 4L necrosis (p = 0.002). A nomogram model, containing six independent risk factors, demonstrated a good performance, with the area under the curve (AUC) of 0.921 (95% CI: 0.878-0.964) in the training cohort and 0.892 (95% CI: 0.830-0.954) in the validation cohort. The calibration curve showed a good agreement on the presence of station 4L LNM between the risk estimation according to the model and histopathologic results on surgical specimens. The Hosmer-Lemeshow test demonstrated a non-significant statistic (p = 0.691 and 0.897) in the training and validation cohorts, which indicated no departure from the perfect fit. Decision curve analysis indicated that the model had better diagnostic power for 4L LNM than the traditional LN size criteria.

Conclusions

This model integrated the available clinical and radiological risk factors, facilitating in the precise prediction of 4L LNM in patients with ESCC and aiding in personalized therapeutic decision-making regarding the need for routine prophylactic 4L lymphadenectomy.

A novel web-based dynamic nomogram for recurrent laryngeal nerve lymph node metastasis in esophageal squamous cell carcinoma

Background

Patients with esophageal squamous cell carcinoma (ESCC) are liable to develop recurrent laryngeal nerve (RLN) lymph node metastasis (LNM). We aimed to assess the predictive value of the long diameter (LD) and short diameter (SD) of RLN lymph node (LN) and construct a web-based dynamic nomogram for RLN LNM prediction.

Methods

We reviewed 186 ESCC patients who underwent RLN LN dissection from January 2016 to December 2018 in the Affiliated Hospital of North Sichuan Medical College. Risk factors for left and right RLN LNM were determined by univariate and multivariate analyses. A web-based dynamic nomogram was constructed by using logistic regression. The performance was assessed by the area under the curve (AUC) and Brier score. Models were internally validated by performing five-fold cross-validation.

Results

Patients who underwent left and right RLN LN dissection were categorized as left cohort (n = 132) and right cohort (n = 159), with RLN LNM rates of 15.9% (21/132) and 21.4% (34/159), respectively. The AUCs of the LD (SD) of RLN LN were 0.663 (0.688) in the left cohort and 0.696 (0.705) in the right cohort. The multivariate analysis showed that age, the SD of RLN LN, and clinical T stage were significant risk factors for left RLN LNM (all P < 0.05), while tumor location, the SD of RLN LN, and clinical T stage were significant risk factors for right RLN LNM (all P < 0.05). The dynamic nomograms showed reliable performance after five-fold cross-validation [(left (right), mean AUC: 0.814, range: 0.614–0.891 (0.775, range: 0.084–0.126); mean Brier score: 0.103, range: 0.084–0.126 (0.145, range: 0.105–0.206)], available at https://mpthtw.shinyapps.io/leftnomo/ and https://mpthtw.shinyapps.io/rightnomo/.

Conclusion

The LD and SD of RLN LN are inadequate to predict RLN LNM accurately, but online dynamic nomograms by combined risk factors show better prediction performance and convenient clinical application.

Development and validation of a nomogram for prediction of cervical lymph node metastasis in middle and lower thoracic esophageal squamous cell carcinoma

Background

Estimates of cervical lymph node (LN) metastasis in patients with middle and lower thoracic esophageal squamous cell carcinoma (ESCC) are important. A nomogram is a useful tool for individualized prediction.

Methods

A total of 235 patients were enrolled in this study. Univariate and multivariate analyses were performed to screen for independent risk factors and construct a nomogram to predict the risk of cervical LN metastasis. The nomogram performance was assessed by discrimination, calibration, and clinical use.

Results

Totally, four independent predictors, including the maximum diameter of tumor, paraesophageal lymph node status, recurrent laryngeal nerve lymph node status, and the CT-reported cervical LN status, were enrolled in the nomogram. The AUC of the nomogram model in the training and validation dataset were 0.833 (95% CI 0.762–0.905), 0.808 (95% CI 0.696–0.920), respectively. The calibration curve demonstrated a strong consistency between nomogram and clinical findings in predicting cervical LN metastasis. Decision curve analysis demonstrated that the nomogram was clinically useful.

Conclusion

We developed a nomogram that could be conveniently used to predict the individualized risk of cervical LN metastasis in patients with middle and lower thoracic ESCC.

Preoperative Clinical Characteristics Predict Recurrent Laryngeal Nerve Lymph Node Metastasis and Overall Survival in Esophageal Squamous Cell Carcinoma: A Retrospective Study With External Validation

BACKGROUND

Recurrent laryngeal nerve (RLN) lymph node metastasis (LNM) is not rare in patients with esophageal squamous cell carcinoma (ESCC). We aimed to develop and externally validate a preoperative nomogram using clinical characteristics to predict RLN LNM in patients with ESCC and evaluate its prognostic value.

METHODS

A total of 430 patients with ESCC who underwent esophagectomy with lymphadenectomy of RLN LNs at two centers between May 2015 and June 2019 were reviewed and divided into training (center 1, n = 283) and external validation cohorts (center 2, n = 147). Independent risk factors for RLN LNM were determined by multivariate logistic regression, and a nomogram was developed. The performance of the nomogram was assessed in terms of discrimination, calibration, clinical usefulness, and prognostic value. The nomogram was internally validated by the bootstrap method and externally validated by the external validation cohort.

RESULTS

Multivariate analysis indicated that clinical T stage (P <0.001), endoscopic tumor length (P = 0.003), bioptic tumor differentiation (P = 0.004), and preoperative carcinoembryonic antigen level (P = 0.001) were significantly associated with RLN LNM. The nomogram had good discrimination with the area under the curve of 0.770 and 0.832 after internal and external validations. The calibration curves and decision curve analysis confirmed the good calibration and clinical usefulness of this model. High-risk of RLN LNM predicted by the nomogram was associated with worse overall survival in the external validation cohort (P <0.001).

CONCLUSION

A nomogram developed by preoperative clinical characteristics demonstrated a good performance to predict RLN LNM and prognosis for patients with ESCC.