Learning curve for minimally invasive oesophagectomy of oesophageal cancer and survival analysis

Study on the learning curve for thoracoscopic and laparoscopic radical resection of esophageal cancer

BACKGROUND

The procedure for thoracoscopic and laparoscopic radical resection of esophageal cancer is complicated, so the operation time is long, which can easily negatively affect the self-confidence of young thoracic surgeons. This retrospective cohort study aimed to improve young thoracic surgeons' understanding of this type of surgery by analyzing the learning curve.

METHODS

From October 2017 to August 2018, 64 patients who underwent thoracoscopic and laparoscopic radical resection of esophageal cancer by a single team were reviewed by a retrospective cohort study. These patients were divided into four groups according to the date of operation. The baseline data, operation time, the amount of bleeding during the operation, and the number of lymph nodes sampled were compared. Then, the quality of the different stages of the operation was analyzed and evaluated.

RESULTS

There were no significant differences in the general baseline data, chest tube duration, or number of samples collected from the right laryngeal nodes among the four groups (p > 0.05). With the accumulation of experience, several key measures of surgical benefit were significantly different among the four groups. Specifically, the operation time became shorter, the amount of bleeding gradually decreased, the number of lymph nodes sampled gradually increased, and the number of left para-recurrent laryngeal nerve lymph nodes sampled gradually increased (p < 0.05).

CONCLUSION

According to the learning curve, approximately 16 patients needed to complete this type of operation in 300 min, and 22 patients needed to be independently sampled from more than 20 lymph nodes.

Prognostic factors in locally advanced oesophageal squamous cell carcinoma: a clinical and radiomic analysis of neoadjuvant immunochemotherapy before surgery

BACKGROUND

The treatment of locally advanced oesophageal squamous cell carcinoma (LAESCC) without distant metastasis remains a subject of debate. Neoadjuvant immunochemotherapy (NIC) combined with surgery is the preferred initial approach for managing LAESCC. However, information on the clinical efficacy and survival of patients with LAESCC treated with NIC followed by surgery is limited.

METHODS

This retrospective analysis aimed to identify predictors NIC treatment effectiveness and on patient survival. We developed a Cox proportional hazards model and Kaplan-Meier curve to estimate progression-free survival (PFS) and overall survival (OS) following NIC treatment and surgery.

RESULTS

Overall, 225 patients with LAESCC were divided into training (157) and test set (68) (7:3). After a median follow-up of 2.86 years, death was observed as a positive event in 41 patients (26.1%). It is statistically significant to construct a prediction model combining radiomics features pre- and post-NIC with clinical features to predict the PFS and OS of LAESCC. The combined model showed the highest performance in predicting both disease-free survival and OS compared with the clinical or radiomics models. multivariate Cox regression analysis identified smoking (HR = 1.417, 95% confidence interval [CI]: 0.875-2.293, p = 0.156), Ki67(HR = 2.426, 95% confidence interval [CI]: 1.506-3.908, p = 0.000) and postRad-S1 (HR = 1.867, 95% CI: 1.053-3.311, p = 0.033) as significant independent covariates associated with high PFS. While Ki67 and postRad-S2 were prognostic factors significantly associated with OS (HR = 1.521, 95% CI: 0.821-2.818, p = 0.183; HR = 1.912, 95% CI: 1.001-3.654, p = 0.050, respectively).

CONCLUSION

For patients with LAESCC treated with NIC followed by surgery, the combined model effectively evaluated the efficacy of NIC and predicted PFS and OS. Additionally, different independent predictors were associated with PFS and OS, providing clues for future studies.

Risk factor analysis and predictive model construction for postoperative bleeding in early esophageal cancer

OBJECTIVE

A multivariate logistic regression model was developed to identify the risk factors for postoperative bleeding in patients undergoing endoscopic submucosal dissection (ESD) for early esophageal cancer.

METHODS

The clinical data of 258 patients with early esophageal cancer who received ESD in Jiujiang Number One People's Hospital from April 2019 to March 2022 were retrospectively analyzed. Patients with or without postoperative bleeding were included into a bleeding group and a control group, respectively, and general information with statistically significant difference between the two groups was included in the multivariate logistic regression model to screen the risk factors for postoperative bleeding in the patients. The risk factors were then used to construct a nomogram prediction model for postoperative bleeding, and internal (training set) and external (validation set) validation was performed.

RESULTS

(1) The incidence of post-ESD bleeding was 12.02% in the 258 patients with early esophageal cancer. (2) History of hypertension, lesion diameter, submucosal fibrosis, C-reactive protein (CRP), and albumin (ALB) were independent risk factors for postoperative bleeding after ESD in the patients (P<0.05). (3) The results of receiver operator characteristic curve (ROC) showed that the area under the curve (AUC) was 0.821 for the training set and 0.740 for the validation set. (4) The correction curve showed that the actual and predicted values of the training and validation sets were well fitted.

CONCLUSION

Hypertension history, lesion diameter, submucosal fibrosis, CRP, and ALB are risk factors for postoperative bleeding in patients with early esophageal cancer undergoing ESD. The nomograms established based on these factors has good predictive value for postoperative bleeding in these patients.

Exploring the learning curve in minimally invasive esophagectomy: a systematic review

Minimally invasive esophagectomy (MIE) has been shown to be superior to open esophagectomy with reduced morbidity, mortality, and comparable lymph node (LN) harvest. However, MIE is technically challenging. This study aims to perform a pooled analysis on the number of cases required to surmount the learning curve (LC), i.e. NLC in MIE. PubMed, Embase, Scopus, and the Cochrane Library were systematically searched for articles from inception to June 2022. Inclusion criteria were articles that reported LC in video-assisted MIE (VAMIE) and/or robot-assisted MIE (RAMIE). Poisson means (95% confidence interval [CI]) was used to determine NLC. Negative binomial regression was used for comparative analysis. There were 41 articles with 45 data sets (n = 7755 patients). The majority of tumors were located in the lower esophagus or gastroesophageal junction (66.7%, n = 3962/5939). The majority of data sets on VAMIE (n = 16/26, 61.5%) used arbitrary analysis, while the majority of data sets (n = 14/19, 73.7%) on RAMIE used cumulative sum control chart analysis. The most common outcomes reported were overall operating time (n = 30/45) and anastomotic leak (n = 28/45). Twenty-four data sets (53.3%) reported on LN harvest. The overall NLC was 34.6 (95% CI: 30.4-39.2), 68.5 (95% CI: 64.9-72.4), 27.5 (95% CI: 24.3-30.9), and 35.9 (95% CI: 32.1-40.2) for hybrid VAMIE, total VAMIE, hybrid RAMIE, and total RAMIE, respectively. NLC was significantly lower for total RAMIE compared to total VAMIE (incidence rate ratio: 0.52, P = 0.032). Studies reporting NLC in MIE are heterogeneous. Further studies should clearly define prior surgical experiences and assess long-term oncological outcomes using non-arbitrary analysis.

Construction a new nomogram prognostic model for predicting overall survival after radical resection of esophageal squamous cancer

Background

Esophageal cancer is one of the deadliest malignancies in the world, and 5-year overall survival (OS) of esophageal cancer ranges from 12% to 20%. Surgical resection remains the principal treatment. The American Joint Commission on Cancer (AJCC) TNM (tumor, node, and metastasis) staging system is a key guideline for prognosis and treatment decisions, but it cannot fully predict outcomes. Therefore, targeting the molecular and biological features of each patient's tumor, and identifying key prognostic biomarkers as effective survival predictors and therapeutic targets are highly important to clinicians and patients.

Methods

In this study, three different methods, including Univariate Cox regression, Lasso regression, and Randomforest regression were used to screen the independent factors affecting the prognosis of esophageal squamous cell carcinoma and construct a nomogram prognostic model. The accuracy of the model was verified by comparing with TNM staging system and the reliability of the model was verified by internal cross validation.

Results

Preoperative neutrophil lymphocyte ratio(preNLR), N-stage, p53 level and tumor diameter were selected to construct the new prognostic model. Patients with higher preNLR level, higher N-stage, lower p53 level and larger tumor diameter had worse OS. The results of C-index, Decision Curve Analysis (DCA), and integrated discrimination improvement (IDI) showed that the new prognostic model has a better prediction than the TNM staging system.

Conclusion

The accuracy and reliability of the nomogram prognostic model were higher than that of TNM staging system. It can effectively predict individual OS and provide theoretical basis for clinical decision making.