Nomograms for predicting overall and recurrence-free survival after trimodality therapy for esophageal adenocarcinoma

Perineural Invasion Is a Significant Indicator of High Malignant Degree and Poor Prognosis in Esophageal Cancer: A Systematic Review and Meta-Analysis

Background

Perineural invasion (PNI) is a malignant metastatic mode of tumors and has been reported in many tumors including esophageal cancer (EC). However, the role of PNI in EC has been reported differently. This systematic review and meta-analysis aims to focus on the role of PNI in EC.

Methods

Eight databases of CNKI, VIP, Wanfang, Scopus, Wiley, ISI, PubMed, and EBSCO are used for literature search. The association of PNI with gender, pathological stages of T and N (pT and pN), lymphovascular invasion (LVI), lymph node metastasis, 5-year overall survival (OS), and 5-year disease-free survival (DFS) was examined in the meta-analysis by Revman5.0 Software. The pooled OR/HR and 95% CI were used to assess the risk and prognostic value.

Results

Sixty-nine published studies were screened for analysis of PNI in EC. The incidence of PNI in esophageal squamous carcinoma (ESCC) and esophageal adenocarcinoma (EAC) was different, but not statistically significant (p > 0.05). The PNI-positive patients had a significantly higher risk of pT stage (OR = 3.85, 95% CI = 2.45–6.05, p < 0.00001), pN stage (OR = 1.86, 95% CI = 1.52–2.28, p < 0.00001), LVI (OR = 2.44, 95% CI = 1.55–3.85, p = 0.0001), and lymph node metastasis (OR = 2.87, 95% CI = 1.56–5.29, p = 0.0007). Furthermore, the cumulative analysis revealed a significant correlation between PNI and poor OS (HR = 1.37, 95% CI = 1.24–1.51, p < 0.0001), as well as poor DFS (HR = 1.55, 95% CI = 1.38–1.74, p < 0.0001).

Conclusion

PNI occurrence is significantly related to tumor stage, LVI, lymph node metastasis, OS, and DFS. These results indicate that PNI can serve as an indicator of high malignant degree and poor prognosis in EC.

Prognostic Nomogram for Predicting Long-Term Overall Survival of Esophageal Cancer Patients Receiving Neoadjuvant Chemoradiotherapy Plus Surgery: A Population-Based Study

Objective

Neoadjuvant chemoradiotherapy (nCRT) plays an important role in patients with locally advanced esophageal cancer (EC). We aim to determine the prognostic risk factors and establish a reliable nomogram to predict overall survival (OS) based on SEER population.

Methods

Patients with EC coded by 04–15 in the SEER database were included. The data were divided into training group and verification group (7:3). The Cox proportional-risk model was evaluated by using the working characteristic curve (receiver operating characteristic curve, ROC) and the area under the curve (AUC), and a nomogram was constructed. The calibration curve was used to measure the consistency between the predicted and the actual results. Decision curve analysis (DCA) was used to evaluate its clinical value. The best cut-off value of nomogram score in OS was determined by using X-tile software, and the patients were divided into low-risk, medium-risk, and high-risk groups.

Results

A total of 2,209 EC patients who underwent nCRT were included in further analysis, including 1,549 in the training cohort and 660 in the validation group. By Cox analysis, sex, marital status, T stage, N stage, M stage, and pathological grade were identified as risk factors. A nomogram survival prediction model was established to predict the 36-, 60-, and 84-month survival. The ROC curve and AUC showed that the model had good discrimination ability. The correction curve was in good agreement with the prediction results. DCA further proved the effective clinical value of the nomogram model. The results of X-tile analysis showed that the long-term prognosis of patients in the low-risk subgroup was better in the training cohort and the validation cohort (p &lt; 0.001).

Conclusion

This study established an easy-to-use nomogram risk prediction model consisting of independent prognostic factors in EC patients receiving nCRT, helping to stratify risk, identify high-risk patients, and provide personalized treatment options.

Predictive Model for Overall Survival and Cancer-Specific Survival in Patients with Esophageal Adenocarcinoma

Objective

Recent years, there has been a rapid increase in the incidence of esophageal adenocarcinoma (EAC), while the prognosis for patients diagnosed remains poor and has slightly improved.

Methods

We extracted 6,466 cases with detailed demographical characteristics including age at diagnosis, sex, ethnicity, marital status, and clinical features, involving tumor grade and stage at diagnosis and treatment modalities (radiation therapy, chemotherapy, and surgery) from the Surveillance, Epidemiology, and End Results (SEER) (1975–2017) dataset. They were further randomly divided into the training and validating cohorts. Univariate and multivariate Cox analyses were conducted to determine significant variables for construction of nomogram. The predictive power of the model was then assessed by Harrell concordance index (C-index) and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.

Results

Multivariate analysis revealed that age, marital status, insurance, tumor grade, TNM stage, surgery, and chemotherapy all showed a significant association with overall survival (OS) and cancer-specific survival (CSS). These characteristics were employed to build a nomogram. Particularly, the discrimination of nomogram for OS and CSS prediction in the training set were excellent (C-index = 0.762, 95% CI: 0.754–0.770 and C-index = 0.774, 95% CI: 0.766–0.782). The AUC of the nomogram for predicting 2- and 5-year OS was 0.834 and 0.853 and CSS was 0.844 and 0.866. Similar results were observed in the internal validation set.

Conclusion

We have successfully established a novel nomogram for predicting OS and CSS in EAC patients with good accuracy, which can help clinicians predict the survival of individual patient survival and provide optimal treatment strategies.