Prognostic nomograms and risk-stratifying systems for predicting survival in patients with resected pT2-4aN0M0 esophageal carcinoma

Clinical characteristics, prognosis, and nomogram for upper esophageal cancer: a SEER database analysis

Upper esophageal cancer (ESCA) is a distinct subtype of ESCA that accounts for < 10% of ESCA cases. However, its unique clinical characteristics remain unclear, and without specialized prognostic model. We aimed to clarify its unique clinical characteristics and develop a specialized prognostic model. Data for a total of 1371 upper ESCA cases and 15,434 cases of ESCA at other segments were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Compared with that of patients with ESCA at other segments, a greater proportion of patients with upper ESCA were older and female; had an abnormal marital status; had tumors at the T4 stage, N0 stage, and M0 stage; and had squamous cell carcinoma and differentiation grade II. Moreover, the prognosis of upper ESCA was significantly poorer, and the constituent ratio stratified by the above characteristics from 2004 to 2015 showed no significant changes of average annual percent change (AAPC). Cox regression analysis was used to identify independent prognostic factors. Age, sex, marital status, histologic type, grade, and T, N and M stage were included in the development of the nomogram. The C-indexes of the training cohort and validation cohort were 0.64 (95% CI 0.62-0.66) and 0.62 (95% CI 0.58-0.64), respectively. The area under the curve (AUC), calibration curve, and decision curve analysis (DCA) results confirmed the good performance of the upper ESCA model. The C-index, integrated discrimination improvement (IDI), net reclassification improvement (NRI), time-dependent AUC, and DCA and survival analysis results confirmed that the upper ESCA model performed better than the TNM model in predicting the prognosis of upper ESCA. Finally, compared with the total ESCA model, which is based on a total of 16,805 ESCA cases, the upper ESCA model showed better performance in predicting the prognosis of upper ESCA. In conclusion, we outlined the unique clinical characteristics of upper ESCA and developed a specialized prognostic model that exhibited better performance in predicting the prognosis of upper ESCA than did the TNM model and total ESCA model.

A nomogram to predict long-term survival after resection for esophageal cancer: An observational study in northeast China

OBJECTIVE

This study aims to create a prognostic nomogram by combining clinicopathologic variables that are linked to the overall survival following the surgical removal of esophageal squamous cell carcinoma.

METHODS

A total of 224 patients with esophageal cancer who underwent surgical R0 resection were included. The construction of the nomogram involved using a multivariable Cox proportional hazards regression model. To evaluate the model's effectiveness, Kaplan-Meier curves and calibration plots were used for discrimination and calibration, respectively.

RESULTS

Nearly half of the patients were >60 years old (45.1%), and 95.5% of the patients were male. After esophageal cancer resection, 35.7% of patients experienced complications, with 23.7% developing anastomotic stenosis and 4.5% developing a fistula. Using the backward selection of clinically relevant variables, we found that tumor located in middle thoracic (hazard ratio 2.299, 95% confidence interval 1.008-5.244), anastomotic fistula (3.028, 1.436-6.384), and vascular invasion (2.175, 1.496-3.108) were independently associated with mortality (all P < .05), whereas lymph node clearance ≥15 nodes is associated with longer survival (0.444, 0.278-0.710) (P = .001). On the basis of these factors, a nomogram was created to predict survival of esophageal squamous cell carcinoma after resection. Discrimination using Kaplan-Meier curves, calibration curves, and bootstrap cross-validation revealed good predictive abilities (C index, 0.673).

CONCLUSIONS

A nomogram was created based on the experience from northeast China to forecast overall survival following resection for esophageal squamous cell carcinoma. The validation process demonstrated accurate distinction and calibration, indicating the practical value of the nomogram in enhancing personalized survival predictions for patients who undergo esophageal squamous cell carcinoma resection in this study population.

Nomograms for prognosis prediction in esophageal adenocarcinoma: realities and challenges

Prognostic assessment is of great significance for individualized treatment and care of cancer patients. Although the TNM staging system is widely used as the primary prognostic classifier for solid tumors in clinical practice, the complexity of tumor occurrence and development requires more personalized probability prediction models than an ordered staging system. By integrating clinical, pathological, and molecular factors into digital models through LASSO and Cox regression, a nomogram could provide more accurate personalized survival estimates, helping clinicians and patients develop more appropriate treatment and care plans. Esophageal adenocarcinoma (EAC) is a common pathological subtype of esophageal cancer with poor prognosis. Here, we screened and comprehensively reviewed the studies on EAC nomograms for prognostic prediction, focusing on performance evaluation and potential prognostic factors affecting survival. By analyzing the strengths and limitations of the existing nomograms, this study aims to provide assistance in constructing high-quality prognostic models for EAC patients.

Occult Non-Small Cell Lung Cancer: An Underappreciated Disease

Background: The number of researches on occult non-small cell lung cancer (NSCLC) is modest. Herein, we defined the clinicopathological features, prognosis and survival outcome of this underappreciated tumor, with purpose of obtaining a clearer picture on this disease. Methods: The entire cohort was categorized into two groups (occult NSCLC and other NSCLC) and further into five groups (occult, T1, T2, T3 and T4). A least absolute shrinkage and selection operator (LASSO) penalized Cox regression model was performed to identify the prognostic indicators. A nomogram and a risk-classifying system were formulated. Kaplan–Meier with Log-rank method was carried out to compare overall survival (OS) and cancer specific survival (CSS) differences between groups. Results: 59,046 eligible NSCLC cases (occult NSCLC: 1158 cases; other NSCLC: 57,888 cases) were included. Occult NSCLC accounted for 2.0% of the included cases. Multivariate analysis revealed that age, sex, tumor location, histology, grade and surgery were prognostic factors for OS. The corresponding prognostic nomogram classified occult NSCLC patients into low-risk and high-risk group, and its performance was acceptable. Survival curves demonstrated that occult NSCLC patients exhibited worse survivals than other NSCLC. In further analyses, the survival of low-risk occult NSCLC and stage T3 NSCLC were comparable, and the high-risk occult NSCLC patients still owned the worst survival rate. Conclusions: Occult NSCLC was an aggressive tumor with poor prognosis, and surgery was the preferred treatment. More attention should be paid to this overlooked disease due to no evidence of tumor imaging.