A Population-Based Study: How to Identify High-Risk T1-2 Esophageal Cancer Patients?

Construction and validation of a risk-scoring model to predict lymph node metastasis in T1b-T2 esophageal cancer

BACKGROUND

Lymph node status is an important factor in determining preoperative treatment strategies for stage T1b-T2 esophageal cancer (EC). Thus, the aim of this study was to investigate the risk factors for lymph node metastasis (LNM) in T1b-T2 EC and to establish and validate a risk-scoring model to guide the selection of optimal treatment options.

METHODS

Patients who underwent upfront surgery for pT1b-T2 EC between January 2016 and December 2022 were analyzed. On the basis of the independent risk factors determined by multivariate logistic regression analysis, a risk-scoring model for the prediction of LNM was constructed and then validated. The area under the receiver operating characteristic curve (AUC) was used to assess the discriminant ability of the model.

RESULTS

The incidence of LNM was 33.5% (214/638) in our cohort, 33.4% (169/506) in the primary cohort and 34.1% (45/132) in the validation cohort. Multivariate analysis confirmed that primary site, tumor grade, tumor size, depth, and lymphovascular invasion were independent risk factors for LNM (all P < 0.05), and patients were grouped based on these factors. A 7-point risk-scoring model based on these variables had good predictive accuracy in both the primary cohort (AUC, 0.749; 95% confidence interval 0.709-0.786) and the validation cohort (AUC, 0.738; 95% confidence interval 0.655-0.811).

CONCLUSION

A novel risk-scoring model for lymph node metastasis was established to guide the optimal treatment of patients with T1b-T2 EC.

Nomogram models for the prognosis of cervical cancer: A SEER-based study

Background

Cervical cancer (CC) is one of the most common cancers in women. This study aimed to investigate the clinical and non-clinical features that may affect the prognosis of patients with CC and to develop accurate prognostic models with respect to overall survival (OS) and cancer-specific survival (CSS).

Methods

We identified 11,148 patients with CC from the SEER (Surveillance, Epidemiology, and End Results) database from 2010 to 2016. Univariate and multivariate Cox regression models were used to identify potential predictors of patients’ survival outcomes (OS and CSS). We selected meaningful independent parameters and developed nomogram models for 1-, 3-, and 5-year OS and CSS via R tools. Model performance was evaluated by C-index and receiver operating characteristic curve. Furthermore, calibration curves were plotted to compare the predictions of nomograms with observed outcomes, and decision curve analysis (DCA) and clinical impact curves (CICs) were used to evaluate the clinical effectiveness of the nomograms.

Results

All eligible patients (n=11148) were randomized at a 7:3 ratio into training (n=7803) and validation (n=3345) groups. Ten variables were identified as common independent predictors of OS and CSS: insurance status, grade, histology, chemotherapy, metastasis number, tumor size, regional nodes examined, International Federation of Obstetrics and Gynecology stage, lymph vascular space invasion (LVSI), and radiation. The C-index values for OS (0.831 and 0.824) and CSS (0.844 and 0.841) in the training cohorts and validation cohorts, respectively, indicated excellent discrimination performance of the nomograms. The internal and external calibration plots indicated excellent agreement between nomogram prediction and actual survival, and the DCA and CICs reflected favorable potential clinical effects.

Conclusions

We constructed nomograms that could predict 1-, 3-, and 5-year OS and CSS in patients with CC. These tools showed near-perfect accuracy and clinical utility; thus, they could lead to better patient counseling and personalized and tailored treatment to improve clinical prognosis.