A Nutrition and Inflammation-Related Nomogram to Predict Overall Survival in Surgically Resected Esophageal Squamous Cell Carcinoma (ESCC) Patients

The combination of handgrip strength and CONUT predicts overall survival in patients with gastrointestinal cancer: A multicenter cohort study

BACKGROUND

The controlled nutritional status score (CONUT) and handgrip strength (HGS) were both predictive indexes for the prognosis of cancers. However, the combination of CONUT and HGS for predicting the prognosis of gastrointestinal cancer had not been developed. This study aimed to explore the combination of CONUT and HGS as the potential predictive prognosis in patients with gastric and colorectal cancer.

METHODS

A cohort study was conducted with gastric and colorectal cancer patients in multicenter in China. Based on the optimal HGS cutoff value for different sex, the HGS cutoff value was determined. The patients were divided into high and low HGS groups based on their HGS scores. A CONUT score of 4 or less was defined as a low CONUT, whereas scores higher than 4 were defined as high CONUT. The Kaplan-Meier method was used to create survival curves, and the log-rank test was used to compare time-event relationships between groups. A Cox proportional hazard regression model was used to determine independent risk factors for overall survival (OS).

RESULTS

A total 2177 gastric and colorectal patients were enrolled in this study, in which 1391 (63.9%) were men (mean [SD] age, 66.11 [11.60] years). Multivariate analysis revealed that patients with high HGS had a lower risk of death than those with low HGS (hazard ratio [HR],0.87; 95% confidence interval [CI], 0.753-1.006, P = 0.06), while high CONUT had a higher risk of death than those with low CONUT (HR, 1.476; 95% CI, 1.227-1.777, P < 0.001). Patients with both low HGS and high CONUT had 1.712 fold increased risk of death (HR, 1.712; 95% CI, 1.364-2.15, P < 0.001). Moreover, cancer type and sex were stratified and found that patients with high CONUT and low HGS had lower survival rate than those with low CONUT and high HGS in both gastric or colorectal cancer, and both male and female.

CONCLUSION

A combination of low HGS and high CONUT was associated with poor prognosis in patients with gastrointestinal cancer, which could probably predict the prognosis of gastrointestinal cancer more accurate than HGS or CONUT alone.

Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma

The current prognostic tools for esophageal squamous cell carcinoma (ESCC) lack the necessary accuracy to facilitate individualized patient management strategies. To address this issue, this study was conducted to develop a machine learning (ML) prediction model for ESCC patients' survival management. Six ML approaches, including Rpart, Elastic Net, GBM, Random Forest, GLMboost, and the machine learning-extended CoxPH method, were employed to develop risk prediction models. The model was trained on a dataset of 1954 ESCC patients with 27 clinical features and validated on a dataset of 487 ESCC patients. The discriminative performance of the models was assessed using the concordance index (C-index). The best performing model was used for risk stratification and clinical evaluation. The study found that N stage, T stage, surgical margin, tumor grade, tumor length, sex, MPV, AST, FIB, and Mg are the important feature for ESCC patients' survival. The machine learning-extended CoxPH model, Elastic Net, and Random Forest had similar performance in predicting the mortality risk of ESCC patients, and outperformed GBM, GLMboost, and Rpart. The risk scores derived from the CoxPH model effectively stratified ESCC patients into low-, intermediate-, and high-risk groups with distinctly different 3-year overall survival (OS) probabilities of 80.8%, 58.2%, and 29.5%, respectively. This risk stratification was also observed in the validation cohort. Furthermore, the risk model demonstrated greater discriminative ability and net benefit than the AJCC8th stage, suggesting its potential as a prognostic tool for predicting survival events and guiding clinical decision-making. The classical algorithm of the CoxPH method was also found to be sufficiently good for interpretive studies.

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.

Prognostic Value of a Modified Albumin-Bilirubin Score Designed for Patients with Esophageal Squamous Cell Carcinoma After Radical Resection

BACKGROUND

The albumin-bilirubin (ALBI) score was originally developed to assess the severity of liver dysfunction in patients with hepatocellular carcinoma and has subsequently been used as a prognostic marker for that disease. Here, we examined the value of the preoperative ALBI score as a prognostic marker for patients with esophageal squamous cell carcinoma (ESCC) after radical esophagectomy.

METHODS

We retrospectively analyzed data from 449 patients who underwent curative resection for ESCC. The ALBI score was calculated as (log₁₀ serum bilirubin [μmol/l] × 0.66) + (serum albumin [g/l] × - 0.0852). Receiver operating characteristic curve analysis was used to define a preoperative modified ALBI (mALBI) score for patient stratification.

RESULTS

Of the 449 ESCC patients, 232 and 217 were assigned to mALBI Grade 1 or Grade 2 groups based on preoperative ALBI scores of ≤ - 3.33 or > - 3.33, respectively. Preoperative mALBI grade was significantly associated with age, excessive alcohol consumption, squamous cell carcinoma antigen level, and clinical disease stage. The mALBI Grade 2 group had significantly shorter disease-specific and recurrence-free survival than the mALBI Grade 1 group. Multivariate analysis demonstrated that mALBI Grade 2 was an independent prognostic factor for disease-specific survival (hazard ratio 1.86, 95% confidence interval 1.18-2.93, P = 0.0074). In most subgroup analyses, mALBI Grade 2 was associated with a greater risk of disease-specific death.

CONCLUSIONS

mALBI grade serves as a simple and useful prognostic marker for disease-specific survival in patients with ESCC after radical esophagectomy.