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Background: This study explores the significance of computed tomography (CT) radiomic features, along with inflammation and nutrition biomarkers, in the prognosis of postoperative patients with T3N0M0 esophageal squamous cell carcinoma (ESCC). The study aims to construct a related nomogram., Methods: A total of 114 patients were enrolled and randomly assigned to training and validation cohorts in a 7:3 ratio. Radiomic features were extracted from their preoperative chest-enhanced CT arterial images of the primary tumor, and inflammatory and nutritional indices, including neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and prognostic nutritional index (PNI), were calculated based on laboratory data from the 3 days before surgery. Intra-class correlations coefficient (ICC) and least absolute shrinkage and selection operator (Lasso) were applied to screen valuable radiomics features predicting overall survival (OS), and the Rad-score was calculated. In the training cohort, univariate and multivariate Cox regression analyses identified independent prognostic factors, which were adopted to establish the nomogram., Results: Eight radiomic features were selected for Rad-score calculation. Multivariate Cox regression revealed Rad-score, PNI, NLR, and PLR as independent prognostic factors for ESCC patients (p < 0.05). A nomogram was constructed based on these variables. The concordance index (C-index) for the nomogram was 0.797 (95% CI: 0.726-0.868) in the training cohort and 0.796 (95% CI: 0.702-0.890) in the validation cohort. Calibration curves indicated good calibration ability, and the receiver operating characteristic (ROC) analysis demonstrated superior discriminative ability for the nomogram in comparison to the Rad-score alone. Decision curve analysis (DCA) confirmed the clinical utility of the nomogram., Conclusion: We developed and validated a nomogram for predicting the OS of postoperative T3N0M0 ESCC patients, integrating nutritional, inflammatory markers, and radiomic signature. The combined nomogram can serve as a robust tool for risk stratification and clinical management., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Preoperative laboratory data indicators significantly affect the prognosis of a variety of tumors. Nevertheless, the combined effect of systemic immune-inflammation index (SII) and prognostic nutritional index (PNI) on overall survival (OS) in patients with esophageal carcinoma remains unclear. Thus, we examined these associations among patients with postoperative staged T3N0M0 esophageal carcinoma. The data of 246 patients with postoperative staged T3N0M0 esophageal carcinoma from January 1, 2010, to December 31, 2022, were retrospectively analyzed. OS was measured from the date of pathological diagnosis until either death or the last follow-up. The Kaplan-Meier method and multivariate Cox regression model were used to analyze the relationship between neutrophil-to-lymphocyte ratio (NLR), Platelet-to-lymphocyte ratio (PLR), Platelet-to-lymphocyte ratio (LMR), SII, PNI, and OS. The predictive value of SII and PNI as a combined index was analyzed by the receiver operating characteristic curve (ROC). A total of 246 patients aged 65.5 ± 7.4 years were included in this study and 181 (73.6%) were male. The univariate analysis revealed that differentiation, vessel involvement, postoperative treatment, NLR, SII, PLR, LMR, PNI were predictors of OS (P < .05). After adjusted for potential confounds, multivariate Cox regression analysis showed that the differentiation, SII, PNI, and postoperative treatment were independent prognostic factors correlated with OS in patients with postoperative staged T3N0M0 esophageal carcinoma (P < .05). SII and PNI, as a combined indicator, have a higher predictive value for OS. The NLR, SII, PLR, LMR, and PNI could all be used as independent predictors of OS in patients with postoperative staged T3N0M0 esophageal carcinoma. The combination of SII and PNI can significantly improve the accuracy of prediction., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

In this study, we explored the prognostic risk factors of elderly patients (≥65 years old) with lymph node-negative esophageal cancer (EC) and established a nomogram to evaluate the cancer-specific survival of patients. The surveillance, epidemiology, and end results database was used to collect data on patients diagnosed with EC. Univariate and multivariate Cox analyses were used to determine independent prognostic factors, and the nomogram for predicting cancer-specific survival of EC patients was constructed based on the independent prognostic factors obtained from the multivariate Cox analysis. To evaluate the predictive ability of the nomogram, calibration curves, concordance index (C-index), receiver operating characteristic curves, and decision curve analysis were conducted. Kaplan-Meier method was used to analyze the long-term outcomes of EC patients with different risk stratifications. A total of 3050 cases with lymph node-negative EC were randomized into the training cohort (1525) and the validation cohort (1525). Cancer-specific mortality at 1, 3, and 5 years in the entire cohort was 30.7%, 41.8%, and 59.2%, respectively. In multivariate Cox analysis, age (P < .001), marital status (P < .001), tumor size (P < .001), Tumor-node-metastasis stage (P < .001), chemotherapy (P = .011), radiotherapy (P < .001), and surgery (P < .001) were independent prognostic factors. The C-index for the training cohort was 0.740 (95% confidence interval [CI]: 0.722-0.758), and the C-index for the validation cohort was 0.738 (95% CI: 0.722-0.754). The calibration curve demonstrated the great calibration ability of the nomogram. Based on the area under the receiver operating characteristic curve, the nomogram demonstrated a higher sensitivity than the tumor-node-metastasis stage. Decision curve analysis showed the good clinical utility of the nomogram. The risk stratification system was established using the Kaplan-Meier curve and verified by the log-rank test (P < .001). The nomogram and risk stratification system can improve the accuracy of prediction to help clinicians identify high-risk patients and make treatment decisions., Competing Interests: The authors have no funding and conflicts of interest to disclose., (Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc.)

Objective: To investigate the capability of computed tomography (CT) radiomic features to predict the therapeutic response and local control of the locoregional recurrence lymph node (LN) after curative esophagectomy by chemoradiotherapy., Methods: This retrospective study included 129 LN from 77 patients (training cohort: 102 LN from 59 patients; validation cohort: 27 LN from 18 patients) with postoperative esophageal squamous cell carcinoma (ESCC). The region of the tumor was contoured in pretreatment contrast-enhanced CT images. The least absolute shrinkage and selection operator with logistic regression was used to identify radiomic predictors in the training cohort. Model performance was evaluated using the area under the receiver operating characteristic curves (AUC). The Kaplan-Meier method was used to determine the local recurrence time of cancer., Results: The radiomic model suggested seven features that could be used to predict treatment response. The AUCs in training and validated cohorts were 0.777 (95% CI: 0.667-0.878) and 0.765 (95% CI: 0.556-0.975), respectively. A significant difference in the radiomic scores (Rad-scores) between response and nonresponse was observed in the two cohorts (p < 0.001, 0.034, respectively). Two features were identified for classifying whether there will be relapse in 2 years. AUC was 0.857 (95% CI: 0.780-0.935) in the training cohort. The local control time of the high Rad-score group was higher than the low group in both cohorts (p < 0.001 and 0.025, respectively). As inferred from the Cox regression analysis, the low Rad-score was a high-risk factor for local recurrence within 2 years., Conclusions: The radiomic approach can be used as a potential imaging biomarker to predict treatment response and local control of recurrence LN in ESCC patients., (© 2021 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.)

The long noncoding RNASBF2-AS1 can promote the occurrence and development of many kinds of tumours, but its role in oesophageal squamous cell carcinoma (ESCC) is unknown. We found that SBF2-AS1 was up-regulated in ESCC, and its expression was positively correlated with tumor size (P = 0.0001), but was not related to gender, age, TNM stage, histological grade, and lymphnode metastasis (P > 0.05). It was further found that the higher the expression of SBF2-AS1, the lower the survival rate. COX multivariate analysis showed that the expression of SBF2-AS1 was an independent prognostic factor. Functional experiments show that inhibition of SBF2-AS1 can inhibit the proliferation of ESCC through in vivo and in vitro, and overexpression of SBF2-AS1 can promote the proliferation of ESCC and inhibit its apoptosis. In mechanism, SBF2-AS1/miR-338-3P, miR-362-3P/E2F1 axis are involved in the regulation of ESCC growth. In general, SBF2-AS1 may be used as ceRNA to combine with miR-338-3P and miR-362-3P to up-regulate the expression ofE2F1, and ultimately play a role in promoting cancer. It may be used as a therapeutic target and a biomarker for prognosis.