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Introduction: At present, clinical factors and hematological indicators have been proved to have great potential in predicting the prognosis of cancer patients, and no one has combined these two valuable indicators to establish a prognostic model for esophageal squamous cell carcinoma (ESCC) patients with stage T1-3N0M0 after R0 resection. To verify, we aimed to combine these potential indicators to establish a prognostic model., Methods: Stage T1-3N0M0 ESCC patients from two cancer centers (including training cohort: N = 819, and an external validation cohort: N = 177)-who had undergone esophagectomy in 1995-2015 were included. We integrated significant risk factors for death events by multivariable logistic regression methods and applied them to the training cohort to build Esorisk. The parsimonious aggregate Esorisk score was calculated for each patient; the training set was divided into three prognostic risk classes according to the 33rd and 66th percentiles of the Esorisk score. The association of Esorisk with cancer-specific survival (CSS) was assessed using Cox regression analyses., Results: The Esorisk model was: [10 + 0.023 × age + 0.517 × drinking history - 0.012 × hemoglobin-0.042 × albumin - 0.032 × lymph nodes]. Patients were grouped into three classes-Class A (5.14-7.26, low risk), Class B (7.27-7.70, middle risk), and Class C (7.71-9.29, high risk). In the training group, five-year CSS decreased across the categories (A: 63%; B: 52%; C: 30%, Log-rank P < 0.001). Similar findings were observed in the validation group. Additionally, Cox regression analysis showed that Esorisk aggregate score remained significantly associated with CSS in the training cohort and validation cohort after adjusting for other confounders., Conclusions: We combined the data of two large clinical centers, and comprehensively considered their valuable clinical factors and hematological indicators, established and verified a new prognostic risk classification that can predict CSS of stage T1-3N0M0 ESCC patients., (© 2023. The Author(s).)

Background: The postoperative survival effect of the number of examined lymph nodes on patients of R0-resected esophageal squamous cell carcinoma with pathological stage T1-3N0M0 is still unclear., Methods: Patients diagnosed with pathological stage T1-3N0M0 esophageal squamous cell carcinoma from two cancer databases-our cancer center (N = 707), and Surveillance Epidemiology and End Results (N = 151). The primary clinical endpoint was overall survival. The X-tile software was used to determine the optimal cutoff value of the number of examined lymph nodes, and propensity score matching was conducted to reduce selection bias according to the results of X-tile software. The cohort of 151 patients from another database was used for validation., Results: X-tile software provided an optimal cutoff value of 15 examined lymph nodes based on 707 patients, and 231 pairs of matched patients were included. In the unmatched cohort, Cox proportional hazard regression analysis revealed better overall survival in patients with more than 15 examined lymph nodes (adjusted hazard ratio, 0.566, 95% confidence interval, 0.445-0.720; p < 0.001) compared with patients with 15 or fewer examined lymph nodes. In the validation cohort, patients with more than 15 examined lymph nodes also had better overall survival (adjusted hazard ratio 0.665, p = 0.047)., Conclusions: The number of examined lymph nodes is a significant prognostic factor in esophageal squamous cell carcinoma patients with pathological stage T1-3N0M0, and more than 15 examined lymph nodes are associated with better overall survival. Although the difference is not significant, the survival curve of patients with examined lymph nodes > 30 is better than those with examined lymph nodes 15-30. We believe that the number of examined lymph nodes can provide prognostic guidance for those patients, and the more examined lymph nodes cause lesser occult lymph nodes metastasis and lead to a better prognosis. Therefore, surgeons and pathologists should try to examine as many lymph nodes as possible to evaluate the pathological stage precisely. However, we need more validation from other studies., (© 2022. The Author(s).)

Currently, the incidence of esophageal squamous cell carcinoma (ESCC) in China is high and its prognosis is poor. To evaluate the prognosis of patients with ESCC, we performed computerized quantitative analyses on diagnostic computed tomography (CT) and digital histopathological slices. A retrospective study was conducted to assess the prognosis of ESCC in 153 patients who underwent esophagectomy, and the cohort was selected based on strict clinical criteria. Each patient had an enhanced CT image, and there were two imaging protocols for CT images of all patients. Each patient in the cohort also had a histopathological tissue slide after hematoxylin-eosin staining. Under an electron microscope, the tissue slide was scanned as an image of large size. We then performed quantitative analyses to identify factors related to the prognosis of ESCC on digital histological images and diagnostic CT images. For CT images, we used the radiomics method. For histological images, we designed a set of quantitative features based on machine learning algorithms, such as K-means and principal component analysis. These features describe the patterns of different cell types in histopathological images. Subsequently, we used the survival analysis model established using only CT image features as the baseline. We also compared multiple machine learning models and adopted a five-fold cross-validation method to establish a robust survival model. In establishing survival models, we first used CT image features to establish survival models, and the C-index from the Weibull Cox model on the test set reached 0.624. Then we used histopathlogical features to establish survival models, and the C-index from the Weibull Cox model on the test set reached 0.664, which was obviously better than CT's. Lastly, we combined CT image features and histopathological image features to establish survival models. The performance was better than that in the models built using only CT image features or histopathological image features, and the C-index from the regularized Cox model on the test set reached 0.694. We also proved the effectiveness of the quantified histopathological image features in terms of prognosis using the log-rank test. Histopathological image features are more relevant to prognosis than features extracted from CT images using radiomics. The results of this study provide clinicians with a reference to improve the survival rate of patients with ESCC after surgery. These results have implications for advancing the process of explaining the poor prognosis of esophageal cancer., (© 2021 Institute of Physics and Engineering in Medicine.)

Background: To explore the postoperative prognosis of esophageal squamous cell carcinoma (ESCC) patients with stage IB/IIA, using a prognostic score (PS)., Methods: Stage IB/IIA ESCC patients who underwent esophagectomy from 1999 to 2010 were included. We retrospectively recruited 153 patients and extracted their medical records. Moreover, we analyzed the programmed death ligand-1 (PD-L1) expression of their paraffin tissue. The cohort were randomly divided into a training group (N = 123) and a validation group (N = 30). We selected overall survival (OS) as observed endpoint. Prognostic factors with a multivariable two-sided P < 0.05 met standard of covariate inclusion., Results: Univariable and multivariable analyses identified pTNM stage, the number of lymph nodes (NLNs) and PD-L1 expression as independent OS predictors. Primary prognostic score which comprised above three covariates adversely related with OS in two cohorts. PS discrimination of OS was comparable between the training and internal validation cohorts (C-index = 0.774 and 0.801, respectively). In addition, the PS system had an advantage over pTNM stage in the identification of high-risk patients (C-index = 0.774 vs. C-index = 0.570, P < 0.001). Based on PS cutoff, training and validation datasets generated low-risk and high-risk groups with different OS. Our three-factor PS predicted OS (low-risk subgroup vs. high-risk subgroup 60-month OS, 74% vs. 23% for training cohort and 83% vs. 45% for validation cohort)., Conclusion: Our study suggested a PS for significant clinical stratification of IB/IIA ESCC to screen out subgroups with poor prognosis.

Background: This study aimed to explore the significance of preoperative levels of squamous cell carcinoma antigen (SCC-Ag) and albumin on the cancer-specific survival (CSS) of patients with stage T1-3N0M0 in esophageal squamous cell cancer (ESCC)., Methods: The data of 308 patients who underwent esophagectomy between 1996 and 2011 were analyzed. SCC-Ag and albumin levels were measure 1 week before surgery. The optimal cutoff levels of SCC-Ag and albumin were determined using the X-Tile software, which were 1.0 μg/L and 39.8 g/L, respectively. The associations between SCC-Ag and albumin levels and clinicopathological characteristics were assessed using the χ 2 test, Student's t-test and Fisher's exact test. Cox univariable and multivariable analyses were computed to identify SCC-Ag and albumin levels as independent prognostic factors related to the CSS of patients with ESCC. We used the Kaplan-Meier survival curve to determine the significance of SCC-Ag and albumin level on ESCC in the long-term follow-up., Results: The 5-year CSS rate for the entire cohort was 65.0%. There was a significant difference in CSS between the low and high SCC-Ag level groups (hazard ratio [HR], 1.828, 95% confidence interval [CI], 1.203-2.778; P = 0.005). Patients with ESCC with low albumin level had a worse CSS than those with high albumin level (HR, 0.540; 95% CI, 0.348-0.838; P = 0.006). Patients with both high SCC-Ag and low albumin levels had worse 5-year CSS than patients with low SCC-Ag and high albumin levels (P < 0.05)., Conclusions: Preoperative serum SCC-Ag and albumin levels can predict survival in patients ESCC with stage T1-3N0M0. Patients with ESCC with high SCC-Ag and low albumin levels may have a poor survival outcome.