Objective To investigate the influencing factors for the clinical remission of advanced esophageal squamous cell carcinoma (ESCC) after neoadjuvant chemotherapy, establish an individualized nomogram model to predict the clinical remission of advanced ESCC with neoadjuvant chemotherapy and evaluate its efficacy, providing serve for the preoperative adjuvant treatment of ESCC.Methods The clinical data of patients with esophageal cancer who underwent neoadjuvant chemotherapy (nedaplatin 80 mg/m2, day 3+docetaxel 75 mg/m2, day 1, 2 cycles, 21 days per cycle interval) in the Department of Thoracic Surgery, Affiliated Hospital of North Sichuan Medical College from February 2016 to August 2020 were analyzed retrospectively. According to the WHO criteria for efficacy assessment of solid tumors, tumors were divided into complete remission (CR), partial remission (PR), stable disease (SD) and progressive disease (PD). CR and PR were defined as effective neoadjuvant chemotherapy, and SD and PD were defined as ineffective neoadjuvant chemotherapy. Univariate and multivariate analyses were used to analyze the influencing factors for the short-term efficacy of neoadjuvant chemotherapy. The R software was used to establish a nomogram model for predicting the clinical remission of advanced ESCC with neoadjuvant chemotherapy, and Bootstrap method for internal verification of the model. C-index, calibration curve and receiver operating characteristic (ROC) curve were used to evaluate the predictive performance of the nomogram.Results Finally 115 patients were enrolled, including 93 males and 22 females, aged 40-75 (64.0±8.0) years. After receiving docetaxel+nedaplatin neoadjuvant chemotherapy for 2 cycles, there were 9 patients with CR, 56 patients with PR, 43 patients with SD and 7 patients with PD. Among them, chemotherapy was effective (CR+PR) in 65 patients and ineffective (SD+PD) in 50 patients, with the clinical effective rate of about 56.5% (65/115). Univariate analysis showed that there were statistical differences in smoking history, alcoholism history, tumor location, tumor differentiation degree, and cN stage before chemotherapy between the effective neoadjuvant chemotherapy group and the ineffective neoadjuvant chemotherapy group (P<0.05). Logistic regression analysis showed that low-differentiation advanced ESCC had the worst clinical response to neoadjuvant chemotherapy, moderately-highly differentiated ESCC responded better (P<0.05). Stage cN0 advanced ESCC responded better to neoadjuvant chemotherapy than stage cN1 and cN2 (P<0.05). The C-index and the area under the ROC curve of the nomogram were both 0.763 (95%CI 0.676-0.850), the calibration curve fit well, the best critical value of the nomogram calculated by the Youden index was 70.04 points, and the sensitivity and specificity of the critical value were 80.0% and 58.0%, respectively.ConclusionThe established clinical prediction model has good discrimination and accuracy, and can provide a reference for individualized analysis of the clinical remission of advanced ESCC with neoadjuvant chemotherapy and the screening of new adjuvant treatment subjects.
ObjectiveTo summarise the application research progress of clinical prediction models in postoperative complications of gastric cancer, in order to reduce the risk of complications after gastric cancer surgery. MethodThe literature on the study of postoperative complications of gastric cancer at home and abroad was read and reviewed. ResultsAt present, the main way of treating gastric cancer was still radical resection, and the occurrence of complications after surgical treatment seriously affected the recovery and survival quality of patients. With the deepening of research, the prediction models of postoperative complications in gastric cancer were constantly constructed, and these models provided strong evidence for the early judgement of postoperative complications in gastric cancer, and provided a scientific basis for the improvement of patients’ life quality. ConclusionClinical predictive models are expected to become risk screening tools for predicting the risk of postoperative complications of gastric cancer with clinical utility.
ObjectiveTo explore the predictive value of the pre-treatment systemic immune-inflammation index (SII) for major pathological response (MPR) after neoadjuvant immunochemotherapy (nICT) in esophageal cancer, and to construct a clinical prediction model combined with relevant clinical characteristics. Methods Retrospective collection of clinical data from patients with locally advanced esophageal cancer who received nICT followed by radical surgery at the First People's Hospital of Jining from January 2022 to June 2023. The receiver operating characteristic (ROC) curve was used to evaluate the predictive value of pre-treatment SII and neutrophil-lymphocyte ratio (NLR) for the efficacy of nICT in esophageal cancer. The optimal cut-off value was determined based on the maximum Youden index. Further, univariate and multivariate logistic regression analyses were employed to identify predictors for MPR after nICT in esophageal cancer and to construct a nomogram model. The model was evaluated using the area under the ROC curve (AUC), and internal validation was conducted using the Bootstrap method. ResultsA total of 63 patients were included, with 38 males and 25 females, and a median age of 67 (49-79) years. The ROC curve indicated that the optimal cut-off value for pre-treatment SII was 521.7, with an AUC of 0.701 [95%CI (0.564, 0.838)] for predicting MPR after nICT in esophageal cancer. The ROC curve showed that the optimal cut-off value for pre-treatment NLR was 2.32, with an AUC of 0.681 [95%CI (0.544, 0.818)]. Multivariate logistic regression analysis results revealed cT stage [OR=0.232, 95%CI (0.071, 0.759), P=0.016] and SII [OR=5.477, 95%CI (1.584, 18.939), P<0.001] as independent predictors for MPR after nICT in esophageal cancer. Based on the multivariate logistic regression results, a clinical prediction model was constructed, with an AUC of 0.789 on the ROC curve. The calibration plot showed a good agreement between the prediction curve and the ideal curve. ConclusionPre-treatment SII can serve as an independent predictive indicator for MPR in patients with esophageal cancer after nICT. The clinical model, established in combination with cT stage, can better predict the efficacy of nICT in esophageal cancer.