The number of cycles of adjuvant chemotherapy in stage III and high-risk stage II rectal cancer: a nomogram and recursive partitioning analysis.

OBJECTIVE: The prognostic role of the number of cycles of adjuvant chemotherapy (ACT) after total mesorectal excision in stage III and high-risk stage II rectal cancer is unknown. As a result of this, our study was designed to assess the effect of the number of cycles of ACT on the prediction of cancer-specific survival. METHODS: Four hundred patients that were diagnosed as stage III and high-risk stage II rectal cancer from January 2012 to January 2018 and who had received total mesorectal excision were enrolled in this study. A nomogram incorporating the number of cycles of ACT was also developed in this study. For internal validation, the bootstrap method was used and the consistency index was used to evaluate the accuracy of the model. The patients were stratified into risk groups according to their tumor characteristics by recursive partitioning analysis. RESULTS: We found that the risk of death was decreased by 26% (HR = 0.74, 95% CI: 0.61-0.89, P = 0.0016) with each increasing ACT cycle. The N stage, positive lymph node ratio (PLNR), carcinoembryonic antigen, neutrophil-to-lymphocyte ratio, and the number of cycles of ACT were chosen and entered into the nomogram model. Recursive partitioning analysis-based risk stratification revealed a significant difference in the prognosis in rectal cancer patients with high-risk, intermediate-risk, and low-risk (3-year cancer-specific survival: 0.246 vs. 0.795 vs. 0.968, P < 0.0001). Seven or more cycles of ACT yielded better survival in patients with PLNR ≥ 0.28 but not in patients with PLNR < 0.28. CONCLUSION: In conclusion, the nomogram prognosis model based on the number of cycles of ACT predicted individual prognosis in rectal cancer patients who had undergone total mesorectal excision. These findings further showed that in patients with PLNR ≥ 0.28, no fewer than 7 cycles of ACT are needed to significantly reduce the patient's risk of death.

Development and Internal Validation of a Nomogram-Based Model to Predict Three-Year and Five-Year Overall Survival in Patients with Stage II/III Colon Cancer.

OBJECTIVE: The aim of this study was to develop a nomogram-based model to predict the three-year and five-year overall survival (OS) of patients with stage II/III colon cancer following radical resection. METHODS: A total of 1156 patients with stage II/III colon cancer who underwent radical resection at the Affiliated Hospital of Guizhou Medical University between December 2012 and December 2018 were enrolled. Lasso regression was used to screen out 12 variables: age, prealbumin, albumin, degree of differentiation, total tumor-node-metastasis (TNM) stage, T stage, N stage, prognostic nutritional index (PNI), platelet/lymphocyte count, carcinoembryonic antigen, carbohydrate antigen 19-9 (CA19-9), and postoperative adjuvant chemotherapy. The data set was then randomly split into a modeling set and a validation set, and the bootstrap method was used to verify the internal validity of the final model. A nomogram was then used to present the model, and the risk groups were categorized according to the total score in the nomogram. RESULTS: This study established and developed a simple, easy-to-use predictive model that included age, degree of differentiation, N stage, CA19-9, PNI, and postoperative chemotherapy as variables. In the multivariate Cox regression analysis, only postoperative chemotherapy was identified as an independent risk factor for death in patients with colon cancer. The receiver operating characteristic curve showed that the model demonstrated good resolution, with an area under the curve of 0.803. Decision curve analysis indicated that the model had a good positive net gain, and the bootstrap method was used to verify its stability. In the OS rate, the C-index was 0.78. According to the total score of the nomogram, the risk group was layered by drawing the Kaplan-Meier (K-M) curve. In the three-year OS K-M curve, the survival rates of the low-risk group, the medium-risk group, and the high-risk group were 96%, 93%, and 82%, respectively. In the five-year OS K-M curve, the survival rates of the low-risk group, the medium-risk group, and the high-risk group were 94%, 90%, and 73%, respectively. CONCLUSION: The nomogram-based prediction model developed in this study is stable and has good resolution, reliability, and net gain. It will therefore be useful for clinicians performing risk stratification and postoperative monitoring and in the development of personalized treatment options for patients with stage II/III colon cancer.

Two decoupling methods for non-isothermal DSC results of AIBN decomposition.

During thermal decomposition of azobisisobutyronitrile (AIBN), the endothermic process of phase transition disturbed exothermic decomposition, which brought deformation in its thermal graphs. Therefore, exact kinetic parameters of the decomposition could not be obtained by the existing kinetics analytic models, and the accurate enthalpy data of the decomposition and phase transition were not available. Two methods, i.e., a solvent method and a mathematical method, were introduced in this paper to resolve the coupling phenomenon. In the former method, AIBN was dissolved into aniline to eliminate the endothermic process and obtain curves of the liquid-state decomposition. In the latter method, MATLAB software was employed to get the "pure" exothermic decomposition curve without the influence of phase transition by fitting coupling curves within the section after the transition point and extrapolating to the initial stage of decomposition. Moreover, the kinetic parameters of the "pure" exothermic decomposition of AIBN obtained by the mathematical fitting agreed with the results from the solvent method, verifying the accuracy of the decoupling. The research is of great significance for comprehending the exact characteristics of thermal behaviors and safety parameters of AIBN. It also provides a great help to determine the safe operating temperature and alarm temperature for processes in industry.