Background: To investigate the prognostic value of pretreatment primary gross tumor with (GTVp) and without retropharyngeal lymph nodes (GTVnx) for predicting survival outcomes in patients with localregional advanced nasopharyngeal carcinoma (NPC) after intensity-modulated radiation therapy (IMRT).Methods: From Jan 2012 to Dec 2017, 148 patients with local-regional advanced NPC who had undergone definitive radiotherapy were identified. GTVnx volume and retropharyngeal lymph nodes (GTVrLNs) volume were measured based on registration of MRI with contrast-enhanced CT images. The Kaplan-Meier method was used for survival analysis. Univariate and multivariate prognostic analyses was performed by using the Cox proportional hazard model. Receiver operating characteristic (ROC) curves were used to identify the cut-off point and assess the prognostic value for GTVnx, GTVp and GTVrLNs.Results: The median follow-up time for the entire group was 27 months (ranging 7 to 80 months). The 3-year overall survival (OS) rate was 85%, and the 3-year local failure-free rate (LFFR), distant failurefree rate (DFFR) and disease-free survival (DFS) rates were 93%, 81%, and 73%, respectively. A positive correlation between GTVnx or GTVp volume and T stage was observed (both P<0.001). The 3-year LFFR, OS, and DFS rate, but not for DMFS, in NPC patients with GTVnx ≤42.7 cm 3 was significantly better than those with >42.7 cm 3 (all P<0.05). Multivariate analysis indicated that GTVnx volume (P=0.041) was the only independent prognostic factor for LFFR, while age and AJCC stage were two independent prognostic factors for OS. Conclusions:The GTVnx is an independent prognostic factor for local control, while the prognostic value of GTVrLNs is limited. Physicians are recommended to distinguish between GTVnx and retropharyngeal lymph nodes (RLN) involvement when assessing the risk for local recurrence in advanced NPC.
IntroductionIt’s very necessary to predict the survival status of patients based on their prognosis. This can assist physicians in evaluating treatment decisions. Random Forest is an excellent machine learning algorithm even without any modification. We propose a new Random Forest weighting method and apply it to the gastric cancer patient data from the Surveillance, Epidemiology, and End Results (SEER) program, and then evaluated the generalization ability of this weighted Random Forest algorithm on 10 public medical datasets. Furthermore, for the same weighting mode, the difference between using out-of-bag (OOB) data and all training sets as the weighting basis is explored.Material and methods110697 cases of gastric cancer patients diagnosed between 1975 and 2016 obtained from the SEER database were contained in the experiment. In addition, 10 public medical datasets are used for the generalization ability evaluation of this weighted Random Forest algorithm.ResultsThrough experimental verification, on the SEER gastric cancer patient data, the weighted Random Forest algorithm improves the accuracy by 0.79% compared with the original Random Forest. In AUC, Macro-averaging increased by 2.32% and Micro-averaging increased by 0.51% on average. Among the 10 public datasets, the Random Forest weighted in accuracy has the best performance on 6 datasets, with an average increase of 1.44% in accuracy and an average increase of 1.2% in AUC.ConclusionsCompared with the original Random Forest, the weighted Random Forest model has a significant improvement in performance, and the effect of using all training data as the weighting basis is better than using OOB data.
Introduction: There are numerous types of surgery for patients with primary gastric tumour, which can be summarized as radical surgery or palliative surgery. Different surgical procedures will have further effects for different stage of patients.Aim: We will use the resources of the SEER database (2010)(2011)(2012)(2013)(2014)(2015) to explore the therapeutic value of surgery and prognostic factors.Material and methods: Kaplan-Meier analysis/log-rank testing for data analysis and multivariate analysis was conducted through a Cox proportional model.Results: Fourteen thousand five hundred and seven cases of primary gastric tumours identified in the period from 2010 to 2015. In a multivariate cox regression analysis, the following factors were associated with better primary gastric patients survival (Surgical method, Age at diagnosis, histological grade). Through Kaplan-Meier analysis (p < 0.005) we also found that for the patient group the survival rate of using gastrectomy (partial, subtotal, hemi-) surgery is the lowest.Conclusions: Among patients with multivariate Cox regression model, type of surgery, age at diagnosis, and histological grade were the top 3 factors affecting patient survival. In palliative surgery, laser excision is the best surgical method of local tumour excision, and the survival of patients of this group is obviously better than in other groups. In radical surgery, near-total gastrectomy and radical gastrectomy, in continuity with the resection of other organs, are better surgical methods, while gastrectomy (partial, subtotal, hemi-) is the worst type of surgery in terms of prognosis, and even the survival rate in the later stage (after 3 years) is worse than in the group without surgery.
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