scholarly journals Prevalence of frailty and prediction of mortality in Chinese cancer patients using a frailty index‐based clinical algorithm—A multicentre study

2021 ◽  
Author(s):  
Xi Jin ◽  
Yue Ren ◽  
Li Shao ◽  
Zengqing Guo ◽  
Chang Wang ◽  
...  
Keyword(s):  
Multicentre Study ◽  
Cancer Patients ◽  
Frailty Index ◽  
Clinical Algorithm ◽  
Prediction Of Mortality ◽  
Chinese Cancer Patients

The Prevalence of Frailty in Cancer Patients and Mortality Prediction With a Novel Frailty Index Based Clinical Algorithm-A Multicenter, Prospective, Observational Study

2020 ◽  
Author(s):  
Xi Jin ◽  
Yue Ren ◽  
Li Shao ◽  
Zengqing Guo ◽  
Chang Wang ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Frailty Index ◽  
National Level ◽  
Laboratory Data ◽  
Mortality Prediction ◽  
Prediction Algorithm ◽  
Tumor Type ◽  
Risk Of Death ◽  
Increased Risk ◽  
Chinese Cancer Patients

Abstract Purpose To investigate the prediction capacity and status of frailty in Chinese cancer patients in national level, through establishing a novel prediction algorithm. Methods The percentage of frailty in different ages, provinces and tumor type groups of Chinese cancer patients were revealed. The predictioncapacity of frailty on mortality of Chinese cancer patients was analyzed by FI-LAB that is composed of routine laboratory data from accessible blood test and calculated as the ratio of abnormal factors in 22 variables. Establishment of a novel algorithm MCP(mortality of cancer patients)to predict the five-year mortality in Chinese cancer patients was accomplished and its prediction capacity was tested in the training and validation sets using ROC analysis. ResultsWe found that the increased risk of death in cancer patients can be successfully identified through FI-LAB. The univariable and multivariable Cox regression were used to evaluate the effect of frailty on death. In the 5-year follow-up, 20.6% of the 2959 participants (age = 55.8 ± 11.7 years; 43.5% female) were dead while the mean FI-LAB score in baseline was 0.23 (standard deviation = 0.13; range = 0 to 0.73).Frailty (after adjusting for gender, age, and other confounders) could be directly correlated with increased risk of death, with a hazard ratio of 12.67 (95% confidence interval CI: 7.19, 22.31) in comparison with those without frailty. In addition, MCP algorithm presented an area under the ROC (AUC) of 0.691 (95% CI: 0.659-0.684) and 0.648 (95% CI: 0.613-0.684) in the training and validation set, respectively. Conclusion Frailty is common in cancer patients and FI-LAB has high prediction capacity on mortality. The MCP algorithm is a good supplement for frailty evaluation and mortality prediction in cancer patients.

Exploration of the MSI landscape in Chinese pan-cancer patient by Next-Generation Sequencing.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e14576-e14576
Author(s):  
Xinlu Liu ◽  
Jiasheng Xu ◽  
Jian Sun ◽  
Deng Wei ◽  
Xinsheng Zhang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Correlation Analysis ◽  
Cancer Patients ◽  
Cancer Type ◽  
Multiple Tumor ◽  
Treatment Plans ◽  
Cancer Types ◽  
Chinese Cancer Patients ◽  
Colorectal Cancer Patients ◽  
Pan Cancer

e14576 Background: Clinically, MSI had been used as an important molecular marker for the prognosis of colorectal cancer and other solid tumors and the formulation of adjuvant treatment plans, and it had been used to assist in the screening of Lynch syndrome. However, there were currently few reports on the incidence of MSI-H in Chinese pan-cancer patients. This study described the occurrence of MSI in a large multi-center pan-cancer cohort in China, and explored the correlation between MSI and patients' TMB, age, PD-L1 expression and other indicators. Methods: The study included 8361 patients with 8 cancer types from multiple tumor centers. Use immunohistochemistry to detect the expression of MMR protein (MLH1, MSH2, MSH6 and PMS2) in patients with various cancer types to determine the MSI status and detect the expression of PD-L1 in patients. Through NGS technology, 831 genes of 8361 Chinese cancer patients were sequenced and the tumor mutation load of the patients was calculated. The MSI mutations of patients in 8 cancer types were analyzed and the correlation between MSI mutations of patients and the patient's age, TMB and PD-L1 expression was analyzed. Results: The test results showed that MSI patients accounted for 1.66% of pan-cancers. Among them, MSI-H patients accounted for the highest proportion in intestinal cancer, reaching 7.2%. The correlation analysis between MSI and TMB was performed on patients of various cancer types. The results showed that: in each cancer type, MSI-H patients had TMB greater than 10, and 26.83% of MSI-H patients had TMB greater than 100 in colorectal cancer patients. The result of correlation analysis showed that there was no significant correlation between the patient's age and the risk of MSI mutation ( P> 0.05). In addition to PAAD and LUAD, the expression of PD-L1 in MSI-H patients was higher than that in MSS patients in other cancer types( P< 0.05). The correlation analysis between PD-L1 expression and TMB in patients found that in colorectal cancer, the higher the expression of PD-L1, the higher the patient's TMB ( P< 0.05). Conclusions: In this study, we explored the incidence of MSI-H in pan-cancer patients in China and found that the TMB was greater than 10 in patients with MSI-H. Compared with MSS patients, MSI-H patients have higher PD-L1 expression, and the higher the PD-L1 expression in colorectal cancer, the higher the TMB value of patients.

Comprehensive unmet needs and correlations with quality of life in Chinese cancer patients

2018 ◽  
Vol 27 (2) ◽  
pp. e12813 ◽  
Author(s):  
Y.P. Zhang ◽  
Y. Zhang ◽  
W.H. Liu ◽  
Y.T. Yan ◽  
H.H. Wei
Keyword(s):  
Quality Of Life ◽  
Cancer Patients ◽  
Unmet Needs ◽  
Chinese Cancer Patients

scholarly journals Application of Data Mining for the Prediction of Mortality and Occurrence of Complications for Gastric Cancer Patients

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1163 ◽  
Author(s):  
Cristiana Neto ◽  
Maria Brito ◽  
Vítor Lopes ◽  
Hugo Peixoto ◽  
António Abelha ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Data Mining ◽  
Cancer Patients ◽  
Healthcare Delivery ◽  
Treatment Strategies ◽  
Data Mining Algorithms ◽  
Prediction Of Mortality ◽  
Hospital Stays ◽  
Gastric Cancer Patients ◽  
Mining Algorithms

The development of malign cells that can grow in any part of the stomach, known as gastric cancer, is one of the most common causes of death worldwide. In order to increase the survival rate in patients with this condition, it is essential to improve the decision-making process leading to a better and more efficient selection of treatment strategies. Nowadays, with the large amount of information present in hospital institutions, it is possible to use data mining algorithms to improve the healthcare delivery. Thus, this study, using the CRISP methodology, aims to predict not only the mortality associated with this disease, but also the occurrence of any complication following surgery. A set of classification models were tested and compared in order to improve the prediction accuracy. The study showed that, on one hand, the J48 algorithm using oversampling is the best technique to predict the mortality in gastric cancer patients, with an accuracy of approximately 74%. On the other hand, the rain forest algorithm using oversampling presents the best results when predicting the possible occurrence of complications among gastric cancer patients after their in-hospital stays, with an accuracy of approximately 83%.

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