NRI and SIRI are the optimal combinations for prognostic risk stratification in patients with non-small cell lung cancer after EGFR-TKI therapy.

BACKGROUND: Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) have become the standard treatment for advanced non-small cell lung cancer (NSCLC) with EGFR mutations. However, NSCLC heterogeneity leads to differences in efficacy; thus, potential biomarkers need to be explored to predict the prognosis of patients. Recently, the prognostic importance of pre-treatment malnutrition and systemic inflammatory response in cancer patients has received increasing attention. METHODS: In this study, clinical information from 363 NSCLC patients receiving EGFR-TKI treatment at our clinical center was used for analysis. RESULTS: High nutritional risk index (NRI) and systemic inflammation response index (SIRI) were significantly associated with poor overall survival (OS) and progression-free survival (PFS) in NSCLC patients (P < 0.05). Importantly, NRI and SIRI were the best combination models for predicting clinical outcomes of NSCLC patients and independent OS and PFS predictors. Moreover, a nomogram model was constructed by combining NRI/SIRI, sex, smoking history, EGFR mutation, TNM stage, and surgery treatment to visually and personally predict the 1-, 2-, 3-, 4-, and 5-year OS of patients with NSCLC. Notably, risk stratification based on the nomogram model was better than that based on the TNM stage. CONCLUSION: NRI and SIRI were the best combination models for predicting clinical outcomes of NSCLC patients receiving EGFR-TKI treatment, which may be a novel biomarker for supplement risk stratification in NSCLC patients.

The prediction and analysis of COVID-19 epidemic trend by combining LSTM and Markov method.

Corona Virus Disease 2019 (COVID-19) has spread rapidly to countries all around the world from the end of 2019, which caused a great impact on global health and has had a huge impact on many countries. Since there is still no effective treatment, it is essential to making effective predictions for relevant departments to make responses and arrangements in advance. Under the limited data, the prediction error of LSTM model will increase over time, and its prone to big bias for medium- and long-term prediction. To overcome this problem, our study proposed a LSTM-Markov model, which uses Markov model to reduce the prediction error of LSTM model. Based on confirmed case data in the US, Britain, Brazil and Russia, we calculated the training errors of LSTM and constructed the probability transfer matrix of the Markov model by the errors. And finally, the prediction results were obtained by combining the output data of LSTM model with the prediction errors of Markov Model. The results show that: compared with the prediction results of the classical LSTM model, the average prediction error of LSTM-Markov is reduced by more than 75%, and the RMSE is reduced by more than 60%, the mean [Formula: see text] of LSTM-Markov is over 0.96. All those indicators demonstrate that the prediction accuracy of proposed LSTM-Markov model is higher than that of the LSTM model to reach more accurate prediction of COVID-19.

Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: Case studies in Russia, Peru and Iran.

The COVID-19 outbreak in late December 2019 is still spreading rapidly in many countries and regions around the world. It is thus urgent to predict the development and spread of the epidemic. In this paper, we have developed a forecasting model of COVID-19 by using a deep learning method with rolling update mechanism based on the epidemical data provided by Johns Hopkins University. First, as traditional epidemical models use the accumulative confirmed cases for training, it can only predict a rising trend of the epidemic and cannot predict when the epidemic will decline or end, an improved model is built based on long short-term memory (LSTM) with daily confirmed cases training set. Second, considering the existing forecasting model based on LSTM can only predict the epidemic trend within the next 30 days accurately, the rolling update mechanism is embedded with LSTM for long-term projections. Third, by introducing Diffusion Index (DI), the effectiveness of preventive measures like social isolation and lockdown on the spread of COVID-19 is analyzed in our novel research. The trends of the epidemic in 150 days ahead are modeled for Russia, Peru and Iran, three countries on different continents. Under our estimation, the current epidemic in Peru is predicted to continue until November 2020. The number of positive cases per day in Iran is expected to fall below 1000 by mid-November, with a gradual downward trend expected after several smaller peaks from July to September, while there will still be more than 2000 increase by early December in Russia. Moreover, our study highlights the importance of preventive measures which have been taken by the government, which shows that the strict controlment can significantly reduce the spread of COVID-19.