Evaluation of overall survival and disease-free survival of adjuvant chemotherapy and hormone therapy in patients with breast cancer

Abstract : Background: This study evaluated the effect of adjuvant chemotherapy and hormone therapy on overall survival and disease-free survival in patients with breast cancer with hormone receptor-positive, HER2-negative tumor without lymph node involvement. Methods: Breast cancer patients with hormone receptor-positive, HER2-negative, and no lymph node involvement were included in this retrospective cohort study. Patient records were used to collect data on sex, age, time of disease onset, tumor subtype, tumor size, grade, lymphovascular and perineural involvement, ki67, and treatment protocols. Patients were divided into 2 groups: Patients who received both adjuvant chemotherapy and hormonal therapy and patients who received hormonal therapy only. Disease-free survival index (DFS) and overall survival index (OS) were evaluated. Results: Sixty-seven female patients were enrolled in this study. Of them, 68.2% received both adjuvant chemotherapy and hormonal therapy and 31.6% received hormonal therapy only. During follow-up, recurrences occurred in 8 patients. The 3-year and 5-year DFS were 93.4% and 90%, respectively. The 3-year and 5-year DFS were 94% and 92%, respectively, in patients who received both adjuvant chemotherapy and hormonal therapy, and 91% and 85%, respectively, in patients who received hormonal therapy. None of the factors studied affected the 3-year and 5-year DFS. The 3-year and 5-year DFS OS were 98.6% and 96.9%, respectively CONCLUSION: Adjuvant chemotherapy in patients with breast cancer with hormone receptor-positive, HER2-negative, and no lymph node involvement compared with similar patients receiving hormone therapy alone had no significant difference in disease-free survival index and overall survival index. Keywords: breast cancer; disease-free survival index; overall survival inde

Evaluate effect of 126 pre-processing methods on various artificial intelligence models accuracy versus normal mode to predict groundwater level (case study: Hamedan-Bahar Plain, Iran)

The estimation of groundwater levels is crucial and an important step in ensuring sustainable management of water resources. In this paper, selected piezometers of the Hamedan-Bahar plain located in west of Iran. The main objective of this study is to compare effect of various pre-processing methods on input data for different artificial intelligence (AI) models to predict groundwater levels (GWLs). The observed GWL, evaporation, precipitation, and temperature were used as input variables in the AI algorithms. Firstly, 126 method of data pre-processing was done by python programming which are classified into three classes: 1- statistical methods, 2- wavelet transform methods and 3- decomposition methods; later, various pre-processed data used by four types of widely used AI models with different kernels, which includes: Support Vector Machine (SVR), Artificial Neural Network (ANN), Long-Short Term memory (LSTM), and Pelican Optimization Algorithm (POA) - Artificial Neural Network (POA-ANN) are classified into three classes: 1- machine learning (SVR and ANN), 2- deep learning (LSTM) and 3- hybrid-ML (POA-ANN) models, to predict groundwater levels (GWLs). Akaike Information Criterion (AIC) were used to evaluate and validate the predictive accuracy of algorithms. According to the results, based on summation (train and test phases) of AIC value of 1778 models, average of AIC values for ML, DL, hybrid-ML classes, was decreased to −25.3%, −29.6% and −57.8%, respectively. Therefore, the results showed that all data pre-processing methods do not lead to improvement of prediction accuracy, and they should be selected very carefully by trial and error. In conclusion, wavelet-ANN model with daubechies 13 and 25 neurons (db13_ANN_25) is the best model to predict GWL that has −204.9 value for AIC which has grown by 5.23% (−194.7) compared to the state without any pre-processing method (ANN_Relu_25).Validerad;2024;Nivå 2;2024-04-16 (hanlid);Funder: Ministry of Science and ICT, South Korea (20230166-001);Full text license: CC BY 4.0</p

An Integrated Statistical-Machine Learning Approach for Runoff Prediction

Nowadays, great attention has been attributed to the study of runoff and its fluctuation over space and time. There is a crucial need for a good soil and water management system to overcome the challenges of water scarcity and other natural adverse events like floods and landslides, among others. Rainfall–runoff (R-R) modeling is an appropriate approach for runoff prediction, making it possible to take preventive measures to avoid damage caused by natural hazards such as floods. In the present study, several data-driven models, namely, multiple linear regression (MLR), multiple adaptive regression splines (MARS), support vector machine (SVM), and random forest (RF), were used for rainfall–runoff prediction of the Gola watershed, located in the south-eastern part of the Uttarakhand. The rainfall–runoff model analysis was conducted using daily rainfall and runoff data for 12 years (2009 to 2020) of the Gola watershed. The first 80% of the complete data was used to train the model, and the remaining 20% was used for the testing period. The performance of the models was evaluated based on the coefficient of determination (R2), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and percent bias (PBAIS) indices. In addition to the numerical comparison, the models were evaluated. Their performances were evaluated based on graphical plotting, i.e., time-series line diagram, scatter plot, violin plot, relative error plot, and Taylor diagram (TD). The comparison results revealed that the four heuristic methods gave higher accuracy than the MLR model. Among the machine learning models, the RF (RMSE (m3/s), R2, NSE, and PBIAS (%) = 6.31, 0.96, 0.94, and −0.20 during the training period, respectively, and 5.53, 0.95, 0.92, and −0.20 during the testing period, respectively) surpassed the MARS, SVM, and the MLR models in forecasting daily runoff for all cases studied. The RF model outperformed in all four models’ training and testing periods. It can be summarized that the RF model is best-in-class and delivers a strong potential for the runoff prediction of the Gola watershed.Validerad;2022;Nivå 2;2022-07-05 (sofila);Funder: , G.B. Pant University of Agriculture and Technology, India; Gola Barrage gauge station Haldwani–Kathgodam, India; Portuguese Foundation for Science and Technology (PTDC/CTA-OHR/30561/2017, WinTherface)</p

A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration

Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (NES), root mean square error (RMSE), Willmott’s index of agreement (WI), and Legates–McCabe’s index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Qt-n) and suspended sediment concentration (St-n) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (St-1, St-2, St-3, St-4) outperformed the other developed models with the lowest RMSE = 95.02 mg/l and the highest NES = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71 mg/l and the highest NES = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships.Full text license: CC BY 4.0; Funder: Open access funding provided by Lulea University of Technology; King Saud University, Riyadh, Saudi Arabia (RSPD2024R958);</p