Treatment Strategies and Prognostic Factors in Patients with Stage T3 and T4 Laryngeal Carcinoma / 肿瘤防治研究

Objective To investigate the selection of treatment strategies and prognostic factors for patients with stage T3 and T4 laryngeal carcinoma. Methods A total of 132 patients with stage T3 and T4 laryngeal cancer admitted to our hospital from March 2010 to March 2019 were retrospectively selected as research objects. According to the different treatment strategies, the patients were divided into simple surgery group (group A, 57 cases), simple chemoradiotherapy group (group B, 32 cases), and surgery combined with chemoradiotherapy group (group C, 43 cases). The general data and clinicopathological features of the three groups were compared, and a survival curve was drawn by the Kaplan–Meier method. The 3-year survival rates of the three groups were compared. Then, the same 132 patients were divided into survival and death groups. The clinical data of the two groups were compared, and the prognostic factors were analyzed by multivariate logistic regression. A back propagation (BP) neural network model was constructed, and its differentiation and accuracy were evaluated. Results The proportions and 3 year survival rates of patients with poor differentiation, lymphatic vascular invasion, and involvement of lymph nodes outside the capsule in group C were significantly higher than those in groups A and B (P < 0.05). The 3 year survival rate of 132 patients was 68.94%(41/132). Poor differentiation, N2-N3 stage, lymphatic vascular invasion, and involvement of lymph nodes outside the capsule were risk factors for death (P < 0.05), whereas surgery combined with radiotherapy and chemotherapy were protective factors (P < 0.05). The BP neural network model exhibited good discrimination and high accuracy. Conclusion Surgery combined with radiotherapy and chemotherapy can significantly improve survival rate in patients with poor differentiation, lymphatic vascular invasion, and involvement of lymph nodes outside the capsule. Close attention should be paid to patients with stage N2-N3 in the formulation of reasonable treatment strategies.

Postmortem Interval Estimation Using Protein Chip Technology Combined with Multivariate Analysis Methods / 法医学杂志

OBJECTIVES@#To estimate postmortem interval (PMI) by analyzing the protein changes in skeletal muscle tissues with the protein chip technology combined with multivariate analysis methods.@*METHODS@#Rats were sacrificed for cervical dislocation and placed at 16 ℃. Water-soluble proteins in skeletal muscles were extracted at 10 time points (0 d, 1 d, 2 d, 3 d, 4 d, 5 d, 6 d, 7 d, 8 d and 9 d) after death. Protein expression profile data with relative molecular mass of 14 000-230 000 were obtained. Principal component analysis (PCA) and orthogonal partial least squares (OPLS) were used for data analysis. Fisher discriminant model and back propagation (BP) neural network model were constructed to classify and preliminarily estimate the PMI. In addition, the protein expression profiles data of human skeletal muscles at different time points after death were collected, and the relationship between them and PMI was analyzed by heat map and cluster analysis.@*RESULTS@#The protein peak of rat skeletal muscle changed with PMI. The result of PCA combined with OPLS discriminant analysis showed statistical significance in groups with different time points (P<0.05) except 6 d, 7 d and 8 d after death. By Fisher discriminant analysis, the accuracy of internal cross-validation was 71.4% and the accuracy of external validation was 66.7%. The BP neural network model classification and preliminary estimation results showed the accuracy of internal cross-validation was 98.2%, and the accuracy of external validation was 95.8%. There was a significant difference in protein expression between 4 d and 25 h after death by the cluster analysis of the human skeletal muscle samples.@*CONCLUSIONS@#The protein chip technology can quickly, accurately and repeatedly obtain water-soluble protein expression profiles in rats' and human skeletal muscles with the relative molecular mass of 14 000-230 000 at different time points postmortem. The establishment of multiple PMI estimation models based on multivariate analysis can provide a new idea and method for PMI estimation.

Study of cognitive function of patients with brain metastases from lung cancer after whole brain radiotherapy / 中华放射肿瘤学杂志

Objective:To assessthe cognitive function of patients with brain metastases fromlung cancer after whole brain radiotherapy (WBRT).Methods:Clinical data of 146 patients with brain metastases from lung cancer admitted to Jinshazhou Hospital, Guangzhou University of Chinese Medicine from January 2020 to February 2021 were retrospectively analyzed. According to the presence or absence of neurological symptoms, they were divided into group A (presence, n=95) and group B (absence, n=51). Risk factors were determined by using multivariate logistic regression analysis.Nomogram prediction model was constructed and evaluated. Results:After WBRT, 124 cases (84.9%) were clinically effective, and 22 (15.1%) were ineffective. After WBRT, the mini-mental state examination (MMSE) scores of patientsin group A were significantly improved (all P<0.05), with the most significant increase at 4 months, followed by slight decrease. The MMSE scores of patients in group B were decreased, with the most notable decline at 4 months, followed by some recovery. Multivariate logistic analysis showed that 3 cycles of chemotherapy, simultaneous integrated boost, planning target volumeradiation dose of 40 Gy, no hippocampus protection and hippocampus radiation dose of>30 Gy were the independent risk factors for cognitive decline (all P<0.05). The evaluation results of the nomograph prediction model showed that the nomograph prediction model yielded high discrimination, accuracy and effectiveness. According to the risk stratification system, all patients were divided into 4risk groups: extremely low (total score<90), low (90≤total score<128), medium (128≤total score<152) and high (total score≥152) risk groups. Conclusions:For patients with brain metastases from lung cancer, WBRT has high clinical efficacy, whereas it exerts an impact on the cognitive function of patients, with the most significant effect at 4 months. Three cycles of chemotherapy, simultaneous integrated boost, planning target volume radiation dose of 40 Gy, no protection of hippocampus, and radiation dose of>30 Gy in hippocampus are the independent risk factors for cognitive decline.

Multi-index optimization of extraction process of Fengyin Decoction based on BAS-GA-BP neural network combined with entropy weight method / 中国中药杂志

To optimize the ethanol extraction technology parameters of Fengyin Decoction by orthogonal experiment combined with beetle antennae search(BAS)-genetic algorithm(GA)-back propagation neural network(BPNN). Based on single factor investigation, the extraction temperature, ethanol volume, extraction time, and ethanol concentration were used as orthogonal experiment factors, and entropy weight method was used to calculate the comprehensive scores of aloe-emodin, glycyrrhizic acid ammonium salt, rhein, emodin, chrysophanol, physcion, cinnamaldehyde, 6-gingerol, extraction ratio and fingerprint similarity. BAS-BPNN model was established, and then, GA was used to predict the optimal extraction process. The results showed that BAS-BPNN was optimized to obtain the optimal ethanol extraction process of Fengyin Decoction as follows: extraction temperature of 87 ℃, adding 9 times of 75 % ethanol, and extracting for 47 minutes, with a comprehensive score of 1.052 9. Meanwhile, the optimal process parameters obtained by orthogonal design were as follows: the extraction temperature of 80 ℃, adding 10 times of 75% ethanol, extracting for 30 minutes, with a comprehensive score of 1.003 7. The comprehensive score of the process obtained from the BAS-BPNN model was slightly better than that from the orthogonal test, indicating that the optimized process from BAS-BPNN model was more ideal, so it was finally determined as the best extraction process for Fengyin Decoction. The process of Fengyin Decoction obtained from BAS-GA-BPNN has high extraction efficiency and good stability, which provides reference for the subsequent development and quality control.

Analysis of factors influencing hospitalization cost based on back propagation neural network model / 中华医院管理杂志

Objective@#To analyze the influencing factors of hospitalization cost by back propagation(BP)neural network.@*Methods@#Inpatients with a total of 50 611 were collected from the hospital information system in a tertiary hospital. BP Neural network modeling was developed by SPSS 22.0 software to study the factors which influence the hospitalization cost.@*Results@#The hospitalization expenses prediction accuracies of training sample and test sample were 81.1% and 79.0% respectively. The top three factors were pharmaceutical cost(100.0%), general medical operation cost(88.8%)and disposable medical materials cost(60.7%).@*Conclusions@#The results show that BP model is suitable for dependent variable with the hospitalization expenses. The pharmaceutical cost, disposable medical materials cost and general medical operation cost were greater influencing factors than others. We should take actions by improving the treatment level and measures, so as to reduce the economic burden of patients and society

Multi class disorder detection of magnetic resonance brain images using composite features and neural network

Brain disorder recognition has becoming a promising area of study. In reality, some disorders share similar features and signs, making the task of diagnosis and treatment challenging. This paper presents a rigorous and robust computer aided diagnosis system for the detection of multiple brain abnormalities which can assist physicians in the diagnosis and treatment of brain diseases. In this system, we used energy of wavelet sub bands, textural features of gray level co-occurrence matrix and intensity feature of MR brain images. These features are ranked using Wilcoxon test. The composite features are classifi ed using back propagation neural network. Bayesian regulation is adopted to fi nd the optimal weights of neural network. The experimentation is carried out on datasets DS-90 and DS-310 of Harvard Medical School. To enhance the generalization capability of the network, fi vefold stratifi ed cross validation technique is used. The proposed system yields multi class disease classifi cation accuracy of 100% in diff erentiating 90 MR brain images into 18 classes and 97.81% in diff erentiating 310 MR brain images into 6 classes. The experimental results reveal that the composite features along with BPNN classifi er create a competent and reliable system for the identifi cation of multiple brain disorders which can be used in clinical applications. The Wilcoxon test outcome demonstrates that standard deviation feature along with energies of approximate and vertical sub bands of level 7 contribute the most in achieving enhanced multi class classifi cation performance results.

Predictive effect of back propagation neural network model on hematoma enlargement in patients with cerebral hemorrhage / 中国脑血管病杂志

Objective To study predicting results of the back propagation (BP)neural network model for hematoma enlargement (HE)in patients with intracerebral hemorrhage. Methods The clinical data of 128 patients with cerebral hemorrhage admitted to the 309th hospital of People′s Liberation Army from January 2011 to December 2014 were analyzed retrospectively. The Matlab 7. 14 software was used to achieve BP neural network model for predicting hematoma enlargement within 24 hours in patients with intracerebral hemorrhage (HE ≥6. 0 ml and HE ≥12. 5 ml). The mean square error (MSE)of the model and the accuracy of the overall prediction were calculated. The receiver operation characteristic (ROC) curve was drawn for predicting HE. Results When the BP neural network predicted HE ≥6. 0 ml and HE ≥12. 5 ml,the mean square deviations of the training set,validation set,and test set were 0. 061, 0. 143,0. 052 and 0. 023,0. 057,and 0. 065,respectively. The best fitting performance verification of hematoma enlargement was as follows:≥ 6. 0 ml for network training 11 times and the error value 0. 224;≥12. 5 ml for network training 20 times,and the error value 0. 057. The overall accuracies of predicting HE ≥6. 0 ml and HE ≥12. 5 ml were 92. 2% (118/ 128)and 96. 9% (124/ 128)respectively. Conclusion The BP neural network model have no special limitation for data. It can accurately fit the hematoma expansion model of cerebral hemorrhage.

Optimization of Preparation Technology of Stachydrine in Fructus Leonuri Based on Back-propagation Neural Network and Genetic Algorithm / 广州中医药大学学报

Objective To optimize the preparative procedure for stachydrine in Fructus Leonuri. Methods The preparation was screened by orthogonal experiment, and a mathematical model of relationship of extraction time, methanol concentration, and solid-liquid ratio with the content of stachydrine hydrochloride was established by using back-propagation (BP) neural network. And the process parameters were optimized with genetic algorithm (GA) . Results The optimum process parameters were as follows: extraction with 69% of methanol concentration and with solid-liquid ratio being 11 times for 62 min. The content of stachydrine obtained by BP neural network modeling and GA was higher than that achieved by orthogonal experiment. Conclusion The optimum preparative procedure could be achieved by combining BP modeling with GA. The model developed in this study was proved to be predictable and feasible for the optimization of process parameters of multi-dimension nonlinear system.