OBJECTIVE: To probe into the effects of living habit, diet, drug and hereditary factors related to daily life so as to reduce the incidence of carcinoma of large intestine through changing the life style.DATA SOURCES: Articles on carcinoma of large intestine published between January 1980 and December 2003 were retrieved in NCBI Entrez PubMed with the key words of "carcinoma of large intestine, epidemiology and prevention" and the language restricted to English. Meanwhile, articles on carcinoma of large intestine published between January 1994 and December 2003 were searched for in CNKI database with the Chinese key words of "carcinoma of large intestine, epidemiology and prevention" and the language restricted to Chinese.STUDY SELECTION: The related literature was selected by the primary tions, hormone level and genetic factors on the morbidity of carcinoma of excluding blind method was not required. Exclusion criteria: review articles and papers of meta-analysis and replicated studies.DATA EXTRACTION: Totally 84 related articles were collected, including 17 retrieved with retrospective approach. Fifty-eight articles met the inclusion criteria, and 26 papers were excluded. Among the excluded papers, 6 papers were about the basic biological and chemical research on carcinoma of large intestine, 12 were about different pathological types of carcinoma of large intestine and distribution of hospitalized cases, 4 were meta-analysis, and 4 were of popular science and delayed diagnosis due to physicians or patients themselves. The collected data showed that the morbidity and distribution of large intestine carcinoma were related to region,age, dietary factor, verminosis, heredity, hormone and chronic intestinal diseases and long-term stress.characteristics, trace element intake and living habits on the morbidity of responding preventive measures for reducing the incidence of large intestine carcinoma.CONCLUSION: With the improvement of living standard, the occurrence .and distribution of carcinoma of large intestine increase, presenting a tendency to occur in young people. Developing good life style and dietary habit and doing moderate exercise can prevent the occurrence of carcinoma of large intestine.

Objective To preliminarily analyze the prediction efficiency of multimodal MRI?based radiomics model for preoperative glioma IDH1 gene expression type. Methods The MRI data of 108 surgery?proven glioma patients from May 2015 to January 2019 were retrospectively analyzed, and the MRI data included axial T1WI,T2WI,fluid attenuated inversion recovery (FLAIR),DWI imaging and enhanced T1WI sequence.Forty-seven cases were IDH1 mutant type, and 61 cases were IDH1 wild type. All patients were divided into training and validation groups according to the 7∶3 ratio of the random forest model. Seventy-three cases were in training group, and 35 cases were in validation group. Independent predictors of IDH1 mutation were screened by univariate analysis combined with multivariate logistic regression analysis (P<0.05) in order to construct a random forest diagnosis model of general clinical information and conventional MRI morphological features.General clinical information and conventional MRI morphological features included gender, age, umbers of cases of left and right hemispheres, location of tumors, maximum diameter of tumors, peritumoral edema, intratumoral cystic degeneration, enhancement and ADC value of tumors. The ROI was manually outlined by MaZda software in the most obvious level of 5 sequences of tumor mass and the radiomics features were extracted, including the gray?level co?occurrence matrix(GLCM), the run?length matrix(RUN), the absolute gradient(GRA),the auto?regressive model(ARM) and wavelets transform (WAV). The least absolute shrinkage and selection operator (LASSO)regression were used to select image radiomics features with a method of 10 fold cross?validation and to reduce the dimensions. The screened image radiomics labels were combined with the conventional morphological feature independent predictors to construct a multimodal MRI?based random forest model, and the validation data set was used to evaluate the accuracy and diagnostic efficiency of each model. The sensitivity and specificity of conventional MRI morphological feature model and multimodal MRI?based radiomics prediction model were evaluated dynamically by drawing ROC curves, and the prediction efficiency of the two models was quantified by using AUC statistical indicators. The model classification error rate under different outcomes and the classification error rate of out of bag(OOB)were used to evaluate the stability of the multimodal MRI?based random forest model. The contribution rate of each variable to the model was reflected by the characteristic variables importance assessment map. Results Univariate regression analysis of the conventional MRI morphological characteristics showed that peritumoral edema, cystic degeneration and enhancement were the three independent predictors of IDH1 gene expression (P<0.01). LASSO algorithm and 10?fold cross?validation identified six robust radiomic features including high frequency coefficients of wavelet transform (WavEnHH_s?4) of T2WI, S(4, 4) inverse difference of gray uniformity measurement (InvDfMom), S(5, 0) Entropy (entropy), WavEnHH_s?4 of T1WI enhancement, S(1, 1) InvDfMom, S(1, -1) Entropy Difference (DifEntrp)of Flair.The error rate of classification for different outcomes and classification error rate of random forest OOB data of multimodal MRI radiomics diagnosis model finally stabilized at 10%. The results of Characteristic Variable Importance Assessment Map: Mean Decrease Accuracy and Mean Decrease Gini index were consistent, which showed that besides three conventional MRI morphological predictors peritumoral edema, enhancement and cystic degeneration, the radiomics labels also played a key role in the model. The results of ROC curve showed that the accuracy, specificity,sensitivity and AUC of conventional MRI morphological feature model were 82.7%, 68.4%, 90.9% and 0.835, respectively, and those of multimodal MRI?based radiomics model were 88.5%, 89.5%, 87.8% and 0.956 respectively. Conclusion Multimodal MRI?based radiomics random forest model can improve the predictive efficiency of preoperative glioma IDH1 gene expression type more quantitatively.

Objective:To evaluate the quantitative diagnostic value of controlled attenuation parameter (CAP) in health checkup groups with asymptomatic nonalcoholic fatty liver disease.Methods:A multicenter prospective study was conducted among Chinese individuals undergoing regular health checkups; a total of 173 subjects were investigated. Human body indexes such as height, weight, and blood pressure were measured, and complete blood count, liver function, blood lipid, FibroScan, and MRI-PDFF examinations were performed. Correlation between MRI-PDFF and CAP was described using Spearman′s and Pearson′s coefficients. Diagnostic efficacy of the CAP was evaluated using the subject work characteristic curve and the area under this curve, and the optimal cut-off value was determined according to the Youden index.Results:The average age and body mass index of the subjects were 45.0±10.5 years and 25.8±4.0 kg/m 2, respectively. A linear correlation was found between CAP and lg transformed magnetic resonance imaging-based proton density fat fraction results (Pearson′s coefficient 0.772, P<0.001). When optimized for ≥90% sensitivity, the CAP cutoff for staging ≥S1 steatosis was 244 dB/m. Conclusions:The CAP result was significantly correlated with the liver fat fraction measured by MRI-PDFF, and capable of differentiating steatosis grades. CAP can be used as a tool for screening fatty liver in health checkup groups.

Objective: To preliminarily analyze the prediction efficiency of multimodal MRI-based radiomics model for preoperative glioma IDH1 gene expression type. Methods: The MRI data of 108 surgery-proven glioma patients from May 2015 to January 2019 were retrospectively analyzed, and the MRI data included axial T₁WI,T₂WI,fluid attenuated inversion recovery (FLAIR),DWI imaging and enhanced T₁WI sequence.Forty-seven cases were IDH1 mutant type, and 61 cases were IDH1 wild type. All patients were divided into training and validation groups according to the 7∶3 ratio of the random forest model. Seventy-three cases were in training group, and 35 cases were in validation group. Independent predictors of IDH1 mutation were screened by univariate analysis combined with multivariate logistic regression analysis (P<0.05) in order to construct a random forest diagnosis model of general clinical information and conventional MRI morphological features.General clinical information and conventional MRI morphological features included gender, age, umbers of cases of left and right hemispheres, location of tumors, maximum diameter of tumors, peritumoral edema, intratumoral cystic degeneration, enhancement and ADC value of tumors. The ROI was manually outlined by MaZda software in the most obvious level of 5 sequences of tumor mass and the radiomics features were extracted, including the gray-level co-occurrence matrix(GLCM), the run-length matrix(RUN), the absolute gradient(GRA),the auto-regressive model(ARM) and wavelets transform(WAV). The least absolute shrinkage and selection operator (LASSO)regression were used to select image radiomics features with a method of 10 fold cross -validation and to reduce the dimensions. The screened image radiomics labels were combined with the conventional morphological feature independent predictors to construct a multimodal MRI-based random forest model, and the validation data set was used to evaluate the accuracy and diagnostic efficiency of each model. The sensitivity and specificity of conventional MRI morphological feature model and multimodal MRI-based radiomics prediction model were evaluated dynamically by drawing ROC curves, and the prediction efficiency of the two models was quantified by using AUC statistical indicators. The model classification error rate under different outcomes and the classification error rate of out of bag(OOB)were used to evaluate the stability of the multimodal MRI-based random forest model. The contribution rate of each variable to the model was reflected by the characteristic variables importance assessment map. Results: Univariate regression analysis of the conventional MRI morphological characteristics showed that peritumoral edema, cystic degeneration and enhancement were the three independent predictors of IDH1 gene expression (P<0.01). LASSO algorithm and 10-fold cross-validation identified six robust radiomic features including high frequency coefficients of wavelet transform (WavEnHH_s-4) of T₂WI, S(4,4) inverse difference of gray uniformity measurement (InvDfMom),S(5,0) Entropy (entropy),WavEnHH_s-4 of T₁WI enhancement, S(1,1) InvDfMom,S(1, -1) Entropy Difference (DifEntrp)of Flair.The error rate of classification for different outcomes and classification error rate of random forest OOB data of multimodal MRI radiomics diagnosis model finally stabilized at 10%. The results of Characteristic Variable Importance Assessment Map: Mean Decrease Accuracy and Mean Decrease Gini index were consistent, which showed that besides three conventional MRI morphological predictors peritumoral edema, enhancement and cystic degeneration, the radiomics labels also played a key role in the model. The results of ROC curve showed that the accuracy, specificity, sensitivity and AUC of conventional MRI morphological feature model were 82.7%, 68.4%, 90.9% and 0.835, respectively, and those of multimodal MRI-based radiomics model were 88.5%, 89.5%, 87.8% and 0.956 respectively. Conclusion Multimodal MRI-based radiomics random forest model can improve the predictive efficiency of preoperative glioma IDH1 gene expression type more quantitatively.