BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation gr...BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation grade of CRC is of great value.AIM To develop and validate machine learning-based models for predicting the differ-entiation grade of CRC based on T2-weighted images(T2WI).METHODS We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023.Patients were randomly assigned to a training cohort(n=220)or a validation cohort(n=95)at a 7:3 ratio.Lesions were delineated layer by layer on high-resolution T2WI.Least absolute shrinkage and selection operator regression was applied to screen for radiomic features.Radiomics and clinical models were constructed using the multilayer perceptron(MLP)algorithm.These radiomic features and clinically relevant variables(selected based on a significance level of P<0.05 in the training set)were used to construct radiomics-clinical models.The performance of the three models(clinical,radiomic,and radiomic-clinical model)were evaluated using the area under the curve(AUC),calibration curve and decision curve analysis(DCA).RESULTS After feature selection,eight radiomic features were retained from the initial 1781 features to construct the radiomic model.Eight different classifiers,including logistic regression,support vector machine,k-nearest neighbours,random forest,extreme trees,extreme gradient boosting,light gradient boosting machine,and MLP,were used to construct the model,with MLP demonstrating the best diagnostic performance.The AUC of the radiomic-clinical model was 0.862(95%CI:0.796-0.927)in the training cohort and 0.761(95%CI:0.635-0.887)in the validation cohort.The AUC for the radiomic model was 0.796(95%CI:0.723-0.869)in the training cohort and 0.735(95%CI:0.604-0.866)in the validation cohort.The clinical model achieved an AUC of 0.751(95%CI:0.661-0.842)in the training cohort and 0.676(95%CI:0.525-0.827)in the validation cohort.All three models demonstrated good accuracy.In the training cohort,the AUC of the radiomic-clinical model was significantly greater than that of the clinical model(P=0.005)and the radiomic model(P=0.016).DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process.CONCLUSION In this study,we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC.This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.展开更多
Objective:To achieve precision medicine,the use of imaging methods to help the clinical detection of cerebral infarction is conducive to the clinical development of a treatment plan and increase of the cure rate and i...Objective:To achieve precision medicine,the use of imaging methods to help the clinical detection of cerebral infarction is conducive to the clinical development of a treatment plan and increase of the cure rate and improvement of the prognosis of patients.Methods:In this work,T2-weighted imaging(T2WI),diffusion-weighted imaging(DWI),susceptibility-weighted imaging(SWI),and diffusion tensor imaging(DTI)examinations were performed on 34 patients with clinically diagnosed cerebral infarction to measure the difference in signal intensity between the lesion and its mirror area and make a comparative analysis by means of the Student-Newman-Keuls method.Results:The detection rate of T2WI was 79%(27/34),the detection rate of DWI was 97%(33/34),the detection rate of SWI was 88%(30/34),and the detection rate of DTI was 94%(32/34).Conclusion:The imaging performance was in the order DWI>DTI>SWI>T2WI for the diagnosis of cerebral infarction,and combined imaging is better than single imaging.展开更多
目的探讨达格列净联合胰岛素泵强化治疗对老年2型糖尿病(T_(2)DM)患者胰岛功能的影响。方法选取2019年8月至2022年8月接受治疗的80例老年T_(2)DM患者作为研究对象,按照抽签法将其分为对照组和研究组,每组40例。对照组给予二甲双胍联合...目的探讨达格列净联合胰岛素泵强化治疗对老年2型糖尿病(T_(2)DM)患者胰岛功能的影响。方法选取2019年8月至2022年8月接受治疗的80例老年T_(2)DM患者作为研究对象,按照抽签法将其分为对照组和研究组,每组40例。对照组给予二甲双胍联合胰岛素泵强化治疗,研究组给予达格列净联合胰岛素泵强化治疗。比较两组的治疗效果。结果治疗后,研究组的空腹血糖(FPG)、餐后2 h血糖(2 h PG)水平及糖化血红蛋白(HbA1c)低于对照组(P<0.05)。治疗后,研究组的体重、体质指数(BMI)低于对照组(P<0.05)。治疗后,研究组的空腹胰岛素(FINS)水平与稳态模型胰岛β细胞分泌指数(HOMA-β)高于对照组,稳态模型胰岛素抵抗指数(HOMA-IR)低于对照组(P<0.05)。治疗后,研究组的谷胱甘肽过氧化物酶(GSH-Px)、超氧化物歧化酶(SOD)水平高于对照组,丙二醛(MDA)水平低于对照组(P<0.05)。结论达格列净联合胰岛素泵强化治疗老年T_(2)DM患者的效果显著,可有效降低血糖指标、体重以及BMI,并改善胰岛功能。展开更多
AIM: To investigate dynamic characteristics and pathological mechanism of signal in rabbit VX-2 tumor model on diffusion-weighted imaging (DWI) after chemoembolization. METHODS: Forty New Zealand rabbits were included...AIM: To investigate dynamic characteristics and pathological mechanism of signal in rabbit VX-2 tumor model on diffusion-weighted imaging (DWI) after chemoembolization. METHODS: Forty New Zealand rabbits were included in the study and forty-seven rabbit VX-2 tumor models were raised by implanting directly and intrahepatically after abdominal cavity opened. Forty VX-2 tumor models from them were divided into four groups. DWI was performed periodically and respectively for each group after chemoembolization. All VX-2 tumor samples of each group were studied by pathology. The distinction of VX-2 tumors on DWI was assessed by their apparent diffusion coefficient (ADC) values. The statistical significance between different time groups, different area groups or different b-value groups was calculated by using SPSS12.0 software. RESULTS: Under b-value of 100 s/mm2, ADC values were lowest at 16 h after chemoembolization in area of VX-2 tumor periphery, central, and normal liver parenchyma around tumor, but turned to increase with further elongation of chemoembolization treatment. The distinction of ADC between different time groups was significant respectively (F = 7.325, P < 0.001; F = 2.496, P < 0.048; F = 6.856, P < 0.001). Cellular edema in the area of VX-2 tumor periphery or normal liver parenchyma around tumor, increased quickly in sixteen h after chemoembolization but, from the 16th h to the 48th h, cellular edema in the area of normal liver parenchyma around tumor decreased gradually and that in the area of VX-2 tumor periphery decreased lightly at, and then increased continually. After chemoembolization, Cellular necrosis in the area of VX-2 tumor periphery was more significantly high than that before chemoembolization. The areas of dead cells in VX-2 tumors manifested low signal and high ADC value, while the areas of viable cells manifested high signal and low ADC value. CONCLUSION: DWI is able to detect and differentiate tumor necrotic areas from viable cellular areas before and after chemoembolization. ADC of normal liver parenchyma and VX-2 tumor are influenced by intracellular edema, tissue cellular death and microcirculation disturbance after chemoembolization.展开更多
基金the Fujian Province Clinical Key Specialty Construction Project,No.2022884Quanzhou Science and Technology Plan Project,No.2021N034S+1 种基金The Youth Research Project of Fujian Provincial Health Commission,No.2022QNA067Malignant Tumor Clinical Medicine Research Center,No.2020N090s.
文摘BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation grade of CRC is of great value.AIM To develop and validate machine learning-based models for predicting the differ-entiation grade of CRC based on T2-weighted images(T2WI).METHODS We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023.Patients were randomly assigned to a training cohort(n=220)or a validation cohort(n=95)at a 7:3 ratio.Lesions were delineated layer by layer on high-resolution T2WI.Least absolute shrinkage and selection operator regression was applied to screen for radiomic features.Radiomics and clinical models were constructed using the multilayer perceptron(MLP)algorithm.These radiomic features and clinically relevant variables(selected based on a significance level of P<0.05 in the training set)were used to construct radiomics-clinical models.The performance of the three models(clinical,radiomic,and radiomic-clinical model)were evaluated using the area under the curve(AUC),calibration curve and decision curve analysis(DCA).RESULTS After feature selection,eight radiomic features were retained from the initial 1781 features to construct the radiomic model.Eight different classifiers,including logistic regression,support vector machine,k-nearest neighbours,random forest,extreme trees,extreme gradient boosting,light gradient boosting machine,and MLP,were used to construct the model,with MLP demonstrating the best diagnostic performance.The AUC of the radiomic-clinical model was 0.862(95%CI:0.796-0.927)in the training cohort and 0.761(95%CI:0.635-0.887)in the validation cohort.The AUC for the radiomic model was 0.796(95%CI:0.723-0.869)in the training cohort and 0.735(95%CI:0.604-0.866)in the validation cohort.The clinical model achieved an AUC of 0.751(95%CI:0.661-0.842)in the training cohort and 0.676(95%CI:0.525-0.827)in the validation cohort.All three models demonstrated good accuracy.In the training cohort,the AUC of the radiomic-clinical model was significantly greater than that of the clinical model(P=0.005)and the radiomic model(P=0.016).DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process.CONCLUSION In this study,we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC.This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.
文摘Objective:To achieve precision medicine,the use of imaging methods to help the clinical detection of cerebral infarction is conducive to the clinical development of a treatment plan and increase of the cure rate and improvement of the prognosis of patients.Methods:In this work,T2-weighted imaging(T2WI),diffusion-weighted imaging(DWI),susceptibility-weighted imaging(SWI),and diffusion tensor imaging(DTI)examinations were performed on 34 patients with clinically diagnosed cerebral infarction to measure the difference in signal intensity between the lesion and its mirror area and make a comparative analysis by means of the Student-Newman-Keuls method.Results:The detection rate of T2WI was 79%(27/34),the detection rate of DWI was 97%(33/34),the detection rate of SWI was 88%(30/34),and the detection rate of DTI was 94%(32/34).Conclusion:The imaging performance was in the order DWI>DTI>SWI>T2WI for the diagnosis of cerebral infarction,and combined imaging is better than single imaging.
文摘目的探讨达格列净联合胰岛素泵强化治疗对老年2型糖尿病(T_(2)DM)患者胰岛功能的影响。方法选取2019年8月至2022年8月接受治疗的80例老年T_(2)DM患者作为研究对象,按照抽签法将其分为对照组和研究组,每组40例。对照组给予二甲双胍联合胰岛素泵强化治疗,研究组给予达格列净联合胰岛素泵强化治疗。比较两组的治疗效果。结果治疗后,研究组的空腹血糖(FPG)、餐后2 h血糖(2 h PG)水平及糖化血红蛋白(HbA1c)低于对照组(P<0.05)。治疗后,研究组的体重、体质指数(BMI)低于对照组(P<0.05)。治疗后,研究组的空腹胰岛素(FINS)水平与稳态模型胰岛β细胞分泌指数(HOMA-β)高于对照组,稳态模型胰岛素抵抗指数(HOMA-IR)低于对照组(P<0.05)。治疗后,研究组的谷胱甘肽过氧化物酶(GSH-Px)、超氧化物歧化酶(SOD)水平高于对照组,丙二醛(MDA)水平低于对照组(P<0.05)。结论达格列净联合胰岛素泵强化治疗老年T_(2)DM患者的效果显著,可有效降低血糖指标、体重以及BMI,并改善胰岛功能。
基金the National Natural Science Foundation of China, No. 30070235, 30470508
文摘AIM: To investigate dynamic characteristics and pathological mechanism of signal in rabbit VX-2 tumor model on diffusion-weighted imaging (DWI) after chemoembolization. METHODS: Forty New Zealand rabbits were included in the study and forty-seven rabbit VX-2 tumor models were raised by implanting directly and intrahepatically after abdominal cavity opened. Forty VX-2 tumor models from them were divided into four groups. DWI was performed periodically and respectively for each group after chemoembolization. All VX-2 tumor samples of each group were studied by pathology. The distinction of VX-2 tumors on DWI was assessed by their apparent diffusion coefficient (ADC) values. The statistical significance between different time groups, different area groups or different b-value groups was calculated by using SPSS12.0 software. RESULTS: Under b-value of 100 s/mm2, ADC values were lowest at 16 h after chemoembolization in area of VX-2 tumor periphery, central, and normal liver parenchyma around tumor, but turned to increase with further elongation of chemoembolization treatment. The distinction of ADC between different time groups was significant respectively (F = 7.325, P < 0.001; F = 2.496, P < 0.048; F = 6.856, P < 0.001). Cellular edema in the area of VX-2 tumor periphery or normal liver parenchyma around tumor, increased quickly in sixteen h after chemoembolization but, from the 16th h to the 48th h, cellular edema in the area of normal liver parenchyma around tumor decreased gradually and that in the area of VX-2 tumor periphery decreased lightly at, and then increased continually. After chemoembolization, Cellular necrosis in the area of VX-2 tumor periphery was more significantly high than that before chemoembolization. The areas of dead cells in VX-2 tumors manifested low signal and high ADC value, while the areas of viable cells manifested high signal and low ADC value. CONCLUSION: DWI is able to detect and differentiate tumor necrotic areas from viable cellular areas before and after chemoembolization. ADC of normal liver parenchyma and VX-2 tumor are influenced by intracellular edema, tissue cellular death and microcirculation disturbance after chemoembolization.
