Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Hepatocellular carcinoma (HCC) is the most common primary malignant tumor of the liver. MRI has become an important imaging method for non-invasive diagnosis and evaluation of HCC in clinics because of its advantageous aspects, such as its non-radiative nature, superior detection, and qualitative accuracy over CT and ultrasound. Various MRI techniques, including hepatobiliary-specific contrast agents, magnetic resonance elastography, diffusion-weighted imaging, and others, can diagnose HCC or evaluate its malignant biological behavior from different dimensions such as blood supply, cell function, tissue hardness, and water molecule diffusion. This article introduces the current status and prospects of various MRI techniques for HCC diagnosis and evaluation.

Objective:To carry out a study on the cost analysis of the clinical use of Artificial Intelligence-assisted Diagnosis Technology for Coronary CT Angiography(CCTA-AI)to explore the cost differences and cost effects of Coronary CT Angiography(CCTA)examinations before and after the introduction of Artificial Intelligence(AI),and analyze the impact of the application of AI technology on the high-quality development of public hospitals.Methods:The operation cost method was used to measure the changes in the cost and efficiency of CCTA examinations before and after the application of AI technology in five sample hospitals in Beijing,and diagnostic accuracy was used as the effect value to calculate the cost effect of CCTA-AI diagnosis versus CCTA-manual diagnosis,and to analyze the main factors affecting the unit cost.Results:The average cost of examination in 5 sample hospitals after the application of AI-assisted diagnosis system was 1 074.90 yuan,and 1 266.61 yuan before the application,with a large difference between institutions.The cost-effectiveness analysis based on diagnostic accuracy showed that the AI group had an absolute advantage over the manual group,with a cost of 1 074 900 yuan for the AI group and an effectiveness of 855.05 persons,and a cost of 1 266 610 yuan for the physician group,with an effectiveness of 815.07 persons for the high-year-end physician group,and an effectiveness of 793.40 persons for the low-year-end physician group;0.46 man-hours could be saved for each patient examined;the unit cost of CCTA examination was affected by a number of factors,among which"the number of annual examinations"and"the number of CT units involved in CCTA examination"had the greatest influence on the unit cost of CCTA examination.Conclusion:The application of AI-assisted diagnostic technology can promote the improvement of quality and efficiency in public hospitals in a certain extent,and help optimize the overall distribution of medical resources at the system level.In the future,the cost analysis of AI technology should be further strengthened to comprehensively assess its actual contribution to the healthcare system.

Objective:To carry out a study on the cost analysis of the clinical use of Artificial Intelligence-assisted Diagnosis Technology for Coronary CT Angiography(CCTA-AI)to explore the cost differences and cost effects of Coronary CT Angiography(CCTA)examinations before and after the introduction of Artificial Intelligence(AI),and analyze the impact of the application of AI technology on the high-quality development of public hospitals.Methods:The operation cost method was used to measure the changes in the cost and efficiency of CCTA examinations before and after the application of AI technology in five sample hospitals in Beijing,and diagnostic accuracy was used as the effect value to calculate the cost effect of CCTA-AI diagnosis versus CCTA-manual diagnosis,and to analyze the main factors affecting the unit cost.Results:The average cost of examination in 5 sample hospitals after the application of AI-assisted diagnosis system was 1 074.90 yuan,and 1 266.61 yuan before the application,with a large difference between institutions.The cost-effectiveness analysis based on diagnostic accuracy showed that the AI group had an absolute advantage over the manual group,with a cost of 1 074 900 yuan for the AI group and an effectiveness of 855.05 persons,and a cost of 1 266 610 yuan for the physician group,with an effectiveness of 815.07 persons for the high-year-end physician group,and an effectiveness of 793.40 persons for the low-year-end physician group;0.46 man-hours could be saved for each patient examined;the unit cost of CCTA examination was affected by a number of factors,among which"the number of annual examinations"and"the number of CT units involved in CCTA examination"had the greatest influence on the unit cost of CCTA examination.Conclusion:The application of AI-assisted diagnostic technology can promote the improvement of quality and efficiency in public hospitals in a certain extent,and help optimize the overall distribution of medical resources at the system level.In the future,the cost analysis of AI technology should be further strengthened to comprehensively assess its actual contribution to the healthcare system.

Objective:To carry out a study on the cost analysis of the clinical use of Artificial Intelligence-assisted Diagnosis Technology for Coronary CT Angiography(CCTA-AI)to explore the cost differences and cost effects of Coronary CT Angiography(CCTA)examinations before and after the introduction of Artificial Intelligence(AI),and analyze the impact of the application of AI technology on the high-quality development of public hospitals.Methods:The operation cost method was used to measure the changes in the cost and efficiency of CCTA examinations before and after the application of AI technology in five sample hospitals in Beijing,and diagnostic accuracy was used as the effect value to calculate the cost effect of CCTA-AI diagnosis versus CCTA-manual diagnosis,and to analyze the main factors affecting the unit cost.Results:The average cost of examination in 5 sample hospitals after the application of AI-assisted diagnosis system was 1 074.90 yuan,and 1 266.61 yuan before the application,with a large difference between institutions.The cost-effectiveness analysis based on diagnostic accuracy showed that the AI group had an absolute advantage over the manual group,with a cost of 1 074 900 yuan for the AI group and an effectiveness of 855.05 persons,and a cost of 1 266 610 yuan for the physician group,with an effectiveness of 815.07 persons for the high-year-end physician group,and an effectiveness of 793.40 persons for the low-year-end physician group;0.46 man-hours could be saved for each patient examined;the unit cost of CCTA examination was affected by a number of factors,among which"the number of annual examinations"and"the number of CT units involved in CCTA examination"had the greatest influence on the unit cost of CCTA examination.Conclusion:The application of AI-assisted diagnostic technology can promote the improvement of quality and efficiency in public hospitals in a certain extent,and help optimize the overall distribution of medical resources at the system level.In the future,the cost analysis of AI technology should be further strengthened to comprehensively assess its actual contribution to the healthcare system.