BACKGROUND The incidence rate of breast cancer has exceeded that of lung cancer,and it has become the most malignant type of cancer in the world.BI-RADS 4 breast nodules have a wide range of malignant risks and are as...BACKGROUND The incidence rate of breast cancer has exceeded that of lung cancer,and it has become the most malignant type of cancer in the world.BI-RADS 4 breast nodules have a wide range of malignant risks and are associated with challenging clinical decision-making.AIM To explore the diagnostic value of artificial intelligence(AI)automatic detection systems for BI-RADS 4 breast nodules and to assess whether conventional ultrasound BI-RADS classification with AI automatic detection systems can reduce the probability of BI-RADS 4 biopsy.METHODS A total of 107 BI-RADS breast nodules confirmed by pathology were selected between June 2019 and July 2020 at Hwa Mei Hospital,University of Chinese Academy of Sciences.These nodules were classified by ultrasound doctors and the AI-SONIC breast system.The diagnostic values of conventional ultrasound,the AI automatic detection system,conventional ultrasound combined with the AI automatic detection system and adjusted BI-RADS classification diagnosis were statistically analyzed.RESULTS Among the 107 breast nodules,61 were benign(57.01%),and 46 were malignant(42.99%).The pathology results were considered the gold standard;furthermore,the sensitivity,specificity,accuracy,Youden index,and positive and negative predictive values were 84.78%,67.21%,74.77%,0.5199,66.10%and 85.42%for conventional ultrasound BI-RADS classification diagnosis,86.96%,75.41%,80.37%,0.6237,72.73%,and 88.46%for automatic AI detection,80.43%,90.16%,85.98%,0.7059,86.05%,and 85.94%for conventional ultrasound BI-RADS classification with automatic AI detection and 93.48%,67.21%,78.50%,0.6069,68.25%,and 93.18%for adjusted BI-RADS classification,respectively.The biopsy rate,cancer detection rate and malignancy risk were 100%,42.99%and 0%and 67.29%,61.11%,and 1.87%before and after BI-RADS adjustment,respectively.CONCLUSION Automatic AI detection has high accuracy in determining benign and malignant BI-RADS 4 breast nodules.Conventional ultrasound BI-RADS classification combined with AI automatic detection can reduce the biopsy rate of BI-RADS 4 breast nodules.展开更多
目的分析乳腺影像报告及数据系统(breast imaging reporting and data system,BI-RADS)4类乳腺良恶性结节采用超声弹性成像(ultrasonic elastography,UE)技术诊断的价值。方法选择2023年1—11月济南市章丘区人民医院收治的102例疑似BI-R...目的分析乳腺影像报告及数据系统(breast imaging reporting and data system,BI-RADS)4类乳腺良恶性结节采用超声弹性成像(ultrasonic elastography,UE)技术诊断的价值。方法选择2023年1—11月济南市章丘区人民医院收治的102例疑似BI-RADS 4类乳腺实性结节患者为研究对象,全部患者均接受彩色多普勒超声与UE检查。以病理结果为“金标准”,针对BI-RADS 4类乳腺良恶性结节经常规超声与UE技术诊断的结果及效能,以及对UE技术诊断结果与病理结果的一致性进行评价。结果BI-RADS 4类乳腺良恶性结节经穿刺活检结果证实为恶性36例,良性66例,UE技术诊断结果与病理诊断结果具有极好的一致性(kappa=0.890)。UE技术对BI-RADS 4类乳腺良恶性结节诊断的灵敏度94.44%、特异度95.45%、准确度95.10%均高于常规超声的72.22%、81.82%、78.43%,差异有统计学意义(χ^(2)=6.400、6.092、12.337,P均<0.05)。结论UE在BI-RADS 4类乳腺良恶性结节中具有较为理想的诊断价值,进一步提高了诊断效能,为临床提供可靠的参考信息。展开更多
Breast Imaging Reporting and Data System,also known as BI-RADS is a universal system used by radiologists and doctors.It constructs a comprehensive language for the diagnosis of breast cancer.BI-RADS 4 category has a ...Breast Imaging Reporting and Data System,also known as BI-RADS is a universal system used by radiologists and doctors.It constructs a comprehensive language for the diagnosis of breast cancer.BI-RADS 4 category has a wide range of cancer risk since it is divided into 3 categories.Mathematicalmodels play an important role in the diagnosis and treatment of cancer.In this study,data of 42 BI-RADS 4 patients taken fromthe Center for Breast Health,Near East University Hospital is utilized.Regarding the analysis,a mathematical model is constructed by dividing the population into 4 compartments.Sensitivity analysis is applied to the parameters with the desired outcome of a reduced range of cancer risk.Numerical simulations of the parameters are demonstrated.The results of the model have revealed that an increase in the lactation rate and earlymenopause have a negative correlation with the chance of being diagnosed with BI-RADS 4 whereas a positive correlation increase in age,the palpable mass,and family history is distinctive.Furthermore,the negative effects of smoking and late menopause on BI-RADS 4C diagnosis are vehemently outlined.Consequently,the model showed that the percentages of parameters play an important role in the diagnosis of BI-RADS 4 subcategories.All things considered,with the assistance of the most effective parameters,the range of cancer risks in BI-RADS 4 subcategories will decrease.展开更多
文摘BACKGROUND The incidence rate of breast cancer has exceeded that of lung cancer,and it has become the most malignant type of cancer in the world.BI-RADS 4 breast nodules have a wide range of malignant risks and are associated with challenging clinical decision-making.AIM To explore the diagnostic value of artificial intelligence(AI)automatic detection systems for BI-RADS 4 breast nodules and to assess whether conventional ultrasound BI-RADS classification with AI automatic detection systems can reduce the probability of BI-RADS 4 biopsy.METHODS A total of 107 BI-RADS breast nodules confirmed by pathology were selected between June 2019 and July 2020 at Hwa Mei Hospital,University of Chinese Academy of Sciences.These nodules were classified by ultrasound doctors and the AI-SONIC breast system.The diagnostic values of conventional ultrasound,the AI automatic detection system,conventional ultrasound combined with the AI automatic detection system and adjusted BI-RADS classification diagnosis were statistically analyzed.RESULTS Among the 107 breast nodules,61 were benign(57.01%),and 46 were malignant(42.99%).The pathology results were considered the gold standard;furthermore,the sensitivity,specificity,accuracy,Youden index,and positive and negative predictive values were 84.78%,67.21%,74.77%,0.5199,66.10%and 85.42%for conventional ultrasound BI-RADS classification diagnosis,86.96%,75.41%,80.37%,0.6237,72.73%,and 88.46%for automatic AI detection,80.43%,90.16%,85.98%,0.7059,86.05%,and 85.94%for conventional ultrasound BI-RADS classification with automatic AI detection and 93.48%,67.21%,78.50%,0.6069,68.25%,and 93.18%for adjusted BI-RADS classification,respectively.The biopsy rate,cancer detection rate and malignancy risk were 100%,42.99%and 0%and 67.29%,61.11%,and 1.87%before and after BI-RADS adjustment,respectively.CONCLUSION Automatic AI detection has high accuracy in determining benign and malignant BI-RADS 4 breast nodules.Conventional ultrasound BI-RADS classification combined with AI automatic detection can reduce the biopsy rate of BI-RADS 4 breast nodules.
文摘Breast Imaging Reporting and Data System,also known as BI-RADS is a universal system used by radiologists and doctors.It constructs a comprehensive language for the diagnosis of breast cancer.BI-RADS 4 category has a wide range of cancer risk since it is divided into 3 categories.Mathematicalmodels play an important role in the diagnosis and treatment of cancer.In this study,data of 42 BI-RADS 4 patients taken fromthe Center for Breast Health,Near East University Hospital is utilized.Regarding the analysis,a mathematical model is constructed by dividing the population into 4 compartments.Sensitivity analysis is applied to the parameters with the desired outcome of a reduced range of cancer risk.Numerical simulations of the parameters are demonstrated.The results of the model have revealed that an increase in the lactation rate and earlymenopause have a negative correlation with the chance of being diagnosed with BI-RADS 4 whereas a positive correlation increase in age,the palpable mass,and family history is distinctive.Furthermore,the negative effects of smoking and late menopause on BI-RADS 4C diagnosis are vehemently outlined.Consequently,the model showed that the percentages of parameters play an important role in the diagnosis of BI-RADS 4 subcategories.All things considered,with the assistance of the most effective parameters,the range of cancer risks in BI-RADS 4 subcategories will decrease.
