AIM: To determine whether contrast-enhanced ultrasound(CEUS) can improve the precision of breast imaging reporting and data system(BI-RADS) categorization. METHODS: A total of 230 patients with 235 solid breast lesion...AIM: To determine whether contrast-enhanced ultrasound(CEUS) can improve the precision of breast imaging reporting and data system(BI-RADS) categorization. METHODS: A total of 230 patients with 235 solid breast lesions classified as BI-RADS 4 on conventional ultrasound were evaluated. CEUS was performed within one week before core needle biopsy or surgical resection and a revised BI-RADS classification was assigned based on 10 CEUS imaging characteristics. Receiver operating characteristic curve analysis was then conducted to evaluate the diagnostic performance of CEUS-based BI-RADS assignment with pathological examination as reference criteria. RESULTS: The CEUS-based BI-RADS evaluation classified 116/235(49.36%) lesions into category 3, 20(8.51%), 13(5.53%) and 12(5.11%) lesions into categories 4A, 4B and 4C, respectively, and 74(31.49%) into category 5. Selecting CEUS-based BI-RADS category 4A as an appropriate cut-off gave sensitivity and specificity values of 85.4% and 87.8%, respectively, for the diagnosisof malignant disease. The cancer-to-biopsy yield was 73.11% with CEUS-based BI-RADS 4A selected as the biopsy threshold compared with 40.85% otherwise, while the biopsy rate was only 42.13% compared with 100% otherwise. Overall, only 4.68% of invasive cancers were misdiagnosed.CONCLUSION: This pilot study suggests that evaluation of BI-RADS 4 breast lesions with CEUS results in reduced biopsy rates and increased cancer-to-biopsy yields.展开更多
AIM: To build and evaluate predictive models for contrast-enhanced ultrasound(CEUS) of the breast to distinguish between benign and malignant lesions. METHODS: A total of 235 breast imaging reporting and data system(B...AIM: To build and evaluate predictive models for contrast-enhanced ultrasound(CEUS) of the breast to distinguish between benign and malignant lesions. METHODS: A total of 235 breast imaging reporting and data system(BI-RADS) 4 solid breast lesions were imaged via CEUS before core needle biopsy or surgical resection. CEUS results were analyzed on 10 enhancing patterns to evaluate diagnostic performance of three benign and three malignant CEUS models, with pathological results used as the gold standard. A logistic regression model was developed basing on the CEUS results, and then evaluated with receiver operating curve(ROC). RESULTS: Except in cases of enhanced homogeneity, the rest of the 9 enhancement appearances were statistically significant(P < 0.05). These 9 enhancement patterns were selected in the final step of the logistic regression analysis, with diagnostic sensitivity and specificity of 84.4% and 82.7%, respectively, and the area under the ROC curve of 0.911. Diagnostic sensitivity, specificity, and accuracy of the malignant vs benign CEUS models were 84.38%, 87.77%, 86.38% and 86.46%, 81.29% and 83.40%, respectively. CONCLUSION: The breast CEUS models can predict risk of malignant breast lesions more accurately, decrease false-positive biopsy, and provide accurate BIRADS classification.展开更多
自2013年美国放射学会出版第二版乳腺影像报告和数据系统(Breast Imaging Reporting and Data System,BI-RADS)后,乳腺超声的临床实践与科学研究均从中获益。本文总结了2013年版超声BI-RADS出版这10年间,乳腺超声影像技术临床应用与革...自2013年美国放射学会出版第二版乳腺影像报告和数据系统(Breast Imaging Reporting and Data System,BI-RADS)后,乳腺超声的临床实践与科学研究均从中获益。本文总结了2013年版超声BI-RADS出版这10年间,乳腺超声影像技术临床应用与革新、存在的问题与面临的挑战及未来的发展机遇,以期为临床诊治、指南推广与应用提供帮助。展开更多
目的分析基于超声乳腺影像报告和数据系统(Breast Imaging Reporting and Data System,BI-RADS)分类对不同病理类型乳腺肿块诊断的结果。方法方便选取2022年3月—2023年8月在枣庄市台儿庄区人民医院和济南市平阴县中医医院进行检查的74...目的分析基于超声乳腺影像报告和数据系统(Breast Imaging Reporting and Data System,BI-RADS)分类对不同病理类型乳腺肿块诊断的结果。方法方便选取2022年3月—2023年8月在枣庄市台儿庄区人民医院和济南市平阴县中医医院进行检查的74例女性患者为研究对象。以病理检查为金标准,经病理证实乳腺肿块共80个,采用kappa检验分析超声检查与病理结果的一致性。结果病理结果显示,良性肿块61个,恶性肿块19个(kappa值为0.710,P<0.01)。超声BI-RADS分类诊断的灵敏度、特异度、准确度分别为96.72%、84.21%、93.75%。超声BI-RADS对良性肿瘤的诊断符合率均≥85.71%,而对恶性肿瘤诊断中,符合率最低时为75.00%。结论虽然不同病理类型乳腺肿块的超声诊断符合率较高,但在实际操作过程中仍然存在漏诊误诊等情况,故需要临床医师了解超声检查的不足,对超声诊断质量做好把控。展开更多
Background:Structured reports are not widely used and thus most reports exist in the form of free text.The process of data extraction by experts is time-consuming and error-prone,whereas data extraction by natural lan...Background:Structured reports are not widely used and thus most reports exist in the form of free text.The process of data extraction by experts is time-consuming and error-prone,whereas data extraction by natural language processing (NLP) is a potential solution that could improve diagnosis efficiency and accuracy.The purpose of this study was to evaluate an NLP program that determines American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) descriptors and final assessment categories from breast magnetic resonance imaging (MRI) reports.Methods:This cross-sectional study involved 2330 breast MRI reports in the electronic medical record from 2009 to 2017.We used 1635 reports for the creation of a revised BI-RADS MRI lexicon and synonyms lists as well as the iterative development of an NLP system.The remaining 695 reports that were not used for developing the system were used as an independent test set for the final evaluation of the NLP system.The recall and precision of an NLP algorithm to detect the revised BI-RADS MRI descriptors and BI-RADS categories from the free-text reports were evaluated against a standard reference of manual human review.Results:There was a high level of agreement between two manual reviewers,with a κ value of 0.95.For all breast imaging reports,the NLP algorithm demonstrated a recall of 78.5% and a precision of 86.1% for correct identification of the revised BI-RADS MRI descriptors and the BI-RADS categories.NLP generated the total results in <1 s,whereas the manual reviewers averaged 3.38 and 3.23 min per report,respectively.Conclusions:The NLP algorithm demonstrates high recall and precision for information extraction from free-text reports.This approach will help to narrow the gap between unstructured report text and structured data,which is needed in decision support and other applications.展开更多
Purpose: To apply and evaluate a super-resolution scheme based on the super-resolution convolutional neural network (SRCNN) for enhancing image resolution in digital mammograms. Materials and Methods: A total of 711 m...Purpose: To apply and evaluate a super-resolution scheme based on the super-resolution convolutional neural network (SRCNN) for enhancing image resolution in digital mammograms. Materials and Methods: A total of 711 mediolateral oblique (MLO) images including breast lesions were sampled from the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM). We first trained the super-resolution convolutional neural network (SRCNN), which is a deep-learning based super-resolution method. Using this trained SRCNN, high-resolution images were reconstructed from low-resolution images. We compared the image quality of the super-resolution method and that obtained using the linear interpolation methods (nearest neighbor and bilinear interpolations). To investigate the relationship between the image quality of the SRCNN-processed images and the clinical features of the mammographic lesions, we compared the image quality yielded by implementing the SRCNN, in terms of the breast density, the Breast Imaging-Reporting and Data System (BI-RADS) assessment, and the verified pathology information. For quantitative evaluation, peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were measured to assess the image restoration quality and the perceived image quality. Results: The super-resolution image quality yielded by the SRCNN was significantly higher than that obtained using linear interpolation methods (p p Conclusion: SRCNN can significantly outperform conventional interpolation methods for enhancing image resolution in digital mammography. SRCNN can significantly improve the image quality of magnified mammograms, especially in dense breasts, high-risk BI-RADS assessment groups, and pathology-verified malignant cases.展开更多
文摘AIM: To determine whether contrast-enhanced ultrasound(CEUS) can improve the precision of breast imaging reporting and data system(BI-RADS) categorization. METHODS: A total of 230 patients with 235 solid breast lesions classified as BI-RADS 4 on conventional ultrasound were evaluated. CEUS was performed within one week before core needle biopsy or surgical resection and a revised BI-RADS classification was assigned based on 10 CEUS imaging characteristics. Receiver operating characteristic curve analysis was then conducted to evaluate the diagnostic performance of CEUS-based BI-RADS assignment with pathological examination as reference criteria. RESULTS: The CEUS-based BI-RADS evaluation classified 116/235(49.36%) lesions into category 3, 20(8.51%), 13(5.53%) and 12(5.11%) lesions into categories 4A, 4B and 4C, respectively, and 74(31.49%) into category 5. Selecting CEUS-based BI-RADS category 4A as an appropriate cut-off gave sensitivity and specificity values of 85.4% and 87.8%, respectively, for the diagnosisof malignant disease. The cancer-to-biopsy yield was 73.11% with CEUS-based BI-RADS 4A selected as the biopsy threshold compared with 40.85% otherwise, while the biopsy rate was only 42.13% compared with 100% otherwise. Overall, only 4.68% of invasive cancers were misdiagnosed.CONCLUSION: This pilot study suggests that evaluation of BI-RADS 4 breast lesions with CEUS results in reduced biopsy rates and increased cancer-to-biopsy yields.
文摘AIM: To build and evaluate predictive models for contrast-enhanced ultrasound(CEUS) of the breast to distinguish between benign and malignant lesions. METHODS: A total of 235 breast imaging reporting and data system(BI-RADS) 4 solid breast lesions were imaged via CEUS before core needle biopsy or surgical resection. CEUS results were analyzed on 10 enhancing patterns to evaluate diagnostic performance of three benign and three malignant CEUS models, with pathological results used as the gold standard. A logistic regression model was developed basing on the CEUS results, and then evaluated with receiver operating curve(ROC). RESULTS: Except in cases of enhanced homogeneity, the rest of the 9 enhancement appearances were statistically significant(P < 0.05). These 9 enhancement patterns were selected in the final step of the logistic regression analysis, with diagnostic sensitivity and specificity of 84.4% and 82.7%, respectively, and the area under the ROC curve of 0.911. Diagnostic sensitivity, specificity, and accuracy of the malignant vs benign CEUS models were 84.38%, 87.77%, 86.38% and 86.46%, 81.29% and 83.40%, respectively. CONCLUSION: The breast CEUS models can predict risk of malignant breast lesions more accurately, decrease false-positive biopsy, and provide accurate BIRADS classification.
文摘自2013年美国放射学会出版第二版乳腺影像报告和数据系统(Breast Imaging Reporting and Data System,BI-RADS)后,乳腺超声的临床实践与科学研究均从中获益。本文总结了2013年版超声BI-RADS出版这10年间,乳腺超声影像技术临床应用与革新、存在的问题与面临的挑战及未来的发展机遇,以期为临床诊治、指南推广与应用提供帮助。
文摘目的分析基于超声乳腺影像报告和数据系统(Breast Imaging Reporting and Data System,BI-RADS)分类对不同病理类型乳腺肿块诊断的结果。方法方便选取2022年3月—2023年8月在枣庄市台儿庄区人民医院和济南市平阴县中医医院进行检查的74例女性患者为研究对象。以病理检查为金标准,经病理证实乳腺肿块共80个,采用kappa检验分析超声检查与病理结果的一致性。结果病理结果显示,良性肿块61个,恶性肿块19个(kappa值为0.710,P<0.01)。超声BI-RADS分类诊断的灵敏度、特异度、准确度分别为96.72%、84.21%、93.75%。超声BI-RADS对良性肿瘤的诊断符合率均≥85.71%,而对恶性肿瘤诊断中,符合率最低时为75.00%。结论虽然不同病理类型乳腺肿块的超声诊断符合率较高,但在实际操作过程中仍然存在漏诊误诊等情况,故需要临床医师了解超声检查的不足,对超声诊断质量做好把控。
文摘Background:Structured reports are not widely used and thus most reports exist in the form of free text.The process of data extraction by experts is time-consuming and error-prone,whereas data extraction by natural language processing (NLP) is a potential solution that could improve diagnosis efficiency and accuracy.The purpose of this study was to evaluate an NLP program that determines American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) descriptors and final assessment categories from breast magnetic resonance imaging (MRI) reports.Methods:This cross-sectional study involved 2330 breast MRI reports in the electronic medical record from 2009 to 2017.We used 1635 reports for the creation of a revised BI-RADS MRI lexicon and synonyms lists as well as the iterative development of an NLP system.The remaining 695 reports that were not used for developing the system were used as an independent test set for the final evaluation of the NLP system.The recall and precision of an NLP algorithm to detect the revised BI-RADS MRI descriptors and BI-RADS categories from the free-text reports were evaluated against a standard reference of manual human review.Results:There was a high level of agreement between two manual reviewers,with a κ value of 0.95.For all breast imaging reports,the NLP algorithm demonstrated a recall of 78.5% and a precision of 86.1% for correct identification of the revised BI-RADS MRI descriptors and the BI-RADS categories.NLP generated the total results in <1 s,whereas the manual reviewers averaged 3.38 and 3.23 min per report,respectively.Conclusions:The NLP algorithm demonstrates high recall and precision for information extraction from free-text reports.This approach will help to narrow the gap between unstructured report text and structured data,which is needed in decision support and other applications.
文摘Purpose: To apply and evaluate a super-resolution scheme based on the super-resolution convolutional neural network (SRCNN) for enhancing image resolution in digital mammograms. Materials and Methods: A total of 711 mediolateral oblique (MLO) images including breast lesions were sampled from the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM). We first trained the super-resolution convolutional neural network (SRCNN), which is a deep-learning based super-resolution method. Using this trained SRCNN, high-resolution images were reconstructed from low-resolution images. We compared the image quality of the super-resolution method and that obtained using the linear interpolation methods (nearest neighbor and bilinear interpolations). To investigate the relationship between the image quality of the SRCNN-processed images and the clinical features of the mammographic lesions, we compared the image quality yielded by implementing the SRCNN, in terms of the breast density, the Breast Imaging-Reporting and Data System (BI-RADS) assessment, and the verified pathology information. For quantitative evaluation, peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were measured to assess the image restoration quality and the perceived image quality. Results: The super-resolution image quality yielded by the SRCNN was significantly higher than that obtained using linear interpolation methods (p p Conclusion: SRCNN can significantly outperform conventional interpolation methods for enhancing image resolution in digital mammography. SRCNN can significantly improve the image quality of magnified mammograms, especially in dense breasts, high-risk BI-RADS assessment groups, and pathology-verified malignant cases.
