双雷达
医学
乳房成像
放射科
卡帕
科恩卡帕
乳腺超声检查
超声波
病变
预测值
钙化
活检
乳房磁振造影
词典
核医学
乳腺摄影术
外科
人工智能
乳腺癌
统计
内科学
癌症
语言学
哲学
数学
计算机科学
作者
Hye‐Jeong Lee,Min Jung Kim,Min Jung Kim,Ji Hyun Youk,Jun‐Young Lee,Dae Ryong Kang,Ki Keun Oh
标识
DOI:10.1016/j.ejrad.2007.04.008
摘要
Purpose To evaluate inter- and intra-observer variabilities in breast sonographic feature analysis and management, using the fourth edition of the Breast Imaging Reporting and Data System (BI-RADS). Materials and methods We included 136 patients with 150 breast lesions who underwent breast ultrasound (US) and ultrasound-guided core needle biopsy. A pathological diagnosis was available for all 150 lesions: 77 (51%) malignant and 73 (49%) benign. Four radiologists retrospectively reviewed sonographic images of lesions twice within an 8-week interval. The observers described each lesion, using BI-RADS descriptors and final assessment. Inter- and intra-observer variabilities were assessed with Cohen's kappa statistic. Positive predictive value and negative predictive value (NPV) for final assessment were also calculated. Results For inter-observer agreements for sonographic descriptors, substantial agreement for lesion calcification and final assessment (κ = 0.61 for both), moderate agreement for lesion shape, orientation, boundary, and posterior acoustic features (κ = 0.49, 0.56, 0.59, and 0.49, respectively), and fair agreement for lesion margin and echo pattern (κ = 0.33 and 0.37, respectively) were obtained. For intra-observer agreement, substantial to perfect agreement was found for almost all lesion descriptors and final assessments. NPV for final assessment category 3 was 95%. Positive predictive value (PPV) for final assessment categorized as 4 or 5 were as follows: category 4a, 26%; category 4b, 89%; category 4c, 90%; and category 5, 97%. Conclusion Because inter- and intra-observer agreement with the BI-RADS lexicon for US is good, the use of BI-RADS lexicon can provide accurate and consistent description and assessment for breast US.
科研通智能强力驱动
Strongly Powered by AbleSci AI