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Non-mass lesions detected by breast US: stratification of cancer risk for clinical management.

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Title: Non-mass lesions detected by breast US: stratification of cancer risk for clinical management.
Authors: Park, Ko Woon1 (AUTHOR), Park, Surin1 (AUTHOR), Shon, Insuk2 (AUTHOR), Kim, Min-Ji2 (AUTHOR), Han, Boo-kyung1 (AUTHOR), Ko, Eun Young1 (AUTHOR), Ko, Eun Sook1 (AUTHOR), Shin, Jung Hee1 (AUTHOR), Kwon, Mi-ri1 (AUTHOR), Choi, Ji Soo1 (AUTHOR) jisoo.choi@samsung.com
Source: European Radiology. Mar2021, Vol. 31 Issue 3, p1693-1706. 14p. 1 Color Photograph, 2 Black and White Photographs, 1 Diagram, 5 Charts, 2 Graphs.
Subjects: Breast, Logistic regression analysis, Imaging systems, Odds ratio, Mammograms, Epidermal cyst
Abstract: Objective: To develop a classification system using imaging features to interpret breast non-mass lesions (NMLs) detected on US and to stratify their cancer risk. Methods: This retrospective study included 715 patients with 715 breast NMLs detected on breast US from 2012 to 2016. Each patient underwent mammography at the time of diagnosis. Radiologists assessed US and mammographic features and final BI-RADS categories. Multivariable logistic regression was used to find imaging features associated with malignancy in a development dataset (n = 460). A system to classify BI-RADS categories (3 to 5) was developed based on the odds ratios (ORs) of imaging features significantly associated with malignancy and validated in a distinct validation dataset (n = 255). Results: Among 715 NMLs, 385 (53.8%) were benign and 330 (46.2%) were malignant. In the development dataset, the following B-mode US features were associated with malignancy (all p < 0.001): segmental distribution (OR = 3.03; 95% confidence interval [CI], 1.50–6.15), associated calcifications (OR = 4.26; 95% CI, 1.62–11.18), abnormal ductal change (OR = 4.91; 95% CI, 2.07–11.68), and posterior shadowing (OR = 20.20; 95% CI, 6.46–63.23). The following mammographic features were also associated with malignancy (all p < 0.001): calcifications (OR = 7.98; 95% CI, 3.06–20.81) and focal asymmetry (OR = 4.75; 95% CI, 1.90–11.88). In the validation dataset, our classification system using US and mammography showed a higher area under the curve (0.951–0.956) compared to when it was not applied (0.908–0911) to predict malignancy with BI-RADS categories (p < 0.05). Conclusion: Our classification system which incorporates US and mammographic features of breast NMLs can help interpret and manage all NMLs detected on breast US by stratifying cancer risk according to BI-RADS categories. Key Points: • When diagnosing breast NMLs detected on US, suspicious US features are segmental distribution, associated abnormal ductal change, calcifications, and posterior shadowing within or around the NML on B-mode US, while a probably benign US feature is the presence of multiple small cysts. • Corresponding suspicious mammographic features of breast NMLs detected on US are associated calcifications and focal asymmetry. • Our classification system which incorporates US features with and without mammography can potentially be used to interpret and manage any NMLs detected on breast US in clinical practice. [ABSTRACT FROM AUTHOR]
Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Non-mass lesions detected by breast US: stratification of cancer risk for clinical management.
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  Data: &lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Park%2C+Ko+Woon%22&quot;&gt;Park, Ko Woon&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Park%2C+Surin%22&quot;&gt;Park, Surin&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Shon%2C+Insuk%22&quot;&gt;Shon, Insuk&lt;/searchLink&gt;&lt;relatesTo&gt;2&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Kim%2C+Min-Ji%22&quot;&gt;Kim, Min-Ji&lt;/searchLink&gt;&lt;relatesTo&gt;2&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Han%2C+Boo-kyung%22&quot;&gt;Han, Boo-kyung&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Ko%2C+Eun+Young%22&quot;&gt;Ko, Eun Young&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Ko%2C+Eun+Sook%22&quot;&gt;Ko, Eun Sook&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Shin%2C+Jung+Hee%22&quot;&gt;Shin, Jung Hee&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Kwon%2C+Mi-ri%22&quot;&gt;Kwon, Mi-ri&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Choi%2C+Ji+Soo%22&quot;&gt;Choi, Ji Soo&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;i&gt; jisoo.choi@samsung.com&lt;/i&gt;
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  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22European+Radiology%22&quot;&gt;European Radiology&lt;/searchLink&gt;. Mar2021, Vol. 31 Issue 3, p1693-1706. 14p. 1 Color Photograph, 2 Black and White Photographs, 1 Diagram, 5 Charts, 2 Graphs.
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  Data: Objective: To develop a classification system using imaging features to interpret breast non-mass lesions (NMLs) detected on US and to stratify their cancer risk. Methods: This retrospective study included 715 patients with 715 breast NMLs detected on breast US from 2012 to 2016. Each patient underwent mammography at the time of diagnosis. Radiologists assessed US and mammographic features and final BI-RADS categories. Multivariable logistic regression was used to find imaging features associated with malignancy in a development dataset (n = 460). A system to classify BI-RADS categories (3 to 5) was developed based on the odds ratios (ORs) of imaging features significantly associated with malignancy and validated in a distinct validation dataset (n = 255). Results: Among 715 NMLs, 385 (53.8%) were benign and 330 (46.2%) were malignant. In the development dataset, the following B-mode US features were associated with malignancy (all p &lt; 0.001): segmental distribution (OR = 3.03; 95% confidence interval [CI], 1.50–6.15), associated calcifications (OR = 4.26; 95% CI, 1.62–11.18), abnormal ductal change (OR = 4.91; 95% CI, 2.07–11.68), and posterior shadowing (OR = 20.20; 95% CI, 6.46–63.23). The following mammographic features were also associated with malignancy (all p &lt; 0.001): calcifications (OR = 7.98; 95% CI, 3.06–20.81) and focal asymmetry (OR = 4.75; 95% CI, 1.90–11.88). In the validation dataset, our classification system using US and mammography showed a higher area under the curve (0.951–0.956) compared to when it was not applied (0.908–0911) to predict malignancy with BI-RADS categories (p &lt; 0.05). Conclusion: Our classification system which incorporates US and mammographic features of breast NMLs can help interpret and manage all NMLs detected on breast US by stratifying cancer risk according to BI-RADS categories. Key Points: • When diagnosing breast NMLs detected on US, suspicious US features are segmental distribution, associated abnormal ductal change, calcifications, and posterior shadowing within or around the NML on B-mode US, while a probably benign US feature is the presence of multiple small cysts. • Corresponding suspicious mammographic features of breast NMLs detected on US are associated calcifications and focal asymmetry. • Our classification system which incorporates US features with and without mammography can potentially be used to interpret and manage any NMLs detected on breast US in clinical practice. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00330-020-07168-y
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        Text: English
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        PageCount: 14
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      – SubjectFull: Breast
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              Text: Mar2021
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