Clinical application of S-detect to breast masses on ultrasonography: A study evaluating the diagnostic performance and agreement with a dedicated breast radiologist

Kiwook Kim, Mi Kyung Song, Eun Kyung Kim, Jung Hyun Yoon

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11 Citations (Scopus)


Purpose: The purpose of this study was to evaluate the diagnostic performance of S-Detect when applied to breast ultrasonography (US), and the agreement with an experienced radiologist specializing in breast imaging. Methods: From June to August 2015, 192 breast masses in 175 women were included. US features of the breast masses were retrospectively analyzed by a radiologist who specializes in breast imaging and S-Detect, according to the fourth edition of the American College of Radiology Breast Imaging Reporting and Data System lexicon and final assessment categories. Final assessments from S-Detect were in dichotomized form: possibly benign and possibly malignant. Kappa statistics were used to analyze the agreement between the radiologist and S-Detect. Diagnostic performance of the radiologist and S-Detect was calculated, including sensitivity, specificity, positive predictive value (PPV), negative predictive value, accuracy, and area under the receiving operator characteristics curve. Results: Of the 192 breast masses, 72 (37.5%) were malignant, and 120 (62.5%) were benign. Benign masses among category 4a had higher rates of possibly benign assessment on S-Detect for the radiologist, 63.5% to 36.5%, respectively (P=0.797). When the cutoff was set at category 4a, the specificity, PPV, and accuracy was significantly higher in S-Detect compared to the radiologist (all P<0.05), with a higher area under the receiver operator characteristics curve of 0.725 compared to 0.653 (P=0.038). Moderate agreement (k=0.58) was seen in the final assessment between the radiologist and S-Detect. Conclusion: S-Detect may be used as an additional diagnostic tool to improve the specificity of breast US in clinical practice, and guide in decision making for breast masses detected on US.

Original languageEnglish
Pages (from-to)3-9
Number of pages7
Issue number1
Publication statusPublished - 2017 Jan 1


All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

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