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

Abstract

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.

Keywords: BI-RADS; Breast; Diagnosis, computer-aided; Neoplasms; Ultrasonography.

Conflict of interest statement

No potential conflict of interest relevant to this article was reported.

Figures

Fig. 1.. Representative image showing the setting of the region-of-interest (ROI) for S-Detect analysis.

After the ROI was drawn along the border of the mass, ultrasonographic features were analyzed automatically by the S-Detect program and a final assessment was produced.

Fig. 2.. Receiver operator characteristic (ROC) curve for the radiologist and S-Detect.

Solid blue line, brown interrupted line, and orange interrupted line indicates area under the ROC curve of radiologist with cutoff at ultrasonography (US) Breast Imaging Reporting and Data System (BI-RADS) category 4a (0.653), performance of radiologist with cutoff set at US BI-RADS category 4b (0.772), and area under the ROC curve of S-Detect (0.725), respectively.