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

Abstract

PurposeThe 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.MethodsFrom 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.ResultsOf 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.ConclusionS-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.

Breast Ultrasound Computer-Aided Diagnosis: Analysis of Types of Errors

“…In this CAD system, the final assessment classification was divided into 'possibly benign' and 'possibly malignant' (4,10). The cutoff for differentiating benign and malignant lesions by the radiologist was set at category 4A and category 4B.…”

“…Compared with mammography, breast US is an easily available imaging tool that does not involve a radiation hazard. However, operator dependency remains the main limitation (4).…”

“…Many studies have applied CAD systems to breast US to demonstrate the efficiency of CAD systems and to evaluate the usefulness of CAD systems for improving diagnostic accuracy (4)(5)(6)(7)(8)(9)(10)(11). There are two algorithms in breast US-CAD systems for lesion interpretations.…”

“…One is the interpretation according to the breast imaging reporting and data system (BI-RADS) lexicon, which is known as the knowledge-based analysis, and the other is the deep-learning algorithm, which is known as statistics-based analysis. that is based on a deep-learning algorithm that uses big data and provides assistance in morphological analysis based on the BI-RADS lexicon and final assessment (4,12). S-Detect might be very useful in improving the diagnostic performance of breast US for assistance in either lesion detection or the decisionmaking process during practice.…”

“…S-Detect might be very useful in improving the diagnostic performance of breast US for assistance in either lesion detection or the decisionmaking process during practice. This system has been reported to improve the diagnostic performance of radiologists by proper application of these computer-aided programs (4,11,13).…”