Automated classification of focal breast lesions according to S-detect: validation and role as a clinical and teaching tool

Abstract

Purpose: To assess the diagnostic performance and the potential as a teaching tool of S-detect in the assessment of focal breast lesions.

Methods: 61 patients (age 21-84 years) with benign breast lesions in follow-up or candidate to pathological sampling or with suspicious lesions candidate to biopsy were enrolled. The study was based on a prospective and on a retrospective phase. In the prospective phase, after completion of baseline US by an experienced breast radiologist and S-detect assessment, 5 operators with different experience and dedication to breast radiology performed elastographic exams. In the retrospective phase, the 5 operators performed a retrospective assessment and categorized lesions with BI-RADS 2013 lexicon. Integration of S-detect to in-training operators evaluations was performed by giving priority to S-detect analysis in case of disagreement. 2 × 2 contingency tables and ROC analysis were used to assess the diagnostic performances; inter-rater agreement was measured with Cohen's k; Bonferroni's test was used to compare performances. A significance threshold of p = 0.05 was adopted.

Results: All operators showed sensitivity > 90% and varying specificity (50-75%); S-detect showed sensitivity > 90 and 70.8% specificity, with inter-rater agreement ranging from moderate to good. Lower specificities were improved by the addition of S-detect. The addition of elastography did not lead to any improvement of the diagnostic performance.

Conclusions: S-detect is a feasible tool for the characterization of breast lesions; it has a potential as a teaching tool for the less experienced operators.

Keywords: Breast lesion characterization; Breast tumors; CAD; S-detect; US-elastography.

Conflict of interest statement

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Conflict of interest

Cantisani V. is lecturer for Bracco and Samsung Healthcare; Bartolotta is lecturer for Samsung Healthcare.

Figures

Fig. 1

ROC curves for expert operator and for S-detect: a expert operator; b S-detect

Fig. 2

ROC curve analysis for operators-in-training: a 5th-year with limited experience in breast imaging; b 2nd-year with deeper experience in breast imaging; c 3rd-year with limited experience in breast imaging; d 1st-year with deeper experience in breast imaging

Fig. 3

Performance of the operators-in-training after the integration of S-detect in ambiguous cases (BI-RADS 4a): a 5th-year with limited experience; b 2nd-year with deeper experience; c 3rd-year with limited experience; d 1st-year with deeper experience

Fig. 4

Distribution of the lesions according to elastographic indicators: a elasticity assessment; b Tsukuba Map; c strain ratio (cut-off 1.765)

Fig. 5

ROC curves concerning the integration of various elastographic indicators: a elasticity assessment; b Tsukuba map; c strain ratio

Fig. 6

BI-RADS 5 lesion: a a parallel, hypoechoic, irregular lesion that has angular margins and posterior shadowing and is associated with architectural distortion; b Scarce vascularity at CDUS; c–g Elastographic features with qualitative and semiquantitative stiffness assessment; h Structured report according to S-detect