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TOMMASO VINCENZO BARTOLOTTA

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

  • Autori: Di Segni, Mattia; de Soccio, Valeria; Cantisani, Vito; Bonito, Giacomo; Rubini, Antonello; Di Segni, Gabriele; Lamorte, Sveva; Magri, Valentina; De Vito, Corrado; Migliara, Giuseppe; Bartolotta, Tommaso Vincenzo; Metere, Alessio; Giacomelli, Laura; de Felice, Carlo; D’Ambrosio, Ferdinando
  • Anno di pubblicazione: 2018
  • Tipologia: Articolo in rivista (Articolo in rivista)
  • OA Link: http://hdl.handle.net/10447/325891

Abstract

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.