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

Tommaso Vincenzo Bartolotta, Corrado De Vito, Ferdinando D’Ambrosio, Giuseppe Migliara, Mattia Di Segni, Valentina Magri, Antonello Rubini, Valeria De Soccio, Giacomo Bonito, Gabriele Di Segni, Sveva Lamorte, Carlo De Felice, Alessio Metere, Vito Cantisani, Laura Giacomelli, Carlo De Felice, Carlo De Felice

Risultato della ricerca: Article

13 Citazioni (Scopus)

Abstract

AbstractPURPOSE: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.
Lingua originaleEnglish
pagine (da-a)105-118
Numero di pagine14
RivistaJOURNAL OF ULTRASOUND
Volume21
Stato di pubblicazionePublished - 2018

All Science Journal Classification (ASJC) codes

  • Internal Medicine
  • Radiology Nuclear Medicine and imaging

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    Bartolotta, T. V., De Vito, C., D’Ambrosio, F., Migliara, G., Di Segni, M., Magri, V., Rubini, A., De Soccio, V., Bonito, G., Di Segni, G., Lamorte, S., De Felice, C., Metere, A., Cantisani, V., Giacomelli, L., De Felice, C., & De Felice, C. (2018). Automated classification of focal breast lesions according to S-detect: validation and role as a clinical and teaching tool. JOURNAL OF ULTRASOUND, 21, 105-118.