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

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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.

Riassunto

Obiettivi

Valutare la performance diagnostica ed il potenziale come strumento didattico dell’S-detect nella valutazione delle lesioni mammarie focali.

Metodi

Sono state arruolate 61 pazienti (età: 21–84 anni) con lesioni mammarie benigne in follow-up o con lesioni sospette per malignità candidate a biopsia. Lo studio è stato basato su una fase prospettica ed una retrospettiva. Nella fase prospettica, dopo il completamento dell’ecografia di base da parte di un senologo esperto, 5 operatori con differente livello di esperienza e differentemente dedicati alla senologia hanno eseguito l’esame elastosonografico. Nella fase retrospettiva, i 5 operatori hanno eseguito una valutazione e categorizzazione delle lesioni con BI-RADS 2013. L’integrazione dell’S-detect con la valutazione degli operatori in formazione è stata eseguita dando priorità all’analisi del software in caso di discordanza. Sono state impiegate le tabelle di contingenza 2 × 2 e le curve ROC per valutare le performance diagnostiche; la concordanza tra gli operatori è stata misurata con il test k di Cohen; il test di Bonferroni è stato impiegato per comparare le performance. È stata adottata una soglia di significatività pari a p = 0.05.

Risultati

Tutti gli operatori hanno dimostrato una sensibilità > 90% e specificità variabile (50–75%); l’S-detect ha dimostrato una sensibilità > 90% e specificità del 70,8%, con concordanza con gli operatori compresa tra moderata e buona. Le specificità più basse sono state aumentate dall’aggiunta dell’S-detect. L’aggiunta dell’elastosonografia non ha determinato aumento delle performance diagnostiche.

Conclusioni

L’S-detect è uno strumento impiegabile nella caratterizzazione delle lesioni mammarie ed è un potenziale strumento didattico per gli operatori meno esperti.

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Funding

No funding financed this study.

Author information

Authors and Affiliations

  1. U.O.C. Diagnostica per Immagini, P. O San Paolo - ASL Roma 4, Largo dei Donatori del Sangue 1, 00053, Civitavecchia (RM), Italy

    Mattia Di Segni, Valeria de Soccio, Vito Cantisani, Giacomo Bonito, Antonello Rubini, Sveva Lamorte, Valentina Magri, Carlo de Felice & Ferdinando D’Ambrosio

  2. Arakne S.r.l., Via Edoardo d’Onofrio, 304, 00155, Rome, RM, Italy

    Gabriele Di Segni

  3. Department of Public Health and Infectious Diseases, Sapienza University of Rome, Piazzale Aldo Moro 5, Rome, Italy

    Corrado De Vito & Giuseppe Migliara

  4. Department of Radiology, University of Palermo, 90127, Palermo, Italy

    Tommaso Vincenzo Bartolotta

  5. Department of Surgical Sciences, Sapienza University of Rome, viale del Policlinico 155, 00161, Rome, Italy

    Alessio Metere & Laura Giacomelli

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  1. Mattia Di Segni
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Corresponding author

Correspondence to Mattia Di Segni.

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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.

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Di Segni, M., de Soccio, V., Cantisani, V. et al. Automated classification of focal breast lesions according to S-detect: validation and role as a clinical and teaching tool. J Ultrasound 21, 105–118 (2018). https://doi.org/10.1007/s40477-018-0297-2

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