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.

Automated classification of focal breast lesions according to S-detect: validation and role as a clinical and teaching tool / 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. - In: JOURNAL OF ULTRASOUND. - ISSN 1971-3495. - 21:2(2018), pp. 105-118. [10.1007/s40477-018-0297-2]

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

Di Segni, Mattia
Primo
;
de Soccio, Valeria
Secondo
;
Cantisani, Vito;Bonito, Giacomo;Rubini, Antonello;Lamorte, Sveva;Magri, Valentina;De Vito, Corrado;Migliara, Giuseppe;Metere, Alessio;Giacomelli, Laura;de Felice, Carlo
Penultimo
;
D’Ambrosio, Ferdinando
Ultimo
2018

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.
2018
breast lesion characterization; breast tumors; CAD; S-detect; US-elastography; internal medicine; radiology, nuclear medicine and imaging
01 Pubblicazione su rivista::01a Articolo in rivista
Automated classification of focal breast lesions according to S-detect: validation and role as a clinical and teaching tool / 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. - In: JOURNAL OF ULTRASOUND. - ISSN 1971-3495. - 21:2(2018), pp. 105-118. [10.1007/s40477-018-0297-2]
File allegati a questo prodotto
File Dimensione Formato  
Di-Segni_Automated_2018.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.36 MB
Formato Adobe PDF
1.36 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1118444
Citazioni
  • ???jsp.display-item.citation.pmc??? 25
  • Scopus 53
  • ???jsp.display-item.citation.isi??? 47
social impact