Advertisement

Journal of Ultrasound

, Volume 21, Issue 2, pp 105–118 | Cite as

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

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

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

CAD Breast lesion characterization Breast tumors US-elastography S-detect 

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.

Notes

Funding

No funding financed this study.

Compliance with ethical standards

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.

References

  1. 1.
    Hooley RJ, Scoutt LM, Philpotts LE (2013) Breast ultrasonography: state of the art. Radiology 268:642–659.  https://doi.org/10.1148/radiol.13121606 CrossRefPubMedGoogle Scholar
  2. 2.
    D’Orsi CJ, Sickles EA, Mendelson EB, Morris EA (2013) ACR BI-RADS® Atlas, breast imaging reporting and data system, 5th edn. American College of Radiology, RestonGoogle Scholar
  3. 3.
    Rao AA, Feneis J, Lalonde C, Ojeda-Fournier H (2016) A pictorial review of changes in the BI-RADS fifth edition. RadioGraphics 36:623–639.  https://doi.org/10.1148/rg.2016150178 CrossRefPubMedGoogle Scholar
  4. 4.
    Lee J (2017) Practical and illustrated summary of updated BI-RADS for ultrasonography. Ultrasonography 36:71–81.  https://doi.org/10.14366/usg.16034 CrossRefPubMedGoogle Scholar
  5. 5.
    Spak DA, Plaxco JS, Santiago L, Dryden MJ, Dogan BE (2017) BI-RADS ® fifth edition: a summary of changes. Diagn Interv Imaging 98:179–190.  https://doi.org/10.1016/j.diii.2017.01.001 CrossRefPubMedGoogle Scholar
  6. 6.
    Goddi A, Bonardi M, Alessi S (2012) Breast elastography: a literature review. J Ultrasound 15:192–198.  https://doi.org/10.1016/j.jus.2012.06.009 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Drudi F, Giovagnorio F, Carbone A, Ricci P, Petta S, Cantisani V, Ferrari F, Marchetti F, Passariello R (2006) Transrectal colour Doppler contrast sonography in the diagnosis of local recurrence after radical prostatectomy—comparison with MRI. Ultraschall in der Medizin Eur J Ultrasound 28:146–151.  https://doi.org/10.1055/s-2006-926583 CrossRefGoogle Scholar
  8. 8.
    Cantisani V, Ricci P, Erturk M, Pagliara E, Drudi F, Calliada F, Mortele K, D’Ambrosio U, Marigliano C, Catalano C (2010) Detection of hepatic metastases from colorectal cancer: prospective evaluation of gray scale US versus SonoVue® low mechanical index real-time enhanced US as compared with multidetector-CT or Gd-BOPTA-MRI. Ultraschall in der Medizin-Eur J Ultrasound 31:500–505.  https://doi.org/10.1055/s-0028-1109751 CrossRefGoogle Scholar
  9. 9.
    Bamber J, Cosgrove D, Dietrich C, Fromageau J, Bojunga J, Calliada F, Cantisani V, Correas J-M, D’Onofrio M, Drakonaki E, Fink M, Friedrich-Rust M, Gilja O, Havre R, Jenssen C, Klauser A, Ohlinger R, Saftoiu A, Schaefer F, Sporea I, Piscaglia F (2013) EFSUMB guidelines and recommendations on the clinical use of ultrasound elastography. Part 1: basic principles and technology. Ultraschall in der Medizin Eur J Ultrasound 34:169–184.  https://doi.org/10.1055/s-0033-1335205 CrossRefGoogle Scholar
  10. 10.
    Shiina T, Nightingale KR, Palmeri ML, Hall TJ, Bamber JC, Barr RG, Castera L, Choi BI, Chou Y-H, Cosgrove D, Dietrich CF, Ding H, Amy D, Farrokh A, Ferraioli G, Filice C, Friedrich-Rust M, Nakashima K, Schafer F, Sporea I, Suzuki S, Wilson S, Kudo M (2015) WFUMB guidelines and recommendations for clinical use of ultrasound elastography: part 1: basic principles and terminology. Ultrasound Med Biol 41:1126–1147.  https://doi.org/10.1016/j.ultrasmedbio.2015.03.009 CrossRefPubMedGoogle Scholar
  11. 11.
    Kamble R, Sodhi KS, Thapa BR, Saxena AK, Bhatia A, Dayal D, Khandelwal N (2017) Liver acoustic radiation force impulse (ARFI) in childhood obesity: comparison and correlation with biochemical markers. J Ultrasound 20:33–42.  https://doi.org/10.1007/s40477-016-0229-y CrossRefPubMedGoogle Scholar
  12. 12.
    Giannetti A, Biscontri M, Matergi M, Stumpo M, Minacci C (2016) Feasibility of CEUS and strain elastography in one case of ileum Crohn stricture and literature review. J Ultrasound 19:231–237.  https://doi.org/10.1007/s40477-016-0212-7 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Ricci P, Marigliano C, Cantisani V, Porfiri A, Marcantonio A, Lodise P, D’Ambrosio U, Labbadia G, Maggini E, Mancuso E, Panzironi G, Di Segni M, Furlan C, Masciangelo R, Taliani G (2013) Ultrasound evaluation of liver fibrosis: preliminary experience with acoustic structure quantification (ASQ) software. Radiol Med 118:995–1010.  https://doi.org/10.1007/s11547-013-0940-0 CrossRefPubMedGoogle Scholar
  14. 14.
    Jalalian A, Mashohor SBT, Mahmud HR, Saripan MIB, Ramli ARB, Karasfi B (2013) Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clin Imaging 37:420–426.  https://doi.org/10.1016/j.clinimag.2012.09.024 CrossRefPubMedGoogle Scholar
  15. 15.
    Dromain C, Boyer B, Ferré R, Canale S, Delaloge S, Balleyguier C (2013) Computed-aided diagnosis (CAD) in the detection of breast cancer. Eur J Radiol 82:417–423.  https://doi.org/10.1016/j.ejrad.2012.03.005 CrossRefPubMedGoogle Scholar
  16. 16.
    Itoh A, Ueno E, Tohno E, Kamma H, Takahashi H, Shiina T, Yamakawa M, Matsumura T (2006) Breast disease: clinical application of US elastography for diagnosis. Radiology 239:341–350.  https://doi.org/10.1148/radiol.2391041676 CrossRefPubMedGoogle Scholar
  17. 17.
    Jales RM, Sarian LO, Torresan R, Marussi EF, Álvares BR, Derchain S (2013) Simple rules for ultrasonographic subcategorization of BI-RADS®-US 4 breast masses. Eur J Radiol 82:1231–1235.  https://doi.org/10.1016/j.ejrad.2013.02.032 CrossRefPubMedGoogle Scholar
  18. 18.
    Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Xiao X, Jiang Q, Wu H, Guan X, Qin W, Luo B (2017) Diagnosis of sub-centimetre breast lesions: combining BI-RADS-US with strain elastography and contrast-enhanced ultrasound—a preliminary study in China. Eur Radiol 27:2443–2450.  https://doi.org/10.1007/s00330-016-4628-4 CrossRefPubMedGoogle Scholar
  20. 20.
    de Fleury FC (2015) The importance of breast elastography added to the BI-RADS® (5th edition) lexicon classification. Revista da Associação Médica Brasileira 61:313–316.  https://doi.org/10.1590/1806-9282.61.04.313 CrossRefGoogle Scholar
  21. 21.
    Moon WK, Lo C-M, Cho N, Chang JM, Huang C-S, Chen J-H, Chang R-F (2013) Computer-aided diagnosis of breast masses using quantified BI-RADS findings. Comput Methods Programs Biomed 111:84–92.  https://doi.org/10.1016/j.cmpb.2013.03.017 CrossRefPubMedGoogle Scholar
  22. 22.
    Shen W-C, Chang R-F, Moon WK (2007) Computer aided classification system for breast ultrasound based on breast imaging reporting and data system (BI-RADS). Ultrasound Med Biol 33:1688–1698.  https://doi.org/10.1016/j.ultrasmedbio.2007.05.016 CrossRefPubMedGoogle Scholar
  23. 23.
    Kim K, Song MK, Kim E-K, Yoon JH (2017) Clinical application of S-detect to breast masses on ultrasonography: a study evaluating the diagnostic performance and agreement with a dedicated breast radiologist. Ultrasonography 36:3–9.  https://doi.org/10.14366/usg.16012 CrossRefPubMedGoogle Scholar
  24. 24.
    Cho E, Kim E-K, Song MK, Yoon JH (2017) Application of computer-aided diagnosis on breast ultrasonography: evaluation of diagnostic performances and agreement of radiologists according to different levels of experience: application of computer-aided diagnosis on breast ultrasonography. J Ultrasound Med.  https://doi.org/10.1002/jum.14332 CrossRefPubMedGoogle Scholar
  25. 25.
    Botticelli A, Mazzotti E, Di Stefano D, Petrocelli V, Mazzuca F, La Torre M, Ciabatta FR, Giovagnoli RM, Marchetti P, Bonifacino A (2015) Positive impact of elastography in breast cancer diagnosis: an institutional experience. J Ultrasound 18:321–327.  https://doi.org/10.1007/s40477-015-0177-y CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Cosgrove D, Piscaglia F, Bamber J, Bojunga J, Correas J-M, Gilja O, Klauser A, Sporea I, Calliada F, Cantisani V, D’Onofrio M, Drakonaki E, Fink M, Friedrich-Rust M, Fromageau J, Havre R, Jenssen C, Ohlinger R, Săftoiu A, Schaefer F, Dietrich C (2013) EFSUMB guidelines and recommendations on the clinical use of ultrasound elastography. Part 2: clinical applications. Ultraschall in der Medizin Eur J Ultrasound 34:238–253.  https://doi.org/10.1055/s-0033-1335375 CrossRefGoogle Scholar
  27. 27.
    Barr RG, Nakashima K, Amy D, Cosgrove D, Farrokh A, Schafer F, Bamber JC, Castera L, Choi BI, Chou Y-H, Dietrich CF, Ding H, Ferraioli G, Filice C, Friedrich-Rust M, Hall TJ, Nightingale KR, Palmeri ML, Shiina T, Suzuki S, Sporea I, Wilson S, Kudo M (2015) WFUMB guidelines and recommendations for clinical use of ultrasound elastography: part 2: breast. Ultrasound Med Biol 41:1148–1160.  https://doi.org/10.1016/j.ultrasmedbio.2015.03.008 CrossRefPubMedGoogle Scholar
  28. 28.
    Sadigh G, Carlos RC, Neal CH, Dwamena BA (2012) Accuracy of quantitative ultrasound elastography for differentiation of malignant and benign breast abnormalities: a meta-analysis. Breast Cancer Res Treat 134:923–931.  https://doi.org/10.1007/s10549-012-2020-x CrossRefPubMedGoogle Scholar
  29. 29.
    Sadigh G, Carlos RC, Neal CH, Wojcinski S, Dwamena BA (2013) Impact of breast mass size on accuracy of ultrasound elastography vs. conventional B-mode ultrasound: a meta-analysis of individual participants. Eur Radiol 23:1006–1014.  https://doi.org/10.1007/s00330-012-2682-0 CrossRefPubMedGoogle Scholar
  30. 30.
    Hao S-Y, Jiang Q-C, Zhong W-J, Zhao X-B, Yao J-Y, Li L-J, Luo B-M, Ou B, Zhi H (2016) Ultrasound elastography combined with BI-RADS–US classification system: is it helpful for the diagnostic performance of conventional ultrasonography? Clin Breast Cancer 16:e33–e41.  https://doi.org/10.1016/j.clbc.2015.10.003 CrossRefPubMedGoogle Scholar

Copyright information

© Società Italiana di Ultrasonologia in Medicina e Biologia (SIUMB) 2018

Authors and Affiliations

  • Mattia Di Segni
    • 1
    Email author
  • Valeria de Soccio
    • 1
  • Vito Cantisani
    • 1
  • Giacomo Bonito
    • 1
  • Antonello Rubini
    • 1
  • Gabriele Di Segni
    • 2
  • Sveva Lamorte
    • 1
  • Valentina Magri
    • 1
  • Corrado De Vito
    • 3
  • Giuseppe Migliara
    • 3
  • Tommaso Vincenzo Bartolotta
    • 4
  • Alessio Metere
    • 5
  • Laura Giacomelli
    • 5
  • Carlo de Felice
    • 1
  • Ferdinando D’Ambrosio
    • 1
  1. 1.U.O.C. Diagnostica per Immagini, P. O San Paolo - ASL Roma 4Civitavecchia (RM)Italy
  2. 2.Arakne S.r.l.RomeItaly
  3. 3.Department of Public Health and Infectious DiseasesSapienza University of RomeRomeItaly
  4. 4.Department of RadiologyUniversity of PalermoPalermoItaly
  5. 5.Department of Surgical SciencesSapienza University of RomeRomeItaly

Personalised recommendations