Literature DB >> 29681007

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

Mattia Di Segni1, Valeria de Soccio2, Vito Cantisani2, Giacomo Bonito2, Antonello Rubini2, Gabriele Di Segni3, Sveva Lamorte2, Valentina Magri2, Corrado De Vito4, Giuseppe Migliara4, Tommaso Vincenzo Bartolotta5, Alessio Metere6, Laura Giacomelli6, Carlo de Felice2, Ferdinando D'Ambrosio2.   

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.

Entities:  

Keywords:  Breast lesion characterization; Breast tumors; CAD; S-detect; US-elastography

Mesh:

Year:  2018        PMID: 29681007      PMCID: PMC5972107          DOI: 10.1007/s40477-018-0297-2

Source DB:  PubMed          Journal:  J Ultrasound        ISSN: 1876-7931


  29 in total

1.  Ultrasound Elastography Combined With BI-RADS-US Classification System: Is It Helpful for the Diagnostic Performance of Conventional Ultrasonography?

Authors:  Shao-Yun Hao; Qiong-Chao Jiang; Wen-Jing Zhong; Xin-Bao Zhao; Ji-Yi Yao; Lu-Jing Li; Bao-Ming Luo; Bing Ou; Hui Zhi
Journal:  Clin Breast Cancer       Date:  2015-11-10       Impact factor: 3.225

2.  Transrectal colour Doppler contrast sonography in the diagnosis of local recurrence after radical prostatectomy--comparison with MRI.

Authors:  F M Drudi; F Giovagnorio; A Carbone; P Ricci; S Petta; V Cantisani; F S Ferrari; F Marchetti; R Passariello
Journal:  Ultraschall Med       Date:  2006-04       Impact factor: 6.548

3.  Computer aided classification system for breast ultrasound based on Breast Imaging Reporting and Data System (BI-RADS).

Authors:  Wei-Chih Shen; Ruey-Feng Chang; Woo Kyung Moon
Journal:  Ultrasound Med Biol       Date:  2007-08-03       Impact factor: 2.998

Review 4.  Feasibility of CEUS and strain elastography in one case of ileum Crohn stricture and literature review.

Authors:  Andrea Giannetti; Marco Biscontri; Marco Matergi; Michela Stumpo; Chiara Minacci
Journal:  J Ultrasound       Date:  2016-06-29

Review 5.  WFUMB guidelines and recommendations for clinical use of ultrasound elastography: Part 2: breast.

Authors:  Richard G Barr; Kazutaka Nakashima; Dominique Amy; David Cosgrove; Andre Farrokh; Fritz Schafer; Jeffrey C Bamber; Laurent Castera; Byung Ihn Choi; Yi-Hong Chou; Christoph F Dietrich; Hong Ding; Giovanna Ferraioli; Carlo Filice; Mireen Friedrich-Rust; Timothy J Hall; Kathryn R Nightingale; Mark L Palmeri; Tsuyoshi Shiina; Shinichi Suzuki; Ioan Sporea; Stephanie Wilson; Masatoshi Kudo
Journal:  Ultrasound Med Biol       Date:  2015-03-18       Impact factor: 2.998

Review 6.  BI-RADS® fifth edition: A summary of changes.

Authors:  D A Spak; J S Plaxco; L Santiago; M J Dryden; B E Dogan
Journal:  Diagn Interv Imaging       Date:  2017-01-25       Impact factor: 4.026

7.  Ultrasound evaluation of liver fibrosis: preliminary experience with acoustic structure quantification (ASQ) software.

Authors:  Paolo Ricci; Chiara Marigliano; Vito Cantisani; Andrea Porfiri; Andrea Marcantonio; Pietro Lodise; Ugo D'Ambrosio; Giancarlo Labbadia; Elena Maggini; Ester Mancuso; Giovanna Panzironi; Mattia Di Segni; Caterina Furlan; Raffaele Masciangelo; Gloria Taliani
Journal:  Radiol Med       Date:  2013-06-26       Impact factor: 3.469

Review 8.  Breast ultrasonography: state of the art.

Authors:  Regina J Hooley; Leslie M Scoutt; Liane E Philpotts
Journal:  Radiology       Date:  2013-09       Impact factor: 11.105

9.  Clinical application of S-Detect to breast masses on ultrasonography: a study evaluating the diagnostic performance and agreement with a dedicated breast radiologist.

Authors:  Kiwook Kim; Mi Kyung Song; Eun-Kyung Kim; Jung Hyun Yoon
Journal:  Ultrasonography       Date:  2016-04-14

10.  Practical and illustrated summary of updated BI-RADS for ultrasonography.

Authors:  Jiyon Lee
Journal:  Ultrasonography       Date:  2016-10-03
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  18 in total

Review 1.  Methods for the segmentation and classification of breast ultrasound images: a review.

Authors:  Ademola E Ilesanmi; Utairat Chaumrattanakul; Stanislav S Makhanov
Journal:  J Ultrasound       Date:  2021-01-11

2.  Ultrasound-guided preoperative localization of breast lesions: a good choice.

Authors:  Giorgio Carlino; Pierluigi Rinaldi; Michela Giuliani; Rossella Rella; Enida Bufi; Federico Padovano; Chiara Ciardi; Maurizio Romani; Paolo Belli; Riccardo Manfredi
Journal:  J Ultrasound       Date:  2018-10-26

3.  Computer-aided diagnostic system for thyroid nodule sonographic evaluation outperforms the specificity of less experienced examiners.

Authors:  Daniele Fresilli; Giorgio Grani; Maria Luna De Pascali; Gregorio Alagna; Eleonora Tassone; Valeria Ramundo; Valeria Ascoli; Daniela Bosco; Marco Biffoni; Marco Bononi; Vito D'Andrea; Fabrizio Frattaroli; Laura Giacomelli; Yana Solskaya; Giorgia Polti; Patrizia Pacini; Olga Guiban; Raffaele Gallo Curcio; Marcello Caratozzolo; Vito Cantisani
Journal:  J Ultrasound       Date:  2020-04-03

Review 4.  S-Detect characterization of focal breast lesions according to the US BI RADS lexicon: a pictorial essay.

Authors:  Tommaso Vincenzo Bartolotta; Alessia Angela Maria Orlando; Luigi Spatafora; Mariangela Dimarco; Cesare Gagliardo; Adele Taibbi
Journal:  J Ultrasound       Date:  2020-03-17

Review 5.  AI-enhanced breast imaging: Where are we and where are we heading?

Authors:  Almir Bitencourt; Isaac Daimiel Naranjo; Roberto Lo Gullo; Carolina Rossi Saccarelli; Katja Pinker
Journal:  Eur J Radiol       Date:  2021-07-30       Impact factor: 4.531

6.  An investigation of the classification accuracy of a deep learning framework-based computer-aided diagnosis system in different pathological types of breast lesions.

Authors:  Mengsu Xiao; Chenyang Zhao; Qingli Zhu; Jing Zhang; He Liu; Jianchu Li; Yuxin Jiang
Journal:  J Thorac Dis       Date:  2019-12       Impact factor: 2.895

7.  The diagnostic performance of ultrasound computer-aided diagnosis system for distinguishing breast masses: a prospective multicenter study.

Authors:  Qi Wei; Yu-Jing Yan; Ge-Ge Wu; Xi-Rong Ye; Fan Jiang; Jie Liu; Gang Wang; Yi Wang; Juan Song; Zhi-Ping Pan; Jin-Hua Hu; Chao-Ying Jin; Xiang Wang; Christoph F Dietrich; Xin-Wu Cui
Journal:  Eur Radiol       Date:  2022-01-23       Impact factor: 5.315

8.  S-Detect characterization of focal solid breast lesions: a prospective analysis of inter-reader agreement for US BI-RADS descriptors.

Authors:  Tommaso Vincenzo Bartolotta; Alessia Angela Maria Orlando; Maria Laura Di Vittorio; Francesco Amato; Mariangela Dimarco; Domenica Matranga; Raffaele Ienzi
Journal:  J Ultrasound       Date:  2020-05-23

Review 9.  Artificial intelligence in breast ultrasound.

Authors:  Ge-Ge Wu; Li-Qiang Zhou; Jian-Wei Xu; Jia-Yu Wang; Qi Wei; You-Bin Deng; Xin-Wu Cui; Christoph F Dietrich
Journal:  World J Radiol       Date:  2019-02-28

10.  Diagnostic Value of Breast Lesions Between Deep Learning-Based Computer-Aided Diagnosis System and Experienced Radiologists: Comparison the Performance Between Symptomatic and Asymptomatic Patients.

Authors:  Mengsu Xiao; Chenyang Zhao; Jianchu Li; Jing Zhang; He Liu; Ming Wang; Yunshu Ouyang; Yixiu Zhang; Yuxin Jiang; Qingli Zhu
Journal:  Front Oncol       Date:  2020-07-07       Impact factor: 6.244

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