Literature DB >> 32509917

Automated mammographic density measurement using Quantra™: comparison with the Royal Australian and New Zealand College of Radiology synoptic scale.

Inez Yeo1, Judith Akwo2, Ernest Ekpo1,2.   

Abstract

Purpose: This technology evaluation study assesses the limits of agreement between the mammographic density (MD) measurement of Quantra™ from different breasts and mammographic views and its agreement with the Royal Australian and New Zealand College of Radiologists (RANZCR) synoptic scale. Approach: MD of 800 women was assessed by Quantra™ and seven radiologists using the RANZCR synoptic scale. Bland-Altman analysis was used to assess the limits of agreement between Quantra™ MD measures from both breasts and mammographic views. The agreement between Quantra™ and the RANZCR synoptic scale was assessed using weighted kappa ( K w ). The receiver operating characteristics area under the curve (AUC) was used to assess the performance of Quantra™ in reproducing RANZCR MD ratings.
Results: There was no significant bias in the mean MD of Quantra™ from both breasts: left versus right craniocaudal (CC) views ( B = - 0.14 ; p = 0.36 ) and right versus left mediolateral oblique (MLO) ( B = - 0.021 ; p = 0.18 ). However, MD measures from the same breast but different views showed significant bias: right CC versus right MLO ( B = 0.064 ; p < 0.0001 ) and left CC versus left MLO ( B = 0.56 ; p < 0.0001 ). Quantra™ demonstrated substantial agreement with the RANZCR synoptic scale on four- and two-category scales ( K w = 0.62 ; 0.59 to 0.66 and 0.76; 0.72 to 0.81, respectively). Quantra™ better reproduced the RANZCR synoptic scale on a two-category scale ( AUC = 0.88 ; 0.84 to 0.91) than a four-category scale ( AUC = 0.62 ; 0.58 to 0.67 to 0.78; 0.74 to 0.82). Conclusions: Quantra™ reproduces MD classification using the RANZCR synoptic scale on a two-category scale and should help in identification of women with dense breasts who may need adjunctive imaging for early detection of breast cancer.
© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE).

Entities:  

Keywords:  breast cancer; breast imaging; dense breast; inter-observer variability; screening mammography

Year:  2020        PMID: 32509917      PMCID: PMC7256649          DOI: 10.1117/1.JMI.7.3.035501

Source DB:  PubMed          Journal:  J Med Imaging (Bellingham)        ISSN: 2329-4302


  23 in total

1.  Automatic breast density segmentation: an integration of different approaches.

Authors:  Michiel G J Kallenberg; Mariëtte Lokate; Carla H van Gils; Nico Karssemeijer
Journal:  Phys Med Biol       Date:  2011-04-05       Impact factor: 3.609

2.  The AAPM/RSNA physics tutorial for residents. X-ray attenuation.

Authors:  M H McKetty
Journal:  Radiographics       Date:  1998 Jan-Feb       Impact factor: 5.333

3.  Radiological assessment of breast density by visual classification (BI-RADS) compared to automated volumetric digital software (Quantra): implications for clinical practice.

Authors:  Elisa Regini; Giovanna Mariscotti; Manuela Durando; Gianluca Ghione; Andrea Luparia; Pier Paolo Campanino; Caterina Chiara Bianchi; Laura Bergamasco; Paolo Fonio; Giovanni Gandini
Journal:  Radiol Med       Date:  2014-03-08       Impact factor: 3.469

4.  Automated Volumetric Breast Density Measurements in the Era of the BI-RADS Fifth Edition: A Comparison With Visual Assessment.

Authors:  Ji Hyun Youk; Hye Mi Gweon; Eun Ju Son; Jeong-Ah Kim
Journal:  AJR Am J Roentgenol       Date:  2016-03-02       Impact factor: 3.959

5.  Volumetric breast density assessment: reproducibility in serial examinations and comparison with visual assessment.

Authors:  J M Singh; E M Fallenberg; F Diekmann; D M Renz; R Witlandt; U Bick; F Engelken
Journal:  Rofo       Date:  2013-07-25

6.  Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening.

Authors:  Kathleen R Brandt; Christopher G Scott; Lin Ma; Amir P Mahmoudzadeh; Matthew R Jensen; Dana H Whaley; Fang Fang Wu; Serghei Malkov; Carrie B Hruska; Aaron D Norman; John Heine; John Shepherd; V Shane Pankratz; Karla Kerlikowske; Celine M Vachon
Journal:  Radiology       Date:  2015-12-22       Impact factor: 11.105

Review 7.  Breast Cancer Epidemiology, Prevention, and Screening.

Authors:  Stella Winters; Charmaine Martin; Daniel Murphy; Navkiran K Shokar
Journal:  Prog Mol Biol Transl Sci       Date:  2017-10-10       Impact factor: 3.622

8.  Reliability of automated breast density measurements.

Authors:  Olivier Alonzo-Proulx; Gordon E Mawdsley; James T Patrie; Martin J Yaffe; Jennifer A Harvey
Journal:  Radiology       Date:  2015-02-25       Impact factor: 11.105

Review 9.  Mammographic density and breast cancer risk: current understanding and future prospects.

Authors:  Norman F Boyd; Lisa J Martin; Martin J Yaffe; Salomon Minkin
Journal:  Breast Cancer Res       Date:  2011-11-01       Impact factor: 6.466

10.  Evaluation of breast parenchymal density with QUANTRA software.

Authors:  Shivani Pahwa; Smriti Hari; Sanjay Thulkar; Suveen Angraal
Journal:  Indian J Radiol Imaging       Date:  2015 Oct-Dec
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  1 in total

1.  Development and Validation of an AI-driven Mammographic Breast Density Classification Tool Based on Radiologist Consensus.

Authors:  Veronica Magni; Matteo Interlenghi; Andrea Cozzi; Marco Alì; Christian Salvatore; Alcide A Azzena; Davide Capra; Serena Carriero; Gianmarco Della Pepa; Deborah Fazzini; Giuseppe Granata; Caterina B Monti; Giulia Muscogiuri; Giuseppe Pellegrino; Simone Schiaffino; Isabella Castiglioni; Sergio Papa; Francesco Sardanelli
Journal:  Radiol Artif Intell       Date:  2022-03-16
  1 in total

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