Literature DB >> 25496215

The Added Value of Statistical Modeling of Backscatter Properties in the Management of Breast Lesions at US.

Isabelle Trop1, François Destrempes, Mona El Khoury, André Robidoux, Louis Gaboury, Louise Allard, Boris Chayer, Guy Cloutier.   

Abstract

PURPOSE: To develop a classification method based on the statistical backscatter properties of tissues that can be used as an ancillary tool to the usual Breast Imaging Reporting and Data System (BI-RADS) classification for solid breast lesions identified at ultrasonography (US).
MATERIALS AND METHODS: This study received institutional review board approval, and all subjects provided informed consent. Eighty-nine women (mean age, 50 years; age range, 22-82 years) with 96 indeterminate solid breast lesions (BI-RADS category 4-5; mean size, 13.2 mm; range, 2.6-44.7 mm) were enrolled. Prior to biopsy, additional radiofrequency US images were obtained, and a 3-second cine sequence was used. The research data were analyzed at a later time and were not used to modify patient management decisions. The lesions were segmented manually, and parameters of the homodyned K distribution (α, k, and μn values) were extracted for three regions: the intratumoral zone, a 3-mm supratumoral zone, and a 5-mm infratumoral zone. The Mann-Whitney rank sum test was used to identify parameters with the best discriminating value, yielding intratumoral α, supratumoral k, and infratumoral μn values.
RESULTS: The 96 lesions were classified as follows: 48 BI-RADS category 4A lesions, 16 BI-RADS category 4B lesions, seven BI-RADS category 4C lesions, and 25 BI-RADS category 5 lesions. There were 24 cancers (25%). The area under the receiver operating characteristic curve was 0.76 (95% confidence interval: 0.65, 0.86). Overall, 24% of biopsies (in 17 of 72 lesions) could have been spared. By limiting analysis to lesions with a lower likelihood of malignancy (BI-RADS category 4A-4B), this percentage increased to 26% (16 of 62 lesions). Among benign lesions, the model was used to correctly classify 10 of 38 fibroadenomas (26%) and three of seven stromal fibroses (43%).
CONCLUSION: The statistical model performs well in the classification of solid breast lesions at US, with the potential of preventing one in four biopsies without missing any malignancy. © RSNA, 2014.

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Year:  2014        PMID: 25496215     DOI: 10.1148/radiol.14140318

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  7 in total

Review 1.  Review of Quantitative Ultrasound: Envelope Statistics and Backscatter Coefficient Imaging and Contributions to Diagnostic Ultrasound.

Authors:  Michael L Oelze; Jonathan Mamou
Journal:  IEEE Trans Ultrason Ferroelectr Freq Control       Date:  2016-01-08       Impact factor: 2.725

2.  Hepatic steatosis assessment using ultrasound homodyned-K parametric imaging: the effects of estimators.

Authors:  Zhuhuang Zhou; Qiyu Zhang; Weiwei Wu; Ying-Hsiu Lin; Dar-In Tai; Jeng-Hwei Tseng; Yi-Ru Lin; Shuicai Wu; Po-Hsiang Tsui
Journal:  Quant Imaging Med Surg       Date:  2019-12

3.  Single Particle Differentiation through 2D Optical Fiber Trapping and Back-Scattered Signal Statistical Analysis: An Exploratory Approach.

Authors:  Joana S Paiva; Rita S R Ribeiro; João P S Cunha; Carla C Rosa; Pedro A S Jorge
Journal:  Sensors (Basel)       Date:  2018-02-27       Impact factor: 3.576

4.  Breast-lesions characterization using Quantitative Ultrasound features of peritumoral tissue.

Authors:  Ziemowit Klimonda; Piotr Karwat; Katarzyna Dobruch-Sobczak; Hanna Piotrzkowska-Wróblewska; Jerzy Litniewski
Journal:  Sci Rep       Date:  2019-05-28       Impact factor: 4.379

5.  Quantitative ultrasound, elastography, and machine learning for assessment of steatosis, inflammation, and fibrosis in chronic liver disease.

Authors:  François Destrempes; Marc Gesnik; Boris Chayer; Marie-Hélène Roy-Cardinal; Damien Olivié; Jeanne-Marie Giard; Giada Sebastiani; Bich N Nguyen; Guy Cloutier; An Tang
Journal:  PLoS One       Date:  2022-01-27       Impact factor: 3.240

6.  Breast Tumor Classification Using Intratumoral Quantitative Ultrasound Descriptors.

Authors:  Sabiq Muhtadi
Journal:  Comput Math Methods Med       Date:  2022-03-07       Impact factor: 2.238

7.  Imaging the Effects of Whole-Body Vibration on the Progression of Hepatic Steatosis by Quantitative Ultrasound Based on Backscatter Envelope Statistics.

Authors:  Jui Fang; Ming-Wei Lai; Hao-Tsai Cheng; Anca Cristea; Zhuhuang Zhou; Po-Hsiang Tsui
Journal:  Pharmaceutics       Date:  2022-03-29       Impact factor: 6.525

  7 in total

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