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

Radiology. 2015 Jun;275(3):666-74. doi: 10.1148/radiol.14140318. Epub 2014 Dec 12.

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.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Breast Neoplasms / classification*
  • Breast Neoplasms / diagnostic imaging*
  • Female
  • Humans
  • Middle Aged
  • Models, Statistical*
  • Prospective Studies
  • Ultrasonography, Mammary*
  • Young Adult