Chris K Bent1, Lawrence W Bassett, Carl J D'Orsi, James W Sayre. 1. Department of Radiological Sciences, David Geffen School of Medicine at the University of California, Los Angeles, 200 UCLA Medical Plaza, Rm 165-47, Box 956952, Los Angeles, CA 90095, USA. cbent@ucla.edu
Abstract
OBJECTIVE: The purpose of this article is to retrospectively assess the likelihood of malignancy of microcalcifications according to the BI-RADS descriptors in a digital mammography environment. MATERIALS AND METHODS: The study included 146 women with calcifications who underwent imaging-guided biopsy between April 2005 and July 2006. Digital mammograms procured before biopsy were analyzed independently by two breast imaging subspecialists blinded to biopsy results. Lesions described discordantly were settled by consensus. One of the radiologists provided a BI-RADS final assessment score. RESULTS: The overall positive predictive value of biopsies was 28.8%. The individual morphologic descriptors predicted the risk of malignancy as follows: fine linear/branching, 16 (70%) of 23 cases; fine pleomorphic, 14 (28%) of 50 cases; coarse heterogeneous, two (20%) of 10 cases; amorphous, 10 (20%) of 51 cases; and typically benign, zero (0%) of 12 cases. Fisher-Freeman-Halton exact testing showed statistical significance among morphology descriptors (p < 0.001) and distribution descriptors (p < 0.001). The positive predictive value for malignancy according to BI-RADS assessment categories were as follows: category 2, 0%; category 3, 0%; category 4A, 13%; category 4B, 36%; category 4C, 79%; and category 5, 100%. CONCLUSION: BI-RADS morphology and distribution descriptors can aid in assessing the risk of malignancy of microcalcifications detected on full-field digital mammography. The positive predictive value increased in successive BI-RADS categories (4A, 4B, and 4C), verifying that subdivision provides an improved assessment of suspicious microcalcifications in terms of likelihood of malignancy.
OBJECTIVE: The purpose of this article is to retrospectively assess the likelihood of malignancy of microcalcifications according to the BI-RADS descriptors in a digital mammography environment. MATERIALS AND METHODS: The study included 146 women with calcifications who underwent imaging-guided biopsy between April 2005 and July 2006. Digital mammograms procured before biopsy were analyzed independently by two breast imaging subspecialists blinded to biopsy results. Lesions described discordantly were settled by consensus. One of the radiologists provided a BI-RADS final assessment score. RESULTS: The overall positive predictive value of biopsies was 28.8%. The individual morphologic descriptors predicted the risk of malignancy as follows: fine linear/branching, 16 (70%) of 23 cases; fine pleomorphic, 14 (28%) of 50 cases; coarse heterogeneous, two (20%) of 10 cases; amorphous, 10 (20%) of 51 cases; and typically benign, zero (0%) of 12 cases. Fisher-Freeman-Halton exact testing showed statistical significance among morphology descriptors (p < 0.001) and distribution descriptors (p < 0.001). The positive predictive value for malignancy according to BI-RADS assessment categories were as follows: category 2, 0%; category 3, 0%; category 4A, 13%; category 4B, 36%; category 4C, 79%; and category 5, 100%. CONCLUSION: BI-RADS morphology and distribution descriptors can aid in assessing the risk of malignancy of microcalcifications detected on full-field digital mammography. The positive predictive value increased in successive BI-RADS categories (4A, 4B, and 4C), verifying that subdivision provides an improved assessment of suspicious microcalcifications in terms of likelihood of malignancy.
Authors: Shadi Aminololama-Shakeri; Chris I Flowers; Christine E McLaren; Dorota J Wisner; Jade de Guzman; Joan E Campbell; Lawrence W Bassett; Haydee Ojeda-Fournier; Karen Gerlach; Jonathan Hargreaves; Sarah L Elson; Hanna Retallack; Bonnie N Joe; Stephen A Feig; Colin J Wells Journal: AJR Am J Roentgenol Date: 2017-02-15 Impact factor: 3.959
Authors: Shara I Feld; Kaitlin M Woo; Roxana Alexandridis; Yirong Wu; Jie Liu; Peggy Peissig; Adedayo A Onitilo; Jennifer Cox; C David Page; Elizabeth S Burnside Journal: AMIA Annu Symp Proc Date: 2018-12-05
Authors: Lars J Grimm; David Y Johnson; Karen S Johnson; Jay A Baker; Mary Scott Soo; E Shelley Hwang; Sujata V Ghate Journal: Eur Radiol Date: 2016-10-17 Impact factor: 5.315