PURPOSE: To perform a meta-analysis to report pooled estimates for underestimation of invasive breast cancer (where core-needle biopsy [CNB] shows ductal carcinoma in situ [DCIS] and excision histologic examination shows invasive breast cancer) and to identify preoperative variables that predict invasive breast cancer. MATERIALS AND METHODS: Studies were identified by searching MEDLINE and were included if they provided data on DCIS underestimates (overall and according to preoperative variables). Study-specific and pooled percentages for DCIS underestimates were calculated. By using meta-regression (random effects logistic modeling) the association between each study-level preoperative variable and understaged invasive breast cancer was investigated. RESULTS: Fifty-two studies that included 7350 cases of DCIS with findings at excision histologic examination as the reference standard met the eligibility criteria and were included. There were 1736 underestimates (invasive breast cancer at excision); the random-effects pooled estimate was 25.9% (95% confidence interval: 22.5%, 29.5%). Preoperative variables that showed significant univariate association with higher underestimation included the use of a 14-gauge automated device (vs 11-gauge vacuum-assisted biopsy, P = .006), high-grade lesion at CNB (vs non-high grade lesion, P < .001), lesion size larger than 20 mm at imaging (vs lesions ≤ 20 mm, P < .001), Breast Imaging Reporting and Data System (BI-RADS) score of 4 or 5 (vs BI-RADS score of 3, P for trend = .005), mammographic mass (vs calcification only, P < .001), and palpability (P < .001). CONCLUSION: About one in four DCIS diagnoses at CNB represent understaged invasive breast cancer. Preoperative variables significantly associated with understaging include biopsy device and guidance method, size, grade, mammographic features, and palpability.
PURPOSE: To perform a meta-analysis to report pooled estimates for underestimation of invasive breast cancer (where core-needle biopsy [CNB] shows ductal carcinoma in situ [DCIS] and excision histologic examination shows invasive breast cancer) and to identify preoperative variables that predict invasive breast cancer. MATERIALS AND METHODS: Studies were identified by searching MEDLINE and were included if they provided data on DCIS underestimates (overall and according to preoperative variables). Study-specific and pooled percentages for DCIS underestimates were calculated. By using meta-regression (random effects logistic modeling) the association between each study-level preoperative variable and understaged invasive breast cancer was investigated. RESULTS: Fifty-two studies that included 7350 cases of DCIS with findings at excision histologic examination as the reference standard met the eligibility criteria and were included. There were 1736 underestimates (invasive breast cancer at excision); the random-effects pooled estimate was 25.9% (95% confidence interval: 22.5%, 29.5%). Preoperative variables that showed significant univariate association with higher underestimation included the use of a 14-gauge automated device (vs 11-gauge vacuum-assisted biopsy, P = .006), high-grade lesion at CNB (vs non-high grade lesion, P < .001), lesion size larger than 20 mm at imaging (vs lesions ≤ 20 mm, P < .001), Breast Imaging Reporting and Data System (BI-RADS) score of 4 or 5 (vs BI-RADS score of 3, P for trend = .005), mammographic mass (vs calcification only, P < .001), and palpability (P < .001). CONCLUSION: About one in four DCIS diagnoses at CNB represent understaged invasive breast cancer. Preoperative variables significantly associated with understaging include biopsy device and guidance method, size, grade, mammographic features, and palpability.
Authors: Eline E Deurloo; Jincey D Sriram; Hendrik J Teertstra; Claudette E Loo; Jelle Wesseling; Emiel J Th Rutgers; Kenneth G A Gilhuijs Journal: Eur Radiol Date: 2012-02-26 Impact factor: 5.315
Authors: Bibo Shi; Lars J Grimm; Maciej A Mazurowski; Jay A Baker; Jeffrey R Marks; Lorraine M King; Carlo C Maley; E Shelley Hwang; Joseph Y Lo Journal: Acad Radiol Date: 2017-05-11 Impact factor: 3.173
Authors: Bibo Shi; Lars J Grimm; Maciej A Mazurowski; Jay A Baker; Jeffrey R Marks; Lorraine M King; Carlo C Maley; E Shelley Hwang; Joseph Y Lo Journal: J Am Coll Radiol Date: 2018-02-02 Impact factor: 5.532
Authors: Michael R Harowicz; Ashirbani Saha; Lars J Grimm; P Kelly Marcom; Jeffrey R Marks; E Shelley Hwang; Maciej A Mazurowski Journal: J Magn Reson Imaging Date: 2017-02-09 Impact factor: 4.813