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      Prediction of Underestimation Using Contrast-Enhanced Spectral Mammography in Patients Diagnosed as Ductal Carcinoma In Situ on Preoperative Core Biopsy

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          Most cited references35

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          Ductal carcinoma in situ of the breast.

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            Ductal carcinoma in situ at core-needle biopsy: meta-analysis of underestimation and predictors of invasive breast cancer.

            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. 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. 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). 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.
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              Morbidity results from the NSABP B-32 trial comparing sentinel lymph node dissection versus axillary dissection.

              Three year post-surgical morbidity levels were compared between patients with negative sentinel lymph node dissection alone (SLND) and those with negative sentinel node dissection and negative axillary lymph node dissection (ALND) in the NSABP B-32 trial. A total of 1,975 ALND and 2,008 SLND node negative breast cancer patients had shoulder range of motion and arm volumes assessed along with self reports of arm tingling and numbness. Relative shoulder abduction deficits and relative arm volume differences between ipsilateral and contralateral arms were calculated. Shoulder abduction deficits >or=10% peaked at 1 week for the ALND (75%) and SLND (41%) groups. Arm volume differences >or=10% at 36 months were evident for the ALND (14%) and SLND (8%) groups. Numbness and tingling peaked at 6 months for the ALND (49%, 23%) and SLND (15%, 10%) groups. Logistic regression correlates of residual morbidity included treatment group, age, handedness, tumor size, systemic chemotherapy, and radiation to the axilla. Although residual morbidity for both treatment groups was evident, the results of the NSABP B-32 study indicate the superiority of the SLND compared to the ALND treatment approach relative to post-surgical morbidity outcomes over a 3-year follow-up period. (c) 2010 Wiley-Liss, Inc.

                Author and article information

                Contributors
                Journal
                Clinical Breast Cancer
                Clinical Breast Cancer
                Elsevier BV
                15268209
                April 2022
                April 2022
                : 22
                : 3
                : e374-e386
                Article
                10.1016/j.clbc.2021.10.004
                914e5d26-a210-4ee0-b1f8-41d0cddda31a
                © 2022

                https://www.elsevier.com/tdm/userlicense/1.0/

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