Utility of Diffusion-weighted Imaging to Decrease Unnecessary Biopsies Prompted by Breast MRI: A Trial of the ECOG-ACRIN Cancer Research Group (A6702) Article Swipe
YOU?
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· 2019
· Open Access
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· DOI: https://doi.org/10.1158/1078-0432.ccr-18-2967
Purpose: Conventional breast MRI is highly sensitive for cancer detection but prompts some false positives. We performed a prospective, multicenter study to determine whether apparent diffusion coefficients (ADCs) from diffusion-weighted imaging (DWI) can decrease MRI false positives. Experimental Design: A total of 107 women with MRI-detected BI-RADS 3, 4, or 5 lesions were enrolled from March 2014 to April 2015. ADCs were measured both centrally and at participating sites. ROC analysis was employed to assess diagnostic performance of centrally measured ADCs and identify optimal ADC thresholds to reduce unnecessary biopsies. Lesion reference standard was based on either definitive biopsy result or at least 337 days of follow-up after the initial MRI procedure. Results: Of 107 women enrolled, 67 patients (median age 49, range 24–75 years) with 81 lesions with confirmed reference standard (28 malignant, 53 benign) and evaluable DWI were analyzed. Sixty-seven of 81 lesions were BI-RADS 4 (n = 63) or 5 (n = 4) and recommended for biopsy. Malignancies exhibited lower mean in centrally measured ADCs (mm2/s) than benign lesions [1.21 × 10−3 vs.1.47 × 10−3; P < 0.0001; area under ROC curve = 0.75; 95% confidence interval (CI) 0.65–0.84]. In centralized analysis, application of an ADC threshold (1.53 × 10−3 mm2/s) lowered the biopsy rate by 20.9% (14/67; 95% CI, 11.2%–31.2%) without affecting sensitivity. Application of a more conservative threshold (1.68 × 10−3 mm2/s) to site-measured ADCs reduced the biopsy rate by 26.2% (16/61) but missed three cancers. Conclusions: DWI can reclassify a substantial fraction of suspicious breast MRI findings as benign and thereby decrease unnecessary biopsies. ADC thresholds identified in this trial should be validated in future phase III studies.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1158/1078-0432.ccr-18-2967
- OA Status
- green
- Cited By
- 135
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2910373477
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2910373477Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1158/1078-0432.ccr-18-2967Digital Object Identifier
- Title
-
Utility of Diffusion-weighted Imaging to Decrease Unnecessary Biopsies Prompted by Breast MRI: A Trial of the ECOG-ACRIN Cancer Research Group (A6702)Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-01-15Full publication date if available
- Authors
-
Habib Rahbar, Zheng Zhang, Thomas L. Chenevert, Justin Romanoff, Averi E. Kitsch, Lucy Hanna, Sara Harvey, Linda Moy, Wendy B. DeMartini, Başak E. Doğan, Wei Yang, Lilian C. Wang, Bonnie N. Joe, Karen Y. Oh, Colleen H. Neal, Elizabeth S. McDonald, Mitchell D. Schnall, Constance D. Lehman, Christopher Comstock, Savannah C. PartridgeList of authors in order
- Landing page
-
https://doi.org/10.1158/1078-0432.ccr-18-2967Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/6420847Direct OA link when available
- Concepts
-
Medicine, Biopsy, Confidence interval, Breast cancer, False positive paradox, Diffusion MRI, Effective diffusion coefficient, Radiology, Receiver operating characteristic, Nuclear medicine, Lesion, Prospective cohort study, Breast MRI, Magnetic resonance imaging, Cancer, Mammography, Surgery, Internal medicine, Computer science, Machine learningTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
135Total citation count in OpenAlex
- Citations by year (recent)
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2025: 19, 2024: 22, 2023: 28, 2022: 30, 2021: 16Per-year citation counts (last 5 years)
- References (count)
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39Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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