TY - JOUR
T1 - Utility of Diffusion-weighted Imaging to Decrease Unnecessary Biopsies Prompted by Breast MRI
T2 - A trial of the ECOG-ACRIN Cancer Research group (A6702)
AU - Rahbar, Habib
AU - Zhang, Zheng
AU - Chenevert, Thomas L.
AU - Romanoff, Justin
AU - Kitsch, Averi E.
AU - Hanna, Lucy G.
AU - Harvey, Sara M.
AU - Moy, Linda
AU - DeMartini, Wendy B.
AU - Dogan, Basak
AU - Yang, Wei T.
AU - Wang, Lilian C.
AU - Joe, Bonnie N.
AU - Oh, Karen Y.
AU - Neal, Colleen H.
AU - McDonald, Elizabeth S.
AU - Schnall, Mitchell D.
AU - Lehman, Constance D.
AU - Comstock, Christopher E.
AU - Partridge, Savannah C.
N1 - Funding Information:
W. Yang is a consultant/advisory board member for Seno Medical Advisory Board, GE Healthcare, and Wolters Kluwer. M. Schnall reports receiving commercial research support from Siemens. C. Lehman reports receiving commercial research support from and is a consultant/advisory board member for General ElectricHealthcare.S.C.Partridgereportsreceiving commercial research grants from General Electric Healthcare, and reports receiving commercial research support from Philips Healthcare. No potential conflicts of interest were disclosed by the other authors.
Funding Information:
This study was conducted by the ECOG-ACRIN Cancer Research Group (Peter J. O'Dwyer and Mitchell D. Schnall Group Co-Chairs) and supported by the NCI of the NIH under the following award numbers: CA180820, CA180794, CA180828, CA180801, CA180847, CA180799, CA180816, CA180858, CA180870, CA18086, CA180791, CA151326, CA207290, and CA166104. The authors also acknowledge those individuals who have contributed substantially to the work reported in the manuscript, including the A6702 Trial Team, the patients who participated in the study, and the staff members who contributed to the conduct of the study at the University of Washington (Seattle, WA), the University of Michigan (Ann Arbor, MI), the University of Pennsylvania (Philadelphia, PN), MD Anderson Cancer Center (Houston, TX), University of Wisconsin (Madison, WI), Northwestern University (Evanston, IL), Vanderbilt University (Nashville, TN), New York University (New York, NY), University of California (San Francisco, CA), and Oregon Health Sciences University (Portland, OR).
Publisher Copyright:
© 2019 American Association for Cancer Research.
PY - 2019
Y1 - 2019
N2 - 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 ¼ 63) or 5 (n ¼ 4) and recommended for biopsy. Malignancies exhibited lower mean in centrally measured ADCs (mm 2 /s) than benign lesions [1.21 10 3 vs.1.47 10 3 ; P < 0.0001; area under ROC curve ¼ 0.75; 95% confidence interval (CI) 0.65–0.84]. In centralized analysis, application of an ADC threshold (1.53 10 3 mm 2 /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 mm 2 /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.
AB - 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 ¼ 63) or 5 (n ¼ 4) and recommended for biopsy. Malignancies exhibited lower mean in centrally measured ADCs (mm 2 /s) than benign lesions [1.21 10 3 vs.1.47 10 3 ; P < 0.0001; area under ROC curve ¼ 0.75; 95% confidence interval (CI) 0.65–0.84]. In centralized analysis, application of an ADC threshold (1.53 10 3 mm 2 /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 mm 2 /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.
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U2 - 10.1158/1078-0432.CCR-18-2967
DO - 10.1158/1078-0432.CCR-18-2967
M3 - Article
C2 - 30647080
AN - SCOPUS:85062956163
SN - 1078-0432
VL - 25
SP - 1756
EP - 1765
JO - Clinical Cancer Research
JF - Clinical Cancer Research
IS - 6
ER -