R2* magnetic resonance imaging (R2*-MRI) can quantify hepatic iron content (HIC) by noninvasive means but is not fully investigated. Patients with iron overload completed 1.5T R2*-MRI examination and liver biopsy within 30 days. Fortythree patients (sickle cell anemia, n ؍ 32; -thalassemia major, n ؍ 6; and bone marrow failure, n ؍ 5) were analyzed: median age, 14 years, median transfusion duration, 15 months, average (؎SD) serum ferritin 2718 plus or minus 1994 ng/mL, and average HIC 10.9 plus or minus 6.8 mg Fe/g dry weight liver. Regions of interest were drawn and analyzed by 3 independent reviewers with excellent agreement of their measurements (intraclass correlation coefficient ؍ 0.98). Ferritin and R2*-MRI were weakly but significantly associated (range of correlation coefficients among the 3 reviewers, 0.41-0.48; all P < .01). R2*-MRI was strongly associated with HIC for all 3 reviewers (correlation coefficients, 0.96-0.98; all P < .001). This high correlation confirms prior reports, calibrates R2*-MRI measurements, and suggests its clinical utility for predicting HIC using R2*-MRI. This study was registered at www.clinicaltrials.gov as #NCT00675038. IntroductionMonitoring body iron content is critical for clinical management of patients with iron overload. Prior reports of iron measurements by R2 magnetic resonance imaging (MRI) and R2*-MRI relaxometry in the liver have shown good correlations with hepatic iron content (HIC). 1-3 However, MRI calibration varies according to instrumentation and technique. To calibrate the R2*-MRI technique for noninvasive HIC assessment, we conducted a study to estimate the correlation of R2*-MRI with liver biopsy-proven HIC determination in patients with iron overload. Methods PatientsPatients 7 years of age and older with iron overload (ferritin Ͼ 1000 ng/mL within 3 months of enrollment or Ն 18 erythrocyte transfusions) were eligible. All participants underwent nonsedated liver MRI examination and ferritin measurement, followed within 30 days by liver biopsy with HIC determination. The St Jude Children's Research Hospital Institutional Review Board provided continuing approval, and all participants or legal guardians signed informed consent in accordance with the Declaration of Helsinki. Liver biopsiesTwo liver specimens were obtained, the first for liver iron quantitation (Mayo Laboratories, Rochester MN) and the second for pathology review. All histology was reviewed by a single pathologist blinded to clinical status and HIC values. Liver fibrosis was scored from zero (absent fibrosis) to 6 (cirrhosis). 4 MRI techniqueThe single breath-hold R2*-MRI used a 1.5T MRI scanner (Siemens Symphony, Siemens; Malvern, PA) using a multiecho gradient echo sequence to acquire 20 images with increasing echo times (range, 1.1-17.3 ms). Liver images were obtained in transversal slice orientation through the center at the main portal vein origin. Slice thickness measured 10 mm with in-plane resolution of 3.125 mm. Quantitative T2* maps were calculated offline using custom-...
Purpose Women with proliferative breast lesions are candidates for primary prevention, but few risk models incorporate benign findings to assess breast cancer risk. We incorporated benign breast disease (BBD) diagnoses into the Breast Cancer Surveillance Consortium (BCSC) risk model, the only breast cancer risk assessment tool that uses breast density. Methods We developed and validated a competing-risk model using 2000 to 2010 SEER data for breast cancer incidence and 2010 vital statistics to adjust for the competing risk of death. We used Cox proportional hazards regression to estimate the relative hazards for age, race/ethnicity, family history of breast cancer, history of breast biopsy, BBD diagnoses, and breast density in the BCSC. Results We included 1,135,977 women age 35 to 74 years undergoing mammography with no history of breast cancer; 17% of the women had a prior breast biopsy. During a mean follow-up of 6.9 years, 17,908 women were diagnosed with invasive breast cancer. The BCSC BBD model slightly overpredicted risk (expected-to-observed ratio, 1.04; 95% CI, 1.03 to 1.06) and had modest discriminatory accuracy (area under the receiver operator characteristic curve, 0.665). Among women with proliferative findings, adding BBD to the model increased the proportion of women with an estimated 5-year risk of 3% or higher from 9.3% to 27.8% (P < .001). Conclusion The BCSC BBD model accurately estimates women's risk for breast cancer using breast density and BBD diagnoses. Greater numbers of high-risk women eligible for primary prevention after BBD diagnosis are identified using the BCSC BBD model.
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