NEW YORK (Reuters Health), Aug 10 - The findings from a new study support a role for MRI in detecting ductal carcinoma in situ (DCIS), particularly disease with a high nuclear grade.
"Our study suggests that the sensitivity of film screen or digital mammography for diagnosing DCIS is limited," lead author Dr. Christiane K. Kuhl, from the University of Bonn in Germany, and colleagues note. "Of the 167 intraductal cancers that had been diagnosed during the study period, 72 (43%) were mammographically occult, but were diagnosed by MRI."
The findings, which appear in the August 11th issue of The Lancet, are from a study of 7,319 women who were evaluated with both mammography and MRI for breast cancer screening. The researchers compared the biologic features of mammography-detected DCIS with MRI-detected DCIS and also looked at the women's risk profiles.
A histologic diagnosis of breast cancer was confirmed in 193 women, 167 of whom had undergone mammography and MRI prior to surgery, the report indicates.
Of these 167 cases, 153 (92%) were diagnosed with MRI and 93 (56%) were diagnosed with mammography (p < 0.001).
Eighty-nine cases of high-grade DCIS were identified, including 43 cases that were missed with mammography and only detected with MRI, the authors note. Conversely, MRI detected 87 of the cases, but missed two that were only picked up with mammography.
No significant differences in age, menopausal status, personal or familial breast cancer history, or breast density were seen between women with MRI- versus mammography-detected DCIS.
In a related editorial, Dr. Carla Boetes and Dr. Ritse M. Mann, from Radboud University Nijmegen in the Netherlands, comment that "these findings can only lead to the conclusion that MRI outperforms mammography in tumor detection and diagnosis. MRI should thus no longer be regarded as an adjunct to mammography but as a distinct method to detect breast cancer in its earliest stage."
Last Updated: 2007-08-09 18:30:10 -0400 (Reuters Health)
Lancet 2007;370:459-460,485-492.
Related Reading
MRI beats US for breast screening of at-risk women, but yields more biopsies, August 6, 2007
MRI spots high-grade DCIS more often than mammography: study, June 4, 2007
Breast MR shows exceptional sensitivity for spotting DCIS, January 12, 2007
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![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
