Feature - Screening MRI benefi ts women at average risk of breast cancer
Images in a 55-year-old screening participant. (a, b) Normal digital full-field mediolateral oblique (a) and craniocaudal (b) mammograms (BI-RADS category 1) show a heterogeneously dense breast (ACR category C). (c) Screening ultrasound image shows normal findings (BI-RADS category 1). (d) MR-guided biopsy enabled us to confirm the presence of an invasive high-grade triple-negative cancer (no special type [NST], pT1b, N0, M0). (d) Breast MR image shows a suspicious enhancing mass (arrow) in the left breast (BI-RADS category 5).E
MRI screening improves early diagnosis of breast cancer in all women – not only those at high risk – according to a new study from Germany published online in the journal Radiology.
MRI has long been known as an effective breast cancer screening modality that offers better sensitivity than mammography and ultrasound. Currently, guidelines reserve breast MRI screening for women who have a strong family history or other specific breast cancer risk factors. MRI screening has not been considered necessary for women at average risk, and there has been resistance to expansion of MRI into this population due, in part, to concern over higher costs.
However, with breast cancer remaining a major cause of cancer death in women, there is good reason to pursue the search for improved screening methods, according to the study’s lead author, Christiane Kuhl, M.D., chair of the Department of Radiology at RWTH Aachen University in Aachen, Germany.
Between 2005 and 2013, Dr Kuhl and colleagues studied breast MRI’s impact on 2,120 women, ages 40 to 70, with less than a 15% lifetime risk of breast cancer. The women had normal screening mammograms and, in the case of those with dense breast tissue, normal screening ultrasound. Breast MRI detected 60 additional breast cancers, including 40 invasive cancers, for an overall supplemental cancer detection rate of 15.5 per 1,000 women. Of the 60 cancers detected in the study group over the observation period (7,007 screening rounds), 59 were found only using MRI, one was found also by mammography, and none by mammography or ultrasound alone.
According to Dr Kuhl, the results suggest that MRI can serve as a useful supplemental screening tool for women at average risk, especially those with dense mammographic tissue, and that MRI is superior to supplemental ultrasound for this purpose.
The results also highlight the ability of MRI in the detection of more aggressive types of cancer.
“The faster a cancer grows and the bet-ter it is in seeding metastases, the better will it be picked up early by MRI,” Dr Kuhl said. “In our cohort, cancers found by MRI alone exhibited features of rapid growth at pathology.”
This ability is especially important in women with dense breast tissue in which aggressive cancers may be missed on mammography. Left undetected, these cancers will grow to become clinically palpable cancers, also known as interval cancers. The new study showed that, consistent with previous research, breast MRI can depict these rapidly growing cancers with high reliability.
According to Dr Kuhl, interval cancers exhibit an adverse biologic profile and are the main driver of breast cancer mortality. Additional cancers detected by MRI screening in the study had a skewed distribution towards a higher-than-normal prevalence or incidence of rapidly growing (grade 3) cancers.
“The interval cancer rate in our study was zero percent. Not a single cancer was undetected that became palpable,” she said. “This suggests that MRI finds breast cancers that also mammography would find, but MRI detects them earlier, and it finds the cancers which, if MRI had not been done, would have progressed to interval cancers.”
Neuroimaging technique may predict autism among high-risk infants
Functional connectivity magnetic resonance imaging (fcMRI) may predict which high-risk, 6-month old infants will develop autism spectrum disorder by age 2 years, according to a study funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) and the US National Institute of Mental Health (NIMH), two components of the National Institutes of Health. The study is published in the June 7, 2017, issue of Science Translational Medicine.
In the United States autism affects roughly 1 out of every 68 children. Siblings of children diagnosed with autism are at higher risk of developing the disorder. Although early diagnosis and intervention can help improve outcomes for children with autism, there currently is no method to diagnose the disease before children show symptoms.
“Previous findings suggest that brain-related changes occur in autism before behavioural symptoms emerge,” said Diana Bianchi, M.D., NICHD Director. “If future studies confirm these results, detecting brain differences may enable physicians to diagnose and treat autism earlier than they do today.”
In the current study, a research team at the University of North Carolina at Chapel Hill and Washington University School of Medicine in St. Louis focused on the brain’s functional connectivity -- how regions of the brain work together during different tasks and during rest. Using fcMRI, the researchers scanned 59 highrisk, 6-month-old infants while they slept naturally. The children were deemed high-risk because they have older siblings with autism. At age 2 years, 11 of the 59 infants in this group were diagnosed with autism.
The researchers used a computer-based technology called machine learning, which trains itself to look for differences that can separate the neuroimaging results into two groups -- autism or nonautism -- and predict future diagnoses. One analysis predicted each infant’s future diagnosis by using the other 58 infants’ data to train the computer program. This method identified 82% of the infants who would go on to have autism (9 out of 11), and it correctly identified all of the infants who did not develop autism. In another analysis that tested how well the results could apply to other cases, the computer program predicted diagnoses for groups of 10 infants, at an accuracy rate of 93%.
“Although the findings are early-stage, the study suggests that in the future, neuroimaging may be a useful tool to diagnose autism or help health care providers evaluate a child’s risk of developing the disorder,” said Joshua Gordon, M.D., Ph.D., NIMH Director.
Overall, the team found 974 functional connections in the brains of 6-month-olds that were associated with autism-related behaviours. The authors propose that a single neuroimaging scan may accurately predict autism among high-risk infants, but caution that the findings need to be replicated in a larger group.
|Date of upload: 19th Jul 2017|
Copyright © 2017 MiddleEastHealthMag.com. All Rights Reserved.