News | Breast Density | July 02, 2018

Norwegian Study Confirms Higher Cancer Rate in Women with Dense Breast Tissue

Study supports value of automated breast density measurement as future standard for breast cancer screening

Norwegian Study Confirms Higher Cancer Rate in Women with Dense Breast Tissue

July 2, 2018 — A large Norwegian study using automated breast density measurements found that women with mammographically dense breast tissue have higher recall and biopsy rates, and increased odds of screen-detected and interval breast cancer. The study, published online in the journal Radiology, supports automated measurements as a future standard to ensure objective breast density classification for breast cancer screening, the researchers said.

Previous studies have shown that women with mammographically dense breasts face a higher risk of breast cancer and missed cancers than those with non-dense breasts, partly because the superimposition of dense breast tissue on mammograms leads to a masking effect, causing some cancers to go undetected. However, the majority of those studies relied on subjective density assessments — most commonly, the radiologist’s subjective interpretation using the American College of Radiology’s Breast Imaging Reporting and Data System (BI-RADS). This approach introduces potential mammogram reader variability into density categorization, said the study’s principal investigator, Solveig Hofvind, Ph.D., from the Cancer Registry of Norway in Oslo.

For the new study, Hofvind and colleagues used automated software to help classify mammographic density in 107,949 women ages 50 to 69 from BreastScreen Norway, a national program that offers women screening every two years. The researchers looked at a total of 307,015 digital screening examinations that took place from 2007 to 2015.

The automated software classified breasts as dense in 28 percent of the screening examinations. Rates of screen-detected cancer were 6.7 per 1,000 examinations for women with dense breast tissue and 5.5 for women with non-dense breasts. Interval breast cancer, or breast cancer detected between screenings — usually by palpation — was 2.8 per 1,000 in the dense breast tissue group and 1.2 for women with non-dense tissue.

The recall rate, or rate at which women are called back for additional examination based on suspicious mammographic findings, was 3.6 percent for women with dense breasts, compared with 2.7 percent in women with non-dense breasts. The biopsy rate of 1.4 percent in the dense breasts group was higher than the 1.1 percent rate for women in the non-dense category.

“The odds of screen-detected and interval breast cancer were substantially higher for women with mammographically dense versus fatty breasts in BreastScreen Norway,” Hofvind said. “We also found substantially higher rates of recalls and biopsies among women with mammographically dense breast tissue.”

Mammographic sensitivity for detecting breast cancer was only 71 percent for women with dense breasts, compared to 82 percent for women with non-dense breasts. Cancers detected at screening were more advanced among women in the dense breast tissue group. Average tumor diameter for screen-detected cancers was 16.6 millimeters (mm) in the dense breasts group, compared to 15.1 mm for the non-dense group. Lymph-node-positive disease was found in 24 percent of women with dense breasts, compared with 18 percent for women with non-dense breasts.

The results add another piece to the puzzle of screening protocols for women with dense breasts. Hofvind noted that more than half of states in the U.S. have enacted breast density legislation, with some states requiring that women be informed about their breast density or that additional breast imaging could be beneficial. However, supplemental screening for women with dense breasts currently is not recommended by any of the major healthcare societies or organizations, and more research is needed before widespread changes are made.

“We need well-planned and high-quality studies that can give evidence about the cost-effectiveness of more frequent screening, other screening tools such as tomosynthesis and/or the use of additional screening tools like MRI [magnetic resonance imaging] and ultrasound for women with dense breasts,” she said. “Further, we need studies on automated measurement tools for mammographic density to ensure their validity.”

In the meantime, Hofvind said, the findings will help inform how automated volumetric density categorization will change population-based screening performance and outcomes through a more objective breast density measurement method.

In an accompanying editorial, “Machine Detection of High Breast Density: Worse Outcomes for Our Patients,” Liane E. Philpotts, M.D., FACR, from Yale School of Medicine wrote, “This study is important for two main reasons: (1) the validation that automated means of density classification can correctly identify a percentage of women with dense tissue, and (2) women with dense tissue have poorer performance of screening mammography. It lends support to the density notification movement along with redoubling efforts towards optimizing supplemental screening methods.”

She added that automated volumetric determination could better standardize density measurements and provide greater confidence that women with dense breast tissue could consistently be identified.

“Breast density is here to stay, and it is in everyone’s best interest to embrace understanding and optimization of breast imaging practice to best address the needs of women with dense tissue,” she concluded.

For more information: www.pubs.rsna.org/journal/radiology

Reference

Hofvind S., Moshina N., Sebuødegård S., et al. "Automated Volumetric Analysis of Mammographic Density in a Screening Setting: Worse Outcomes for Women with Dense Breasts," Radiology. June 26, 2018. https://doi.org/10.1148/radiol.2018172972

Related Content

Artificial intelligence (AI)-assisted software was used to identify inflammatory tissues in lung and automatically segment inflammatory lesions. Three-dimensional image shows regions of COVID-19 pneumonia in lung through AI postprocessing. Image courtesy of the American Journal of Roentgenology (AJR)

Artificial intelligence (AI)-assisted software was used to identify inflammatory tissues in lung and automatically segment inflammatory lesions. Three-dimensional image shows regions of COVID-19 pneumonia in lung through AI postprocessing. Image courtesy of the American Journal of Roentgenology (AJR)

News | Coronavirus (COVID-19) | July 10, 2020
July 10, 2020 — An open-access Ameri
Simulation finds starting at age 30 with MRI and mammography to be the preferred strategy; starting at 25 prevented marginally more deaths, but with more testing and emotional stress

Getty Images

News | Breast Imaging | July 09, 2020
July 9, 2020 — Chest radiation is used to treat children with Hodgkin and non-Hodgkin lymphoma as well as lung metast
 Many patients with severe coronavirus disease 2019 (COVID-19) remain unresponsive after surviving critical illness. Investigators led by a team at Massachusetts General Hospital (MGH) now describe a patient with severe COVID-19 who, despite prolonged unresponsiveness and structural brain abnormalities, demonstrated functionally intact brain connections and weeks later he recovered the ability to follow commands

Getty Images

News | Coronavirus (COVID-19) | July 08, 2020
July 8, 2020 — Many patients with severe coronavirus disease 2019 (...
Hologic, Inc. announced he U.S. launch of the SuperSonic MACH 40 ultrasound system, expanding the company’s suite of ultrasound technologies with its first premium, cart-based system.
News | Breast Imaging | July 08, 2020
July 8, 2020 — Hologic, Inc. announced he U.S.
R2* maps of healthy control participants and participants with Alzheimer disease. R2* maps are windowed between 10 and 50 sec21. Differences in iron concentration in basal ganglia are too small to allow visual separation between patients with Alzheimer disease and control participants, and iron levels strongly depend on anatomic structure and subject age. Image courtesy of Radiological Society of North America

R2* maps of healthy control participants and participants with Alzheimer disease. R2* maps are windowed between 10 and 50 sec21. Differences in iron concentration in basal ganglia are too small to allow visual separation between patients with Alzheimer disease and control participants, and iron levels strongly depend on anatomic structure and subject age. Image courtesy of Radiological Society of North America

News | Magnetic Resonance Imaging (MRI) | July 01, 2020
July 1, 2020 — Researchers using magnetic...
Thoracic findings in a 15-year-old girl with Multisystem Inflammatory Syndrome in Children (MIS-C). (a) Chest radiograph on admission shows mild perihilar bronchial wall cuffing. (b) Chest radiograph on the third day of admission demonstrates extensive airspace opacification with a mid and lower zone predominance. (c, d) Contrast-enhanced axial CT chest of the thorax at day 3 shows areas of ground-glass opacification (GGO) and dense airspace consolidation with air bronchograms. (c) This conformed to a mosai

Thoracic findings in a 15-year-old girl with Multisystem Inflammatory Syndrome in Children (MIS-C). (a) Chest radiograph on admission shows mild perihilar bronchial wall cuffing. (b) Chest radiograph on the third day of admission demonstrates extensive airspace opacification with a mid and lower zone predominance. (c, d) Contrast-enhanced axial CT chest of the thorax at day 3 shows areas of ground-glass opacification (GGO) and dense airspace consolidation with air bronchograms. (c) This conformed to a mosaic pattern with a bronchocentric distribution to the GGO (white arrow, d) involving both central and peripheral lung parenchyma with pleural effusions (black small arrow, d). image courtesy of Radiological Society of North America

News | Coronavirus (COVID-19) | June 26, 2020
June 26, 2020 — In recent weeks, a multisystem hyperinflammatory condition has emerged in children in association wit
Case abstraction study period was from 10 March to 7 April 2020. Follow-up of abstracted cases was until 7 May 2020.

Case abstraction study period was from 10 March to 7 April 2020. Follow-up of abstracted cases was until 7 May 2020. Courtesy of Nature Medicine

News | Coronavirus (COVID-19) | June 25, 2020
June 25, 2020 — The characterization of COVID-19
Neurosurgeon Jason Sheehan, M.D., Ph.D., of UVA Health, is pioneering the use of focused ultrasound to treat glioblastoma, the deadliest brain tumor. Image courtesy of UVA Health

Neurosurgeon Jason Sheehan, M.D., Ph.D., of UVA Health, is pioneering the use of focused ultrasound to treat glioblastoma, the deadliest brain tumor. Image courtesy of UVA Health

News | Focused Ultrasound Therapy | June 23, 2020
June 23, 2020 — An innovativ...