News | Artificial Intelligence | December 18, 2019

AI Improves Breast Cancer Risk Prediction

A new study published in Radiology suggests that a type of AI can outperform existing breast cancer screening models

Patient inclusion flowchart shows selection of women in the training and validation samples used for deep neural network development, as well as in the test sample (current study sample). Exclusions are detailed in the footnote. PACS = picture archiving and communication system. Image courtesy of Radiological Society of North America.

Patient inclusion flowchart shows selection of women in the training and validation samples used for deep neural network development, as well as in the test sample (current study sample). Exclusions are detailed in the footnote. PACS = picture archiving and communication system. Image courtesy of Radiological Society of North America.

December 17, 2019 — A sophisticated type of artificial intelligence (AI) can outperform existing models at predicting which women are at future risk of breast cancer, according to a study published in the journal Radiology.

Most existing breast cancer screening programs are based on mammography at similar time intervals — typically, annually or every two years — for all women. This “one size fits all” approach is not optimized for cancer detection on an individual level and may hamper the effectiveness of screening programs.

“Risk prediction is an important building block of an individually adapted screening policy,” said study lead author Karin Dembrower, M.D., breast radiologist and Ph.D. candidate from the Karolinska Institute in Stockholm, Sweden. “Effective risk prediction can improve attendance and confidence in screening programs.”

High breast density, or a greater amount of glandular and connective tissue compared to fat, is considered a risk factor for cancer. While density may be incorporated into risk assessment, current prediction models may fail to fully take advantage of all the rich information found in mammograms. This information has the potential to identify women who would benefit from additional screening with magnetic resonance imaging (MRI).

Dembrower and colleagues developed a risk model that relies on a deep neural network, a type of AI that can extract vast amounts of information from mammographic images. It has inherent advantages over other methods like visual assessment of mammographic density by the radiologist that may not be able to capture all risk-relevant information in the image. The new model was developed and trained on mammograms from cases diagnosed between 2008 and 2012 and then studied on more than 2,000 women ages 40 to 74 who had undergone mammography in the Karolinska University Hospital system. Of the 2,283 women in the study, 278 were later diagnosed with breast cancer.

The deep neural network showed a higher risk association for breast cancer compared to the best mammographic density model. The false negative rate — the rate at which women who were not categorized as high-risk were later diagnosed with breast cancer — was lower for the deep neural network than for the best mammographic density model.

“The deep neural network overall was better than density-based models,” Dembrower said. “And it did not have the same bias as the density-based model. Its predictive accuracy was not negatively affected by more aggressive cancer subtypes.”

The study findings support a future role for AI in breast cancer risk assessment.

“We are not reporting mammographic density currently,” Dembrower said. “In the introduction of individually adapted screening, we use deep learning networks trained to predict cancer rather than taking the indirect route that density offers.”

As an additional benefit, the AI approach can continually be improved with exposure to more high-quality data sets.

“Our deep learning experts at the Royal Institute of Technology in Stockholm are working on an update to the model,” Dembrower said. “After that, we aim to test the model clinically next year by offering MRI to the women who stand to benefit the most.”

For more information: www.rsna.org

 

Related content:

Hologic Announces FDA Approval of 3DQuorum Imaging Technology, Powered by Genius AI

Study Finds iCAD's ProFound AI Improves Efficiency and Accuracy in Breast Cancer Detection

Related Content

#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2

Getty Images

Feature | Coronavirus (COVID-19) | April 07, 2020 | By Melinda Taschetta-Millane and Dave Fornell
In an effort to keep the imaging field updated on the latest information being released on coronavirus (COVID-19), th
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2  The first of three clinical scenarios presented to the panel with final recommendations. Mild features refer to absence of significant pulmonary dysfunction or damage. Pre-test probability is based upon background prevalence of disease and may be further modified by individual’s exposure risk. The absence of resource constraints corresponds to sufficient availability of personnel, personal protective equipment, COVID-19 testing, hospital beds, and/or ve

 The first of three clinical scenarios presented to the panel with final recommendations. Mild features refer to absence of significant pulmonary dysfunction or damage. Pre-test probability is based upon background prevalence of disease and may be further modified by individual’s exposure risk. The absence of resource constraints corresponds to sufficient availability of personnel, personal protective equipment, COVID-19 testing, hospital beds, and/or ventilators with the need to rapidly triage patients. Contextual detail and considerations for imaging with CXR (chest radiography) versus CT (computed tomography) are presented in the text. (Pos=positive, Neg=negative, Mod=moderate). [Although not covered by this scenario and not shown in the figure, in the presence of significant resources constraints, there is no role for imaging of patients with mild features of COVID-19.] Image courtesy of the journal Radiology

News | Coronavirus (COVID-19) | April 07, 2020
April 7, 2020 — A multinational consens...
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2 Chest CT findings of pediatric patients with COVID-19 on transaxial images. (a) Male, 2 months old, 2 days after symptom onset. Patchy ground-glass opacities GGO in the right lower lobe

Chest CT findings of pediatric patients with COVID-19 on transaxial images. Male, 2 months old, 2 days after symptom onset. Patchy ground-glass opacities GGO in the right lower lobe. Image courtesy of Radiology: Cardiothoracic Imaging

News | Coronavirus (COVID-19) | April 06, 2020
April 6, 2020 — Children and teenagers with COVID-19...
A recent study earlier this year in the journal Nature, which included researchers from Google Health London, demonstrated that artificial intelligence (AI) technology outperformed radiologists in diagnosing breast cancer on mammograms
Feature | Breast Imaging | April 06, 2020 | By Samir Parikh
A recent study earlier this year in the journal Nature,
Varian received FDA clearance for its Ethos therapy in February 2020. It is an adaptive intelligence solution that uses onboard AI in the treatment system to take the cone beam CT imaging on the system, compare it to the treatment plan and deliver an entire adaptive treatment plan in a typical 15-minute treatment time slot, from patient setup through treatment delivery.

Varian received FDA clearance for its Ethos therapy in February 2020, shown here displayed for the first time at ASTRO 2019. It is an adaptive intelligence solution that uses onboard AI in the treatment system to take the cone beam CT imaging on the system, compare it to the treatment plan and deliver an entire adaptive treatment plan in a typical 15-minute treatment time slot, from patient setup through treatment delivery.

Feature | Treatment Planning | April 03, 2020 | Dave Fornell, Editor
The traditional treatment planning process takes days to create an optimized radiation therapy delivery plan, but new
Feature | Breast Density | April 03, 2020 | By Dayna Williams M.D., Shivani Chaudhry, M.D., and Laurie R. Margolies, M.D.
Breast cancer is the most common cance
An example of Philips’ TrueVue technology, which offers photo-realistic rendering and the ability to change the location of the lighting source on 3-D ultrasound images. In this example of two Amplazer transcatheter septal occluder devices in the heart, the operator demonstrating the product was able to push the lighting source behind the devices into the other chamber of the heart. This illuminated a hole that was still present that the occluders did not seal.

An example of Philips’ TrueVue technology, which offers photo-realistic rendering and the ability to change the location of the lighting source on 3-D ultrasound images. In this example of two Amplazer transcatheter septal occluder devices in the heart, the operator demonstrating the product was able to push the lighting source behind the devices into the other chamber of the heart. This illuminated a hole that was still present that the occluders did not seal. Photo by Dave Fornell

Feature | Radiology Imaging | April 02, 2020 | By Katie Caron
A new year — and decade — offers the opportunity to reflect on the advancements and challenges of years gone by and p
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus

Getty Images

Feature | Coronavirus (COVID-19) | April 02, 2020 | Jilan Liu and HIMSS Greater China Team
Information technologies have played a pivotal role in China’s response to the novel coronavirus...