New Research Confirms iCAD’s ProFound AI Aids Breast Cancer Detection with Digital Breast Tomosynthesis and Improves Radiologists’ Workflow
New research supporting the clinical value of ProFound AI® for Digital Breast Tomosynthesis (DBT) was recently presented at
the Society of Breast Imaging (SBI) Symposium, April 9-11, and at the National
Consortium of Breast Centers (NCBC) Annual Interdisciplinary Breast Center Conference
(NCoBC), April 16-19. Both studies demonstrate ProFound AI helps radiologists
identify normal mammograms, as well as those with increased likelihood of
malignancy, with greater precision.
At the SBI Symposium, Emily Conant, MD, Professor and Division Chief of
Breast Imaging at the University of Pennsylvania Medical Center, presented
findings from a retrospective analysis involving ProFound AI for DBT in a
presentation titled “Feasibility of automated identification of low-likelihood
of cancer cases in digital breast tomosynthesis screening.” According to the
study’s findings, ProFound AI for DBT accurately identified 33.4 percent of
normal screening DBT exams with no cancers being missed, based solely on the
ProFound AI Case Score. When researchers also factored in breast density and
age, ProFound AI identified 58.6 percent of normal cases with no false
negatives.
“Our retrospective study demonstrates the feasibility that clinical
algorithms have the potential to triage and reduce screening DBT workload by
flagging normal mammograms using an AI system, and also prioritizing complex
cases that are more likely to require additional review or evaluation,” said
Dr. Conant. “We are pleased to have our research add to the important growing
body of evidence supporting the significance and value of AI in breast
screening.”
At the NCoBC Interdisciplinary Breast Center Conference, Mark Traill, MD,
University of Michigan Health, presented findings from a study titled “Correlation
between BI-RADS Assessment Categories and Artificial Intelligence Case Scores,”
which was a winner in the “Breast Disease Diagnosis and Management” category. The
study was conducted to evaluate the thresholds at which the ProFound AI system could be used for triaging DBT exams to reach a
minimum rate of false negatives per 1,000 screened in an enriched dataset of
506 biopsy-proven cancer cases and 1,293 non-cancer cases with 320 days of
negative follow-up. A consecutive series of cases were collected from 18 sites
in the United States and three sites in France.
In the retrospective analysis, researchers used ProFound AI on 890
consecutive DBT studies and 50 consecutive cases with biopsy-proven breast
cancer detected with DBT. Results showed a strong positive correlation between
a ProFound AI Case Score of less than 60 percent and patients
assessed as likely to be normal (BI-RADS 1 or 2), while most of the
biopsy-proven cancers had a Case Score of greater than 60 percent.
“We wanted to describe
the Case Score distribution in a screening population to better understand the
significance of score value as a clinical decision tool,” said Dr. Traill. “We
found a very strong correlation between a Case Score of less than 60 percent
and a BI-RADS score assessment of 1 or 2. Also, only 15 percent of the Case
Scores were greater than 60 percent, but this group contained most of the
detected cancers. As a clinical decision tool, a Case Score above 60 percent is
an independent indicator of higher chance of underlying malignancy. This is
very helpful in guiding the intensity of the cancer search, while improving
workflow functionality and reducing stress for the reading radiologist.”
“These two studies both suggest that ProFound AI Case Scores
provide valuable insights that can help clinicians more efficiently identify normal
mammograms, which may directly translate to time-savings benefits,” said
Michael Klein, Chairman and CEO of iCAD. “In addition, ProFound AI is
clinically proven to improve radiologists’ sensitivity while simultaneously
improving their specificity, which is a huge performance achievement in breast
care. With the addition of these two important abstracts, research now shows
how Case Scores can be used in the clinical setting to help radiologists feel
more confident in their decisions about when a mammogram is normal.”
ProFound AI for DBT is a high-performance, deep-learning
workflow solution trained to detect malignant soft-tissue densities and
calcifications. It became the first 3D tomosynthesis software using artificial
intelligence (AI) to be FDA cleared in December 2018. Built with the latest in deep-learning
technology, ProFound AI for DBT rapidly analyzes each tomosynthesis image,
detecting malignant soft tissue densities. Certainty of Finding and Case Scores are relative scores
computed by the ProFound AI algorithm and represent its confidence that a
detection or case is malignant. The Certainty of Finding scores help
radiologists by aiding in clinical decision making. Case Scores, which are
assigned to each case by the algorithm, help clinicians to gain a sense of case
complexity, which may be useful for prioritizing the reading worklist. In a reader study published in Radiology: Artificial
Intelligence,
ProFound AI for DBT Version 2.0 was clinically proven to reduce reading time for
radiologists by 52.7 percent, improve radiologists’ sensitivity by 8 percent,
and reduce the rate of false positives and unnecessary patient recalls by 7.2
percent.[1]
The latest version of ProFound AI for
DBT, version 3.0, was recently cleared by the FDA in March 2021. Compared to
previous versions of the software, the ProFound AI 3.0 algorithm offers up to an
additional 10 percent improvement in specificity performance and up to an
additional 1 percent improvement in sensitivity over its previous deep-learning
AI software generation (ProFound AI Version 2.1).[2] ProFound AI version 3.0 also offers up to 40 percent faster
processing on the new PowerLook platform.2 ProFound AI version
3.0 was developed using over five million images from 30,000 cases, including
almost 8,000 biopsy-proven cancers, and validated on approximately one million
images from 3,500 cases that included 1,200 biopsy-proven cancers.
iCAD’s Breast Health Solutions suite also includes software to evaluate breast density, ProFound AI
for 2D Mammography, and ProFound AI Risk, the world’s first and only clinical
decision support tool that provides an accurate two-year breast cancer risk
estimation that is truly personalized for each woman, based only on a screening
mammogram.[3]
[1] Conant, E et al. (2019). Improving
Accuracy and Efficiency with Concurrent Use of Artificial Intelligence for
Digital Breast Tomosynthesis. Radiology: Artificial Intelligence. 1 (4).
Accessed via https://pubs.rsna.org/doi/10.1148/ryai.2019180096
[2] iCAD data on file. Standalone
performance varies by vendor. FDA Cleared.
[3] Eriksson M, Czene K, Strand F, et al.
Identification of Women at High Risk of Breast Cancer Who Need Supplemental
Screening. [published online ahead of print September 8, 2020]. Radiology.
Accessed via https://doi.org/10.1148/radiol.2020201620