Cleveland Clinic Radiologist Presents Use Case of iCAD's Breast AI Technology at Siemens Healthineers Innovations for Healthcare Professionals Education Symposium

Dr. Laura Dean to discuss how AI solutions minimizes
interval cancers and helps clinicians overcome challenges associated with
digital breast tomosynthesis

Laura Dean, MD, a radiologist at the Cleveland Clinic, will
discuss the use of iCAD’s Breast AI Suite for patients and clinicians in a
presentation titled “Minimizing Interval Cancers with AI” on Thursday, April 20
at the Siemens Healthineers Innovations for Healthcare Professionals Education
Symposium, taking place April 18-20 in New Orleans, LA. iCAD will also showcase
this technology in booth #9 during the meeting.

“Leading facilities worldwide trust our solutions to
detect cancer and we are pleased that Dr. Laura Dean will be discussing her
experience using our technology at the Cleveland Clinic at the upcoming Siemens
Healthineers Symposium,” said Dana Brown, President and CEO of iCAD, Inc. “iCAD’s
Breast AI Suite offers a 360-degree solution of cancer detection, density
assessment, and risk evaluation technologies that are uniquely positioned to
address the top challenges clinicians face today. Not only is it clinically
proven to improve accuracy and efficiency for radiologists reading mammography,
it offers critical information about a woman’s present and future breast
health, which can help clinicians find more interval breast cancers at their
earliest stage possible.”

Interval cancers, or those that are diagnosed in between
screening exams after a “normal” mammogram, tend to be larger and more
aggressive, diagnosed at a later stage and with worse prognoses than those
found on screening mammograms.[1] In a study of 69,025 women, interval
breast cancers accounted for one-fourth of breast cancers in routinely screened
women. These interval cancers were six times more likely to be grade III and
had 3.5 times increased hazards of breast cancer death compared with
screen-detected cancers.

“Digital breast tomosynthesis has improved mammography screening
considerably, particularly in the U.S., where it is available at 82% of breast
imaging sites.[2]
However, DBT has introduced new challenges for radiologists reading
mammography, as it generates an exponential amount of data to review, compared
to 2D mammography. AI is becoming ubiquitous in our daily lives, from
smartphones to smart homes, but it is particularly well-suited for reading DBT,”
said Laura Dean, MD, radiologist, Cleveland Clinic. “Interval cancers tend to
grow and possibly spread more quickly, making them among the most dangerous to
miss. AI helps us to review every pixel of even the most complicated datasets
more closely, and with greater accuracy and efficiency, which ultimately helps
our team at the Cleveland Clinic detect more cancers earlier and hopefully
decrease the incidence of interval cancers.”

Below please find additional details regarding the
presentation:

Title: “Minimizing Interval Cancers Through Breast AI”
Date/time: Thursday, April 20, 2023 from 9:15-10:15am CT (session 2)
Presenters: Laura Dean, MD, Radiologist, Cleveland Clinic Breast
Imaging, Wendy Niazi, Regional Sales Manager, iCAD, Inc.

A growing body of clinical evidence confirms the unique
value iCAD’s Breast AI Suite offers to clinicians and patients alike. According
to a retrospective analysis of 37,367 women published in the Journal ofMedical Screening, ProFound® AI for 2D Mammography may reduce interval
breast cancer rates, as the technology found 93% of interval cancers—including eight
out of nine cancers that presented with minimal signs and six out of six that
were false negatives, or missed in the screening round.[3] Additionally,
a study published in ScienceTranslational Medicine suggests ProFound AI Risk offered high accuracy
in estimating future risk for both screen detected and interval cancers, as
well as invasive and in-situ cancers, in both women with dense and non-dense
breasts.2

The flagship product in iCAD’s Breast AI Suite is
ProFound AI, which became the first technology of its kind to be FDA cleared in
2018. Built with the latest in deep-learning AI, ProFound AI for Digital Breast
Tomosynthesis is clinically proven to improve radiologists’ sensitivity by 8%,
reduce the rate of false positives and unnecessary callbacks by 7%, and slash
reading time by more than half. iCAD’s Breast AI Suite also includes PowerLook Density
Assessment, which automates the process of breast density reporting and empowers
clinicians to further personalize breast cancer screening recommendations for
patients.

The latest addition to iCAD’s Breast AI Suite, ProFound
AI Risk, is the world’s first clinical decision support tool that provides an
accurate short-term breast cancer risk estimation that is truly personalized
for each woman, based only on her mammogram.[4],[5]
This first-in-kind solution uniquely combines age, breast density and subtle
mammographic features, offering superior performance and accuracy in assessing both
short- and long-term risk compared to traditional, commonly-used breast cancer
risk models.3,[6]

“Breast cancer continues to be one of the greatest
threats to women’s health worldwide, but iCAD’s Breast AI Suite arms clinicians
with more intelligence than ever to personalize screening based on individual
risk, which can help them detect cancers at their earliest possible stages,
when they may be more easily treated,” said Ms. Brown. “Using our suite of
tools together provides clinicians with the latest defense in the fight against
breast cancer – not only does it empower them to perform at a higher level, but
it offers valuable information about patients’ individual risk of developing
breast cancer that can help clinicians tailor screening regimens. This
translates to finding more cancers and saving more lives, and we look forward
to sharing our powerful solutions with the clinical community at the Siemens
Healthineers Symposium this week.”

[1] Niraula
S, Biswanger N, Hu P, Lambert P, Decker K. Incidence, Characteristics, and
Outcomes of Interval Breast Cancers Compared With Screening-Detected Breast
Cancers. JAMA Netw Open. 2020;3(9):e2018179.
doi:10.1001/jamanetworkopen.2020.18179

[2] MQSA
National Statistics. Accessed via https://www.fda.gov/radiation-emitting-products/mqsa-insights/mqsa-national-statistics.

[3]
Graewingholt A, Rossi PG. (2021). Retrospective analysis of the effect on
interval cancer rate of adding an artificial intelligence algorithm to the
reading process for two-dimensional full-field digital mammography. J Med
Screen. 0(0) 1-3.

Accessed via
https://journals.sagepub.com/doi/10.1177/0969141320988049

[4]
Eriksson, M et al. A risk model for digital breast tomosynthesis to predict
breast cancer and guide clinical care. Science Translational Medicine. 14
(644). 2022 May 11. Accessed via
https://www.science.org/doi/10.1126/scitranslmed.abn3971.

[5] Eriksson
M, Czene K, Strand F et al. Identification of Women at High Risk of Breast
Cancer Who Need Supplemental Screening. Radiology. 2020 Sept 8,

[6] Eriksson
M, CzeneK , Vachon C, Conant E, Hall P. Long-Term Performance of an Image-Based
Short-Term Risk Model for Breast Cancer. Journal of Clinical Oncology. DOI:
10.1200/JCO.22.01564.