New Research Confirms iCAD’s ProFound AI Aids Breast Cancer Detection with Digital Breast Tomosynthesis, Improves Radiologists’ Workflow

NASHUA, N.H., April 19, 2021 (GLOBE NEWSWIRE) -- iCAD, Inc. (NASDAQ: ICAD), a global medical technology leader providing innovative cancer detection and therapy solutions, today announced that new research supporting the clinical value of ProFound AI® for Digital Breast Tomosynthesis (DBT) was 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.

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,” at the SBI Symposium. 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.

“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.”

According to study findings presented by Dr. Conant at the SBI Symposium, 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.”

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 addition, new data presented by Dr. Mark Traill at the NCoBC Interdisciplinary Breast Center Conference highlights the comparison of ProFound AI Case Scores to BI-RADS assessment categories determined by a single radiologist without using AI in a 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.”

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

About iCAD, Inc.

Headquartered in Nashua, NH, iCAD is a global medical technology leader providing innovative cancer detection and therapy solutions. For more information, visit www.icadmed.com and www.xoftinc.com.

Forward-Looking Statements

Certain statements contained in this News Release constitute “forward-looking statements” within the meaning of the Private Securities Litigation Reform Act of 1995, including statements about the Company’s technology platforms and products. Such forward-looking statements involve a number of known and unknown risks, uncertainties and other factors which may cause the actual results, performance, or achievements of the Company to be materially different from any future results, performance, or achievements expressed or implied by such forward-looking statements. Such factors include, but are not limited, to the Company’s ability to achieve business and strategic objectives, the willingness of patients to undergo mammography screening in light of risks of potential exposure to Covid-19, whether mammography screening will be treated as an essential procedure, whether ProFound AI will improve reading efficiency, improve specificity and sensitivity, reduce false positives and otherwise prove to be more beneficial for patients and clinicians, the impact of supply and manufacturing constraints or difficulties on our ability to fulfill our orders, uncertainty of future sales levels, to defend itself in litigation matters, protection of patents and other proprietary rights, product market acceptance, possible technological obsolescence of products, increased competition, government regulation, changes in Medicare or other reimbursement policies, risks relating to our existing and future debt obligations, competitive factors, the effects of a decline in the economy or markets served by the Company; and other risks detailed in the Company’s filings with the Securities and Exchange Commission. The words “believe,” “demonstrate,” “intend,” “expect,” “estimate,” “will,” “continue,” “anticipate,” “likely,” “seek,” and similar expressions identify forward-looking statements. Readers are cautioned not to place undue reliance on those forward-looking statements, which speak only as of the date the statement was made. The Company is under no obligation to provide any updates to any information contained in this release. For additional disclosure regarding these and other risks faced by iCAD, please see the disclosure contained in our public filings with the Securities and Exchange Commission, available on the Investors section of our website at http://www.icadmed.com and on the SEC’s website at http://www.sec.gov. 

Contacts:
Media inquiries:
Jessica Burns, iCAD
+1-201-423-4492
jburns@icadmed.com

Investor Relations:
Jeremy Feffer, LifeSci Advisors
+1-212-915-2568
jeremy@lifesciadvisors.com

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