SABCS 2024: New Research Assesses ICAD's Image Based AI-Risk, Detection, and Breast Arterial Calcifications (BAC) Assessment Across Diverse Populations
SABCS 2024: New Research Assesses ICAD's Image Based AI-Risk, Detection, and Breast Arterial Calcifications (BAC) Assessment Across Diverse Populations
Reveals Higher Prevalence of Breast Arterial Calcifications (BAC) in Breast Cancer Patients, Suggesting Potential Need for Cardiovascular Assessment Alongside Oncological Treatment
顯示乳腺癌患者中乳腺動脈鈣化(BAC)的發生率較高,這表明在腫瘤治療的同時可能需要進行心血管評估
NASHUA, N.H., Dec. 11, 2024 (GLOBE NEWSWIRE) -- iCAD, Inc. (NASDAQ: ICAD) ("iCAD" or the "Company") a global leader in clinically proven AI-powered cancer detection solutions, announced today that four novel AI-driven breast cancer research abstracts have been accepted for presentation at the 2024 San Antonio Breast Cancer Symposium (SABCS), taking place from December 10-13, 2024. These clinical abstracts highlight the latest research in breast health AI, focusing on improving detection and risk prediction accuracy and assessing disparities across diverse populations.
新罕布什爾州納舒亞,2024年12月11日(環球新聞稿)-- iCAD公司(納斯達克:ICAD)("iCAD"或"公司")是全球領先的臨床驗證的人工智能驅動的癌症檢測解決方案供應商,今天宣佈,四篇新的人工智能驅動的乳腺癌研究摘要已被接受在2024年聖安東尼奧乳腺癌研討會(SABCS)上進行展示,該研討會將於2024年12月10日至13日舉行。這些臨床摘要重點介紹了乳腺健康人工智能的最新研究,着重提高檢測和風險預測的準確性,並評估不同群體之間的差異。
Presenting Author Chirag R. Parghi, MD, MBA, Chief Medical Officer at Solis Mammography, will showcase three research abstracts during Poster Session 2, scheduled for Wednesday, December 11, 2024, from 5:30 to 7:00 p.m. CST. Additional contributing authors include Jennifer Pantleo, R.N., BSN; Julie Shisler, BS; Jeff Hoffmeister, M.D., MSEE; Zi Zhang, M.D., M.P.H; Avi Sharma, M.D.; and, Wei Zhang, PhD.
展示作者Chirag R. Parghi,醫學博士,工商管理碩士,Solis Mammography首席醫療官,將在2024年12月11日星期三的海報會議2中展示三篇研究摘要,時間爲下午5:30至下午7:00(CST)。其他貢獻作者包括Jennifer Pantleo,註冊護士,護理學學士;Julie Shisler,理學學士;Jeff Hoffmeister,醫學博士,電子學碩士;Zi Zhang,醫學博士,公共衛生碩士;Avi Sharma,醫學博士;以及Wei Zhang,博士。
Additionally, presenting Author Mikael Eriksson, PhD, epidemiologist at Karolinska Institute, Sweden, will present research during general session 2, scheduled for Thursday, December 12, 2024 from 9:00 a.m. to 12:30 p.m. CST. demonstrating a 10-year image-derived AI-risk model, based on iCAD's ProFound Risk solution, for primary prevention of breast cancer showed higher discriminatory performance than the clinical Tyrer-Cuzick v8 risk model.
此外,展示作者Mikael Eriksson,醫學博士,瑞典卡羅林斯卡學院流行病學家,將在2024年12月12日星期四的大會會議2中展示研究,時間爲上午9:00至下午12:30(CST)。研究顯示基於iCAD的ProFound Risk解決方案的十年影像衍生人工智能風險模型,在全乳腺癌的初級預防中展示了比臨床Tyrer-Cuzick v8風險模型更高的辨別性能。
Advancing Breast Health with AI
利用人工智能推動乳腺健康
"These studies exemplify the critical role the ProFound AI Breast Health Suite can play in not only improving early breast cancer detection and risk prediction but also in addressing health disparities in diverse populations," said Dana Brown, President and CEO of iCAD. "We are proud to collaborate with Solis Mammography and Karolinska Institute contributing to groundbreaking research that can elevate the standard of care in breast health worldwide. These partnerships demonstrate the potential of our technology to improve patient outcomes, and also opens pathways to broader adoption of AI in healthcare, driving growth in key markets."
「這些研究展示了ProFound人工智能乳腺健康套件在提高早期乳腺癌檢測和風險預測方面的重要作用,同時也爲不同人群解決健康差距,」iCAD的總裁兼首席執行官達納·布朗表示。「我們很自豪能與Solis Mammography和卡羅林斯卡學院合作,推動能夠提升全球乳腺健康護理標準的開創性研究。這些合作關係展示了我們的科技改善患者結果的潛力,同時也爲人工智能在醫療領域的更廣泛應用打開了道路,爲關鍵市場的增長驅動。」
Dr. Chirag Parghi, Chief Medical Officer at Solis Mammography, added: "These findings underscore the transformative potential of AI in empowering clinicians to improve outcomes regardless of age, race or breast density. By addressing traditional gaps in breast cancer detection and risk assessment, AI has the potential to exponentially improve current and future state breast cancer detection."
Solis Mammography的首席醫療官Chirag Parghi博士補充道:「這些發現強調了人工智能在賦能臨床醫生改善結果方面的變革潛力,無論年齡、種族或乳腺密度如何。通過解決乳腺癌檢測和風險評估中的傳統差距,人工智能有潛力在當前和未來的乳腺癌檢測中實現指數級改善。」
Poster Details:
海報詳情:
P2-06-20: Use of an AI Algorithm to Determine the Prevalence of Breast Arterial Calcifications in Women Undergoing Screening Mammograms Based on Race, Age, and Cancer Status (SESS-2141)
P2-06-20:使用人工智能算法來判斷女性在接受篩查乳腺X光檢查時乳腺動脈鈣化的流行率,依據種族、年齡和癌症狀態(SESS-2141)
This poster explores the potential of an AI algorithm to identify Breast Arterial Calcifications (BAC), which are calcium deposits in the arteries of the breast that are commonly detected during routine mammograms. The study demonstrates that the weighted prevalence and distribution of BAC increases with age, as expected in a screening population. Interestingly, BAC prevalence did not vary by race, suggesting that it could serve as an effective cardiovascular biomarker across racial groups. Furthermore, the AI-based BAC detection algorithm highlighted a higher prevalence of BAC in women with mammographically detected breast cancer, suggesting women with increased BAC and breast cancer may benefit from cardiovascular assessment in addition to their oncological treatment. In that sense, a conventional mammogram could identify the cardiac needs of patients prior to or at the time of breast cancer diagnosis, providing an opportunity for early cardiovascular intervention.
這張海報探討了一種人工智能算法識別乳腺動脈鈣化(BAC)的潛力,BAC是乳腺動脈中的鈣沉積,通常在常規乳腺X光檢查中發現。研究表明,BAC的加權流行率和分佈隨着年齡的增長而增加,這在篩查人群中是預期的。有趣的是,BAC的流行率在不同種族間並沒有差異,這表明它可能作爲所有種族群體中有效的心血管生物標誌物。此外,基於人工智能的BAC檢測算法在乳腺癌患者中顯示出更高的BAC流行率,這表明在乳腺癌患者中,增加的BAC和乳腺癌可能在其腫瘤治療的基礎上受益於心血管評估。從這個意義上說,常規的乳腺X光檢查可以在乳腺癌診斷前或診斷時識別患者的心臟需求,爲早期心血管幹預提供機會。
P2-06-24: Effect of an Image-Derived Short-Term Breast Cancer Risk Score in the Analysis of Breast Cancer Prevalence in Screening Populations by Race and Breast Density (SESS-2148)
P2-06-24:基於圖像衍生的短期乳腺癌風險評分對按種族和乳腺密度篩查人群乳腺癌流行率分析的影響(SESS-2148)
This study delves into the development and validation of an AI-driven short-term breast cancer risk assessment score based on image-derived features, including mammographic density, and age. AI-generated case scores were shown to effectively stratify mammograms into categories with varying frequencies of cancer. The case scores did not vary significantly across racial subgroups in our dataset, suggesting that the accuracy of the AI software was consistent across races. The study concludes that an image-derived AI risk model is equally effective across race and density, providing accurate insight into short-term breast cancer risk. Based on the results, image-based risk scoring could offset known gaps in breast cancer detection by traditional mammography in patients with dense breast tissue and help address existing disparities across races. Findings from this study highlight the potential of AI to offer more consistent and equitable breast cancer risk assessments, improving both diagnostic accuracy and patient outcomes across diverse populations.
本研究深入探討了一種基於圖像特徵(包括乳腺密度和年齡)開發和驗證的人工智能驅動的短期乳腺癌風險評估評分。研究顯示,人工智能生成的病例評分可以有效地將乳腺X光篩查分類爲具有不同癌症頻率的類別。我們的數據集中,病例評分在不同種族亞組間沒有顯著差異,這表明人工智能軟件的準確性在各種族間是一致的。研究得出結論,基於圖像的人工智能風險模型在種族和密度方面同樣有效,提供了有關短期乳腺癌風險的準確見解。根據結果,基於圖像的風險評分可以彌補傳統乳腺X光在密集乳腺組織患者中的乳腺癌檢測已知的差距,並幫助解決不同種族之間現有的差異。本研究的發現突顯了人工智能在提供更一致和公平的乳腺癌風險評估方面的潛力,提高了不同人群的診斷準確性和患者結果。
P2-06-25: Is Mammography Artificial Intelligence Consistent Across Race and Density? (SESS-2135)
P2-06-25:乳腺攝影的人工智能在不同種族和乳腺密度中是否一致?(SESS-2135)
This research focuses on the consistency of AI-based mammographic case scoring across different racial and breast density groups. The study emphasizes the potential of AI to provide equitable and reliable screening results, regardless of the patient's race or breast tissue density, two factors known to impact traditional mammography outcomes. For women with non-dense or fatty breast tissue, a low case score corresponded to a significantly lower frequency of cancer (1 in 11,363) compared to women with dense breast tissue who had a low case score (1 in 1,952). Although this finding was not statistically significant according to the Mann-Whitney U test, the difference between categories is notable, and the lack of statistical confirmation is likely due to the low absolute number of cancer cases in the low case score, non-dense cohort. Therefore, the negative predictive value of a low case score on a screening mammogram is presumably higher in women with non-dense breast tissue across a large dataset, suggesting a more reliable assessment for this group.
本研究重點研究基於人工智能的乳腺攝影病例評分在不同種族和乳腺密度組之間的一致性。研究強調人工智能提供公平、可靠的篩查結果的潛力,無論患者的種族或乳腺組織密度如何,這兩個因素已知對傳統乳腺攝影結果有影響。對於乳腺組織不密集或脂肪型的女性,低病例評分對應的癌症發生頻率明顯較低(1/11,363),而密集乳腺組織的女性則爲(1/1,952)。儘管根據Mann-Whitney U檢驗,該發現並沒有統計學顯著性,但不同類別之間的差異顯著,缺乏統計確認的原因可能是低病例評分中不密集組的癌症病例絕對數較低。因此,基於篩查乳腺攝影的低病例評分對乳腺組織不密集的女性的負預測值在大數據集中被推測爲更高,表明對該群體的評估更爲可靠。
GS2-10: A long-term image-derived AI risk model for primary prevention of breast cancer
GS2-10:用於乳腺癌初級預防的長期圖像派生人工智能風險模型
The research analyzed a two-site case-cohort of women aged 30-90 in a population-based screening study in Minnesota and the KARMA cohort from Sweden using an image-derived AI-risk model compared with the clinical Tyrer-Cuzick v8 model using clinical guidelines. Analyses were performed for risk of all breast cancer and restricted to invasive cancer alone. Using the National Institute for Health and Care Excellence (NICE) guidelines, considering women at 8% as high risk, 32% of breast cancers could be subject to preventive strategies in the 9.7% of women at high 10-year risk based on the AI risk model, the 10-year image-derived AI-risk model showed good discriminatory performance and calibration in the two case-cohorts and, showed a significantly higher discriminatory performance than the clinical Tyrer-Cuzick v8 risk model in KARMA. Demonstrating the image-derived AI-risk model has the potential for clinical use in primary prevention and targets up to one third of breast cancers.
該研究分析了明尼蘇達州基於人群的篩查研究中30-90歲女性的兩地病例隊列,以及來自瑞典的KARMA隊列,使用圖像派生的人工智能風險模型與根據臨床指南的Tyrer-Cuzick v8模型進行比較。風險分析包括所有乳腺癌,並限制爲僅侵襲性癌症。根據國家健康與護理卓越研究所(NICE)指南,將8%的女性視爲高風險,預計在基於人工智能風險模型的高10年風險女性中,有32%的乳腺癌可能適用預防策略,該10年圖像派生的人工智能風險模型在兩個病例隊列中顯示了良好的區分性能和校準,並且在KARMA中表現出的區分性能顯著高於臨床的Tyrer-Cuzick v8風險模型。表明圖像派生的人工智能風險模型在初級預防中具有臨床應用潛力,目標是乳腺癌的三分之一。
Join Us at SABCS 2024
加入我們參加SABCS 2024
Attendees are invited to view these posters during Poster Session 2 on December 11, 2024, from 5:30 to 7:00 p.m. CST. To learn more about iCAD's AI solutions, including the ProFound AI Breast Health Suite, visit iCAD's website or contact iCAD for an interview at SABCS.
與會者被邀請在2024年12月11日的海報會議2期間查看這些海報,時間爲下午5:30至晚上7:00 CST。要了解更多關於iCAD的人工智能解決方案,包括ProFound AI乳腺健康套件,請訪問iCAD的網站或聯繫iCAD進行SABCS的採訪。
About iCAD, Inc.
iCAD, Inc. (NASDAQ: ICAD) is a global leader on a mission to create a world where cancer can't hide by providing clinically proven AI-powered solutions that enable medical providers to accurately and reliably detect cancer earlier and improve patient outcomes. Headquartered in Nashua, N.H., iCAD's industry-leading ProFound Breast Health Suite provides AI-powered mammography analysis for breast cancer detection, density assessment and risk evaluation. Used by thousands of providers serving millions of patients, ProFound is available in over 50 countries. In the last five years alone, iCAD estimates reading more than 40 million mammograms worldwide, with nearly 30% being tomosynthesis. For more information, including the latest in regulatory clearances, please visit .
關於iCAD公司
iCAD公司(納斯達克:ICAD)是全球領先的企業,致力於創造一個癌症無法隱藏的世界,通過提供經過臨床驗證的人工智能解決方案,使醫療提供者能夠更準確可靠地早期檢測癌症,提高患者的治療效果。iCAD總部位於新罕布什爾州納舒厄,行業領先的ProFound乳腺健康套件提供乳腺癌檢測、密度評估和風險評估的人工智能乳腺攝影分析。ProFound已被數千名醫療提供者在超過50個國家使用,服務數百萬患者。在過去五年中,iCAD估計全球共讀取了超過4000萬份乳腺攝影,其中近30%爲層析成像。有關更多信息,包括最新的監管批准,請訪問。
ProFound Detection v4 is FDA Cleared. ProFound AI v3 is FDA Cleared. CE Marked. Health Canada Licensed. ProFound AI Risk is CE Marked and Health Canada Licensed. Solutions may not be available in all geographies.
ProFound Detection v4 已獲得 FDA 清除。ProFound AI v3 已獲得 FDA 清除。已獲得 CE 認證。獲得加拿大健康許可。ProFound AI 風險已獲得 CE 認證並獲得加拿大健康許可。解決方案可能並非在所有地區均可用。
Forward-Looking Statements
前瞻性聲明
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 expansion of access to the Company's products, improvement of performance, acceleration of adoption, expected benefits of ProFound AI, the benefits of the Company's products, and future prospects for the Company's technology platforms and products. Such forward-looking statements involve a number of known and unknown risks, uncertainties, and other factors that 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, 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 and on the SEC's website at http://www.sec.gov.
本新聞稿中包含的某些聲明構成《1995年私人證券訴訟改革法案》定義下的「前瞻性聲明」,包括有關公司產品獲取途徑擴展、業績改善、採納加速、ProFound AI 預期收益、公司產品的好處以及公司技術平台和產品的未來前景的聲明。這些前瞻性聲明涉及多種已知和未知的風險、不確定性和其他因素,這些因素可能導致公司的實際結果、表現或成就與任何未來結果、表現或成就存在重大差異,這些差異由這些前瞻性聲明所表達或暗示。這些因素包括但不限於公司的商業和戰略目標達成能力、患者進行乳腺X線檢查的意願、乳腺X線檢查是否將被視爲必要程序、ProFound AI 是否能改善閱讀效率、提高特異性和敏感性、減少假陽性並對患者和臨床醫生更具益處、供應和製造限制或困難對公司履行訂單能力的影響、未來銷售水平的不確定性、在訴訟事務中進行自我辯護、保護專利和其他專有權利、產品市場接受度、產品技術的可能過時、競爭加劇、政府監管、醫療保險或其他報銷政策的變化、與現有和未來債務義務相關的風險、競爭因素、公司經濟或市場的下滑影響,以及公司與美國證券交易委員會的文件中詳細說明的其他風險。「相信」、「展示」、「打算」、「預計」、「估計」、「將」、「繼續」、「預期」、「可能」、「尋求」及類似表達識別前瞻性聲明。提醒讀者不要對這些前瞻性聲明過分依賴,這些聲明僅在聲明發布的日期生效。公司沒有任何義務提供任何本聲明中包含信息的更新。有關iCAD面臨的這些和其他風險的進一步披露,請參閱我們在證券交易委員會的公開文件中包含的披露,這些文件可在我們網站的投資者部分以及美國證券交易委員會網站 http://www.sec.gov 上找到。
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