AI performance by mammographic density in a retrospective cohort study of 99,489 participants in BreastScreen Norway.

IF 4.7 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING European Radiology Pub Date : 2024-10-01 Epub Date: 2024-03-25 DOI:10.1007/s00330-024-10681-z

Marie Burns Bergan, Marthe Larsen, Nataliia Moshina, Hauke Bartsch, Henrik Wethe Koch, Hildegunn Siv Aase, Zhanbolat Satybaldinov, Ingfrid Helene Salvesen Haldorsen, Christoph I Lee, Solveig Hofvind

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引用次数: 0

Abstract

Objective: To explore the ability of artificial intelligence (AI) to classify breast cancer by mammographic density in an organized screening program.

Materials and method: We included information about 99,489 examinations from 74,941 women who participated in BreastScreen Norway, 2013-2019. All examinations were analyzed with an AI system that assigned a malignancy risk score (AI score) from 1 (lowest) to 10 (highest) for each examination. Mammographic density was classified into Volpara density grade (VDG), VDG1-4; VDG1 indicated fatty and VDG4 extremely dense breasts. Screen-detected and interval cancers with an AI score of 1-10 were stratified by VDG.

Results: We found 10,406 (10.5% of the total) examinations to have an AI risk score of 10, of which 6.7% (704/10,406) was breast cancer. The cancers represented 89.7% (617/688) of the screen-detected and 44.6% (87/195) of the interval cancers. 20.3% (20,178/99,489) of the examinations were classified as VDG1 and 6.1% (6047/99,489) as VDG4. For screen-detected cancers, 84.0% (68/81, 95% CI, 74.1-91.2) had an AI score of 10 for VDG1, 88.9% (328/369, 95% CI, 85.2-91.9) for VDG2, 92.5% (185/200, 95% CI, 87.9-95.7) for VDG3, and 94.7% (36/38, 95% CI, 82.3-99.4) for VDG4. For interval cancers, the percentages with an AI score of 10 were 33.3% (3/9, 95% CI, 7.5-70.1) for VDG1 and 48.0% (12/25, 95% CI, 27.8-68.7) for VDG4.

Conclusion: The tested AI system performed well according to cancer detection across all density categories, especially for extremely dense breasts. The highest proportion of screen-detected cancers with an AI score of 10 was observed for women classified as VDG4.

Clinical relevance statement: Our study demonstrates that AI can correctly classify the majority of screen-detected and about half of the interval breast cancers, regardless of breast density.

Key points: • Mammographic density is important to consider in the evaluation of artificial intelligence in mammographic screening. • Given a threshold representing about 10% of those with the highest malignancy risk score by an AI system, we found an increasing percentage of cancers with increasing mammographic density. • Artificial intelligence risk score and mammographic density combined may help triage examinations to reduce workload for radiologists.

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挪威乳腺癌筛查（BreastScreen）99,489 名参与者的回顾性队列研究显示，乳腺 X 线造影密度对 AI 的影响。

目的探索人工智能（AI）在有组织的筛查项目中根据乳腺X光密度对乳腺癌进行分类的能力：我们纳入了2013-2019年期间参加挪威乳腺癌筛查项目的74941名妇女的99489次检查信息。所有检查均采用 AI 系统进行分析，该系统为每次检查分配一个从 1（最低）到 10（最高）的恶性肿瘤风险分数（AI 分数）。乳腺密度分为 Volpara 密度等级 (VDG)，VDG1-4；VDG1 表示脂肪型乳房，VDG4 表示密度极高的乳房。根据 VDG 对筛查出的癌症和 AI 分值为 1-10 的间期癌症进行了分层：我们发现 10,406 例（占总数的 10.5%）检查结果的 AI 风险评分为 10，其中 6.7%（704/10,406）为乳腺癌。这些癌症占筛查出癌症的 89.7%（617/688），占间隔期癌症的 44.6%（87/195）。20.3%（20178/99489）的检查结果被归类为 VDG1，6.1%（6047/99489）被归类为 VDG4。在筛查出的癌症中，84.0%（68/81，95% CI，74.1-91.2）的 VDG1 AI 得分为 10，88.9%（328/369，95% CI，85.2-91.9）的 VDG2 AI 得分为 10，92.5%（185/200，95% CI，87.9-95.7）的 VDG3 AI 得分为 10，94.7%（36/38，95% CI，82.3-99.4）的 VDG4 AI 得分为 10。就间期癌而言，VDG1 的人工智能评分为 10 分的百分比为 33.3%（3/9，95% CI，7.5-70.1），VDG4 为 48.0%（12/25，95% CI，27.8-68.7）：经测试的人工智能系统在所有密度类别的癌症检测方面都表现良好，尤其是在极致密乳房方面。在被归类为 VDG4 的女性中，AI 得分为 10 的筛查出癌症比例最高：我们的研究表明，无论乳腺密度如何，人工智能都能对大部分筛查出的乳腺癌和大约一半的间期乳腺癌进行正确分类：- 乳腺密度是评估人工智能乳腺筛查的重要依据。- 人工智能系统的恶性肿瘤风险评分阈值约为 10%，我们发现随着乳腺密度的增加，癌症的比例也在增加。- 人工智能风险评分与乳腺X光密度相结合，可能有助于分流检查，减轻放射科医生的工作量。

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来源期刊

European Radiology 医学-核医学

CiteScore

11.60

自引率

8.50%

发文量

874

审稿时长

2-4 weeks

期刊介绍： European Radiology (ER) continuously updates scientific knowledge in radiology by publication of strong original articles and state-of-the-art reviews written by leading radiologists. A well balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes ER an indispensable source for current information in this field. This is the Journal of the European Society of Radiology, and the official journal of a number of societies. From 2004-2008 supplements to European Radiology were published under its companion, European Radiology Supplements, ISSN 1613-3749.