Deep-LIBRA:一种用于数字化乳房x线摄影中全自动乳腺密度评估的人工智能方法

O. H. Maghsoudi, Scott Christopher, A. Gastounioti, Lauren Pantalone, Fang-Fang Wu, Eric A. Cohen, Winham Stacey, E. Conant, C. Vachon, D. Kontos
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For the PD estimation module we used a total of 3,304 FFDM images from 1,652 individual women; manual PD scores obtained with the widely used Cumulus software were used as the \"gold standard\" in a three-fold cross-validation setting to assess the accuracy of Deep-LIBRA in PD estimation. PD estimates from Deep-LIBRA were also compared with breast density estimates from the commercially available Volpara software. Breast segmentation had a Dice coefficient of 95.31% when compared to ground-truth manual breast segmentation in the validation set. Deep-LIBRA average differences from ground-truth PD scores in the three cross-validation folds were 4.91%, 4.65%, and 4.22%, while Volpara had corresponding average differences of 6.20%, 6.01%, and 5.94%. Deep-LIBRA PD scores were also significantly different from Volpara PD (t-test p-value Citation Format: Omid Haji Maghsoudi, Scott Christopher, Aimilia Gastounioti, Lauren Pantalone, Fang-Fang Wu, Eric A. Cohen, Winham Stacey, Emily F. 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引用次数: 1

摘要

乳房x线摄影乳房密度的准确估计有助于增强接受全场数字乳房x线摄影(FFDM)乳房筛查的妇女的乳腺癌风险评估。乳腺密度可以从FFDM中估计出来,在临床上最常用的评估方法是根据美国放射学会BI-RADS定义的四种视觉分级之一进行评估。然而,BI-RADS密度评估是高度主观的,不能提供定量的、连续的百分比密度(PD)测量,这将允许更精确的风险分层和密度变化评估。在这里,我们介绍Deep-LIBRA,一种人工智能(AI)工具,用于从FFDM图像中全自动评估乳房PD。Deep-LIBRA的核心是两个关键模块:1)用于乳房分割的改进U-Net架构的实现;2)在分割的乳房区域内执行PD估计的放射学机器学习模块。为了开发和验证Deep-LIBRA,回顾性分析了两次乳腺癌筛查中获得的原始(即“用于处理”)FFDM图像(Selenia Dimensions, Hologic Inc.)。对于乳房分割模块,我们使用了来自2,200名女性个体的12,100个FFDM研究和90%-10%的分裂样本训练验证方法,使用Dice系数来评估Deep-LIBRA与ground-truth手动乳房分割的准确性。对于PD估计模块,我们总共使用了来自1,652名女性的3,304张FFDM图像;使用广泛使用的Cumulus软件获得的手动PD评分作为“金标准”,在三重交叉验证设置中评估Deep-LIBRA在PD估计中的准确性。还比较了来自Deep-LIBRA的PD估计值和来自市售Volpara软件的乳腺密度估计值。在验证集中,与人工乳房分割相比,乳房分割的Dice系数为95.31%。在三个交叉验证折叠中,Deep-LIBRA与真实PD评分的平均差异分别为4.91%、4.65%和4.22%,而Volpara与真实PD评分的平均差异分别为6.20%、6.01%和5.94%。Deep-LIBRA PD得分也与Volpara PD有显著差异(t检验p值引文格式:Omid Haji Maghsoudi, Scott Christopher, Aimilia Gastounioti, Lauren Pantalone, Fang-Fang Wu, Eric A. Cohen, Winham Stacey, Emily F. Conant, Celine Vachon, Despina Kontos)。Deep-LIBRA:一种用于数字乳房x线摄影中全自动乳房密度评估的人工智能方法[摘要]。见:美国癌症研究协会2021年年会论文集;2021年4月10日至15日和5月17日至21日。费城(PA): AACR;癌症杂志,2021;81(13 -增刊):摘要nr 2600。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Abstract 2600: Deep-LIBRA: An artificial intelligence approach for fully-automated assessment of breast density in digital mammography
Accurate estimation of mammographic breast density could aid in augmenting breast cancer risk assessment for women undergoing breast screening with full-field digital mammography (FFDM). Breast density can be estimated from FFDM and is most commonly assessed in the clinic by visual grading into one of the four categories defined by the American College of Radiology BI-RADS. However, BI-RADS density assessment is highly subjective and does not provide a quantitative, continuous measure of percent density (PD), which would allow for more refined risk stratification and assessment of density changes. Here, we introduce Deep-LIBRA, an artificial intelligence (AI) tool for fully-automated assessment of breast PD from FFDM images. Two key modules form the core of Deep-LIBRA: 1) an implementation of a modified U-Net architecture for breast segmentation and 2) a radiomic machine learning module that performs PD estimation within the segmented breast region. To develop and validate Deep-LIBRA, raw (i.e., "For Processing") FFDM images (Selenia Dimensions, Hologic Inc.) acquired at two breast cancer screening practices were retrospectively analyzed. For the breast segmentation module, we used a total of 12,100 FFDM studies from 2,200 individual women and a 90%-10% split-sample training-validation approach, using the Dice coefficient to evaluate the accuracy of Deep-LIBRA versus ground-truth manual breast segmentation. For the PD estimation module we used a total of 3,304 FFDM images from 1,652 individual women; manual PD scores obtained with the widely used Cumulus software were used as the "gold standard" in a three-fold cross-validation setting to assess the accuracy of Deep-LIBRA in PD estimation. PD estimates from Deep-LIBRA were also compared with breast density estimates from the commercially available Volpara software. Breast segmentation had a Dice coefficient of 95.31% when compared to ground-truth manual breast segmentation in the validation set. Deep-LIBRA average differences from ground-truth PD scores in the three cross-validation folds were 4.91%, 4.65%, and 4.22%, while Volpara had corresponding average differences of 6.20%, 6.01%, and 5.94%. Deep-LIBRA PD scores were also significantly different from Volpara PD (t-test p-value Citation Format: Omid Haji Maghsoudi, Scott Christopher, Aimilia Gastounioti, Lauren Pantalone, Fang-Fang Wu, Eric A. Cohen, Winham Stacey, Emily F. Conant, Celine Vachon, Despina Kontos. Deep-LIBRA: An artificial intelligence approach for fully-automated assessment of breast density in digital mammography [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2600.
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