Rigaud Bastien, Weaver Olena O, Dennison Jennifer B, Awais Muhammad, Anderson Brian M, Chiang Ting-Yu D, Yang Wei T, Leung Jessica W T, Hanash Samir M, Brock Kristy K
Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Cancers (Basel). 2022 Oct 13;14(20):5003. doi: 10.3390/cancers14205003.
Recently, convolutional neural network (CNN) models have been proposed to automate the assessment of breast density, breast cancer detection or risk stratification using single image modality. However, analysis of breast density using multiple mammographic types using clinical data has not been reported in the literature. In this study, we investigate pre-trained EfficientNetB0 deep learning (DL) models for automated assessment of breast density using multiple mammographic types with and without clinical information to improve reliability and versatility of reporting. 120,000 for-processing and for-presentation full-field digital mammograms (FFDM), digital breast tomosynthesis (DBT), and synthesized 2D images from 5032 women were retrospectively analyzed. Each participant underwent up to 3 screening examinations and completed a questionnaire at each screening encounter. Pre-trained EfficientNetB0 DL models with or without clinical history were optimized. The DL models were evaluated using BI-RADS (fatty, scattered fibroglandular densities, heterogeneously dense, or extremely dense) versus binary (non-dense or dense) density classification. Pre-trained EfficientNetB0 model performances were compared using inter-observer and commercial software (Volpara) variabilities. Results show that the average Fleiss' Kappa score between-observers ranged from 0.31-0.50 and 0.55-0.69 for the BI-RADS and binary classifications, respectively, showing higher uncertainty among experts. Volpara-observer agreement was 0.33 and 0.54 for BI-RADS and binary classifications, respectively, showing fair to moderate agreement. However, our proposed pre-trained EfficientNetB0 DL models-observer agreement was 0.61-0.66 and 0.70-0.75 for BI-RADS and binary classifications, respectively, showing moderate to substantial agreement. Overall results show that the best breast density estimation was achieved using for-presentation FFDM and DBT images without added clinical information. Pre-trained EfficientNetB0 model can automatically assess breast density from any images modality type, with the best results obtained from for-presentation FFDM and DBT, which are the most common image archived in clinical practice.
最近，有人提出了卷积神经网络（CNN）模型，以利用单一图像模态实现乳腺密度评估、乳腺癌检测或风险分层的自动化。然而，利用临床数据对多种乳腺X线摄影类型的乳腺密度进行分析在文献中尚未见报道。在本研究中，我们研究了预训练的EfficientNetB0深度学习（DL）模型，用于利用多种乳腺X线摄影类型（有或无临床信息）对乳腺密度进行自动化评估，以提高报告的可靠性和通用性。我们对来自5032名女性的120,000张用于处理和展示的全视野数字乳腺X线摄影（FFDM）、数字乳腺断层合成（DBT）以及合成的二维图像进行了回顾性分析。每位参与者最多接受3次筛查检查，并在每次筛查时填写一份问卷。对有或无临床病史的预训练EfficientNetB0 DL模型进行了优化。使用BI-RADS（脂肪型、散在纤维腺体型、不均匀致密型或极度致密型）与二元（非致密型或致密型）密度分类对DL模型进行评估。使用观察者间和商业软件（Volpara）的变异性比较预训练EfficientNetB0模型的性能。结果表明，观察者间的平均Fleiss' Kappa评分在BI-RADS分类中为0.31 - 0.50，在二元分类中为0.55 - 0.69，表明专家之间的不确定性较高。Volpara与观察者在BI-RADS分类和二元分类中的一致性分别为0.33和0.54，显示出中等至中等偏高的一致性。然而，我们提出的预训练EfficientNetB0 DL模型与观察者在BI-RADS分类和二元分类中的一致性分别为0.61 - 0.66和0.70 - 0.75，显示出中等至高度的一致性。总体结果表明，使用用于展示的FFDM和DBT图像且不添加临床信息时，可实现最佳的乳腺密度估计。预训练的EfficientNetB0模型可以从任何图像模态类型自动评估乳腺密度，从用于展示的FFDM和DBT中获得的结果最佳，这两种是临床实践中最常见存档的图像。
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