Towards Clinically Effective AI for Screening Mammography

摘要 乳腺癌是妇女中最常见的癌症，也是癌症死亡的主要原因。早期 乳腺癌的检测可以降低死亡率和发病率，这导致了广泛的乳房X光检查 筛查，建议50-74岁的女性每年或每两年进行一次。阅读乳房X光片 由于发生的罕见性，难以通过图像来确定是否存在癌症-在筛查人群中， 99.5%的女性没有癌症-以及发现非常微妙的异常的视觉挑战 在复杂的背景下。这一困难，再加上每年3900万次的乳房X光检查， 在美国-已经导致了各种各样的解决方案，包括被称为计算机辅助诊断的软件 （CAD）。尽管早期的承诺，这些解决方案并没有发挥其潜力，改善成果， 主要是为了增加口译时间。生产力日益受到关注，因为快速增长的 使用数字乳腺断层合成摄影（DBT或“3D”乳腺X射线摄影），已证明癌症发生率较高 这一技术的检测率高于传统的2D乳腺X线摄影，但也需要更长的时间来解释。作为一种潜在的解决方案， 人们对将深度学习应用于乳房X线摄影术产生了极大的兴趣。深度学习（DL）是一种强大的 机器学习领域，它以端到端的方式从数据中学习图像特征，并已被用于 在许多成像模式识别任务上实现人类水平的性能。这项建议旨在 创建基于DL的乳腺X射线摄影软件，通过（1）准确， 对不同患者人群的稳健预测，（2）DL模型的可解释结果（无“黑盒”） 答案），和（3）适用于2D乳房X线摄影和DBT。在第一阶段，目标是改进模型 通过在额外的数据上进行训练并结合额外的算法进步来提高性能。一期将 最后，通过临床阅片师研究将软件性能与放射科医生进行比较。在第二阶段，目标 是通过自动化质量检测、合并先前检查来提高软件的临床有效性 模型，并扩大训练数据集，以确保结果推广到任何有资格获得 筛查性乳房X光检查这些改进将适用于2D和DBT。实现期望的 性能水平将使产品能够提高放射科医生的工作效率，并确保一致性和 为患者提供准确的解释。这个项目的成功将是朝着翻译国家迈出的一大步。 将最先进的人工智能转化为乳腺X光筛查的临床有效工具。

Abstract Breast cancer is the most common cancer among women and a leading cause of cancer mortality. Early detection of breast cancer can reduce mortality and morbidity, which has led to widespread mammography screening, recommended for women ages 50-74 on a yearly or bi-yearly basis. Reading the mammogram images to decide if cancer may be present is difficult due to the rarity of occurrence – in a screening population, 99.5% of women do not have cancer – and the visual challenge of finding what can be a very subtle abnormality on a complex background. This difficulty, combined with the high volume of mammograms – 39 million per year in the US – has led to a variety of proffered solutions including software known as computer-aided diagnosis (CAD). Despite early promise, such solutions have not fulfilled their potential in improving outcomes and are largely thought to increase interpretation times. Productivity is increasingly a concern due to the rapidly growing use of digital breast tomosynthesis (DBT or “3D” mammography), which has demonstrated higher cancer detection rates than traditional 2D mammography, but also takes much longer to interpret. As a potential solution, there has been significant interest in applying deep learning to mammography. Deep learning (DL) is a powerful field of machine learning which learns image features in an end-to-end fashion from data, and has been used to achieve human level performance on a number of imaging pattern recognition tasks. This proposal seeks to create DL-based software for mammography that can be effective in a clinical setting through (1) accurate and robust predictions on a diverse population of patients, (2) interpretable results from the DL model (no “black box” answers), and (3) applicability to both 2D mammography and DBT. In Phase I, the aim is to improve model performance by training on additional data and incorporating additional algorithmic advances. Phase I will conclude with a clinical reader study comparing performance of the software to radiologists. In Phase II, the aim is to improve the clinical effectiveness of the software by automating quality detection, incorporating prior exams into the model, and expanding the training dataset to ensure results generalize to any woman eligible for screening mammography. These improvements will apply to both 2D and DBT. Achieving the desired performance levels will enable a product that will improve productivity for radiologists and ensure consistent and accurate interpretations for patients. Success in this project would be a large step towards translating state-of- the-art artificial intelligence to clinically effective tools for screening mammography.