Towards Clinically Effective AI for Screening Mammography

摘要 乳腺癌是女性最常见的癌症，也是癌症死亡的主要原因。早些时候 检测乳腺癌可以减少死亡率和发病率，这导致了广泛的乳房X光检查 建议50-74岁的女性每年或每两年进行一次筛查。阅读乳房X光照片 由于在筛查人群中发生的罕见，很难通过图像来确定是否存在癌症， 99.5%的女性没有患癌症--寻找可能是非常细微的异常的视觉挑战 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-已经提供了各种解决方案，包括被称为计算机辅助诊断的软件 (CAD)。尽管早期有希望，但这些解决方案并没有发挥其改善结果的潜力，而且 很大程度上被认为会增加口译时间。由于经济的快速增长，生产力日益受到关注。 使用数字化乳房断层摄影术(DBT或“3D”乳房X光摄影)，显示出较高的癌症 检测率比传统的2D乳房X光检查更高，但也需要更长的时间来解读。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光检查有很大的兴趣。深度学习是一种强大的 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中达到人类水平的性能。这项建议旨在 创建基于DL的乳房X光摄影软件，通过以下方式在临床环境中有效：(1)准确和 对不同患者群体的可靠预测，(2)来自DL模型的可解释结果(没有“黑匣子” 答案)，以及(3)对2D乳房X光摄影和DBT的适用性。在第一阶段，目标是改进模型 通过对更多数据进行培训并结合更多算法进步来提高性能。第一阶段将 最后，进行了一项临床读者研究，比较了该软件与放射科医生的性能。在第二阶段，目标是 是通过自动质量检测，结合先前的检查来提高软件的临床有效性 添加到模型中，并扩展训练数据集以确保结果适用于任何符合条件的女性 筛查乳房X光检查。这些改进将同时适用于2D和DBT。实现预期目标 性能水平将使产品能够提高放射科医生的工作效率，并确保一致和 为患者提供准确的解读。这个项目的成功将是朝着翻译现状迈出的一大步 最先进的人工智能到临床有效的乳房X光检查工具。

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.