论文标题
一种用于降低误解乳房X线摄影的误报的深度学习算法
A deep learning algorithm for reducing false positives in screening mammography
论文作者
论文摘要
筛查乳房X线摄影通过实现早期检测和治疗来改善乳腺癌的结局。但是，筛选考试的其他成像的假阳性回调会导致不必要的程序，患者焦虑和经济负担。这项工作证明了一种AI算法，该算法通过识别乳腺癌不怀疑的乳房X线照片来降低误报。我们培训了该算法，使用123,248 2D数字乳房X线照片（6,161次癌症）确定癌症的缺失，并对15个美国和3英国遗址进行了14,831次筛查考试（1,026次癌症）的回顾性研究。 Retrospective evaluation of the algorithm on the largest of the US sites (11,592 mammograms, 101 cancers) a) left the cancer detection rate unaffected (p=0.02, non-inferiority margin 0.25 cancers per 1000 exams), b) reduced callbacks for diagnostic exams by 31.1% compared to standard clinical readings, c) reduced benign needle biopsies by 7.4%, d）在模拟的临床工作流程中，筛查检查的筛查检查减少了41.6％。这项工作为半自治的乳腺癌筛查系统奠定了基础，这些系统可以通过减少误报，不必要的程序，患者焦虑和费用来使患者和医疗保健系统受益。
Screening mammography improves breast cancer outcomes by enabling early detection and treatment. However, false positive callbacks for additional imaging from screening exams cause unnecessary procedures, patient anxiety, and financial burden. This work demonstrates an AI algorithm that reduces false positives by identifying mammograms not suspicious for breast cancer. We trained the algorithm to determine the absence of cancer using 123,248 2D digital mammograms (6,161 cancers) and performed a retrospective study on 14,831 screening exams (1,026 cancers) from 15 US and 3 UK sites. Retrospective evaluation of the algorithm on the largest of the US sites (11,592 mammograms, 101 cancers) a) left the cancer detection rate unaffected (p=0.02, non-inferiority margin 0.25 cancers per 1000 exams), b) reduced callbacks for diagnostic exams by 31.1% compared to standard clinical readings, c) reduced benign needle biopsies by 7.4%, and d) reduced screening exams requiring radiologist interpretation by 41.6% in the simulated clinical workflow. This work lays the foundation for semi-autonomous breast cancer screening systems that could benefit patients and healthcare systems by reducing false positives, unnecessary procedures, patient anxiety, and expenses.
