Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
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DOI：
10.1001/jama.2017.14585
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发表时间：
2017-12-12
影响因子：
120.7
通讯作者：
van der Laak, Jeroen A. W. M.
中科院分区：
文献类型：
--
作者：
Bejnordi, Babak Ehteshami;Veta, Mitko;van der Laak, Jeroen A. W. M.
深度学习算法的重要性应用于全扫描病理学图像可以潜在地提高诊断准确性和效率。目标评估自动深度学习算法在检测苏木精和曙红染色的组织中的转移中的自动化深度学习算法的性能，并将其与病理学家的诊断诊断的乳腺癌竞争相结合。 （CamelyOn16）开发用于检测淋巴结转移的自动解决方案（2015年11月至2016年11月至11月）。提供了（n = 110）和没有（n = 160）淋巴结转移的训练数据集，这些训练数据集通过免疫组织化学染色验证，并挑战参与者构建算法。在一个独立的129个全斜图图像（49个带有转移的49个图像）的独立测试集中评估了算法性能。还通过一组11个病理学家的小组评估了相同的相应载玻片，从荷兰进行了时间限制（WTC），从荷兰进行时间限制（WTC），以确定每个滑动的可能性在2小时的灵活的2小时疗程中，并模拟常规病理学工作流程，并通过1个病理学家进行挑战（WOTC）竞争（WOTC）。结局和测量特定转移灶的存在以及使用接收器操作特征曲线分析在幻灯片或图像中不存在淋巴结转移的情况。参加仿真运动的11位病理学家将其诊断信心定为绝对正常，可能是正常的，可能的模棱两可，可能是肿瘤或绝对的肿瘤。在0.556至0.994之间，对接收器操作特征曲线（AUC）下的区域进行了验证。最佳表现算法达到了与病理学家WOTC（72.4％[95％CI，64.3％-80.4％]）相当的病变级，真实阳性的分数，平均为0.0125的假阳性图像。对于全扫描的图像分类任务，最佳算法（AUC，0.994 [95％CI，0.983-0.999]）的表现明显优于诊断模拟中的病理学家WTC（平均AUC，0.810，0.810 [range，0.738-0.884; p; p; P]
IMPORTANCE Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency.OBJECTIVE Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting.DESIGN, SETTING, AND PARTICIPANTS Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC).EXPOSURES Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation.MAIN OUTCOMES AND MEASURES The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor.RESULTS The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4%[95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P