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ARCH PATHOL LAB MED:基于人工智能检测乳腺癌淋巴结转移

2019-07-18 Gladiator MedSci原创

原发性肿瘤的淋巴结转移会影响多种癌症的治疗。但是,淋巴结内肿瘤细胞的组织学鉴定是一项费力且容易出错的工作,尤其是对小肿瘤病灶。 本研究评估一种先进的基于深度学习的人工智能算法(淋巴结助手或LYNA)在前哨淋巴结活检中检测转移性乳腺癌的应用和临床实施情况。 研究人员从399例患者(公共可用的Camelyon16 challenge数据集)的苏木精<span lang="EN-US" st

原发性肿瘤的淋巴结转移会影响多种癌症的治疗。但是,淋巴结内肿瘤细胞的组织学鉴定是一项费力且容易出错的工作,尤其是对小肿瘤病灶。

本研究评估一种先进的基于深度学习的人工智能算法(淋巴结助手或LYNA)在前哨淋巴结活检中检测转移性乳腺癌的应用和临床实施情况。

研究人员从399例患者(公共可用的Camelyon16 challenge数据集)的苏木精-伊红染色淋巴结中获得完整的读片图像。使用270张读片开发了LYNA,并对其余129张幻灯片进行了评估。此外,将研究结果与使用不同扫描仪测量重现性的独立实验室(20例患者的108张切片/86块切片)的结果进行了比较。

研究发现,在Camelyon16评估数据集上,当每个患者1个假阳性时,LYNA在接收方操作特性(AUC)99%的情况下实现了滑动水平区域,在肿瘤水平敏感性为91%。此外,还发现了两张含有微转移的“正常”幻灯片。当应用于第二个数据集时,LYNA获得了99.6%AUCLYNA不受常见的组织学伪影如过度固定、染色不良和气泡的影响。

研究表明,人工智能算法可以详尽地评估读片上的每个组织块,实现比病理学家更高的肿瘤级别敏感性和可与之媲美的读片级别性能。这些技术可以提高病理学家的工作效率,减少与肿瘤细胞形态学检测相关的假阴性。研究人员提供了一个框架,以帮助执业病理学家评估这些算法,并将其应用到他们的工作流程中(类似于病理学家评估免疫组织化学结果的方法)

原始出处:

Yun Liu, PhD; Timo Kohlberger, PhD;Artificial IntelligenceBased Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists

本文系梅斯医学(MedSci)原创编译整理,转载需授权!


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    2019-12-27 yb6560
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    2019-07-20 sodoo
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    2019-07-18 phoebeyan520

    学习了

    0

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