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融合空间和多尺度特征的乳腺癌免疫组化Ki-67指数定量分析

熊学春 吴焕文 任菲 崔莉 梁智勇 赵泽

熊学春, 吴焕文, 任菲, 崔莉, 梁智勇, 赵泽. 融合空间和多尺度特征的乳腺癌免疫组化Ki-67指数定量分析[J]. 协和医学杂志, 2022, 13(4): 581-589. doi: 10.12290/xhyxzz.2022-0158
引用本文: 熊学春, 吴焕文, 任菲, 崔莉, 梁智勇, 赵泽. 融合空间和多尺度特征的乳腺癌免疫组化Ki-67指数定量分析[J]. 协和医学杂志, 2022, 13(4): 581-589. doi: 10.12290/xhyxzz.2022-0158
XIONG Xuechun, WU Huanwen, REN Fei, CUI Li, LIANG Zhiyong, ZHAO Ze. An Automatic Quantitative Analysis Method of Ki-67 Index for Breast Cancer Immunohistochemistry Based on Fusion of Spatial and Multi-scale Features[J]. Medical Journal of Peking Union Medical College Hospital, 2022, 13(4): 581-589. doi: 10.12290/xhyxzz.2022-0158
Citation: XIONG Xuechun, WU Huanwen, REN Fei, CUI Li, LIANG Zhiyong, ZHAO Ze. An Automatic Quantitative Analysis Method of Ki-67 Index for Breast Cancer Immunohistochemistry Based on Fusion of Spatial and Multi-scale Features[J]. Medical Journal of Peking Union Medical College Hospital, 2022, 13(4): 581-589. doi: 10.12290/xhyxzz.2022-0158

融合空间和多尺度特征的乳腺癌免疫组化Ki-67指数定量分析

doi: 10.12290/xhyxzz.2022-0158
基金项目: 

国家重点研发计划 2021YFF1201005

中国科学院A类战略性先导科技专项 XDA16021400

中国科学院网络安全和信息化专项应用示范项目 CAS-WX2021SF-0101

详细信息
    通讯作者:

    梁智勇, E-mail: liangzy@pumch.cn

    赵泽, E-mail: zhaoze@ict.ac.cn

    熊学春、吴焕文对本文同等贡献

    熊学春、吴焕文对本文同等贡献

  • 中图分类号: R737.9

An Automatic Quantitative Analysis Method of Ki-67 Index for Breast Cancer Immunohistochemistry Based on Fusion of Spatial and Multi-scale Features

Funds: 

National Key Research and Development Program of China 2021YFF1201005

Strategic Priority Research Program of the Chinese Academy of Sciences XDA16021400

Chinese Academy of Sciences Network Security and Informatization Special Application Demonstration Project CAS-WX2021SF-0101

More Information
  • 摘要:   目的  针对乳腺癌免疫组化全视野数字图像(whole slide image, WSI),提出一种智能化定量分析Ki-67指数的方法。  方法  回顾性纳入2020年1—12月北京协和医院乳腺癌患者的病理切片,将其以40倍率扫描为WSI图像,并由2名病理科医生按照2019年国际乳腺癌Ki-67工作组制订的指南对Ki-67指数进行人工判读。按5:8的比例随机将WSI图像分为A、B两个数据集(A数据集按7:1:2比例随机分为训练集、验证集和测试集)。病理科医生对A数据集人工标注热点区域后,40倍视野下将每张WSI随机裁剪为2000个512×512像素的图块,随机选取其中的50个图块,对肿瘤细胞进行标注并计算Ki-67指数。采用条件随机场模型融合图块的空间特征,经ResNet34预训练模型进行特征提取后构建热点区域识别模型,并采用准确率评价其性能。在热点区域内,40倍视野下随机选取10个视野,模型可自动完成细胞分类,并计算Ki-67指数均值。以人工判读结果为金标准,计算模型对B数据集Ki-67指数评估结果的准确率,并采用Bland-Altman法对人工判读与模型分析结果进行一致性评价。  结果  共入选符合纳入和排除标准的乳腺癌患者病理切片132张。其中A数据集50张(训练集、验证集和测试集分别为35张、5张、10张,分别包含图块70 000个、10 000个、20 000个),B数据集82张。模型对测试集热点区域识别的平均准确率为81.5%,对B数据集Ki-67指数计算结果的准确率为90.2%。Bland-Altman法分析显示,人工判读和模型计算的Ki-67指数的一致性良好。  结论  本研究提出智能化定量分析Ki-67指数的方法准确率高,可辅助病理医师实现Ki-67指数的高效判读。
    作者贡献:熊学春负责对人工智能分析计算方法流程的实现及论文撰写;吴焕文负责病理图像收集、标注及论文撰写;任菲负责选题构思、论文修订;崔莉负责分析方法技术指导;梁智勇负责病理诊断流程制定、结果评测;赵泽负责智能分析方法指导、深度学习相关方案设计、论文修订与审核。
    利益冲突:所有作者均声明不存在利益冲突
  • 图  1  乳腺癌Ki-67指数免疫组化病理图像标注示例

    A.热点区域;B、C.正常组织区域

    图  2  Ki-67指数智能化定量分析方法整体框架

    WSI: 全视野数字图像

    图  3  热点区域内图块标注示例

    A.热点区域内随机选取的图块;B.标注结果,其中实心圆为Ki-67阳性肿瘤细胞,实心矩形为Ki-67阴性肿瘤细胞,“X”为非肿瘤细胞

    图  4  图块之间的概率无向图模型示意图

    图  5  乳腺癌WSI热点区域识别模型整体框架图

    WSI: 同图 2

    图  6  肿瘤细胞计数方法

    图  7  热点区域可视化识别结果

    A.病理科医生人工标注;B.模型预测的热点区域概率热力图;C.模型预测的二值热力图;D.模型预测的热点区域

    图  8  模型对细胞分类计数结果

    A.Ki-67阳性肿瘤细胞(标注红“+”);B.Ki-67阴性肿瘤细胞(标注绿“●”);C.非肿瘤细胞(标注红“*”)

    图  9  Ki-67指数一致性评价结果的Bland-Altman图

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出版历程
  • 收稿日期:  2022-03-28
  • 录用日期:  2022-05-26
  • 刊出日期:  2022-07-30

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