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

  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。

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

  Objective  To propose an intelligent quantitative analysis method of Ki-67 index for breast cancer immunohistochemical whole slide image (WSI).  Methods  The pathological sections of patients with breast cancer diagnosed and treated in Peking Union Medical College Hospital from January 2020 to December 2020 were retrospectively collected, and scanned at 40 magnification as WSI images. Manual interpretation of the Ki-67 index was conducted by 2 pathologists according to the guidelines formulated by the International Breast Cancer Ki-67 Working Group in 2019, which is considered the gold standard. According to the ratio of 5:8, WSI was randomly divided into two data sets, A and B (data set A was randomly divided into training set, validation set and test set according to a ratio of 7:1:2). After the hot spot area in WSI of the data set A was manually marked, each WSI randomly cropped 2000 512×512 pixel patches in the 40 field of view, and 50 patches of them were randomly selected to label tumor cells and calculate the Ki-67 index. The conditional random field model was used to fuse the spatial features of the image blocks, the features were extracted by the ResNet34 pre-training model to construct a hot spot recognition model, and its performance (accuracy) was evaluated in the test set. In the hot spot area, 10 fields of view were randomly selected under the high-power field of view (×40), and the model automatically completed the cell classification and calculated the average Ki-67 index. Taking the results of manual interpretation as the gold standard, the accuracy of the Ki-67 index evaluation results of the data set B by the model was calculated, and the Bland-Altman method was used to evaluate the consistency between the results of manual interpretation and model analysis.  Results  A total of 132 pathological sections of patients with breast cancer which met the inclusion and exclusion criteria were selected. There were 50 images in data set A (35, 5, and 10 images in training set, validation set, and test set, including 70 000, 10 000, and 20 000 patches, respectively), and 82 images in data set B. The average accuracy of the model for identifying hot spots in the test set was 81.5%, and the accuracy of the Ki-67 index calculation results for the B data set was 90.2%. Bland-Altman analysis showed that the Ki-67 index calculated by manual interpretation and model was in good agreement.  Conclusion  The intelligent quantitative analysis method of Ki-67 index proposed in this study has high accuracy and can assist pathologists to achieve efficient interpretation of Ki-67 index.