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.