Method and system for predicting tumor mutation burden (TMB) in triple negative breast cancer (TNBC) based on nuclear scores and histopathological whole slide images (WSIs)

A method for predicting tumor mutation burden (TMB) in triple-negative breast cancer (TNBC) based on nuclear scores and histopathological whole slide images (WSIs), comprising the following steps:

• S1:
• screening the histopathological WSIs of TNBC from histopathological images of breast cancer;
• S2:
• calculating a TMB value of each patient according to gene mutation of each patient with TNBC, and dividing the TMB values into two groups with high and low TMB according to a set threshold, denoted as TMB-H group and TMB-L group respectively, as a label corresponding to the WSI of each patient;
• S3:
• dividing the WSIs into patches of a set size and performing preprocessing;
• S4:
• screening patches with the nuclear scores meeting a threshold from the preprocessed patches according to a nuclear score function;
• S5:
• building a convolutional neural network (CNN) classification model, and stochastically initializing parameters in the CNN classification model;
• S6:
• standardizing color of the patches with the nuclear scores meeting the threshold, and inputting the patches after color standardization and corresponding labels into the CNN classification model to train a TMB classifier, wherein each patch belongs to the corresponding WSI, and the label corresponding to the patch is the label of the WSI corresponding to the patch;
• S7:
• predicting the TMB in the TNBC using the trained TMB classifier.