Predicting Response to Neoadjuvant Therapy in Breast Cancer Using Deep Learning on Primary Core Needle Biopsy Slides postprint

Abstract: Background  Preoperative neoadjuvant therapy（NAT）is a standardized treatment for locally advanced breast cancer. However，only a portion of patients are sensitive to NAT，hence it is very important to predict the treatment efficacy before NAT. Previous studies have used statistical methods combined with clinical data or deep learning methods combined with medical imaging to predict the efficacy of NAT in breast cancer，but without good results. Objective  A deep learning model based on core-needle biopsy whole slide images（WSI）of breast cancer（DL-CNB）was trained using the multiple instance learning（MIL）method to predict pathological complete response（pCR）and visualize related tumor areas. Methods  A retrospective study was conducted to collect the clinical data and biopsy hematoxylin-eosin（H-E）stained slides of breast cancer patients who received neoadjuvant therapy in Beijing Chaoyang Hospital from April 2019 to April 2022. A total of 195 patients were selected according to the inclusion and exclusion criteria. Patients were divided into pCR group（MP=5，n=40） and non-pCR group（MP=1-4，n=155）according to Miller-Payne（MP）grading. The clinical data were analyzed and the Logistic regression model of pCR influencing factors was constructed. All WSI images were randomly divided into training set and test set in a ratio of 4 ∶ 1，and 25% of the data from the training set was taken as verification set. All tumor cell regions in each WSI were labeled，and the training set was prepared by sliding window extraction，data screening，data enhancement，and normalization. Compared with five convolutional neural network models，the optimal model was selected as the feature extractor of DL-CNB. Parameters were set to train the DL-CNB model. The predictive value of DL-CNB was evaluated by using independent test set. To realize the visualization of the important regions related to prediction in the WSI，heat map was drawn according to the weights obtained by the attention-based module.Results　 The proportion of patients with high histological grade，ER negative， PR negative，HER2 positive and Ki-67 high expression in pCR group was higher than that in non-pCR group，and the difference was statistically significant（P<0.05）. Compared with the HR+/HER2-，HR-/HER2+（OR=10.189，95%CI= 3.225-32.187） and HR+/HER2+（OR=3.349，95%CI=1.152-9.737）had statistical significance in achieving pCR（P<0.05）. Logistic regression model predicted an AUC of 0.769，with an accuracy of 81.000%. The AUC of DL-CNB model in the independent test set was 0.914，and the accuracy was 84.211%. Pieces of tumor region labeled non-pCR and pCR in the independent test set were randomly selected for visual display. Conclusion  The DL-CNB model enables the prediction of pCR in neoadjuvant therapy and visualization of important regions by WSI of breast cancer biopsies. The prediction results were better than the clinical data Logistic regression method. Therefore，we can provide clinical decision-making reference for breast cancer patients who meet the indications of NAT，and assist the realization of individualized precision treatment，which is of great significance to improve the quality of life and survival expectancy for patients.