Introduction
Triple negative breast cancer (TNBC) is an aggressive subcategory of breast cancer with poor prognosis and high risk of recurrence. Systemic chemotherapy is offered prior to surgery, so called neo-adjuvant chemotherapy (NAC), to downstage the disease. This study aims to predict the outcome of NAC with deep learning technology based on the microscopic morphological characteristics in whole slide images from the tumor biopsy prior to therapy.
Material and methods
A convolutional neural network was trained on 221 H&E stained biopsies of carcinoma of no special type from 205 patients. Cases were divided in three cohorts, with a good, moderate or bad response to NAC, defined as residual tumor < 10%, 10-50% and > 50% respectively. Manual segmentation of the tumor area was performed comprising invasive carcinoma. The model was tested on 52 new biopsies of 50 patients. Because of the relative low number of moderate and bad responder cases, and in order to achieve a better discrimination for potential visual biomarkers, the moderate and bad response cohorts were merged.
Results and discussion
The predictive performance of the model was calculated by means of the area under the receiver operator curve (AUC ROC). 95% Confidence intervals (CI) were calculated for better understanding of the range of values. In the test set the AUC ROC performance score was 0.696 with a CI of 0.532 - 0.861.
Conclusion
This proof-of-concept study shows that H&E pre-operative biopsies from TNBC, by means of deep learning technology, contain valuable information having predictive value for the outcome of NAC resulting in an AUC value of 0.696.