In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
In triple negative breast cancer (TNBC) treatment, early prediction of pathological complete response (PCR) from chemotherapy before surgical operations is crucial for optimal treatment planning. We propose a novel deep learning-based system to predict PCR to neoadjuvant chemotherapy for TNBC patients with multi-stained histopathology images of serial tissue sections. By first performing tumor cell detection and recognition in a cell detection module, we produce a set of feature maps that capture cell type, shape, and location information. Next, a newly designed spatial attention module integrates such feature maps with original pathology images in multiple stains for enhanced PCR prediction in a dedicated prediction module. We compare it with baseline models that either use a single-stained slide or have no spatial attention module in place. Our proposed system yields 78.3% and 87.5% of accuracy for patch-, and patient-level PCR prediction, respectively, outperforming all other baseline models. Additionally, the heatmaps generated from the spatial attention module can help pathologists in targeting tissue regions important for disease assessment. Our system presents high efficiency and effectiveness and improves interpretability, making it highly promising for immediate clinical and translational impact.
Duanmu Hongyi, Bhattarai Shristi, Li Hongxiao, Cheng Chia Cheng, Wang Fusheng, Teodoro George, Janssen Emiel A M, Gogineni Keerthi, Subhedar Preeti, Aneja Ritu, Kong Jun
Breast cancer, Convolutional neural network, Pathological complete response, Serial pathology images, Spatial attention
