| Autor: |
Duanmu H; Stony Brook University, Stony Brook, NY 11794, USA., Bhattarai S; Georgia State University, Atlanta, GA 30302, USA., Li H; Georgia State University, Atlanta, GA 30302, USA., Cheng CC; Stony Brook University, Stony Brook, NY 11794, USA., Wang F; Stony Brook University, Stony Brook, NY 11794, USA., Teodoro G; Federal University of Minas Gerais, Belo Horizonte 31270-010, Brazil., Janssen EAM; Department of Pathology, Stavanger University Hospital, Stavanger, Norway., Gogineni K; Emory University, Atlanta, GA 30322, USA., Subhedar P; Emory University, Atlanta, GA 30322, USA., Aneja R; Georgia State University, Atlanta, GA 30302, USA., Kong J; Georgia State University, Atlanta, GA 30302, USA.; Emory University, Atlanta, GA 30322, USA. |
| Abstrakt: |
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. |
