A transformer model guided by histopathological image information for DCE-MRI-based prediction of response to neoadjuvant chemotherapy in breast cancer

Pathologic diagnosis is the "gold standard" for diagnosing breast cancer and is increasingly used to assess the response to Neoadjuvant Chemotherapy (NACT). Despite its high accuracy and sensitivity, pathology is invasive and requires biopsy of the patient's breast tissue. Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is the standard of care in breast cancer management and is critical for noninvasive prediction of pathological response to NACT. To this end, we propose a transformer model based on DCE-MRI that is guided by histopathological image data to predict responses to NACT. A cross-attention mechanism was developed to facilitate information interaction between histopathological images and DCE-MRI. Specifically, we designed a modality information transfer module to synthesize histopathological image features from DCE-MRI features. During the training stage, we propose to stochastically use synthesize histopathological image features rather than the real features as network inputs. This strategy enables us to predict the response to NACT by using DCE-MRI alone, regardless of the availability of histopathological images. In this study, 239 patients with paired DCE-MRI and histologic images were included; 32 patients (13.4%) achieved a pathological Complete Response (pCR), while 207 patients (13.4%) had nonpCR. A total of 146 samples were used as the training set, and 93 samples were used as the testing set. The experimental results showed that the proposed histopathological information-guided model using DCE-MRI and histopathological images had a greater predictive performance (AUC=0.824) than either the traditional DCE-MRI (AUC=0.687) or histopathological image-based model (AUC=0.765) in predicting the response to NACT in patients with breast cancer.