Deep learning predicts HER2 status in invasive breast cancer from multimodal ultrasound and MRI.

The preoperative human epidermal growth factor receptor type 2 (HER2) status of breast cancer is typically determined by pathological examination of a core needle biopsy, which influences the efficacy of neoadjuvant chemotherapy (NAC). However, the highly heterogeneous nature of breast cancer and the limitations of needle aspiration biopsy increase the instability of pathological evaluation. The aim of this study was to predict HER2 status in preoperative breast cancer using deep learning (DL) models based on ultrasound (US) and magnetic resonance imaging (MRI).

Multiple instance learning-based prediction of programmed death-ligand 1 (PD-L1) expression from hematoxylin and eosin (H&E)-stained histopathological images in breast cancer.

Programmed death-ligand 1 (PD-L1) is an important biomarker increasingly used as a predictive marker in breast cancer immunotherapy. Immunohistochemical quantification remains the standard method for assessment. However, it presents challenges related to time, cost, and reliability.

MRI-Based Breast Cancer Classification and Localization by Multiparametric Feature Extraction and Combination Using Deep Learning.

Background: Deep learning (DL) have been reported feasible in breast MRI. However, the effectiveness of DL method in mpMRI combinations for breast cancer detection has not been well investigated.

Purpose: To implement a DL method for breast cancer classification and detection using feature extraction and combination from multiple sequences.