Publications by authors named "Wenke Hu"
Article Synopsis
- MRI radiomics is being studied to classify breast cancer based on HER2 expression levels (HER2-zero, HER2-low, HER2-positive), but understanding how predictions are made is still limited.
- Researchers developed machine-learning models using multiparametric MRI data from 737 breast cancer patients to differentiate between HER2 expression levels, employing SHAP analysis for interpretability.
- Model performance showed promise, particularly with the combined model achieving an AUC of 0.762 for task 1, and SHAP analysis highlighted the significance of early-phase dynamic contrast-enhanced imaging features in the predictions.
Objective: This study aimed to establish a MRI-based deep learning radiomics (DLR) signature to predict the human epidermal growth factor receptor 2 (HER2)-low-positive status and further verified the difference in prognosis by the DLR model.
Methods: A total of 481 patients with breast cancer who underwent preoperative MRI were retrospectively recruited from two institutions. Traditional radiomics features and deep semantic segmentation feature-based radiomics (DSFR) features were extracted from segmented tumors to construct models separately.
Abbreviated protocols could allow wider adoption of MRI in patients undergoing breast cancer neoadjuvant chemotherapy (NAC). However, abbreviated MRI has been explored primarily in screening settings. The purpose of this article was to compare diagnostic performance of abbreviated MRI and full-protocol MRI for evaluation of breast cancer NAC response, stratifying by radiologists' breast imaging expertise.
View Article and Find Full Text PDFObjectives: Triple-negative breast cancer (TNBC) is a heterogeneous disease, and different histological subtypes of TNBC have different clinicopathological features and prognoses. Therefore, this study aimed to establish a nomogram model to predict the histological heterogeneity of TNBC: including Metaplastic Carcinoma (MC) and Non-Metaplastic Carcinoma (NMC).
Methods: We evaluated 117 patients who had pathologically confirmed TNBC between November 2016 and December 2020 and collected preoperative multiparameter MRI and clinicopathological data.
Background: The monitoring of immunotherapies is still based on changes in the tumor size in imaging, with a long evaluation period and low sensitivity.
Purpose: To investigate the effectiveness of diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing the therapeutic efficacy of anti-programmed death-1 (PD-1) therapy in a mouse triple negative breast cancer (TNBC) model.
Study Type: Prospective.
Objective: To systematically investigate the effect of imaging features at different DCE-MRI phases to optimise a radiomics model based on DCE-MRI for the prediction of tumour-infiltrating lymphocyte (TIL) levels in breast cancer.
Materials And Methods: This study retrospectively collected 133 patients with pathologically proven breast cancer, including 73 patients with low TIL levels and 60 patients with high TIL levels. The volumes of breast cancer lesions were manually delineated on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and each phase of DCE-MRI, followed by 6250 quantitative feature extractions.