Publications by authors named "Lifu Lin"
Objectives: To explore the effectiveness of radiomics in predicting axillary lymph node metastasis (ALNM) and the relationship between radiomics features and genes.
Method: The 379 patients with breast cancer (186 ALNM-positive and 193 ALNM-negative) recruited from three hospitals were divided into the training (n=224), testing (n=96), and validation (n=59) cohorts. The Cancer Imaging Archive-The Cancer Genome Atlas (TCIA-TCGA) group included 107 patients with breast cancer.
Article Synopsis
- - A study aimed to create an MRI-based radiomics model that predicts axillary lymph node metastasis (ALNM) in breast cancer patients by analyzing both the tumor and surrounding tissue images.
- - The research involved 376 patients and utilized advanced machine learning algorithms to combine various imaging features, achieving a high diagnostic accuracy with an area under the curve (AUC) of 0.820 for the model and up to 0.855 when combined with other clinical data.
- - The model's decision-making process was clarified using SHAP analysis, suggesting it can enhance predictions of ALNM status and assist in making informed clinical treatment choices.
Background: Transcatheter arterial chemoembolization (TACE) is a mainstay treatment for intermediate and advanced hepatocellular carcinoma (HCC), with the potential to enhance patient survival. Preoperative prediction of postoperative response to TACE in patients with HCC is crucial.
Purpose: To develop a deep neural network (DNN)-based nomogram for the non-invasive and precise prediction of TACE response in patients with HCC.