Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Aug 11, 2025
Deep learning has made notable strides in the ultrasonic diagnosis of lymph nodes, yet it faces three primary challenges: a limited number of lymph node images and a scarcity of annotated data; difficulty in comprehensively learning both local and gl...
OBJECTIVE: To develop explainable machine learning models that integrate multimodal imaging and pathological biomarkers to predict axillary lymph node metastasis (ALNM) in breast cancer patients and assess their clinical utility.
Cervical cancer is a leading cause of death from malignant tumors in women, and accurate evaluation of occult lymph node metastasis (OLNM) is crucial for optimal treatment. This study aimed to develop several predictive models-including Clinical mode...
The purpose of this study was to create and validate an ultrasound-based graph convolutional network (US-based GCN) model for the prediction of axillary lymph node metastasis (ALNM) in patients with breast cancer. A total of 820 eligible patients wit...
This study sought to develop a radiomics model capable of predicting axillary lymph node metastasis (ALNM) in patients with invasive breast cancer (IBC) based on dual-sequence magnetic resonance imaging(MRI) of diffusion-weighted imaging (DWI) and dy...
BACKGROUND: Endobronchial ultrasound (EBUS) is a widely used imaging modality for evaluating thoracic lymph nodes (LNs), particularly in the staging of lung cancer. Artificial intelligence (AI)-assisted EBUS has emerged as a promising tool to enhance...
To develop and validate a machine learning-based prediction model to predict axillary lymph node (ALN) metastasis in triple negative breast cancer (TNBC) patients using magnetic resonance imaging (MRI) and clinical characteristics. This retrospective...
BACKGROUND: This study developed an explainable machine learning model for baseline internal mammary lymph node metastasis (IMNM) in breast cancer patients.
The rapid advancements in spatially resolved transcriptomics (SRT) enable the characterization of gene expressions while preserving spatial information. However, high dropout rates and noise hinder accurate spatial domain identification for understan...
INTRODUCTION: Early and accurate diagnosis of tuberculosis (TB) is central to ensuring the proper treatment and curbing the transmission of the disease. Despite the significant burden, the diagnosis of peripheral lymph node(LN)TB, the most prevalent ...
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