Biomedical physics & engineering express
Dec 3, 2025
Lung cancer remains one of the deadliest forms of cancer worldwide, making early and accurate pulmonary-nodule classification essential for improving patient prognosis. This study presents a robust ensemble-stacking framework that integrates Histogra...
BACKGROUND: Detection of axillary lymph node (LN) involvement is essential for staging breast cancer and optimizing treatment. This proof-of-concept two-center study explored the feasibility of magnetic resonance imaging (MRI) radiomics-based machine...
BACKGROUND: Preoperative endometrial cancer (EC) diagnosis often depends on radiologists' expertise, which introduces subjectivity. Recent studies have explored radiomics-based machine learning (ML) models for detecting myometrial invasion (MI), but ...
BACKGROUND: To construct and validate an optimal machine learning (ML)-based clinical-radiomics model integrating clinical and radiomics features for predicting recurrence risk within 2 years after radical surgery in patients with sinonasal squamous ...
BACKGROUND: Radiomics and artificial intelligence have shown strong predictive capabilities in urinary stone research, particularly concerning stone composition, characteristics, and treatment outcomes. However, the association of stone radiomics and...
Breast cancer is a significant public health concern, and early detection is critical for triaging high-risk patients. Sequential screening mammograms can provide important spatiotemporal information about changes in breast tissue over time, which ma...
Advanced cervical cancer (aCC) is associated with a poor prognosis. This study aimed to develop and validate a deep learning-based risk stratification model to predict overall survival in aCC patients using pre-treatment CT images. A total of 396 pat...
This study introduces a unique magnetic resonance imaging dataset focusing on metastatic breast cancer to the brain, a significant clinical challenge in cancer treatment. Comprising 297 T1-weighted post-contrast images from 165 patients, this dataset...
OBJECTIVE: To develop and validate an interpretable delta ultrasound radiomics model for predicting live birth following single vitrified-warmed blastocyst transfer (SVBT).
Accurate assessment of thyroid cartilage invasion is crucial for treatment decision-making and prognosis evaluation in laryngeal squamous cell carcinoma (LSCC). This study aimed to compare the performance of the radiomics and deep learning (DL) model...
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