OBJECTIVE: To compare iodine density (ID) and contrast-enhanced attenuation value (CEAV) from dual-layer spectral computed tomography (DLSCT) scans of lymphomatous, metastatic squamous cell carcinoma (SCCA), and normal cervical lymph nodes.
Asian Pacific journal of cancer prevention : APJCP
Jan 1, 2025
OBJECTIVE: This study aimed to develop a simple machine-learning model incorporating lymph node metastasis status with F-18 Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) and clinical information for predicting regio...
International journal of surgery (London, England)
Jan 1, 2025
BACKGROUND: This study aimed to use artificial intelligence (AI) to integrate various radiological and clinical pathological data to identify effective predictors of contralateral central lymph node metastasis (CCLNM) in patients with papillary thyro...
International journal of surgery (London, England)
Jan 1, 2025
BACKGROUND: Papillary thyroid carcinoma (PTC) is the predominant form of thyroid cancer globally, especially when lymph node metastasis (LNM) occurs. Molecular heterogeneity, driven by genetic alterations and tumor microenvironment components, contri...
OBJECTIVE: To comprehensively investigate the predictive value of thyroid hormone sensitivity parameters for cervical lymph node metastasis in patients diagnosed with differentiated thyroid cancer (DTC) undergoing total thyroidectomy and neck lymph n...
RATIONALE AND OBJECTIVES: To develop and validate a multimodal deep learning (DL) model based on computed tomography (CT) images and clinical knowledge to predict lymph node metastasis (LNM) in early lung adenocarcinoma.
BACKGROUND: Thyroid cancer, a common endocrine malignancy, has seen increasing incidence, making lymph node metastasis (LNM) a critical factor for recurrence and survival. Radiomics and deep learning (DL) advancements offer the potential for improved...
OBJECTIVE: To explore the value of combined radiomics and deep learning models using different machine learning algorithms based on mammography (MG) and magnetic resonance imaging (MRI) for predicting axillary lymph node metastasis (ALNM) in breast c...
BACKGROUND: This study aimed to explore the correlation between primary tumors (PT) and paired metastatic lymph nodes (LN) and to develop a predictive model to provide evidence for forecasting patient prognoses.
PURPOSE: This study aimed to develop and evaluate a machine learning model combining clinical, radiomics, and deep learning features derived from PET/CT imaging to predict lymph node metastasis (LNM) in patients with non-small cell lung cancer (NSCLC...
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