AIMC Topic: Radiomics
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A cutting-edge deep learning-and-radiomics-based ultrasound nomogram for precise prediction of axillary lymph node metastasis in breast cancer patients ≥ 75 years.
Frontiers in endocrinology
Jul 12, 2024
OBJECTIVE: The objective of this study was to develop a deep learning-and-radiomics-based ultrasound nomogram for the evaluation of axillary lymph node (ALN) metastasis risk in breast cancer patients ≥ 75 years.
Predicting Secondary Vertebral Compression Fracture After Vertebral Augmentation via CT-Based Machine Learning Radiomics-Clinical Model.
Academic radiology
Jul 10, 2024
RATIONALE AND OBJECTIVES: Secondary vertebral compression fractures (SVCF) are very common in patients after vertebral augmentation (VA). The aim of this study was to establish a radiomic-based model to predict SVCF and specify appropriate treatment ...
Habitat radiomics and deep learning fusion nomogram to predict EGFR mutation status in stage I non-small cell lung cancer: a multicenter study.
Scientific reports
Jul 10, 2024
Develop a radiomics nomogram that integrates deep learning, radiomics, and clinical variables to predict epidermal growth factor receptor (EGFR) mutation status in patients with stage I non-small cell lung cancer (NSCLC). We retrospectively included ...
Multimodal Deep Learning-based Radiomics Approach for Predicting Surgical Outcomes in Patients with Cervical Ossification of the Posterior Longitudinal Ligament.
Spine
Jul 8, 2024
STUDY DESIGN: A retrospective analysis.
A comprehensive approach for evaluating lymphovascular invasion in invasive breast cancer: Leveraging multimodal MRI findings, radiomics, and deep learning analysis of intra- and peritumoral regions.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jul 8, 2024
PURPOSE: To evaluate lymphovascular invasion (LVI) in breast cancer by comparing the diagnostic performance of preoperative multimodal magnetic resonance imaging (MRI)-based radiomics and deep-learning (DL) models.
Machine learning-based CT radiomics enhances bladder cancer staging predictions: A comparative study of clinical, radiomics, and combined models.
Medical physics
Jul 8, 2024
BACKGROUND: Predicting the accurate preoperative staging of bladder cancer (BLCA), which markedly affects treatment decisions and patient outcomes, using traditional clinical parameters is challenging. Nevertheless, emerging studies in radiomics, esp...
F-FDG PET/CT Radiomics-Based Multimodality Fusion Model for Preoperative Individualized Noninvasive Prediction of Peritoneal Metastasis in Advanced Gastric Cancer.
Annals of surgical oncology
Jul 8, 2024
PURPOSE: This study was designed to develop and validate a machine learning-based, multimodality fusion (MMF) model using F-fluorodeoxyglucose (FDG) PET/CT radiomics and kernelled support tensor machine (KSTM), integrated with clinical factors and nu...
Integrating Radiomics and Neural Networks for Knee Osteoarthritis Incidence Prediction.
Arthritis & rheumatology (Hoboken, N.J.)
Jul 8, 2024
OBJECTIVE: Accurately predicting knee osteoarthritis (KOA) is essential for early detection and personalized treatment. We aimed to develop and test a magnetic resonance imaging (MRI)-based joint space (JS) radiomic model (RM) to predict radiographic...
Predicting the Prognosis of HIFU Ablation of Uterine Fibroids Using a Deep Learning-Based 3D Super-Resolution DWI Radiomics Model: A Multicenter Study.
Academic radiology
Jul 4, 2024
RATIONALE AND OBJECTIVES: To assess the feasibility and efficacy of a deep learning-based three-dimensional (3D) super-resolution diffusion-weighted imaging (DWI) radiomics model in predicting the prognosis of high-intensity focused ultrasound (HIFU)...
LightGBM is an Effective Predictive Model for Postoperative Complications in Gastric Cancer: A Study Integrating Radiomics with Ensemble Learning.
Journal of imaging informatics in medicine
Jun 28, 2024
Postoperative complications of radical gastrectomy seriously affect postoperative recovery and require accurate risk prediction. Therefore, this study aimed to develop a prediction model specifically tailored to guide perioperative clinical decision-...
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