Radiomics
Showing 281 to 290 of 781 articles
Performance of Radiomics-based machine learning and deep learning-based methods in the prediction of tumor grade in meningioma: a systematic review and meta-analysis.
Neurosurgical review
Jan 24, 2025
Currently, the World Health Organization (WHO) grade of meningiomas is determined based on the biopsy results. Therefore, accurate non-invasive preoperative grading could significantly improve treatment planning and patient outcomes. Considering rece...
Delta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases.
JCO clinical cancer informatics
Jan 24, 2025
PURPOSE: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during daily scans could be analyzed using delta-radiomics to predict disease progression events. We evalu...
CT-based machine learning radiomics predicts Ki-67 expression level and its relationship with overall survival in resectable pancreatic ductal adenocarcinoma.
Abdominal radiology (New York)
Jan 22, 2025
BACKGROUND: The prognostic prediction of pancreatic ductal adenocarcinoma (PDAC) remains challenging. This study aimed to develop a radiomics model to predict Ki-67 expression status in PDAC patients using radiomics features from dual-phase enhanced ...
Radiomics for prediction of perineural invasion in colorectal cancer: a systematic review and meta-analysis.
Abdominal radiology (New York)
Jan 22, 2025
BACKGROUND: Perineural invasion (PNI) in colorectal cancer (CRC) is a significant prognostic factor associated with poor outcomes. Radiomics, which involves extracting quantitative features from medical imaging, has emerged as a potential tool for pr...
Multi-parametric MRI combined with radiomics for the diagnosis and grading of endometrial fibrosis.
Abdominal radiology (New York)
Jan 22, 2025
PURPOSE: To evaluate the application of multi-parametric MRI (MP-MRI) combined with radiomics in diagnosing and grading endometrial fibrosis (EF).
Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy.
Cancer imaging : the official publication of the International Cancer Imaging Society
Jan 21, 2025
BACKGROUND: Radiomic analysis of quantitative features extracted from segmented medical images can be used for predictive modeling of prognosis in brain tumor patients. Manual segmentation of the tumor components is time-consuming and poses significa...
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Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors.
BMC gastroenterology
Jan 21, 2025
OBJECTIVE: To develop and validate a computed tomography (CT)-based deep learning radiomics model to predict treatment response and progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (uHCC) treated with transarteri...
Retrospective Studies
Tomography, X-Ray Computed
Female
Aged
Male
Middle Aged
Deep Learning
Adult
Radiomics
Humans
Prognosis
Combined Modality Therapy
Chemoembolization, Therapeutic
Protein Kinase Inhibitors
Tyrosine Kinase Inhibitors
Liver Neoplasms
Carcinoma, Hepatocellular
Immune Checkpoint Inhibitors
Antineoplastic Combined Chemotherapy Protocols
CT-based Machine Learning Radiomics Modeling: Survival Prediction and Mechanism Exploration in Ovarian Cancer Patients.
Academic radiology
Jan 17, 2025
RATIONALE AND OBJECTIVES: To create a radiomics model based on computed tomography (CT) to predict overall survival in ovarian cancer patients. To combine Rad-score with genomic data to explore the association between gene expression and Rad-score.
Development of a clinical prediction model for benign and malignant pulmonary nodules with a CTR ≥ 50% utilizing artificial intelligence-driven radiomics analysis.
BMC medical imaging
Jan 17, 2025
OBJECTIVE: In clinical practice, diagnosing the benignity and malignancy of solid-component-predominant pulmonary nodules is challenging, especially when 3D consolidation-to-tumor ratio (CTR) ≥ 50%, as malignant ones are more invasive. This study aim...
Automatic machine learning accurately predicts the efficacy of immunotherapy for patients with inoperable advanced non-small cell lung cancer using a computed tomography-based radiomics model.
Diagnostic and interventional radiology (Ankara, Turkey)
Jan 16, 2025
PURPOSE: Patients with advanced non-small cell lung cancer (NSCLC) have varying responses to immunotherapy, but there are no reliable, accepted biomarkers to accurately predict its therapeutic efficacy. The present study aimed to construct individual...
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