AIMC Topic: Radiomics
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Machine learning and radiomics for ventricular tachyarrhythmia prediction in hypertrophic cardiomyopathy: insights from an MRI-based analysis.
Acta radiologica (Stockholm, Sweden : 1987)
Oct 1, 2024
BACKGROUND: Myocardial fibrosis is often detected in patients with hypertrophic cardiomyopathy (HCM), which causes left ventricular (LV) dysfunction and tachyarrhythmias.
Machine learning models based on CT radiomics features for distinguishing benign and malignant vertebral compression fractures in patients with malignant tumors.
Acta radiologica (Stockholm, Sweden : 1987)
Oct 1, 2024
BACKGROUND: Radiomics has become an important tool for distinguishing benign and malignant vertebral compression fractures (VCFs). It is more clinically significant to concentrate on patients who have malignant tumors and differentiate between benign...
Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning.
Cancer imaging : the official publication of the International Cancer Imaging Society
Sep 30, 2024
BACKGROUND: Lung cancer (LC) is a leading cause of cancer-related mortality, and immunotherapy (IO) has shown promise in treating advanced-stage LC. However, identifying patients likely to benefit from IO and monitoring treatment response remains cha...
Radiomics prediction models of left atrial appendage hypercoagulability based on machine learning algorithms: an exploration about cardiac computed tomography angiography imaging.
The international journal of cardiovascular imaging
Sep 25, 2024
Transesophageal echocardiography (TEE) is the standard method for diagnosing left atrial appendage (LAA) hypercoagulability in patients with atrial fibrillation (AF), which means LAA thrombus/sludge, dense spontaneous echo contrast and slow LAA blood...
Atrial Appendage
Atrial Fibrillation
Male
Thrombophilia
Middle Aged
Multidetector Computed Tomography
Predictive Value of Tests
Risk Assessment
Female
Humans
Computed Tomography Angiography
Anticoagulants
Nomograms
Reproducibility of Results
Machine Learning
Blood Coagulation
Risk Factors
Decision Support Techniques
Coronary Angiography
Aged
Retrospective Studies
Radiographic Image Interpretation, Computer-Assisted
Radiomics
Pretreatment CT-based machine learning radiomics model predicts response in unresectable hepatocellular carcinoma treated with lenvatinib plus PD-1 inhibitors and interventional therapy.
Journal for immunotherapy of cancer
Sep 25, 2024
Deep Learning and Habitat Radiomics for the Prediction of Glioma Pathology Using Multiparametric MRI: A Multicenter Study.
Academic radiology
Sep 24, 2024
RATIONALE AND OBJECTIVES: Recent radiomics studies on predicting pathological outcomes of glioma have shown immense potential. However, the predictive ability remains suboptimal due to the tumor intrinsic heterogeneity. We aimed to achieve better pat...
Development of machine learning models to predict papillary carcinoma in thyroid nodules: The role of immunological, radiologic, cytologic and radiomic features.
Auris, nasus, larynx
Sep 20, 2024
OBJECTIVE: Approximately 30 % of thyroid nodules yield an indeterminate diagnosis through conventional diagnostic strategies. The aim of this study was to develop machine learning (ML) models capable of identifying papillary thyroid carcinomas using ...
Radiomics-Based Support Vector Machine Distinguishes Molecular Events Driving the Progression of Lung Adenocarcinoma.
Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
Sep 19, 2024
INTRODUCTION: An increasing number of early-stage lung adenocarcinomas (LUAD) are detected as lung nodules. The radiological features related to LUAD progression warrant further investigation. Exploration is required to bridge the gap between radiomi...
Prediction of intraoperative press-fit stability of the acetabular cup in total hip arthroplasty using radiomics-based machine learning models.
European journal of radiology
Sep 19, 2024
BACKGROUND: Preoperative prediction of the acetabular cup press-fit stability in total hip arthroplasty is necessary for clinical decision-making. This study aims to establish and validate machine learning models to investigate the feasibility of pre...
Deep radiomics-based prognostic prediction of oral cancer using optical coherence tomography.
BMC oral health
Sep 19, 2024
BACKGROUND: This study aims to evaluate the integration of optical coherence tomography (OCT) and peripheral blood immune indicators for predicting oral cancer prognosis by artificial intelligence.
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