AIMC Topic: Radiomics
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A computed tomography-based deep learning radiomics model for predicting the gender-age-physiology stage of patients with connective tissue disease-associated interstitial lung disease.
Computers in biology and medicine
Jun 1, 2025
OBJECTIVES: To explore the feasibility of using a diagnostic model constructed with deep learning-radiomics (DLR) features extracted from chest computed tomography (CT) images to predict the gender-age-physiology (GAP) stage of patients with connecti...
Prediction of clinical stages of cervical cancer via machine learning integrated with clinical features and ultrasound-based radiomics.
Scientific reports
May 29, 2025
To investigate the prediction of a model constructed by combining machine learning (ML) with clinical features and ultrasound radiomics in the clinical staging of cervical cancer. General clinical and ultrasound data of 227 patients with cervical can...
Deep learning radiomics fusion model to predict visceral pleural invasion of clinical stage IA lung adenocarcinoma: a multicenter study.
Journal of cardiothoracic surgery
May 28, 2025
AIM: To assess the predictive performance, risk stratification capabilities, and auxiliary diagnostic utility of radiomics, deep learning, and fusion models in identifying visceral pleural invasion (VPI) in lung adenocarcinoma.
Development and validation of a CT-based radiomics machine learning model for differentiating immune-related interstitial pneumonia.
International immunopharmacology
May 27, 2025
INTRODUCTION: Immune checkpoint inhibitor-related interstitial pneumonia (CIP) poses a diagnostic challenge due to its radiographic similarity to other pneumonias. We developed a non-invasive model using CT imaging to differentiate CIP from other pne...
Clinical, radiological, and radiomics feature-based explainable machine learning models for prediction of neurological deterioration and 90-day outcomes in mild intracerebral hemorrhage.
BMC medical imaging
May 26, 2025
BACKGROUND: The risks and prognosis of mild intracerebral hemorrhage (ICH) patients were easily overlooked by clinicians. Our goal was to use machine learning (ML) methods to predict mild ICH patients' neurological deterioration (ND) and 90-day progn...
Deep learning radiomics of left atrial appendage features for predicting atrial fibrillation recurrence.
BMC medical imaging
May 26, 2025
BACKGROUND: Structural remodeling of the left atrial appendage (LAA) is characteristic of atrial fibrillation (AF), and LAA morphology impacts radiofrequency catheter ablation (RFCA) outcomes. In this study, we aimed to develop and validate a predict...
Noninvasive prediction of failure of the conservative treatment in lateral epicondylitis by clinicoradiological features and elbow MRI radiomics based on interpretable machine learning: a multicenter cohort study.
Journal of orthopaedic surgery and research
May 24, 2025
OBJECTIVES: To develop and validate an interpretable machine learning model based on clinicoradiological features and radiomic features based on magnetic resonance imaging (MRI) to predict the failure of conservative treatment in lateral epicondyliti...
Multimodal ultrasound-based radiomics and deep learning for differential diagnosis of O-RADS 4-5 adnexal masses.
Cancer imaging : the official publication of the International Cancer Imaging Society
May 23, 2025
BACKGROUND: Accurate differentiation between benign and malignant adnexal masses is crucial for patients to avoid unnecessary surgical interventions. Ultrasound (US) is the most widely utilized diagnostic and screening tool for gynecological diseases...
Radiomics-based machine learning model for diagnosing internal abdominal hernias: a retrospective study.
Scientific reports
May 22, 2025
Intraperitoneal hernia is an acute abdominal disease, with complex imaging features and variable clinical manifestations that challenge surgeons and emergency physicians in early disease assessment and streamlined diagnosis and treatment procedures. ...
Advanced feature fusion of radiomics and deep learning for accurate detection of wrist fractures on X-ray images.
BMC musculoskeletal disorders
May 20, 2025
OBJECTIVE: The aim of this study was to develop a hybrid diagnostic framework integrating radiomic and deep features for accurate and reproducible detection and classification of wrist fractures using X-ray images.
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