AIMC Topic: Radiomics

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Machine Learning Based on Clinical Information and Integrated CT Radiomics to Predict Local Recurrence of Stage Ia Lung Adenocarcinoma after Microwave Ablation.

Journal of vascular and interventional radiology : JVIR Aug 28, 2024
PURPOSE: To develop and compare 3 different machine learning-based models of clinical information and integrated radiomics features predicting the local recurrence of Stage Ia lung adenocarcinoma after microwave ablation (MWA) for assisting clinical ...
Adenocarcinoma of Lung Radiomics Ablation Techniques Male Middle Aged Humans Radiographic Image Interpretation, Computer-Assisted Machine Learning Neoplasm Recurrence, Local Decision Support Techniques Risk Assessment Tomography, X-Ray Computed Microwaves Time Factors Retrospective Studies Margins of Excision Clinical Decision-Making Lung Neoplasms Female Treatment Outcome Risk Factors Predictive Value of Tests Aged Neoplasm Staging
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Combining Clinical-Radiomics Features With Machine Learning Methods for Building Models to Predict Postoperative Recurrence in Patients With Chronic Subdural Hematoma: Retrospective Cohort Study.

Journal of medical Internet research Aug 28, 2024
BACKGROUND: Chronic subdural hematoma (CSDH) represents a prevalent medical condition, posing substantial challenges in postoperative management due to risks of recurrence. Such recurrences not only cause physical suffering to the patient but also ad...
Humans Machine Learning Female Retrospective Studies Middle Aged Aged Aged, 80 and over Postoperative Period Radiomics Recurrence Male Hematoma, Subdural, Chronic
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US-based Radiomics Analysis of Different Machine Learning Models for Differentiating Benign and Malignant BI-RADS 4A Breast Lesions.

Academic radiology Aug 26, 2024
RATIONALE AND OBJECTIVES: To investigate and authenticate the effectiveness of various radiomics models in distinguishing between benign and malignant BI-RADS 4A lesions.
Diagnosis, Differential Humans Ultrasonography, Mammary Image Interpretation, Computer-Assisted Adult Machine Learning Sensitivity and Specificity Algorithms Breast Neoplasms Female Radiomics Aged Middle Aged
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Radiomic and deep learning analysis of dermoscopic images for skin lesion pattern decoding.

Scientific reports Aug 26, 2024
This study aims to explore the efficacy of a hybrid deep learning and radiomics approach, supplemented with patient metadata, in the noninvasive dermoscopic imaging-based diagnosis of skin lesions. We analyzed dermoscopic images from the Internationa...
Skin Diseases Humans Image Interpretation, Computer-Assisted Skin Neoplasms Dermoscopy Radiomics Deep Learning ROC Curve Image Processing, Computer-Assisted Skin
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Explainable machine-learning-based prediction of QCT/FEA-calculated femoral strength under stance loading configuration using radiomics features.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society Aug 25, 2024
Finite element analysis can provide precise femoral strength assessment. However, its modeling procedures were complex and time-consuming. This study aimed to develop a model to evaluate femoral strength calculated by quantitative computed tomography...
Aged Aged, 80 and over Radiomics Male Middle Aged Humans Machine Learning Tomography, X-Ray Computed Femur Finite Element Analysis
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Ultrasound-Based Deep Learning Radiomics Nomogram for Tumor and Axillary Lymph Node Status Prediction After Neoadjuvant Chemotherapy.

Academic radiology Aug 24, 2024
RATIONALE AND OBJECTIVES: This study aims to explore the feasibility of the deep learning radiomics nomogram (DLRN) for predicting tumor status and axillary lymph node metastasis (ALNM) after neoadjuvant chemotherapy (NAC) in patients with breast can...
Chemotherapy, Adjuvant Middle Aged Deep Learning Ultrasonography Lymph Nodes Nomograms Neoadjuvant Therapy Lymphatic Metastasis Axilla Ultrasonography, Mammary Female Aged Adult Breast Neoplasms Radiomics Humans Retrospective Studies
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Impact of Emerging Deep Learning-Based MR Image Reconstruction Algorithms on Abdominal MRI Radiomic Features.

Journal of computer assisted tomography Aug 22, 2024
OBJECTIVE: This study aims to evaluate, on one MRI vendor's platform, the impact of deep learning (DL)-based reconstruction techniques on MRI radiomic features compared to conventional image reconstruction techniques.
Humans Algorithms Prospective Studies Female Abdomen Aged Magnetic Resonance Imaging Young Adult Deep Learning Adolescent Child, Preschool Adult Child Image Processing, Computer-Assisted Radiomics Male Middle Aged
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Interpretable machine learning models based on shear-wave elastography radiomics for predicting cardiovascular disease in diabetic kidney disease patients.

Journal of diabetes investigation Aug 22, 2024
BACKGROUND: The risk of cardiovascular complications is significantly elevated in patients with diabetic kidney disease (DKD). Recognizing the link between the progression of DKD and an increased risk of cardiovascular disease (CVD), it is crucial to...
Retrospective Studies Female Aged Radiomics Male Humans Machine Learning Prognosis Cardiovascular Diseases Middle Aged Diabetic Nephropathies Elasticity Imaging Techniques
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Multiparametric MRI-Based Deep Learning Radiomics Model for Assessing 5-Year Recurrence Risk in Non-Muscle Invasive Bladder Cancer.

Journal of magnetic resonance imaging : JMRI Aug 21, 2024
BACKGROUND: Accurately assessing 5-year recurrence rates is crucial for managing non-muscle-invasive bladder carcinoma (NMIBC). However, the European Organization for Research and Treatment of Cancer (EORTC) model exhibits poor performance.
Urinary Bladder Multiparametric Magnetic Resonance Imaging Non-Muscle Invasive Bladder Neoplasms Aged Radiomics Male Middle Aged Risk Assessment Deep Learning Magnetic Resonance Imaging Humans Urinary Bladder Neoplasms Retrospective Studies Neoplasm Invasiveness Female Neoplasm Recurrence, Local Reproducibility of Results Risk Factors
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Ensemble learning-based pretreatment MRI radiomic model for distinguishing intracranial extraventricular ependymoma from glioblastoma multiforme.

NMR in biomedicine Aug 20, 2024
This study aims to develop an ensemble learning (EL) method based on magnetic resonance (MR) radiomic features to preoperatively differentiate intracranial extraventricular ependymoma (IEE) from glioblastoma (GBM). This retrospective study enrolled p...
Machine Learning Retrospective Studies Female Aged Young Adult Humans ROC Curve Magnetic Resonance Imaging Diagnosis, Differential Glioblastoma Ependymoma Adult Radiomics Brain Neoplasms Male Middle Aged
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