AIMC Topic: Radiomics

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Integration of radiomic and deep features to reliably differentiate benign renal lesions from renal cell carcinoma.

European journal of radiology Feb 7, 2025
PURPOSE: Accurate differentiation of benign renal lesions from renal cell carcinoma (RCC) is crucial for optimized management, particularly for small renal lesions (≤4 cm in diameter). This study aimed to integrate clinical data, radiomic features, a...
Humans Deep Learning Retrospective Studies Diagnosis, Differential Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Adult Kidney Neoplasms Female Sensitivity and Specificity Reproducibility of Results Carcinoma, Renal Cell Radiomics Aged Male Aged, 80 and over Middle Aged
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Machine learning assisted radiomics in predicting postoperative occurrence of deep venous thrombosis in patients with gastric cancer.

BMC cancer Feb 7, 2025
BACKGROUND: Gastric cancer patients are prone to lower extremity deep vein thrombosis (DVT) after surgery, which is an important cause of death in postoperative patients. Therefore, it is particularly important to find a suitable way to predict the r...
Venous Thrombosis Lower Extremity Radiomics Gastrectomy Female Risk Factors Male Humans Aged Middle Aged Adult Machine Learning Postoperative Complications Stomach Neoplasms Algorithms Retrospective Studies Tomography, X-Ray Computed
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Diagnosis of Sacroiliitis Through Semi-Supervised Segmentation and Radiomics Feature Analysis of MRI Images.

Journal of magnetic resonance imaging : JMRI Feb 6, 2025
BACKGROUND: Sacroiliitis is a hallmark of ankylosing spondylitis (AS), and early detection plays an important role in managing the condition effectively. MRI is commonly used for diagnosing sacroiliitis, traditional methods often depend on subjective...
Young Adult Adult Sensitivity and Specificity Radiomics Male Algorithms Retrospective Studies Female Reproducibility of Results Aged Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Edema Sacroiliac Joint Humans Sacroiliitis Middle Aged Adolescent Child ROC Curve Image Processing, Computer-Assisted
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Integrating manual annotation with deep transfer learning and radiomics for vertebral fracture analysis.

BMC medical imaging Feb 6, 2025
BACKGROUND: Vertebral compression fractures (VCFs) are prevalent in the elderly, often caused by osteoporosis or trauma. Differentiating acute from chronic VCFs is vital for treatment planning, but MRI, the gold standard, is inaccessible for some. Ho...
Tomography, X-Ray Computed Female Aged Aged, 80 and over Radiomics Male Middle Aged Deep Learning ROC Curve Magnetic Resonance Imaging Humans Retrospective Studies Spinal Fractures Fractures, Compression
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A comparison of different machine learning classifiers in predicting xerostomia and sticky saliva due to head and neck radiotherapy using a multi-objective, multimodal radiomics model.

Biomedical physics & engineering express Feb 6, 2025
. Although radiotherapy techniques are a primary treatment for head and neck cancer (HNC), they are still associated with substantial toxicity and side effects. Machine learning (ML) based radiomics models for predicting toxicity mostly rely on featu...
Saliva Xerostomia Middle Aged Head and Neck Neoplasms Support Vector Machine Magnetic Resonance Imaging Bayes Theorem Humans Machine Learning Tomography, X-Ray Computed Female Aged Adult Radiomics Male
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Association between deep learning radiomics based on placental MRI and preeclampsia with fetal growth restriction: A multicenter study.

European journal of radiology Feb 5, 2025
PURPOSE: Preeclampsia (PE) is associated with placental insufficiency and could lead to adverse pregnancy outcomes. The study aimed to develop a placental T2-weighted image-based automatic quantitative model for the identification of PE pregnancies a...
Pre-Eclampsia Adult Image Interpretation, Computer-Assisted Radiomics Fetal Growth Retardation Deep Learning Humans Magnetic Resonance Imaging Retrospective Studies Placenta Pregnancy Female
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Deep learning radiomics nomogram for preoperatively identifying moderate-to-severe chronic cholangitis in children with pancreaticobiliary maljunction: a multicenter study.

BMC medical imaging Feb 5, 2025
BACKGROUND: Long-term severe cholangitis can lead to dense adhesions and increased fragility of the bile duct, complicating surgical procedures and elevating operative risk in children with pancreaticobiliary maljunction (PBM). Consequently, preopera...
Deep Learning Adolescent Child, Preschool Child Female Nomograms Radiomics Chronic Disease Male Cholangitis Infant Pancreaticobiliary Maljunction Severity of Illness Index Humans Retrospective Studies Tomography, X-Ray Computed
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Habitat-Based Radiomics for Revealing Tumor Heterogeneity and Predicting Residual Cancer Burden Classification in Breast Cancer.

Clinical breast cancer Feb 4, 2025
PURPOSE: To investigate the feasibility of characterizing tumor heterogeneity in breast cancer ultrasound images using habitat analysis technology and establish a radiomics machine learning model for predicting response to neoadjuvant chemotherapy (N...
Humans Machine Learning Prognosis Radiomics Middle Aged ROC Curve Neoadjuvant Therapy Ultrasonography, Mammary Retrospective Studies Female Aged Adult Breast Neoplasms Tumor Burden Neoplasm, Residual
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Radiomics Analysis of Different Machine Learning Models based on Multiparametric MRI to Identify Benign and Malignant Testicular Lesions.

Academic radiology Feb 3, 2025
RATIONALE AND OBJECTIVES: To develop and validate a machine learning-based prediction model for the use of multiparametric magnetic resonance imaging(MRI) to predict benign and malignant lesions in the testis.
Radiomics Male Middle Aged Adolescent Contrast Media Testicular Neoplasms Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Retrospective Studies Multiparametric Magnetic Resonance Imaging Aged Humans Young Adult Machine Learning Adult Sensitivity and Specificity
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Machine Learning-Based CT Radiomics Model to Predict the Risk of Hip Fragility Fracture.

Academic radiology Feb 3, 2025
RATIONALE AND OBJECTIVES: This research aimed to develop a combined model based on proximal femur attenuation values and radiomics features at routine CT to predict hip fragility fracture using machine learning methods.
Radiographic Image Interpretation, Computer-Assisted Hip Fractures Aged, 80 and over Radiomics Male Middle Aged Risk Assessment Tomography, X-Ray Computed Female Risk Factors Aged Humans Machine Learning
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