Radiology

Radiomics analysis based on dynamic contrast-enhanced MRI for predicting early recurrence after hepatectomy in hepatocellular carcinoma patients.

This study aimed to develop a machine learning model based on Magnetic Resonance Imaging (MRI) radiomics for predicting early recurrence after curative surgery in patients with hepatocellular carcinoma (HCC).A retrospective analysis was conducted on 200 patients with HCC who underwent curative hepatectomy. Patients were randomly allocated to training (n = 140) and validation (n = 60) cohorts. Preo...

Attention residual network for medical ultrasound image segmentation.

Ultrasound imaging can distinctly display the morphology and structure of internal organs within the human body, enabling the examination of organs like the breast, liver, and thyroid. It can identify the locations of tumors, nodules, and other lesions, thereby serving as an efficacious tool for treatment detection and rehabilitation evaluation. Typically, the attending physician is required to ma...

Brain structural features with functional priori to classify Parkinson's disease and multiple system atrophy using diagnostic MRI.

Clinical two-dimensional (2D) MRI data has seen limited application in the early diagnosis of Parkinson's disease (PD) and multiple system atrophy (MS...

Evaluation of MRI-based synthetic CT for lumbar degenerative disease: a comparison with CT.

Patients with lumbar degenerative disease typically undergo preoperative MRI combined with CT scans, but this approach introduces additional ionizing ...

Innovative deep learning classifiers for breast cancer detection through hybrid feature extraction techniques.

Breast cancer remains a major cause of mortality among women, where early and accurate detection is critical to improving survival rates. This study p...

Quantitative ultrasound classification of healthy and chemically degraded ex-vivo cartilage.

In this study, we explore the potential of ten quantitative (radiofrequency-based) ultrasound parameters to assess the progressive loss of collagen an...

Contrast-enhanced mammography-based interpretable machine learning model for the prediction of the molecular subtype breast cancers.

OBJECTIVE: This study aims to establish a machine learning prediction model to explore the correlation between contrast-enhanced mammography (CEM) ima...

Development and validation of a machine learning model for central compartmental lymph node metastasis in solitary papillary thyroid microcarcinoma via ultrasound imaging features and clinical parameters.

BACKGROUND: Papillary thyroid microcarcinoma (PTMC) is the most common malignant subtype of thyroid cancer. Preoperative assessment of the risk of cen...

Computed tomography-based radiomics predicts prognostic and treatment-related levels of immune infiltration in the immune microenvironment of clear cell renal cell carcinoma.

OBJECTIVES: The composition of the tumour microenvironment is very complex, and measuring the extent of immune cell infiltration can provide an import...

Enhanced pulmonary nodule detection with U-Net, YOLOv8, and swin transformer.

RATIONALE AND OBJECTIVES: Lung cancer remains the leading cause of cancer-related mortality worldwide, emphasizing the critical need for early pulmona...

Ultrasound-based machine learning model to predict the risk of endometrial cancer among postmenopausal women.

BACKGROUND: Current ultrasound-based screening for endometrial cancer (EC) primarily relies on endometrial thickness (ET) and morphological evaluation...

Attention-driven hybrid deep learning and SVM model for early Alzheimer's diagnosis using neuroimaging fusion.

Alzheimer's Disease (AD) poses a significant global health challenge, necessitating early and accurate diagnosis to enable timely interventions. AD is...

Evaluation of a fusion model combining deep learning models based on enhanced CT images with radiological and clinical features in distinguishing lipid-poor adrenal adenoma from metastatic lesions.

OBJECTIVE: To evaluate the diagnostic performance of a machine learning model combining deep learning models based on enhanced CT images with radiolog...

Perilesional dominance: radiomics of multiparametric MRI enhances differentiation of IgG4-Related ophthalmic disease and orbital MALT lymphoma.

BACKGROUND: To develop and validate a diagnostic framework integrating intralesional (ILN) and perilesional (PLN) radiomics derived from multiparametr...

Machine learning-based brain magnetic resonance imaging radiomics for identifying rapid eye movement sleep behavior disorder in Parkinson's disease patients.

BACKGROUND: Traditional clinical diagnostic methods of rapid eye movement sleep behavior disorder (RBD) have certain limitations, especially in the ea...

Advancements in the application of MRI radiomics in meningioma.

Meningiomas are among the most common intracranial tumors, and challenges still remain in terms of tumor classification, treatment, and management. Wi...

MRI's evolving role in rectal cancer in the era of personalized medicine.

Recent decades have seen great advances in the diagnosis and management of rectal cancer, and magnetic resonance imaging (MRI) has become pivotal for ...

Development and validation of a prognostic prediction model for lumbar-disc herniation based on machine learning and fusion of clinical text data and radiomic features.

OBJECTIVE: Based on preoperative clinical text data and lumbar magnetic resonance imaging (MRI), we applied machine learning (ML) algorithms to constr...

Thoracic staging of lung cancers by FDG-PET/CT: impact of artificial intelligence on the detection of associated pulmonary nodules.

This study focuses on automating the classification of certain thoracic lung cancer stages in 3D FDG-PET/CT images according to the 9th Edition of the...

Bidirectional Prototype-Guided Consistency Constraint for Semi-Supervised Fetal Ultrasound Image Segmentation.

Fetal ultrasound (US) image segmentation plays an important role in fetal development assessment, maternal pregnancy management, and intrauterine surg...