AIMC Topic: Tomography, X-Ray Computed
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The value of machine learning based on spectral CT quantitative parameters in the distinguishing benign from malignant thyroid micro-nodules.
BMC cancer
Jul 1, 2025
BACKGROUND AND AIMS: More cases of thyroid micro-nodules have been diagnosed annually in recent years because of advancements in diagnostic technologies and increased public health awareness. To explore the application value of various machine learni...
Enhanced pulmonary nodule detection with U-Net, YOLOv8, and swin transformer.
BMC medical imaging
Jul 1, 2025
RATIONALE AND OBJECTIVES: Lung cancer remains the leading cause of cancer-related mortality worldwide, emphasizing the critical need for early pulmonary nodule detection to improve patient outcomes. Current methods encounter challenges in detecting s...
Deep learning-based lung cancer classification of CT images.
BMC cancer
Jul 1, 2025
Lung cancer remains a leading cause of cancer-related deaths worldwide, with accurate classification of lung nodules being critical for early diagnosis. Traditional radiological methods often struggle with high false-positive rates, underscoring the ...
Computed tomography-based radiomics predicts prognostic and treatment-related levels of immune infiltration in the immune microenvironment of clear cell renal cell carcinoma.
BMC medical imaging
Jul 1, 2025
OBJECTIVES: The composition of the tumour microenvironment is very complex, and measuring the extent of immune cell infiltration can provide an important guide to clinically significant treatments for cancer, such as immune checkpoint inhibition ther...
Radiomics and machine learning for osteoporosis detection using abdominal computed tomography: a retrospective multicenter study.
BMC medical imaging
Jul 1, 2025
OBJECTIVE: This study aimed to develop and validate a predictive model to detect osteoporosis using radiomic features and machine learning (ML) approaches from lumbar spine computed tomography (CT) images during an abdominal CT examination.
Development and validation of CT-based fusion model for preoperative prediction of invasion and lymph node metastasis in adenocarcinoma of esophagogastric junction.
BMC medical imaging
Jul 1, 2025
PURPOSE: In the context of precision medicine, radiomics has become a key technology in solving medical problems. For adenocarcinoma of esophagogastric junction (AEG), developing a preoperative CT-based prediction model for AEG invasion and lymph nod...
Muscle-Driven prognostication in gastric cancer: A multicenter deep learning framework integrating Iliopsoas and erector spinae radiomics for 5-Year survival prediction.
Scientific reports
Jul 1, 2025
This study developed a 5-year survival prediction model for gastric cancer patients by combining radiomics and deep learning, focusing on CT-based 2D and 3D features of the iliopsoas and erector spinae muscles. Retrospective data from 705 patients ac...
Determination of the oral carcinoma and sarcoma in contrast enhanced CT images using deep convolutional neural networks.
Scientific reports
Jul 1, 2025
Oral cancer is a hazardous disease and a major cause of morbidity and mortality worldwide. The purpose of this study was to develop the deep convolutional neural networks (CNN)-based multiclass classification and object detection models for distingui...
Evaluation of MRI-based synthetic CT for lumbar degenerative disease: a comparison with CT.
Scientific reports
Jul 1, 2025
Patients with lumbar degenerative disease typically undergo preoperative MRI combined with CT scans, but this approach introduces additional ionizing radiation and examination costs. To compare the effectiveness of MRI-based synthetic CT (sCT) in dis...
AI-based CT assessment of 3117 vertebrae reveals significant sex-specific vertebral height differences.
Scientific reports
Jul 1, 2025
Predicting vertebral height is complex due to individual factors. AI-based medical imaging analysis offers new opportunities for vertebral assessment. Thereby, these novel methods may contribute to sex-adapted nomograms and vertebral height predictio...
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