AIMC Topic: Tomography, X-Ray Computed

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Development and validation a radiomics combined clinical model predicts treatment response for esophageal squamous cell carcinoma patients.

BMC gastroenterology
PURPOSE: This study is aimed to develop and validate a machine learning model, which combined radiomics and clinical characteristics to predicting the definitive chemoradiotherapy (dCRT) treatment response in esophageal squamous cell carcinoma (ESCC)...

Methodology for a fully automated pipeline of AI-based body composition tools for abdominal CT.

Abdominal radiology (New York)
Accurate, reproducible body composition analysis from abdominal computed tomography (CT) images is critical for both clinical research and patient care. We present a fully automated, artificial intelligence (AI)-based pipeline that streamlines the en...

Predicting Recurrence After Surgical Resection for High-Risk Localized Renal Cell Carcinoma: A Radiomics Clinical Integration Approach.

The Journal of urology
PURPOSE: Adjuvant immunotherapy for clear cell renal cell carcinoma (ccRCC) is controversial because of the absence of reliable biomarkers for identifying patients most likely to benefit. The aim of this study was to develop and validate a quantitati...

Predictive models of severe disease in patients with COVID-19 pneumonia at an early stage on CT images using topological properties.

Radiological physics and technology
Prediction of severe disease (SVD) in patients with coronavirus disease (COVID-19) pneumonia at an early stage could allow for more appropriate triage and improve patient prognosis. Moreover, the visualization of the topological properties of COVID-1...

Machine Learning-Based Radiomics in Malignancy Prediction of Pancreatic Cystic Lesions: Evidence from Cyst Fluid Multi-Omics.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
The malignant potential of pancreatic cystic lesions (PCLs) varies dramatically, leading to difficulties when making clinical decisions. This study aimed to develop noninvasive clinical-radiomic models using preoperative CT images to predict the mali...

The use of a convolutional neural network to automate radiologic scoring of computed tomography of paranasal sinuses.

Biomedical engineering online
BACKGROUND: Chronic rhinosinusitis (CRS) is diagnosed with symptoms and objective endoscopy or computed tomography (CT). The Lund-Mackay score (LMS) is often used to determine the radiologic severity of CRS and make clinical decisions. This proof-of-...

The value of deep learning and radiomics models in predicting preoperative serosal invasion in gastric cancer: a dual-center study.

Abdominal radiology (New York)
PURPOSE: To establish and validate a model based on deep learning (DL), integrating radiomic features with relevant clinical features to generate nomogram, for predicting preoperative serosal invasion in gastric cancer (GC).

A non-invasive diagnostic approach for neuroblastoma utilizing preoperative enhanced computed tomography and deep learning techniques.

Scientific reports
Neuroblastoma presents a wide variety of clinical phenotypes, demonstrating different levels of benignity and malignancy among its subtypes. Early diagnosis is essential for effective patient management. Computed tomography (CT) serves as a significa...

Digital Innovations in Orthognathic Surgery: A Systematic Review of Virtual Surgical Planning, Digital Transfer, and Conventional Model Surgery.

Orthodontics & craniofacial research
OBJECTIVES: Orthognathic surgery has evolved due to the use of virtual surgical planning (VSP) and digital model surgery, which are technological advancements replacing conventional approaches with accurate personalised digital models made from compu...

Vision transformer and deep learning based weighted ensemble model for automated spine fracture type identification with GAN generated CT images.

Scientific reports
The most common causes of spine fractures, or vertebral column fractures (VCF), are traumas like falls, injuries from sports, or accidents. CT scans are affordable and effective at detecting VCF types in an accurate manner. VCF type identification in...