AIMC Topic: Tomography, X-Ray Computed
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Application of machine learning methods for predicting esophageal variceal bleeding in patients with cirrhosis.
European radiology
Dec 21, 2024
OBJECTIVE: To develop and compare machine learning models based on CT morphology features, serum biomarkers, and basic physical conditions to predict esophageal variceal bleeding.
SMDFnet: Saliency multiscale dense fusion network for MRI and CT image fusion.
Computers in biology and medicine
Dec 21, 2024
MRI-CT image fusion technology combines magnetic resonance imaging (MRI) and computed tomography (CT) imaging to provide more comprehensive and accurate image information. This fusion technology can play an important role in medical diagnosis and sur...
Improving the prediction of chemotherapy dose-limiting toxicity in colon cancer patients using an AI-CT-based 3D body composition of the entire L1-L5 lumbar spine.
Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Dec 21, 2024
PURPOSE: Chemotherapy dose-limiting toxicities (DLT) pose a significant challenge in successful colon cancer treatment. Body composition analysis may enable tailored interventions thereby supporting the mitigation of chemotherapy toxic effects. This ...
Identifying threshold of CT-defined muscle loss after radiotherapy for survival in oral cavity cancer using machine learning.
European radiology
Dec 20, 2024
OBJECTIVES: Muscle loss after radiotherapy is associated with poorer survival in patients with oral cavity squamous cell carcinoma (OCSCC). However, the threshold of muscle loss remains unclear. This study aimed to utilize explainable artificial inte...
CT-based clinical-radiomics model to predict progression and drive clinical applicability in locally advanced head and neck cancer.
European radiology
Dec 20, 2024
BACKGROUND: Definitive chemoradiation is the primary treatment for locally advanced head and neck carcinoma (LAHNSCC). Optimising outcome predictions requires validated biomarkers, since TNM8 and HPV could have limitations. Radiomics may enhance risk...
Deep learning model for low-dose CT late iodine enhancement imaging and extracellular volume quantification.
European radiology
Dec 20, 2024
OBJECTIVES: To develop and validate deep learning (DL)-models that denoise late iodine enhancement (LIE) images and enable accurate extracellular volume (ECV) quantification.
Deep Learning
Iodine
Contrast Media
Radiographic Image Interpretation, Computer-Assisted
Computed Tomography Angiography
Myocardial Perfusion Imaging
Radiographic Image Enhancement
Radiation Dosage
Signal-To-Noise Ratio
Female
Aged
Male
Middle Aged
Humans
Retrospective Studies
Tomography, X-Ray Computed
AI model using CT-based imaging biomarkers to predict hepatocellular carcinoma in patients with chronic hepatitis B.
Journal of hepatology
Dec 20, 2024
BACKGROUND & AIMS: Various hepatocellular carcinoma (HCC) prediction models have been proposed for patients with chronic hepatitis B (CHB) using clinical variables. We aimed to develop an artificial intelligence (AI)-based HCC prediction model by inc...
Assessing the prognostic impact of body composition phenotypes on surgical outcomes and survival in patients with spinal metastasis: a deep learning approach to preoperative CT analysis.
Journal of neurosurgery. Spine
Dec 20, 2024
OBJECTIVE: The prognostic significance of body composition phenotypes for survival in patients undergoing surgical intervention for spinal metastases has not yet been elucidated. This study aimed to elucidate the impact of body composition phenotypes...
Automated Identification of Breast Cancer Relapse in Computed Tomography Reports Using Natural Language Processing.
JCO clinical cancer informatics
Dec 20, 2024
PURPOSE: Breast cancer relapses are rarely collected by cancer registries because of logistical and financial constraints. Hence, we investigated natural language processing (NLP), enhanced with state-of-the-art deep learning transformer tools and la...
Artificial Intelligence-Empowered Multistep Integrated Radiation Therapy Workflow for Nasopharyngeal Carcinoma.
International journal of radiation oncology, biology, physics
Dec 19, 2024
PURPOSE: To establish an artificial intelligence (AI)-empowered multistep integrated (MSI) radiation therapy (RT) workflow for patients with nasopharyngeal carcinoma (NPC) and evaluate its feasibility and clinical performance.
Humans
Female
Young Adult
Radiotherapy, Intensity-Modulated
Artificial Intelligence
Middle Aged
Adult
Prospective Studies
Male
Nasopharyngeal Carcinoma
Tomography, X-Ray Computed
Nasopharyngeal Neoplasms
Aged
Organs at Risk
Radiotherapy Planning, Computer-Assisted
Quality of Life
Workflow
Feasibility Studies
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