AIMC Topic: Contrast Media

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Artificial Intelligence-Based Prediction of Contrast Medium Doses for Computed Tomography Angiography Using Optimized Clinical Parameter Sets.

Methods of information in medicine
OBJECTIVES: In this paper, an artificial intelligence-based algorithm for predicting the optimal contrast medium dose for computed tomography (CT) angiography of the aorta is presented and evaluated in a clinical study. The prediction of the contrast...

Prior Clinico-Radiological Features Informed Multi-Modal MR Images Convolution Neural Network: A novel deep learning framework for prediction of lymphovascular invasion in breast cancer.

Cancer medicine
BACKGROUND: Current methods utilizing preoperative magnetic resonance imaging (MRI)-based radiomics for assessing lymphovascular invasion (LVI) in patients with early-stage breast cancer lack precision, limiting the options for surgical planning.

Incidence, Determinants, and Outcome of Contrast-induced Acute Kidney Injury following Percutaneous Coronary Intervention at a Tertiary Care Hospital.

Saudi journal of kidney diseases and transplantation : an official publication of the Saudi Center for Organ Transplantation, Saudi Arabia
Contrast-induced acute kidney injury (CI-AKI) after percutaneous coronary intervention (PCI) is the common cause of in-hospital acquired AKI and is associated with in-hospital mortality and prolonged hospital stay. We studied the incidence of CI-AKI ...

A Convolutional Neural Network Model for Distinguishing Hemangioblastomas From Other Cerebellar-and-Brainstem Tumors Using Contrast-Enhanced MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Hemangioblastoma (HB) is a highly vascularized tumor most commonly occurring in the posterior cranial fossa, requiring accurate preoperative diagnosis to avoid accidental intraoperative hemorrhage and even death.

MRI-Based Kinetic Heterogeneity Evaluation in the Accurate Access of Axillary Lymph Node Status in Breast Cancer Using a Hybrid CNN-RNN Model.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Accurate evaluation of the axillary lymph node (ALN) status is needed for determining the treatment protocol for breast cancer (BC). The value of magnetic resonance imaging (MRI)-based tumor heterogeneity in assessing ALN metastasis in BC...

Machine learning model to preoperatively predict T2/T3 staging of laryngeal and hypopharyngeal cancer based on the CT radiomic signature.

European radiology
OBJECTIVES: To develop and assess a radiomics-based prediction model for distinguishing T2/T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) METHODS: A total of 118 patients with pathologically proven LHSCC were enrolled in t...

Deep-learning reconstruction with low-contrast media and low-kilovoltage peak for CT of the liver.

Clinical radiology
AIM: To compare images using reduced CM, low-kVp scanning and DLR reconstruction with conventional images (no CM reduction, normal tube voltage, reconstructed with HBIR. To compare images using reduced contrast media (CM), low kilovoltage peak (kVp) ...

Discrimination of benign and malignant breast lesions on dynamic contrast-enhanced magnetic resonance imaging using deep learning.

Journal of cancer research and therapeutics
PURPOSE: To evaluate the capability of deep transfer learning (DTL) and fine-tuning methods in differentiating malignant from benign lesions in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Fusion Radiomics-Based Prediction of Response to Neoadjuvant Chemotherapy for Osteosarcoma.

Academic radiology
RATIONALE AND OBJECTIVES: Neoadjuvant chemotherapy (NAC) is the most crucial prognostic factor for osteosarcoma (OS), it significantly prolongs progression-free survival and improves the quality of life. This study aims to develop a deep learning rad...