AIMC Topic: Radiopharmaceuticals

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F-FDG PET/CT-based habitat radiomics combining stacking ensemble learning for predicting prognosis in hepatocellular carcinoma: a multi-center study.

BMC cancer
BACKGROUND: This study aims to develop habitat radiomic models to predict overall survival (OS) for hepatocellular carcinoma (HCC), based on the characterization of the intratumoral heterogeneity reflected in F-FDG PET/CT images.

Accuracy of deep learning-based attenuation correction in Tc-GSA SPECT/CT hepatic imaging.

Radiography (London, England : 1995)
INTRODUCTION: Attenuation correction (AC) is necessary for accurate assessment of radioactive distribution in single photon emission computed tomography (SPECT). The method of computed tomography-based AC (CTAC) is widely used because of its accuracy...

A F-FDG PET/CT-based deep learning-radiomics-clinical model for prediction of cervical lymph node metastasis in esophageal squamous cell carcinoma.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: To develop an artificial intelligence (AI)-based model using Radiomics, deep learning (DL) features extracted from F-fluorodeoxyglucose (F-FDG) Positron emission tomography/Computed Tomography (PET/CT) images of tumor and cervical lymph n...

Human-Artificial Intelligence Symbiotic Reporting for Theranostic Cancer Care.

Cancer biotherapy & radiopharmaceuticals
Reporting of diagnostic nuclear images in clinical cancer management is generally qualitative. Theranostic treatment with Lu radioligands for prostate cancer and neuroendocrine tumors is routinely given as the same arbitrary fixed administered activi...

Using interpretable deep learning radiomics model to diagnose and predict progression of early AD disease spectrum: a preliminary [F]FDG PET study.

European radiology
OBJECTIVES: In this study, we propose an interpretable deep learning radiomics (IDLR) model based on [F]FDG PET images to diagnose the clinical spectrum of Alzheimer's disease (AD) and predict the progression from mild cognitive impairment (MCI) to A...

Early prediction of radiotherapy outcomes in pharyngeal cancer using deep learning on baseline [18F]Fluorodeoxyglucose positron emission Tomography/Computed tomography.

European journal of radiology
OBJECTIVES: This study aimed to develop an integrated segmentation-free deep learning (DL) framework to predict multiple aspects of radiotherapy outcome in pharyngeal cancer patients by analyzing pretreatment 18F-fluorodeoxyglucose (18F-FDG) positron...

Clinical Pilot of a Deep Learning Elastic Registration Algorithm to Improve Misregistration Artifact and Image Quality on Routine Oncologic PET/CT.

Academic radiology
RATIONALE AND OBJECTIVES: Misregistration artifacts between the PET and attenuation correction CT (CTAC) exams can degrade image quality and cause diagnostic errors. Deep learning (DL)-warped elastic registration methods have been proposed to improve...

A support vector machine-based approach to guide the selection of a pseudo-reference region for brain PET quantification.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
A Support Vector Machine (SVM) based approach was developed to identify a pseudo-reference region for brain PET scans with the aim of reducing interscan and intersubject variability. By training a binary linear SVM classifier with PET datasets from t...

Automated deep learning segmentation of cardiac inflammatory FDG PET.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
BACKGROUND: Fluorodeoxyglucose positron emission tomography (FDG PET) with suppression of myocardial glucose utilization plays a pivotal role in diagnosing cardiac sarcoidosis. Reorientation of images to match perfusion datasets and myocardial segmen...