Nuclear Medicine

Comparison of Large Language Models' Performance on 600 Nuclear Medicine Technology Board Examination-Style Questions.

This study investigated the application of large language models (LLMs) with and without retrieval-augmented generation (RAG) in nuclear medicine, particularly their performance across various topics relevant to the field, to evaluate their potential use as reliable tools for professional education and clinical decision-making. We evaluated the performance of LLMs, including the OpenAI GPT-4o ser...

Impact of thyroglobulin changes on clinical outcomes of differentiated thyroid cancer with biochemical incomplete response.

PURPOSE: Partial patients with biochemical incomplete response (BIR) after initial therapy for differentiated thyroid cancer (DTC) may progress to structural recurrence during follow-up. For better-individualized care, this study analyzed predictors of structural recurrence in patients with BIR after initial radioactive iodine therapy (RAIT).

Advances in the application of 18F-sodium fluoride PET in the assessment of atherosclerosis.

Atherosclerosis serves as the primary cause of cardiovascular diseases (CVDs), with its pathological processes encompassing lipid deposition, inflamma...

New Targets for Imaging in Nuclear Medicine.

Nuclear medicine is rapidly evolving with new molecular imaging targets and advanced computational tools that promise to enhance diagnostic precision ...

PSMA PET/CT for prostate cancer diagnosis: current applications and future directions.

Prostate cancer (PCa) requires improved diagnostic strategies beyond conventional imaging. This review aimed to evaluate the role of prostate-specific...

Prediction of anthropogenic I in the South China Sea based on machine learning.

With the rapid increase in the number of nuclear power plants along the China coast and the potential for releases of radioactive substances to marine...

The Use of Maximum-Intensity Projections and Deep Learning Adds Value to the Fully Automatic Segmentation of Lesions Avid for [F]FDG and [Ga]Ga-PSMA in PET/CT.

This study investigated the added value of using maximum-intensity projection (MIP) images for fully automatic segmentation of lesions using deep lear...

Characterization of Effective Half-Life for Instant Single-Time-Point Dosimetry Using Machine Learning.

Single-time-point (STP) image-based dosimetry offers a more convenient approach for clinical practice in radiopharmaceutical therapy (RPT) compared wi...

F-FDG PET/CT-based deep learning models and a clinical-metabolic nomogram for predicting high-grade patterns in lung adenocarcinoma.

BACKGROUND: To develop and validate deep learning (DL) and traditional clinical-metabolic (CM) models based on 18 F-FDG PET/CT images for noninvasivel...

Artificial Delayed-phase Technetium-99m MIBI Scintigraphy From Early-phase Scintigraphy Improves Identification of Hyperfunctioning Parathyroid Lesions in Patients With Hyperparathyroidism.

PURPOSE: The aim of this study was to generate and validate artificial delayed-phase technetium-99m methoxyisobutylisonitrile scintigraphy (aMIBI) ima...

A review of multimodal fusion-based deep learning for Alzheimer's disease.

Alzheimer's Disease (AD) as one of the most prevalent neurodegenerative disorders worldwide, characterized by significant memory and cognitive decline...

Plaque burden improves the detection of ischemic CAD over stenosis from coronary computed tomography angiography.

In symptomatic patients undergoing coronary CTA for suspected coronary artery disease (CAD), we assessed if quantification of plaque burden, in additi...

A measurement-based framework integrating machine learning and morphological dynamics for outdoor thermal regulation.

This study presents a comprehensive investigation into the interplay between machine learning (ML) models, morphological features, and outdoor thermal...

Utilizing Pix2Pix conditional generative adversarial networks to recover missing data in preclinical PET scanner sinogram gaps.

BACKGROUND: The presence of a gap between adjacent detector blocks in Positron Emission Tomography (PET) scanners introduces a partial loss of project...

IT: An interpretable transformer model for Alzheimer's disease prediction based on PET/MR images.

Alzheimer's disease (AD) represents a significant challenge due to its progressive neurodegenerative impact, particularly within an aging global demog...

A Novel Theranostic Strategy for Malignant Pulmonary Nodules by Targeted CECAM6 with Zr/I-Labeled Tinurilimab.

Lung adenocarcinoma (LUAD) constitutes a major cause of cancer-related fatalities worldwide. Early identification of malignant pulmonary nodules const...

Radiomics across modalities: a comprehensive review of neurodegenerative diseases.

Radiomics allows extraction from medical images of quantitative features that are able to reveal tissue patterns that are generally invisible to human...

AI in SPECT Imaging: Opportunities and Challenges.

SPECT is a widely used imaging modality in nuclear medicine which provides essential functional insights into cardiovascular, neurological, and oncolo...

POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation.

Low-dose PET offers a valuable means of minimizing radiation exposure in PET imaging. However, the prevalent practice of employing additional CT scans...

Prediction of PD-L1 expression in NSCLC patients using PET/CT radiomics and prognostic modelling for immunotherapy in PD-L1-positive NSCLC patients.

AIM: To develop a positron emission tomography/computed tomography (PET/CT)-based radiomics model for predicting programmed cell death ligand 1 (PD-L1...