Nuclear Medicine

Preventing exotic pet beetle invasion with an improved lightweight and efficient pest detection model deployed on mobile devices.

BACKGROUND: The illegal smuggling of exotic pet beetles presents a growing threat to global ecosystems. Customs authorities play a critical role in preventing biological invasions, yet current identification methods rely heavily on expert knowledge and time-consuming laboratory analysis, which limits rapid responses at ports of entry. To address this issue, we propose EPB-YOLO-PD, a lightweight, m...

NHOC/NHOP as novel biomarkers for predicting lymph node metastasis in NSCLC using PET/CT radiomics and machine learning: a two-center retrospective study.

PURPOSE: NHOC and NHOP, defined as the normalized distances from peak uptake to tumour centroid and perimeter, are novel PET/CT metrics of tumour aggressiveness. This two-centre study assessed the baseline NHOC/NHOP for predicting lymph node metastasis (LNM) in non-small cell lung cancer (NSCLC), then developed and validated an interpretable machine learning model combining clinical data, NHOC/NHO...

coPERCIST: AI-assisted PET-CT response assessment.

PURPOSE: The PET Response Criteria in Solid Tumours (PERCIST) 1.0 provides a standardized framework for evaluating treatment response using [18F]fluor...

Fully automated volumetric assessment of tumor burden using artificial intelligence on 68Ga-PSMA-11 PET predicts survival after 177Lu-PSMA therapy in metastatic Castration-resistant prostate cancer.

PURPOSE: Despite the rapid development of artificial intelligence (AI)-powered automated segmentation tools for PET/CT imaging, their prognostic value...

Clinical evaluation of deep learning-based CT-free PET reconstruction image: a dual-center study.

PURPOSE: Efforts to reduce the radiation burden of PET/CT have driven the increasing development of AI-based CT-less PET imaging techniques. However, ...

Brain metabolic imaging with 18 F-PET-CT and machine-learning clustering analysis reveal divergent metabolic phenotypes in patients with amyotrophic lateral sclerosis.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by significant clinicopathologic heterogeneity. Th...

Validation of novel low-dose CT methods for quantifying bone marrow in the appendicular skeleton of patients with multiple myeloma: initial results from the [18F]FDG PET/CT sub-study of the Phase 3 GMMG-HD7 Trial.

PURPOSE: The clinical significance of medullary abnormalities in the appendicular skeleton detected by computed tomography (CT) in patients with multi...

Photon-counting detector computed tomography in thoracic oncology: revolutionizing tumor imaging through precision and detail.

Photon-counting detector computed tomography (PCD-CT) is an emerging imaging technology that promises to overcome the limitations of conventional ener...

A New Method of Modeling the Multi-stage Decision-Making Process of CRT Using Machine Learning with Uncertainty Quantification.

Current machine learning-based (ML) models usually attempt to utilize all available patient data to predict patient outcomes while ignoring the associ...

Artificial intelligence-assisted PET imaging for predicting neoadjuvant chemotherapy response in breast cancer: a systematic review and meta-analysis.

PURPOSE: This study aims to evaluate the performance of artificial intelligence (AI)-assisted PET imaging in predicting neoadjuvant chemotherapy (NAC)...

Habitat-aware radiomics and adaptive 2.5D deep learning predict treatment response and long-term survival in ESCC patients undergoing neoadjuvant chemoimmunotherapy.

PURPOSE: Current radiomic approaches inadequately resolve spatial intratumoral heterogeneity (ITH) in esophageal squamous cell carcinoma (ESCC), limit...

Data fusion of medical imaging in neurological disorders.

Medical imaging plays a crucial role in the accurate diagnosis and prognosis of various medical conditions, with each modality offering unique and com...

Interrelations between dopaminergic-, gabaergic- and glutamatergic neurotransmitters in antipsychotic-naïve psychosis patients and the association to initial treatment response.

Preclinical evidence points to disturbances in neural networks in psychosis involving interrelations between dopaminergic-, GABAergic- and glutamaterg...

Enhancing interpretability of AI with radiomics-based deep neural network: proof of concept in the classification of Parkinsonian syndromes with 18F-FDG PET imaging.

OBJECTIVE: Interpretability and reproducibility remain major challenges in applying deep neural network (DNN) to neuroimaging-based diagnosis. This st...

Artificial intelligence-assisted assessment of metabolic response to tebentafusp in metastatic uveal melanoma: a long axial field-of-view [18F]FDG PET/CT study.

PURPOSE: Tebentafusp has emerged as the first systemic therapy to significantly prolong survival in treatment-naïve HLA-A*02:01 + patients with unrese...

A Pilot Study on Quantitative Accuracy and Radiomic Feature Stability of Deep Progressive Learning Reconstruction in NSCLC PET.

Deep progressive learning reconstruction (DPR) is a novel deep learning-based algorithm for PET imaging, yet its impact on quantitative metrics and ra...

An AI-Based Solution for Denoising Fast-Acquisition [18F]FDG PET: Clinical Feasibility and Quantitative Assessment.

Benefits in patient comfort, efficiency, and sustainability can come from reducing positron emission tomography (PET) scan's acquisition duration. Thi...

Digital versus analogue PET in parathyroid imaging: comparison of PET metrics and machine learning-based characterisation of hyperfunctioning lesions (the DIGI-PET study).

PURPOSE: To compare PET-derived metrics between digital and analogue PET/CT in hyperparathyroidism, and to assess whether machine learning (ML) applie...

Spatial imaging features derived from SUVmax location in resectable NSCLC are associated with tumor aggressiveness.

PURPOSE: Accurate non-invasive prediction of histopathologic invasiveness and recurrence risk remains a clinical challenge in resectable non-small cel...

Development and validation of a transformer-based deep learning model for predicting distant metastasis in non-small cell lung cancer using 18FDG PET/CT images.

BACKGROUND: This study aimed to develop and validate a hybrid deep learning (DL) model that integrates convolutional neural network (CNN) and vision t...