Nuclear Medicine

Interpretable predictions from whole-body FDG-PET/CT using parameters associated with clinical outcome.

BACKGROUND: Accurate prediction of clinical outcomes is challenging yet important for patient care. The aim of the study was to evaluate a deep learning-based methodology using tissue-wise information, as a proof of concept, for predicting parameters known to be associated with clinical outcomes. METHODS: We utilized the publicly available autoPET cohort, consisting of 1014 FDG-PET/CT examinations...

AI-based synthetic CT attenuation correction enables reliable quantitative SPECT in unilateral condylar hyperplasia.

PURPOSE: Unilateral condylar hyperplasia (UCH) is a rare mandibular growth disorder in which accurate assessment of condylar metabolic activity is essential for surgical decision-making. Quantitative 99mTc-methylene diphosphonate (MDP) SPECT/CT is commonly used for this purpose; however, CT-based attenuation correction (CTAC) increases radiation exposure and may be affected by registration errors....

Machine learning models for predicting microvascular invasion in hepatocellular carcinoma with three-dimensional whole-lesion 18F-FDG PET radiomics.

OBJECTIVE: To estimate the performance of machine learning models based on preoperative three-dimensional whole-lesion radiomics features for predicti...

Effective elimination of respiratory misregistration-induced attenuation correction errors in PET/CT via deep learning trained on data-driven gated PET from strictly respiratory-phase-matched PET/CT cases.

[Exploration and clinical application of the "digital and intelligent surgery" diagnosis and treatment workflow for oral and maxillofacial tumors].

Tumors in the oral and maxillofacial region present significant clinical challenges due to anatomical complexity and high individual variability, with...

The SPECT/CT radiomics-based classification of skeletal metastases and benign bone lesions.

Hybrid Single Photon Emission Computed Tomography/Computed Tomography (SPECT/CT) improves lesion localization and diagnostic accuracy in detecting ske...

Interpretable multimodal PET/CT-EHR fusion via mixture-of-experts for prognostic stratification in mantle cell lymphoma: a multicenter study.

BACKGROUND: Mantle cell lymphoma (MCL) is a rare, biologically heterogeneous B-cell malignancy with highly variable outcomes. Existing prognostic tool...

Non-invasive diagnosis strategy integrating PSMA PET/CT and mpMRI for patients with suspected prostate cancer: a multi-center study.

BACKGROUND: Multiparametric MRI (mpMRI) and ^68 Ga-PSMA PET/CT are widely used for prostate cancer (PCa) diagnosis but remain limited by false positiv...

Deep learning-guided discovery and engineering of binding peptides for accelerated enzymatic degradation of polyethylene terephthalate.

Enzymatic polyethylene terephthalate (PET) degradation holds promise for environmental restoration. However, limited substrate catalytic capacity hind...

Design and first experimental results of an edgeless PET prototype for breast imaging: DeepBreast.

BACKGROUND: Breast cancer causes the largest number of cancer-related deaths among women worldwide. With the aim of improving Positron Emission Tomogr...

Integrating Machine Learning Tools in Protein Design: A Case of MHETase Engineering for PET Biodeconstruction.

The integration of machine learning tools into protein engineering offers substantial promise, yet linking computational predictions to experimental p...

[177Lu]Lu-PSMA extravasation: from quantitative imaging to clinical guidelines.

Extravasation of therapeutic radioligands such as [177Lu]Lu-PSMA-617 or [177Lu]Lu-DOTATATE is rare but may result in localized radiation injury. In th...

Sharing a whole-/total-body [18F]FDG-PET/CT dataset with CT-derived segmentations: an ENHANCE.PET initiative.

We present a large whole-body and total-body curated dataset of dual-modality 2-deoxy-2-[18F]fluoro-D-glucose (FDG)-Positron Emission Tomography/Compu...

Few shots transfer learning for universal SPECT denoising across diverse acquisition protocols.

Accelerated Single Photon Emission Computed Tomography (SPECT) imaging, achieved by reducing either the number of projection angles or the acquis...

Foundation models for brain imaging: A systematic review.

Foundation models (FMs), large neural networks pretrained on extensive and diverse datasets, have revolutionized artificial intelligence and demonstra...

Machine learning-based radiomics analysis of PET imaging for early prediction of concurrent chemoradiotherapy response in stage II-III cervical squamous cell carcinoma.

BACKGROUND: Cervical squamous cell carcinoma is a major global health burden, with many patients presenting with locally advanced disease requiring co...

PET-CT radiomics and artificial intelligence for predicting lymph node metastasis in cervical cancer: a systematic review and meta-analysis.

PURPOSE: Preoperative identification of lymph node metastasis (LNM) in cervical cancer is crucial for guiding therapeutic strategies but remains clini...

Voxel-wise deep learning segmentation of hydroxyapatite and iodine in spectral photon-counting CT: A quantitative phantom study.

Accurate non-invasive identification of hydroxyapatite (HA) deposits is important for diagnosing calcific musculoskeletal disease and quantifying vasc...

PET imaging of polymyalgia rheumatica: update and future trends.

Polymyalgia rheumatica (PMR) is a common immune-mediated inflammatory disease affecting older adults over 50 years of age and is characterized by cons...

CT-Free Quantitative Thyroid SPECT Based on Artificial Intelligence: A Prospective Multicenter Noninferiority Clinical Trial.

BACKGROUND: Hybrid single-photon emission computed tomography (SPECT)/computed tomography (CT) is used for the differential diagnosis of thyrotoxicosi...