Nuklearmedizin. Nuclear Medicine

No abstract text is available yet for this article.

Radiomics is an emerging field of artificial intelligence that focuses on the extraction and analysis of quantitative features such as intensity, shape, texture and spatial relationships from medical images. These features, often imperceptible to the human eye, can reveal complex patterns and biological insights. They can also be combined with clinical data to create predictive models using machine learning to improve disease characterization in nuclear medicine. This review article examines the current state of radiomics in nuclear medicine and shows its potential to improve patient care...

Nuclear imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) in combination with computed tomography (CT) are established imaging modalities in clinical practice, particularly for oncological problems. Due to a multitude of manufacturers, different measurement protocols, local demographic or clinical workflow variations as well as various available reconstruction and analysis software, very heterogeneous datasets are generated. This review article examines the current state of interoperability and harmonisation of image data and related clinical data in the field of nuclear medicine...

This version of the guideline for radioiodine therapy of benign thyroid disorders is an update of the version, which was published by the German Society of Nuclear Medicine (Deutsche Gesellschaft für Nuklearmedizin, DGN) in co-ordination with the German Society of Endocrinology (Deutsche Gesellschaft für Endokrinologie, DGE, Sektion Schilddrüse) and the German Society of General- and Visceral-Surgery (Deutsche Gesellschaft für Allgemein- und Viszeralchirurgie, DGAV) in 2015. This guideline was harmonized with the recommendations of the European Association of Nuclear Medicine (EANM)...

PURPOSE:  To evaluate the recommendations for or against fine needle biopsy (FNB) of hypofunctioning thyroid nodules (TNs) using of five different Ultrasound (US) -based risk stratification systems (RSSs). METHODS:  German multicenter study with 563 TNs (≥ 10 mm) in 534 patients who underwent thyroid US and surgery. All TNs were evaluated with ACR TI-RADS, EU-TIRADS, ATA, K-TIRADS 2016 and modified K-TIRADS 2021. A correct recommendation was defined as: malignant TN with recommendation for FNB (appropriate) or benign TN without recommendation for FNB (avoided)...

No abstract text is available yet for this article.

No abstract text is available yet for this article.

Routine clinical dosimetry along with radiopharmaceutical therapies is key for future treatment personalization. However, dosimetry is considered complex and time-consuming with various challenges amongst the required steps within the dosimetry workflow. The general workflow for image-based dosimetry consists of quantitative imaging, the segmentation of organs and tumors, fitting of the time-activity-curves, and the conversion to absorbed dose. This work reviews the potential and advantages of the use of artificial intelligence to improve speed and accuracy of every single step of the dosimetry workflow...

Tracer kinetic modelling based on dynamic PET is an important field of Nuclear Medicine for quantitative functional imaging. Yet, its implementation in clinical routine has been constrained by its complexity and computational costs. Machine learning poses an opportunity to improve modelling processes in terms of arterial input function prediction, the prediction of kinetic modelling parameters and model selection in both clinical and preclinical studies while reducing processing time. Moreover, it can help improving kinetic modelling data used in downstream tasks such as tumor detection...

AIM: Despite a vast number of articles on radiomics and machine learning in positron emission tomography (PET) imaging, clinical applicability remains limited, partly owing to poor methodological quality. We therefore systematically investigated the methodology described in publications on radiomics and machine learning for PET-based outcome prediction. METHODS: A systematic search for original articles was run on PubMed. All articles were rated according to 17 criteria proposed by the authors...

Volumetry is crucial in oncology and endocrinology, for diagnosis, treatment planning, and evaluating response to therapy for several diseases. The integration of Artificial Intelligence (AI) and Deep Learning (DL) has significantly accelerated the automatization of volumetric calculations, enhancing accuracy and reducing variability and labor. In this review, we show that a high correlation has been observed between Machine Learning (ML) methods and expert assessments in tumor volumetry; Yet, it is recognized as more challenging than organ volumetry...

Positron emission tomography (PET) is vital for diagnosing diseases and monitoring treatments. Conventional image reconstruction (IR) techniques like filtered backprojection and iterative algorithms are powerful but face limitations. PET IR can be seen as an image-to-image translation. Artificial intelligence (AI) and deep learning (DL) using multilayer neural networks enable a new approach to this computer vision task. This review aims to provide mutual understanding for nuclear medicine professionals and AI researchers...

No abstract text is available yet for this article.

No abstract text is available yet for this article.

BACKGROUND: Therapeutics that specifically address biological processes often require a much finer selection of patients and subclassification of diseases. Thus, diagnostic procedures must describe the diseases in sufficient detail to allow selection of appropriate therapy and to sensitively track therapy response. Anatomical features are often not sufficient for this purpose and there is a need to image molecular and pathophysiological processes. METHOD: Two imaging strategies can be pursued: molecular imaging attempts to image a few biomarkers that play key roles in pathological processes...

BACKGROUND: Machine learning (ML) is considered an important technology for future data analysis in health care. METHODS: The inherently technology-driven fields of diagnostic radiology and nuclear medicine will both benefit from ML in terms of image acquisition and reconstruction. Within the next few years, this will lead to accelerated image acquisition, improved image quality, a reduction of motion artifacts and - for PET imaging - reduced radiation exposure and new approaches for attenuation correction...

BACKGROUND:  Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes...

AIM: The aim of this study was to derive prognostic parameters from 2-[18 F]fluoro-2-deoxy-D-glucose ([18 F]FDG-PET/CT) in patients with low-risk NSCLC and determine their prognostic value. METHODS: 81 (21 female, mean age 66 a) therapy-naive patients that underwent [18F]FDG-PET/CT before histologic confirmation of NSCLC with stadium I and II between 2008-2016 were included. A mean follow-up time of 58 months (13-176), overall and progression free survival (OS, PFS) were registered...

Digitization in the healthcare sector and the support of clinical workflows with artificial intelligence (AI), including AI-supported image analysis, represent a great challenge and equally a promising perspective for preclinical and clinical nuclear medicine. In Germany, the Medical Informatics Initiative (MII) and the Network University Medicine (NUM) are of central importance for this transformation. This review article outlines these structures and highlights their future role in enabling privacy-preserving federated multi-center analyses with interoperable data structures harmonized between site-specific IT infrastructures...

PURPOSE: Prostate-specific membrane antigen (PSMA) is present in the proximal tubule cells of the kidneys. This results in high renal tracer uptake in PSMA-PET, which may contain useful information on renal function. As part of the evaluation for [177 Lu]-PSMA therapies, patients undergo PSMA-PET and additional [99m Tc]-mercapto-acetyltriglycine (MAG3) scintigraphy to assess renal function. Aim of this study was to evaluate estimation of renal function with [18 F]-PSMA-1007-PET/CT (PSMA-PET) by comparison to timely MAG3-scintigraphies...