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Objective To assess the performance of one-view digital breast tomosynthesis (DBT) in breast cancer screening. Methods The Malmö Breast Tomosynthesis Screening Trial is a prospective population-based one-arm study with a planned inclusion of 15000 participants; a random sample of women aged 40–74 years eligible for the screening programme. This is an explorative analysis of the first half of the study population (n = 7500). Participants underwent one-view DBT and two-view digital mammography (DM), with independent double reading and scoring. Primary outcome measures were detection rate, recall rate and positive predictive value (PPV). McNemar's test with 95 % confidence intervals was used. Results Breast cancer was found in sixty-eight women. Of these, 46 cases were detected by both modalities, 21 by DBT alone and one by DM alone. The detection rate for one-view DBT was 8.9/1000 screens (95 % CI 6.9 to 11.3) and 6.3/1000 screens (4.6 to 8.3) for two-view DM (p < 0.0001). The recall rate after arbitration was 3.8 % (3.3 to 4.2) for DBT and 2.6 % (2.3 to 3.0) for DM (p < 0.0001). The PPV was 24 % for both DBT and DM. Conclusion Our results suggest that one-view DBT might be feasible as a stand-alone screening modality. Key Points • One-view DBT as a stand-alone breast cancer screening modality has not been investigated. • One-view DBT increased the cancer detection rate significantly. • The recall rate increased significantly but was still low. • Breast cancer screening with one-view DBT as a stand-alone modality seems feasible.

A framework is proposed for modelling the uncertainty in the measurement processes constituting the dosimetry chain that are involved in internal absorbed dose calculations. The starting point is the basic model for absorbed dose in a site of interest as the product of the cumulated activity and a dose factor. In turn, the cumulated activity is given by the area under a time–activity curve derived from a time sequence of activity values. Each activity value is obtained in terms of a count rate, a calibration factor and a recovery coefficient (a correction for partial volume effects). The method to determine the recovery coefficient and the dose factor, both of which are dependent on the size of the volume of interest (VOI), are described. Consideration is given to propagating estimates of the quantities concerned and their associated uncertainties through the dosimetry chain to obtain an estimate of mean absorbed dose in the VOI and its associated uncertainty. This approach is demonstrated in a clinical example.

Purpose To update the guidelines of the Contrast Media Safety Committee (CMSC) of the European Society of Urogenital Radiology (ESUR) on nephrogenic systemic fibrosis and gadolinium-based contrast media. Areas covered Topics reviewed include the history, clinical features and prevalence of nephrogenic systemic fibrosis and the current understanding of its pathophysiology. The risk factors for NSF are discussed and prophylactic measures are recommended. The stability of the different gadolinium-based contrast media and the potential long-term effects of gadolinium in the body have also been reviewed. Key Points • Clinical features, risk factors and prevention of nephrogenic systemic fibrosis are reviewed • Patients with GFR below 30 ml/min/1.73 m 2 have increased risk of developing NSF • Low stability gadolinium contrast media show the strongest association with NSF • Following guidelines regarding gadolinium contrast agents minimises the risk of NSF • Potential long-term harm from gadolinium accumulation in the body is discussed

Purpose In the last decade, the research community has focused on defining reliable biomarkers for the early detection of Alzheimer’s disease (AD) pathology. In 2017, the Geneva AD Biomarker Roadmap Initiative adapted a framework for the systematic validation of oncological biomarkers to cerebrospinal fluid (CSF) AD biomarkers—encompassing the 42 amino-acid isoform of amyloid-β (Aβ42), phosphorylated-tau (P-tau), and Total-tau (T-tau)—with the aim to accelerate their development and clinical implementation. The aim of this work is to update the current validation status of CSF AD biomarkers based on the Biomarker Roadmap methodology. Methods A panel of experts in AD biomarkers convened in November 2019 at a 2-day workshop in Geneva. The level of maturity (fully achieved, partly achieved, preliminary evidence, not achieved, unsuccessful) of CSF AD biomarkers was assessed based on the Biomarker Roadmap methodology before the meeting and presented and discussed during the workshop. Results By comparison to the previous 2017 Geneva Roadmap meeting, the primary advances in CSF AD biomarkers have been in the area of a unified protocol for CSF sampling, handling and storage, the introduction of certified reference methods and materials for Aβ42, and the introduction of fully automated assays. Additional advances have occurred in the form of defining thresholds for biomarker positivity and assessing the impact of covariates on their discriminatory ability. Conclusions Though much has been achieved for phases one through three, much work remains in phases four (real world performance) and five (assessment of impact/cost). To a large degree, this will depend on the availability of disease-modifying treatments for AD, given these will make accurate and generally available diagnostic tools key to initiate therapy.

Artificial intelligence (AI) will change the face of nuclear medicine and molecular imaging as it will in everyday life. In this review, we focus on the potential applications of AI in the field, both from a physical (radiomics, underlying statistics, image reconstruction and data analysis) and a clinical (neurology, cardiology, oncology) perspective. Challenges for transferability from research to clinical practice are being discussed as is the concept of explainable AI. Finally, we focus on the fields where challenges should be set out to introduce AI in the field of nuclear medicine and molecular imaging in a reliable manner.

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

Artificial intelligence (AI) is coming into the field of nuclear medicine, and it is likely here to stay. As a society, EANM can and must play a central role in the use of AI in nuclear medicine. In this position paper, the EANM explains the preconditions for the implementation of AI in NM and takes position.

Low-dose computed tomography (LDCT) has offered tremendous benefits in radiation-restricted applications, but the quantum noise as resulted by the insufficient number of photons could potentially harm the diagnostic performance. Current image-based denoising methods tend to produce a blur effect on the final reconstructed results especially in high noise levels. In this paper, a deep learning-based approach was proposed to mitigate this problem. An adversarially trained network and a sharpness detection network were trained to guide the training process. Experiments on both simulated and real dataset show that the results of the proposed method have very small resolution loss and achieves better performance relative to state-of-the-art methods both quantitatively and visually.

Purpose [ 18 F]flortaucipir binds to paired helical filament tau and accurately identifies tau in Alzheimer’s disease (AD). However, “off-target” binding interferes with the quantification of [ 18 F]flortaucipir in several brain regions. Recently, other tau PET tracers have been developed. Here, we compare [ 18 F]flortaucipir with the novel tau tracer [ 18 F]RO948 head-to-head in vivo. Methods We included 18 participants with AD, three with amyloid-β-positive amnestic mild cognitive impairment, and four healthy controls. All underwent [ 18 F]flortaucipir (80–100 min) and [ 18 F]RO948 (70–90) PET scans within approximately 1 month. Four study participants underwent 0–100-min dynamic scanning. Standardized uptake value ratios (SUVRs) were created using an inferior cerebellar reference region. Results Neocortical tracer retention was highly comparable using both SUVR and distribution volume ratio-1 values obtained from dynamic scans. However, [ 18 F]RO948 retention was significantly higher in the entorhinal cortex and lower in the basal ganglia, thalamus, and choroid plexus compared with [ 18 F]flortaucipir. Increased off-target binding was observed with age for both tracers. Several cases exhibited strong [ 18 F]RO948 retention in the skull/meninges. This extra-cerebral signal, however, did not affect diagnostic accuracy and remained relatively unchanged when re-examining a subsample after 1 year. Kinetic modeling showed an increase in [ 18 F]flortaucipir SUVR over the scanning interval, compared with a plateau for [ 18 F]RO948. Conclusion [ 18 F]RO948 and [ 18 F]flortaucipir bound comparably in neocortical regions, but [ 18 F]RO948 showed higher retention in the medial temporal lobe and lower intracerebral “off-target” binding. Time-dependent bias of SUVR estimates may prove less of a factor with [ 18 F]RO948, compared with previous tau ligands.

Background To date, the estimated radiation-absorbed dose to organs and tissues in patients undergoing diagnostic examinations in nuclear medicine is derived via calculations based on models of the human body and the biokinetic behaviour of the radiopharmaceutical. An internal dosimetry computer program, IDAC-Dose2.1, was developed based on the International Commission on Radiological Protection (ICRP)-specific absorbed fractions and computational framework of internal dose assessment given for reference adults in ICRP Publication 133. The program uses the radionuclide decay database of ICRP Publication 107 and considers 83 different source regions irradiating 47 target tissues, defining the effective dose as presented in ICRP Publications 60 and 103. The computer program was validated against another ICRP dosimetry program, Dose and Risk Calculation (DCAL), that employs the same computational framework in evaluation of occupational and environmental intakes of radionuclides. IDAC-Dose2.1 has a sub-module for absorbed dose calculations in spherical structures of different volumes and composition; this sub-module is intended for absorbed dose estimates in radiopharmaceutical therapy. For nine specific alpha emitters, the absorbed dose contribution from their decay products is also included in the committed absorbed dose calculations. Results The absorbed doses and effective dose of 131 I-iodide determined by IDAC-Dose2.1 were validated against the dosimetry program DCAL, showing identical results. IDAC-Dose2.1 was used to calculate absorbed doses for intravenously administered 18 F-FDG and orally administered 99m Tc-pertechnetate and 131 I-iodide, three frequently used radiopharmaceuticals. Using the tissue weighting factors from ICRP Publication 103, the effective dose per administered activity was estimated to be 0.016 mSv/MBq for 18 F-FDG, 0.014 mSv/MBq for 99m Tc-pertechnetate, and 16 mSv/MBq for 131 I-iodide. Conclusions The internal dosimetry program IDAC-Dose2.1 was developed and applied to three radiopharmaceuticals for validation against DCAL and to generate improved absorbed dose estimations for diagnostic nuclear medicine using specific absorbed fraction values of the ICRP computational voxel phantoms. The sub-module for absorbed dose calculations in spherical structures 1 mm to 9 cm in diameter and different tissue composition was included to broaden the clinical usefulness of the program. The IDAC-Dose2.1 program is free software for research and available for download at http://www.idac-dose.org .