[en] Soft-tissue and osseous impingement syndromes can be an important cause of chronic ankle pain, particularly in the professional athlete. The classification of ankle impingement syndromes is based to their anatomical location around the tibiotalar joint. The most important impingement syndromes are anterolateral, anterior and posterior impingement with more recent studies describing posteromedial and anteromedial impingement. Usually conventional radiography is the first imaging technique to be performed as it allows assessment of potential bone abnormalities, particularly in anterior and posterior joint compartments. Computed tomography (CT) only plays a role in the assessment of the posterior impingement. Magnetic resonance (MR) imaging is regarded as the modality of choice as it is able to demonstrate both osseous and soft tissue changes, such as bone marrow edema, capsular and ligametous thickening, and localized synovitis. (orig.)

[en] The book on measuring methods and classification in the musculoskeletal radiology covers the following topics: legs; hip joint; knee joint; foot; shoulder joint; elbow joint; wrist joint; spinal column; craniocervical transition region and cervical spine; muscular-skeletal carcinomas; osteoporosis; arthrosis; articular cartilage; hemophilia; rheumatic arthritis; muscular injuries; skeleton age.

[en] Neuroendocrine tumors (neuroendokrine Tumoren) are rare entities. They can be found in all organs and show substantial biologic heterogeneity depending on involved organ, clinical symptoms and histopathologic morphology. Involvement of organs like larynx, cervix uteri, ovary, gallbladder, liver or kidney is extensively rare. The majority of neuroendokrine Tumoren are found in gastrointestinal tract and lung and are classified as neuroendokrine Tumoren of foregut (stomach, duodenum, pancreas, lung), midgut (jejunum, ileum, appendix, right side of the colon) and hindgut (left side of the colon, rectum). The role of imaging is to localize and delineate the primary tumor and to detect metastases. In the diagnosis of neuroendokrine Tumoren radiologic techniques like computed tomography (CT) and magnetic resonance imaging (MRI) are applied. In certain cases nuclear medicine techniques like somatostatin receptor scintigraphy (SRS) and positron emission tomography (PET) using radioactively labelled somatostatin analogues are used. The present article reviews characteristic imaging findings of neuroendokrine Tumoren of the gastrointestinal tract. (orig.)

[en] After a decade of PET/MR, the case of attenuation correction (AC) remains open. The initial four-compartment (air, water, fat, soft tissue) Dixon-based AC scheme has since been expanded with several features, the latest being MR field-of-view extension and a bone atlas. As this potentially changes quantification, we evaluated the impact of these features in PET AC in prostate cancer patients. Two hundred prostate cancer patients were examined with either ${}^{18}$F- or ${}^{68}$Ga-prostate-specific membrane antigen (PSMA) PET/MR. Qualitative and quantitative analysis (SUV${}_{mean}$, SUV${}_{max}$, correlation, and statistical significance) was performed on images reconstructed using different AC schemes: Dixon, Dixon+MLAA, Dixon+HUGE, and Dixon+HUGE+bones for ${}^{18}$F-PSMA data; Dixon and Dixon+bones for ${}^{68}$Ga-PSMA data. Uptakes were compared using linear regression against standard Dixon. High correlation and no visually perceivable differences between all evaluated methods (r > 0.996) were found. The mean relative difference in lesion uptake of ${}^{18}$F-PSMA and ${}^{68}$Ga-PSMA remained, respectively, within 4% and 3% in soft tissue, and within 10% and 9% in bones for all evaluated methods. Bone registration errors were detected, causing mean uptake change of 5% in affected lesions. Based on these results and the encountered bone atlas registration inaccuracy, we deduce that including bones and extending the MR field-of-view did not introduce clinically significant differences in PSMA diagnostic accuracy and tracer uptake quantification in prostate cancer pelvic lesions, facilitating the analysis of serial studies respectively. However, in the absence of ground truth data, we advise against atlas-based methods when comparing serial scans for bone lesions.

[en] The emerging PSMA-targeted radionuclide therapy (RLT) is an effective treatment for metastatic castration-resistant prostate cancer (mCRPC). The European council mandates that treatments should be planned according to the radiation doses delivered to individual patients. However, there is no clinical practical method to predict the dosimetry before RLT, which hampers the realization of treatment planning. Therefore, we aimed to prove the concept to employ artificial intelligence methods to predict the post-therapy dosimetry. Retrospectively 17 patients with metastatic castration-resistant prostate cancer (mCRPC) treated with 177Lu-PSMA I and T RLT were included in this study. Only those cycles with ⁶⁸Ga-PSMA-HEBD-CC PET/CT directly before the treatment and at least 3 planar and 1 SPECT/CT dosimetry imaging were selected. Totally 30 cycles of treatments were considered for this proof-of-concept study. Organ-based mean and max SUV uptake were obtained from pretherapy PET/CT scans. Dosimetry was calculated for kidney, liver, spleen and salivary glands using Hermes Hybrid Dosimetry 4.0. Two machine learning methods, a 3-layer fully connected neural network artificial neural network (ANN) and a random forest regression (RFR) model, were established. 10-folder cross validation was applied to verify the trained network. Our results were compared with population-based dosimetry from literature. ANN has better performance than RFR in the dose prediction for liver and salivary glands, while worse performance for kidney and spleen. Combining the advantages, machine learning achieved the dosimetry prediction error of 18.7±16.0% for kidney, 31.4±27.0% for liver, 24.3±16.2% for salivary glands and 32.1±31.4% for spleen. In contrast, the prediction based on literature population mean has significantly larger error (p<0.01), 46.2±50.4% for kidney, 99.5±238.7% for liver, 705.7±377.7% for salivary glands, 62.3±58.9%. The proof of concept study shows that machine learning can significantly reduce the prediction error compared to generally population-based estimation. Artificial intelligence may provide a practical solution to improve the dosimetry-guided treatment planning for RLT. (author)

No abstract available

[en] Prostate-specific membrane antigen (PSMA) is expressed in most prostate cancers and can be identified by PSMA-ligand imaging, which has already become clinically accepted in several countries in- and outside Europe. PSMA-directed radioligand therapy (PSMA-RLT) with Lutetium-177 (${}^{177}$Lu-PSMA) is currently undergoing clinical validation. Retrospective observational data have documented favourable safety and striking clinical responses. Recent results from a prospective clinical trial (phase II) have been published confirming high response rates, low toxicity and reduction of pain in metastatic castration-resistant prostate cancer (mCRPC) patients who had progressed after conventional treatments. Such patients typically survive for periods less than 1.5 years. This has led some facilities to adopt compassionate or unproven use of this therapy, even in the absence of validation within a randomised-controlled trial. As a result, a consistent body of evidence exists to support efficacy and safety data of this treatment. The purpose of this guideline is to assist nuclear medicine specialists to deliver PSMA-RLT as an “unproven intervention in clinical practice”, in accordance with the best currently available knowledge.

[en] Prostate cancer (PC) is one of the most commonly treated cancer entities with radiation therapy (RT). Risk group-adapted treatment and avoidance of unnecessary toxicities relies primarily on accurate tumor staging. Thus, the introduction of prostate-specific membrane antigen (PSMA) in diagnosis and treatment of PC is a highly interesting development in radiation oncology of urologic tumors. The present work is to evaluate the integration of "6"8Ga-PSMA-PET imaging into standard radiation planning of primary definitive treatment of PC and to determine the impact of PSMA imaging on tumor staging. The data of 15 patients treated for PC between August 2013 and April 2015 were evaluated. Treatment planning included "6"8Ga-PSMA-PET imaging. We analyzed whether the use of PSMA-imaging led to a change of the TNM stage and if it influenced the RT treatment approach or the target volume, due to changes in the gross tumor volume (GTV) or clinical target volume (CTV), in the final treatment plan. In 53.3 % of the analyzed patients a change occurred in the TNM stage based on "6"8Ga-PSMA-PET examination. The RT concept changed in 33.3 % of all patients, leading to relevant changes in the planning target volume. Among these, an additional irradiation of the pelvic lymph drainage due to tracer uptake in lymph nodes was performed in 25 %. Furthermore, boost volumes of PET-positive lymph nodes were added in 80 % of these cases. A down staging due to the "6"8Ga-PSMA-PET examination occurred in 13.3 % of all cases. The integration of "6"8Ga-PSMA-PET-imaging into the RT treatment planning process can be useful for detailed target volume planning. The performance of a "6"8Ga-PSMA-PET frequently leads to changes in the TNM stage, altering the RT treatment regimen and the target volume. A prospective trial is underway to evaluate the impact of "6"8Ga-PSMA-PET based treatment planning on outcome

[en] Radioligand therapy (RLT) with PSMA-ligands is an emerging treatment option for metastatic castration-resistant prostate cancer (mCRPC). Despite the therapy's success, no treatment planning tool exists to estimate individual dose before the treatment. Deep learning was proposed to predict posttherapy dose distribution based on pretherapy imaging. However, it is still challenging for pure data-driven models to dissect the complex pharmacokinetic relation behind theranostics from limited data for dose prediction. In this study, we explored data augmentation with physiological and pharmacological knowledge by introducing a physiologically based pharmacokinetic (PBPK) model as regularization. The PBPK-based predictions are directly integrated within the generator's loss function of a conditional generative adversarial network (cGAN) to improve organ-specific therapy prediction. The proposed method was trained and tested on virtual patients generated from realistic pharmacokinetic, physiological, and physical simulations. This study aimed to assess the impact of the PBPK constraint on the dose accuracy, the mean absolute percentage error (MAPE) was calculated for each studied organ (liver, spleen, and kidneys). The unconstrained model achieved a MAPE of 2.26 ± 4.88% (mean ± standard deviation), 9.82 ± 4.31 and 22.29 ± 10.49% for each organ, respectively. In the case of the PBPK, constrained model the obtained MAPE were 1.13 ± 1.36, 7.68 ± 4.02, and 20.08 ± 9.36%. Our experimental results demonstrate that incorporation of pharmacological and physiological knowledge might help to overcome the limitations of deep learning as a tool for personalized RLT treatment planning. (author)

[en] PET/MR is a new multimodal imaging technique that is expected to improve diagnostic performance of imaging in conditions in which assessment of changes in soft tissue is important such as prostate cancer. Despite substantial changes in PET technology compared to PET/CT, initial studies have demonstrated that integrated PET/MR provides comparable image quality to that of PET/CT, retaining PET quantification efficacy. In this review we briefly describe technological changes compared to PET/CT that made integrated PET/MR possible, propose acquisition protocols for evaluation of prostate cancer with this new multimodal approach, present initial results concerning the application of PET/MR in prostate cancer, and outline the potential for further clinical applications, focusing on potential incremental value compared to present diagnostic performance. (orig.)