Open Access

The integration of machine learning methodologies has become prevalent in the development of clinical prediction models, often suggesting superior performance compared to traditional statistical techniques. Within the scope of low-dimensional datasets, encompassing both classical and machine learning paradigms, we plan to undertake a comparison of variable selection methodologies through simulation-based analysis. The principal aim is the comparison of the variable selection strategies with respect to relative predictive accuracy and its variability, with a secondary aim the comparison of descriptive accuracy. We use six distinct statistical learning approaches across both data generation and model learning. The present manuscript is a protocol for the corresponding simulation study registration (Study registration Open Science Framework ID: k6c8f). We describe the planned steps through the Aims, Data, Estimands, Methods, and Performance framework for simulation study design and reporting.

The integration of machine learning methodologies has become prevalent in the development of clinical prediction models, often suggesting superior performance compared to traditional statistical techniques. Within the scope of low-dimensional datasets, encompassing both classical and machine learning paradigms, we plan to undertake a comparison of variable selection methodologies through simulation-based analysis. The principal aim is the comparison of the variable selection strategies with respect to relative predictive accuracy and its variability, with a secondary aim the comparison of descriptive accuracy. We use six distinct statistical learning approaches across both data generation and model learning. The present manuscript is a protocol for the corresponding simulation study. We describe the planned steps through the Aims, Data, Estimands, Methods, and Performance framework for simulation study design and reporting.

Disclosure: O. Abawi: None. G. Sommer: None. M. Groessl: None. U. Halbsguth: None. S.E. Hannema: None. C. de Bruin: None. E. Charmandari: None. E.L. van den Akker: None. A.B. Leichtle: None. C.E. Flueck: None. Introduction: Current treatment monitoring of children with congenital adrenal hyperplasia (CAH) relies on specialist’s interpretation of clinical and biochemical parameters, but remains dissatisfactory. Comprehensive 24h urine steroid profiling provides detailed insight into adrenal steroid pathways, but its merit in routine treatment monitoring of CAH is not yet established. Aim: To investigate whether 24h urine steroid profiling can predict treatment quality assessment in children with CAH using machine learning (ML). Methods: This prospective observational cohort study included children with genetically confirmed 21-hydroxylase deficiency. Children collected 24h urine at 2 outpatient clinic visits (mean 4.1 ± 0.7 months apart). Using gas chromatography-mass spectrometry, 40 adrenal steroids and metabolites from the classic, backdoor and 11-oxygenated pathways were analysed. Patients were classified as undertreated, optimally treated or overtreated by the pediatric endocrinologist based on detailed clinical and endocrinological evaluation including serum 17-hydroxyprogesterone and androstenedione. We used sparse partial least-squares discriminant analysis (sPLS-DA) to investigate optimal prediction of treatment quality assessment. This ML method computes components (combinations of all input variables) and selects the most discriminative parameters to classify samples (in our case optimally treated vs undertreated) by maximizing between-class variance. We computed area under the ROC-curve (AUC) of two sPLS-DA models: 1. using only 24h urine metabolites; 2. adding also clinical variables age, sex, pubertal status, CAH subtype (classic vs non-classic), medication (hydrocortisone [HC] vs prednisolone), daily HC-equivalent dose, Δbone age minus chronological age, ΔBMI-z, and Δheight-z. Results: We included 112 visits (68 [61%] optimally treated, 44 [39%] undertreated) of 59 patients: 27 (46%) girls, 46 (78%) classic CAH, 19 (32%) prepubertal. Mean age at first visit was 11.9 ± 4.0 years and mean BMI SDS 0.6 ± 1.1. SPLS-DA using 24h urine metabolites showed clear clustering of optimally treated patients on two components, while undertreated patients were more heterogenous (AUC 0.88; 95% CI 0.81-0.94). The model selected pregnanetriol and hydroxypregnanolon contributing to excluding undertreatment and 7 metabolites contributing to excluding optimal treatment: estradiol, cortison, tetrahydroaldosterone, androstenetriol, etiocholanolone, androstenediol, and α-dihydrocortison. Addition of clinical variables did not improve classification (AUC 0.90, 95% CI 0.84-0.96, P=0.59). Discussion: Using ML on 24h urine steroid profiling predicted treatment quality assessment in children with CAH even in absence of clinical data, suggesting that comprehensive 24h urine steroid profiling could improve treatment monitoring in children with CAH. Presentation: Thursday, June 15, 2023

Purpose Swiss BioRef is a nation-wide multicentre infrastructure project, the aim of which is to become a sustainable framework for the estimation and assessment of patient-group-specific reference intervals in laboratory medicine and beyond. In this unprecedented effort, nation-wide multidimensional data from multiple clinical laboratory databases has been combined under the common interoperable semantic framework of the Swiss Personalized Health Network (SPHN) initiative. The consolidated effort enables creating extremely detailed patient group-specific queries via intuitive web applications, allowing the generation of individualised, covariate adjusted reference intervals on-the-fly. Participants The project is a collaborative effort of four major hospitals in Switzerland, the University Hospital Bern (Inselspital, “Insel”), University Hospital Lausanne (CHUV), Swiss Spinal Cord Injury Cohort (“SwiSCI”) and the University Children’s Hospital Zurich (“KiSpi”), and two academic groups in Bern and in Lausanne. Findings to date Within the infrastructure we deployed, the laboratory data from four major hospitals (approximately 9 million measurements from 250’000 patients) is made available to two conceptually different web applications (one centralised and statistically detailed, one decentralised using distributed computing). They enable the inference of reference intervals for more than 40 blood test variables from clinical chemistry, haematology, point-of-care-testing, and coagulation testing, with various patient factors (such as age, sex and a combination of ICD-10 defined diagnoses) and analytical factors (such as type or unique identifiers) that can be used to generate precise reference intervals for the respective groups. Future plan Now that all required basic infrastructure elements for Swiss BioRef are deployed, we are evaluating inter-cohort transferability of semantic standards, “change tracking” in merged databases and biological variation of the blood test variables, in order to generate precise reference intervals. While adjusting the developed web-interfaces to suit the needs of the various end-users, we additionally plan to onboard new national and international partners. Strengths and limitations of this study The Swiss BioRef project is the first multi-cohort infrastructure in Switzerland for the estimation of precise reference intervals in laboratory medicine. With the BioRef consortium agreement a common framework for multi-cohort data sharing, hosting, and accessing has been thoroughly defined. The definition of interoperable data formats and data encoding for Swiss BioRef permits the fusion of the various data sources into a unified infrastructure. Due to differing data management systems at the individual clinical data warehouses, the harmonisation of data contributions requires significant effort which limits direct data provision. Two different web applications with varying data access architectures enable researchers to map the individual complexity of their patients into a substantiated statistical analysis to infer precise and highly relevant reference intervals. Needless to say, anticipating the requirements of an increasingly diverse user base remains a challenging task. Due to the modular expandable architecture of Swiss BioRef, potential national and international partners can easily access and even join the network.

Objectives Reference intervals for the general clinical practice are expected to cover non-pathological values, but also reflect the underlying biological variation present in age- and gender-specific patient populations. Reference intervals can be inferred from routine patient data measured in high capacity using parametric approaches. Stratified reference distributions are obtained which may be transformed to normality via e.g. a Yeo-Johnson transformation. The estimation of the optimal transformation parameter for Yeo-Johnson through maximum likelihood can be highly influenced by the presence of outlying observations, resulting in biased reference interval estimates. Methods To reduce the influence of outlying observations on parametric reference interval estimation, a reweighted M-estimator approach for the Yeo-Johnson (YJ) transformation was utilised to achieve central normality in stratified reference populations for a variety of laboratory test results. The reweighted M-estimator for the YJ transformation offers a robust parametric approach to infer relevant reference intervals. Results The proposed method showcases robustness up to 15 % of outliers present in routine patient data, highlighting the applicability of the reweighted M-estimator in laboratory medicine. Furthermore, reference intervals are personalised based on the patients’ age and gender for a variety of analytes from routine patient data collected in a tertiary hospital, robustly reducing the dimensionality of the data for more data-driven approaches. Conclusions The method shows the advantages for estimating reference intervals directly and parametrically from routine patient data in order to provide expected reference ranges. This approach to locally inferred reference intervals allows a more nuanced comparison of patients’ test results.

Background BRAF V600 mutations are the epitome of targeted therapy. However, not much is known about non-V600 mutations. Using the new data infrastructure of the Swiss Personalized Oncology project of the Swiss Personalized Health Network (SPHN), we evaluated the fate of patients with cancer with non-V600 BRAF mutations in comparison to patients with class 1 mutations. Patients and methods In this retrospective observational multicenter study, we have assembled a cohort of 392 patients with class 1 and 154 patients with nonclass 1 BRAF mutations (76 colorectal cancers, 96 lung cancers, 297 melanomas, and 77 other cancers). We carried out outcome analyses between mutational classes and therapeutic subgroups. Results Overall survival (OS) did not differ significantly between patients with class 1 and nonclass 1 mutations. Upon treatment with BRAF/MEK inhibitors, patients with class 1 mutant melanoma showed numerically longer progression-free survival (PFS; 217 days) than patients with nonclass 1 mutant disease (73 days). Overall, in patients with class 2 or 3 mutations, BRAF and MEK inhibitors showed no benefit over other systemic therapies. However, specific class 2 mutations such as K601E may confer sensitivity to BRAF/MEK inhibitors, with two out of five patients achieving a PFS >400 days. Conclusions The diversity of BRAF mutations presents significant treatment challenges. Despite similar OS, nonclass 1 mutant tumors showed a trend toward lower PFS with BRAF/MEK blockade. Selected class 2 mutations may confer sensitivity to BRAF/MEK inhibitors. This highlights the rationale for a mutation, rather than class-specific, clinical approach against nonclass 1 BRAF-mutant tumors.

MedCo is the first operational system that makes sensitive medical-data available for research in a simple, privacy-conscious and secure way. It enables a consortium of clinical sites to collectively protect their data and to securely share them with investigators, without single points of failure. In this short paper, we report on our ongoing effort for the operational deployment of MedCo within the context of the Swiss Personalized Health Network (SPHN) for the Swiss Molecular Tumor Board.

Idiopathic pulmonary fibrosis (IPF), hypersensitivity pneumonitis (HP) and systemic sclerosis (SSc) are among the most common entities that cause pulmonary fibrosis. Alveolar collapse with subsequent collapse induration of lung tissue is thought to contribute to the fibrotic transformation. The purpose of this study was to examine lung tissue in computed tomography (CT) of non-diseased appearance during expiration for signs of increased density suggesting collapsibility in fibrosing lung diseases. We further analyzed the diaphragmatic movements during the respiratory cycle to determine relationships between density differences and the apex–diaphragm diameter. Significant differences in attenuation changes between inspiration and expiration of unaffected lung parenchyma were detected between IPF and controls and between HP and controls for all lung lobes (p < 0.001). Only minor differences were found between SSc and controls. There was no clinically relevant difference between patients with IPF and those with HP. The measured absolute apex–diaphragm diameter in inspiration and expiration demonstrated a statistically significant difference between patients with IPF versus normal controls. However, the diaphragmatic excursions were not different between these groups. Compared to controls, CT lung density increases significantly more during expiration in the fibrotic lungs of IPF and HP patients. The observed increase in density might indicate the collapse of alveoli during expiration and may represent a common pathophysiologic feature of fibrosing lung diseases. The density changes and lung extensions do not have the same ratios across different diseases and controls.

Die Anwendung massenspektrometrischer Analysenmethoden gewinnt in der klinischen Labordiagnostik zunehmend an Bedeutung. In den vergangenen Jahren stieg die Anzahl von Applikationen in der Laboratoriumsmedizin, Pharmakologie und Toxikologie exponentiell an. Das Anwendungsspektrum reicht gegenwärtig von der Bestimmung einzelner klinisch-chemischer Analyte bis hin zur qualitativen und quantitativen Multiparameteranalyse. Die Kombination der Tandem-Massenspektrometrie mit Atmosphärendruck-Ionisationstechniken stellt heute infolge der hohen analytischen Sensitivität und Spezifität, der Robustheit, der geringen Analysekosten und insbesondere der Möglichkeit von Hochdurchsatzanalysen die Methode der Wahl für die Anwendung in der klinischen Labordiagnostik dar. Gegenwärtig wird diese Plattform bereits routinemäßig für das Neugeborenenscreening auf angeborene Stoffwechseldefekte, für das therapeutische Drugmonitoring sowie in der Hormondiagnostik eingesetzt. Darüber hinaus finden SELDITOF- oder MALDI-TOF-Verfahren Anwendung bei der Suche nach neuen Biomarkern im Rahmen der klinischen Proteomics. Ziel dieser Übersicht ist es, eine kurze Darstellung zum technischen Entwicklungsstand der Massenspektrometrie und zu aktuellen Applikationen in der labormedizinischen Diagnostik zu geben.

Background Accelerated bone loss occurs rapidly following renal transplantation due to intensive immunosuppression and persistent hyperparathyroidism. In renal transplant recipients (RTRs) due to the hyperparathyroidism the non-dominant forearm is often utilized as a peripheral measurement site for dual-energy x-ray absorptiometry (DXA) measurements. The forearm is also the site of previous created distal arteriovenous fistulas (AVF). Although AVF remain patent long after successful transplantation, there are no data available concerning their impact on radial bone DXA measurements. Methods In this cross-sectional study we performed DXA in 40 RTRs with preexisting distal AVF (RTRs-AVF) to assess areal bone mineral density (aBMD) differences between both forearms (three areas) and compared our findings to patients with chronic kidney disease (CKD, n = 40), pre-emptive RTRs (RTRs-pre, n = 15) and healthy volunteers (n = 20). In addition, we assessed relevant demographic, biochemical and clinical aspects. Results We found a marked radial asymmetry between the forearms in RTRs with preexisting AVF. The radial aBMD at the distal AVF forearm was lower compared to the contralateral forearm, resulting in significant differences for all three areas analyzed: the Rad-1/3: median (interquartile range) in g/cm2, Rad-1/3: 0.760 (0.641–0.804) vs. 0.742 (0.642, 0.794), p = 0.016; ultradistal radius, Rad-UD: 0.433 (0.392–0.507) vs. 0.420 (0.356, 0.475), p = 0.004; and total radius, Rad-total: 0.603 (0.518, 0.655) vs. 0.599 (0.504, 0.642), p = 0.001). No such asymmetries were observed in any other groups. Lower aBMD in AVF forearm subregions resulted in misclassification of osteoporosis. Conclusions In renal transplant recipients, a previously created distal fistula may exert a negative impact on the radial bone leading to significant site-to-site aBMD differences, which can result in diagnostic misclassifications.

Multi-cohort projects in medicine provide an opportunity to investigate scientific questions beyond the boundaries of a single institution and endeavor to increase the sample size for obtaining more reliable results. However, the complications of these kinds of collaborations arise during management, with many administrative hurdles. Hands-on approaches and lessons learned from previous collaborations provide solutions for optimized collaboration models. Here, we use our experience in running PGX-link, a Swiss multi-cohort project, to show the strategy we used to tackle different challenges from project setup to obtaining the relevant permits, including ethics approval. We set PGX-link in an international context because our struggles were similar to those encountered during the SYNCHROS (SYNergies for Cohorts in Health: integrating the ROle of all Stakeholders) project. We provide ad hoc solutions for cohorts, general project management strategies, and suggestions for unified protocols between cohorts that would ease current management hurdles. Project managers are not necessarily familiar with medical projects, and even if they are, they are not aware of the intricacies behind decision-making and consequently, of the time needed to set up multi-cohort collaborations. This paper is meant to be a brief overview of what we experienced with our multi-cohort project and provides the necessary practices for future managers.

Background: The criteria for the diagnosis of kidney disease outlined in the Kidney Disease: Improving Global Outcomes guidelines are based on a patient’s current, historical, and baseline data. The diagnosis of acute kidney injury, chronic kidney disease, and acute-on-chronic kidney disease requires previous measurements of creatinine, back-calculation, and the interpretation of several laboratory values over a certain period. Diagnoses may be hindered by unclear definitions of the individual creatinine baseline and rough ranges of normal values that are set without adjusting for age, ethnicity, comorbidities, and treatment. The classification of correct diagnoses and sufficient staging improves coding, data quality, reimbursement, the choice of therapeutic approach, and a patient’s outcome. Objective: In this study, we aim to apply a data-driven approach to assign diagnoses of acute, chronic, and acute-on-chronic kidney diseases with the help of a complex rule engine. Methods: Real-time and retrospective data from the hospital’s clinical data warehouse of inpatient and outpatient cases treated between 2014 and 2019 were used. Delta serum creatinine, baseline values, and admission and discharge data were analyzed. A Kidney Disease: Improving Global Outcomes–based SQL algorithm applied specific diagnosis-based International Classification of Diseases (ICD) codes to inpatient stays. Text mining on discharge documentation was also conducted to measure the effects on diagnosis. Results: We show that this approach yielded an increased number of diagnoses (4491 cases in 2014 vs 11,124 cases of ICD-coded kidney disease and injury in 2019) and higher precision in documentation and coding. The percentage of unspecific ICD N19-coded diagnoses of N19 codes generated dropped from 19.71% (1544/7833) in 2016 to 4.38% (416/9501) in 2019. The percentage of specific ICD N18-coded diagnoses of N19 codes generated increased from 50.1% (3924/7833) in 2016 to 62.04% (5894/9501) in 2019. Conclusions: Our data-driven method supports the process and reliability of diagnosis and staging and improves the quality of documentation and data. Measuring patient outcomes will be the next step in this project.

Antibody testing in inflammatory bowel disease (IBD) can add to diagnostic accuracy of the main subtypes Crohn’s disease (CD) and ulcerative colitis (UC). Whether modern modeling techniques such as supervised and unsupervised machine learning are of value for finer distinction of subtypes such as IBD-unclassified (IBD-U) is not known. We determined the antibody profile of 100 adult IBD patients from the Swiss IBD cohort study with known subtype (50 CD, 50 UC) as well as of 76 IBD-U patients. We included ASCA IgG and IgA, p-ANCA, MPO- and PR3-ANCA, and xANCA measurements for computing different antibody panels as well as machine learning models. The AUC of an optimized antibody panel was 85% (95%CI, 78–92%) to distinguish CD from UC patients. The antibody profile of IBD-U patients was closely related to UC. No specific antibody profile was predictive for IBD-U nor for re-classification. The panel diagnostic was in favor of UC reclassification prediction with a correct assignment rate of 69.2–73.1% depending on the cut-off applied. Supervised machine learning could not distinguish between CD, UC, and IBD-U. More so, unsupervised machine learning suggested only two distinct clusters as a likely number of IBD subtypes. Antibodies in IBD are supportive in confirming clinical determined subtypes CD and UC but have limited capacity to predict IBD-U and reclassification during follow-up. In terms of antibody profiles, IBD-U is not a distinct subtype of IBD.

Aim of the study The aim was to analyse if ibuprofen, as a non-selective cyclooxygenase (COX) inhibitor, has any negative effect on oocyte competence and embryo quality. COX- inhibitors are popular over-the-counter analgesics. Whereas selective COX inhibitors have been shown to impair female fertility, data on non-selective COX inhibitors are poor. Hence, they have not been recommended for women trying to conceive. Methods This is an observational study comparing ibuprofen exposed and unexposed women from 18 to 42 years of age, using the model of natural cycle in vitro fertilisation (IVF) to determine oocyte and embryo quality. Follicular growth was monitored and if the follicle was mature (≥ 15mm size and estimated oestradiol level of ≥ 800pmol/l), ovulation was triggered. Women with luteinising hormone (LH) surge received 400mg ibuprofen every 8 hours to postpone ovulation, whereas women without LH surge received none (controls). Oocyte retrieval rate, oocyte maturity, fertilization rate, embryo development and embryo quality as well as implantation rate were analysed. Results Of the 111 women included, 63 received ibuprofen, and 48 did not. Rates of mature oocytes and implantation rate did not differ. Logistic regression showed no significant association of ibuprofen intake, LH- level or reason for infertility on embryo quality. Conclusion Based on our results, we suggest that, particularly within natural cycle IVF, ibuprofen does no harm around ovulation as analgesic treatment.

Background: Urine flow cytometry (UFC) analyses urine samples and determines parameter counts. We aimed to predict different types of urine culture growth, including mixed growth indicating urine culture contamination. Methods: A retrospective cohort study (07/2017–09/2020) was performed on pairs of urine samples and urine cultures obtained from adult emergency department patients. The dataset was split into a training (75%) and validation set (25%). Statistical analysis was performed using a machine learning approach with extreme gradient boosting to predict urine culture growth types (i.e., negative, positive, and mixed) using UFC parameters obtained by UF-4000, sex, and age. Results: In total, 3835 urine samples were included. Detection of squamous epithelial cells, bacteria, and leukocytes by UFC were associated with the different types of culture growth. We achieved a prediction accuracy of 80% in the three-class approach. Of the n = 126 mixed cultures in the validation set, 11.1% were correctly predicted; positive and negative cultures were correctly predicted in 74.0% and 96.3%. Conclusions: Significant bacterial growth can be safely ruled out using UFC parameters. However, positive urine culture growth (rule in) or even mixed culture growth (suggesting contamination) cannot be adequately predicted using UFC parameters alone. Squamous epithelial cells are associated with mixed culture growth.

The amount of data generated in the field of laboratory medicine has grown to an extent that conventional laboratory information systems (LISs) are struggling to manage and analyze this complex, entangled information (“Big Data”). Statistical learning, a generalized framework from machine learning (ML) and artificial intelligence (AI) is predestined for processing “Big Data” and holds the potential to revolutionize the field of laboratory medicine. Personalized medicine may in particular benefit from AI-based systems, especially when coupled with readily available wearables and smartphones which can collect health data from individual patients and offer new, cost-effective access routes to healthcare for patients worldwide. The amount of personal data collected, however, also raises concerns about patient-privacy and calls for clear ethical guidelines for “Big Data” research, including rigorous quality checks of data and algorithms to eliminate underlying bias and enable transparency. Likewise, novel federated privacy-preserving data processing approaches may reduce the need for centralized data storage. Generative AI-systems including large language models such as ChatGPT currently enter the stage to reshape clinical research, clinical decision-support systems, and healthcare delivery. In our opinion, AI-based systems have a tremendous potential to transform laboratory medicine, however, their opportunities should be weighed against the risks carefully. Despite all enthusiasm, we advocate for stringent added-value assessments, just as for any new drug or treatment. Human experts should carefully validate AI-based systems, including patient-privacy protection, to ensure quality, transparency, and public acceptance. In this opinion paper, data prerequisites, recent developments, chances, and limitations of statistical learning approaches are highlighted.

Objective: From January 1, 2018, until July 31, 2020, our hospital network experienced an outbreak of vancomycin-resistant enterococci (VRE). The goal of our study was to improve existing processes by applying machine-learning and graph-theoretical methods to a nosocomial outbreak investigation. Methods: We assembled medical records generated during the first 2 years of the outbreak period (January 2018 through December 2019). We identified risk factors for VRE colonization using standard statistical methods, and we extended these with a decision-tree machine-learning approach. We then elicited possible transmission pathways by detecting commonalities between VRE cases using a graph theoretical network analysis approach. Results: We compared 560 VRE patients to 86,684 controls. Logistic models revealed predictors of VRE colonization as age (aOR, 1.4 (per 10 years), with 95% confidence interval [CI], 1.3–1.5; P < .001), ICU admission during stay (aOR, 1.5; 95% CI, 1.2–1.9; P < .001), Charlson comorbidity score (aOR, 1.1; 95% CI, 1.1–1.2; P < .001), the number of different prescribed antibiotics (aOR, 1.6; 95% CI, 1.5–1.7; P < .001), and the number of rooms the patient stayed in during their hospitalization(s) (aOR, 1.1; 95% CI, 1.1–1.2; P < .001). The decision-tree machine-learning method confirmed these findings. Graph network analysis established 3 main pathways by which the VRE cases were connected: healthcare personnel, medical devices, and patient rooms. Conclusions: We identified risk factors for being a VRE carrier, along with 3 important links with VRE (healthcare personnel, medical devices, patient rooms). Data science is likely to provide a better understanding of outbreaks, but interpretations require data maturity, and potential confounding factors must be considered.

Radioactive-labelled ligands targeting the prostate-specific membrane antigen (PSMA), a transmembrane protein overexpressed in prostate cancer (PC), have shown promising activity in treatment of metastatic castration-resistant prostate cancer (mCRPC). PSMA-617 and PSMA-I&T (imaging and therapy), both labeled to the beta-emitter lutetium-177 (Lu177), are most frequently used in clinical routine and have shown a favorable side-effect profile. Common side effects are transient xerostomia. Severe side effects, e.g., treatment-associated myelosuppression, are rare. Currently treatment with Lu177-PSMA outside clinical trials is available for compassionate use for patients who exhausted conventional therapies. Previous retro- and prospective studies reported promising results with ≥50% PSA declines observed in at least one third of patients. Retrospective data suggests worse biochemical response in patients with visceral metastases. Preliminary data from the randomized phase II (TheraP) trial showed an improved biochemical response rate of Lu177-PSMA as compared to cabazitaxel in patients progressing after docetaxel. Following these promising data, the results of the randomized, prospective phase III VISION study are eagerly anticipated. A major challenge remains resistance to radioligand therapy with Lu177-PSMA. As an alternative, a PSMA-ligand labeled to the alpha-emitter Actinium-225 (Ac-225) may be offered to patients, which shows promising activity in patients developing progression under Lu177-PSMA at the cost of higher toxicity. Mostly permanent xerostomia is a relevant side effect resulting in treatment discontinuation in up to a quarter of patients. This review summarizes the literature on activity and toxicity of PSMA-targeted radioligand therapy in mCRPC.

Purpose: Renal cysts comprise benign and malignant entities. Risk assessment profits from CT/MRI imaging using the Bosniak classification. While Bosniak-IIF, -III, and -IV cover complex cyst variants, Bosniak-IIF and -III stand out due to notorious overestimation. Contrast-enhanced ultrasound (CEUS) is promising to overcome this deficit but warrants standardization. This study addresses the benefits of a combined CEUS and CT/MRI evaluation of renal cysts. The study provides a realistic account of kidney tumor boards' intricacies in trying to validate renal cysts. Methods: 247 patients were examined over 8 years. CEUS lesions were graded according to CEUS-Bosniak (IIF, III, IV). 55 lesions were resected, CEUS-Bosniak- and CT/MRI-Bosniak-classification were correlated with histopathological diagnosis. Interobserver agreement between the classifications was evaluated statistically. 105 lesions were followed by ultrasound, and change in CEUS-Bosniak-types and lesion size were documented. Results: 146 patients (156 lesions) were included. CEUS classified 67 lesions as CEUS-Bosniak-IIF, 44 as CEUS-Bosniak-III, and 45 as CEUS-Bosniak-IV. Histopathology of 55 resected lesions revealed benign cysts in all CEUS-Bosniak-IIF lesions (2/2), 40% of CEUS-Bosniak-III and 8% of CEUS-Bosniak-IV, whereas malignancy was uncovered in 60% of CEUS-Bosniak-III and 92% of CEUS-Bosniak-IV. Overall, CEUS-Bosniak-types matched CT/MRI-Bosniak types in 58% (fair agreement, κ = 0.28). CEUS-Bosniak resulted in higher stages than CT/MRI-Bosniak (40%). Ultrasound follow-up of 105 lesions detected no relevant differences between CEUS-Bosniak-types concerning cysts size. 99% of lesions showed the same CEUS-Bosniak-type. Conclusion: The CEUS-Bosniak classification is an essential tool in clinical practice to differentiate and monitor renal cystic lesions and empowers diagnostic work-up and patient care.