Dutch Expert Centre for Screening

Birk-Barel syndrome (BIBARS), also known as KCNK9 imprinting syndrome, is an extremely rare genetic condition caused by pathogenic variants in the KCNK9 gene located on the long arm of chromosome 8 (8q24). This gene is paternally imprinted and therefore only expressed from the maternal allele. KCNK9 encodes for a member of the two-pore domain potassium channel (K2P) subfamily. BIBARS syndrome is characterized by congenital hypotonia and weakness of proximal muscles, intellectual disability, behavioural problems, dysmorphic features, feeding problems and epilepsy. The syndrome is implied in neurodevelopment, plays a role in various regulatory systems (eg, blood pressure maintenance, respiratory function, sleep, and cognitive function), and is associated with absence epilepsy. An institutionalized adult female patient with intellectual disability and challenging behaviours was presented whose history was carefully described and in whom extensive somatic, neurological, psychiatric and psychological investigation was performed, in addition to whole exome sequencing. Intellectual disability, developmental delay with severe psychotrauma from early age on, as well as severe aggressive and self-injurious behaviours were established. Whole exome sequencing finally disclosed a pathogenic variant in the KCNK9 gene and an absence epilepsy in association with BIBARS was suspected. After treatment with valproic acid epileptic phenomena no longer occurred and behaviour and emotion regulation improved significantly. A pathogenic heterozygous missense variant in the KCNK9 gene corresponding with a diagnosis of Birk-Barel syndrome was considered to be largely responsible for the severely disinhibited behaviours and causative for the absence epilepsy. For adequate diagnosis and treatment of neurodevelopmental disorders, this case clearly demonstrates the importance of adherence to clinical standards, in particular periodically renewed genetic analysis by means of WES/WGS.

Lynch syndrome is a germline cancer predisposition syndrome caused by a variant in one offour genes. Lynch syndrome places individuals at significantly higher risk for a range of cancers, especially colorectal and endometrial. Depending on which gene is affected, the risk of ovarian, gastric, small bowel, pancreatic, biliary urothelial, brain, and certain skin tumors is also increased. Tailored treatment, cancer surveillance, and consideration of primary prevention measures are critical for at-risk individuals. Despite advancements in genetic testing, Lynch syndrome remains underdiagnosed, with only a small proportion of those affected aware of their genetic predisposition. This article explores the patient experience of living with Lynch syndrome, focusing on the challenges surrounding diagnosis, risk-adjusted prevention, healthcare coordination, and information dissemination. Stages of the patient journey are explored, from awareness and suspicion to diagnosis, treatment and surveillance, psychosocial adaptation, and ongoing management. The need for more comprehensive healthcare strategies and better communication to enhance the quality of care for Lynch syndrome patients is emphasized. We recommend improvements to better meet patient needs.

Genetic tumor risk syndromes (genturis) contribute substantially to the overall cancer burden and provide opportunities for early detection, prevention, and individualized treatment. Yet, many affected individuals remain undiagnosed due to restrictive testing criteria and challenges in variant interpretation. This review summarizes recent advances in the diagnostic evaluation of genturis. We trace the evolution from single-gene testing to multigene panel testing, highlighting gains in diagnostic yield alongside the growing prevalence of uncertain and incidental findings. We then describe emerging functional approaches such as RNA sequencing and proteomics that generate molecular evidence to refine variant classification. Next, we outline how long-read sequencing overcomes technical limitations in complex genomic regions. Finally, we discuss practical aspects of clinical implementation, including reporting practices, workflow integration, and professional education, and propose strategies to improve diagnostic accuracy, efficiency, and equitable access to testing.

• Low delta-T syndrome in cooling coils under part-load is not well understood. • Three different essential cooling coil designs were tested through simulations. • The resulting chilled water temperature differences were presented and analysed. • Temperature differences are dominated by latent heat and cooling coil design. • A below design temperature difference could not be avoided in the tested conditions. Laminar flow in the cooling coil during part-load operation is a debated cause of low delta-T syndrome. Since part-load operation is the primary mode of operation, laminar flow cannot be avoided with two-way controlled cooling coil valves. Therefore, improving fundamental knowledge of part-load behaviour in cooling coils is crucial. This study aimed to provide a better understanding of the chilled water temperature difference for three different airflow rates. Using an element-by-element cooling coil model, the chilled water temperature difference was analysed by means of simulation for three different cooling coils with a predicted decreasing, constant, and increasing chilled water temperature difference in fault-free part-load operation. The results of this simulation study demonstrate both complex and fundamental behaviour of cooling coils for three different design configurations in a climate zone 4A. In all cases, the design chilled water temperature difference is not always achieved. The chilled water temperature difference is affected by (i) the capacity ratio of the ε-NTU method at low loads, (ii) the relative humidity at higher loads, (iii) both dominated by the predicted chilled water temperature difference of the cooling coil in part-load: increasing, constant, or decreasing. For nominal airflow rate, the increasing cooling coil design provides the highest annual normalised mean chilled water temperature difference (109.5 %) and the lowest minimum normalised chilled water temperature difference (71.8 %). The decreasing cooling coil design provides the lowest annual normalised mean chilled water temperature difference (89.3 %) and the highest minimum normalised chilled water temperature difference (83.1 %). The cooling coil with the decreasing or unfavourable chilled water temperature difference in part-load requires the lowest annual water mass of 2.30.10 6 kg, the increasing or favourable resulted in the highest annual water mass of 3.48.10 6 kg. The key implication is that chilled water system designs must consider part-load chilled water temperature differences of cooling coils to deal with possible unpreventable below-design chilled water temperature differences, representing the fault-free case of low delta-T syndrome.

BackgroundTumor heterogeneity presents significant challenges in breast cancer diagnosis and treatment. Four‐dimensional dynamic contrast‐enhanced dedicated breast CT (4D DCE‐bCT) is a novel imaging technique designed to capture contrast agent kinetics with high spatial and temporal resolution, enabling detailed assessment of tumor perfusion and heterogeneity.PurposeTo develop modular breast tumor phantoms capable of simulating a range of physiologically relevant perfusion patterns and structural heterogeneities for validation of 4D DCE‐bCT.MethodsTumor phantoms (1.5 cm diameter) were 3D‐printed using clear resin in several designs: small‐channel phantoms (0.8 and 1.0 mm diameter), a leaking vessel model with permeable outer walls, gyroid structures (1.3 and 1.5 mm pores) mimicking microvascularization, and a dual‐input/output model to replicate heterogeneous perfusion. The phantoms were integrated into a programmable flow system, enabling iodinated contrast (up to 5 mg I/mL) delivery with custom profiles: full wash‐in/wash‐out, persistent, plateau, and partial wash‐out. 4D DCE‐bCT acquisitions with a 65 kV + 0.25 mm Cu filter involved 1 pre‐contrast scan (360 pulses over a 10‐second revolution at 80 mA) followed by three post‐contrast phases (400 pulses over 10 revolutions at 32 mA). Images were reconstructed using 40 projections at 5‐second intervals using prior image constrained compressed sensing (PICCS). Time–intensity curves (TICs) were analyzed in volumes of interest in various sections of the tumor phantoms. A gamma variate function was fitted to each TIC, and the corresponding fit parameters and coefficients of determination (R2) were extracted.ResultsDynamic imaging demonstrated successful capture of expected contrast kinetics. Larger channels (1.0 mm) produced 2.5 times greater enhancement compared to smaller ones (0.8 mm). The R2 values for the 0.8 mm and leaking channel were lower, 0.54 and 0.67 versus 0.85 in the 1 mm channel, due to a higher noise level in the signal. The leaking vessel phantom exhibited delayed wash‐out by ∼30 s. Distinct flow patterns were evident in the dual‐input/output model. The gamma variate parameters showed the expected trends due to the programmed flow patterns.ConclusionThe developed 3D‐printed tumor phantoms effectively simulate key perfusion features and point towards the feasibility of using 4D DCE‐bCT to image detailed perfusion patterns.

BackgroundArtificial intelligence (AI)-based systems have the potential to increase the efficiency and effectiveness of breast cancer screening programs but need to be carefully validated before clinical implementation.PurposeTo retrospectively evaluate an AI system to safely reduce the workload of a double-reading breast cancer screening program.Material and MethodsAll digital mammography (DM) screening examinations of women aged 40-74 years between August 2021 and January 2022 in Östergötland, Sweden were included. Analysis of the interval cancers (ICs) was performed in 2024. Each examination was double-read by two breast radiologists and processed by the AI system, which assigned a score of 1-10 to each examination based on increasing likelihood of cancer. In a retrospective simulation, the AI system was used for triaging; low-risk examinations (score 1-7) were selected for single reading and high-risk examinations (score 8-10) for double reading.ResultsA total of 15,468 DMs were included. Using an AI triaging strategy, 10,473 (67.7%) examinations received scores of 1-7, resulting in a 34% workload reduction. Overall, 52/53 screen-detected cancers were assigned a score of 8-10 by the AI system. One cancer was missed by the AI system (score 4) but was detected by the radiologists. In total, 11 cases of IC were found in the 2024 analysis.ConclusionReplacing one reader in breast cancer screening with an AI system for low-risk cases could safely reduce workload by 34%. In total, 11 cases of IC were found in the 2024 analysis; of them, three were identified correctly by the AI system at the 2021-2022 examination.

Breast cancer screening programmes have shown to reduce mortality, but current methods face challenges such as limited mammographic sensitivity, limited resources, and variability in radiologist expertise. Artificial intelligence (AI) offers potential to improve screening accuracy and efficiency. This study simulated different screening scenarios, evaluating the performance of population-based breast cancer screening when using an AI system as a stand-alone reader or second reader. In this retrospective cohort study, 42 236 consecutive 2D mammograms from 42 100 women attending the Dutch population-based breast cancer screening between Sept 1, 2016, and Aug 31, 2018 were processed by an AI-based cancer detection system (Transpara version 1.7.0, ScreenPoint Medical). Verified outcomes from the Netherlands Cancer Registry on screen-detected cancers, interval cancers, and later-in-future-detected breast cancers were available with 4-year follow-up. We compared sensitivity, specificity, and recall-rate between single human reading, double human reading, stand-alone AI reading, and combined single human reading with AI. Furthermore, we assessed potential differences in performance regarding breast density, tumour size, lymph-node positivity, and invasiveness between cancers identified by single human readers and AI alone. After follow-up, 580 mammograms (579 woman) were labelled positive: 291 screen-detected cancers, 102 interval cancers, and 187 future breast cancers. Double human reading recalled 1244 mammograms (2·9%, 291 screen-detected cancers) and combined single human reading with AI recalled 2112 mammograms (5·0%, 282 screen-detected cancers, 29 interval cancers, 38 future breast cancers), improving the sensitivity by 8·4% (95% CI 5·7-11·2, p<0·0001). No significant difference in performance between combined single human reading with AI across density categories was found. AI-detected future breast cancers and interval cancers missed by human readers were more often invasive cancers (26·7%) or tumours larger than 20 mm in diameter (16·6%) by the time of eventual detection compared with the average screen-detected cancers. Evaluating screening mammograms with one human reader and AI leads to increased breast cancer detection compared with double human reading, independent of breast density. However, an effective arbitration process is needed as the recall rate increases. AI-identified breast cancers that are missed by human readers seem larger and more often invasive by the time they are eventually detected, confirming the clinical relevance of these cases, recognisable by AI at an earlier stage. MARBLE.

Autism has been associated with differences in functional brain network organization. However, the exact nature of these differences across development compared to non-autistic individuals and their relationship to autism-related social cognition, remains unclear. This study first aimed to identify EEG resting-state network characteristics in autistic versus non-autistic children, adolescents, and adults. Second, we investigated associations with social cognition measures. Analyzing resting-state EEG data from the EU-AIMS Longitudinal European Autism Project, we compared network metrics (global efficiency, clustering coefficient, and small-worldness) between 344 autistic and non-autistic individuals within and across age groups in four frequency bands (delta, theta, alpha, and beta). If significant, we explored their relationships to measures of empathy (empathy quotient), complex emotion recognition [reading the mind in the eyes task (RMET)], and theory of mind (animated shapes task). Compared to their non-autistic peers, autistic adolescents showed lower alpha global efficiency, while autistic adults showed lower alpha clustering and small-worldness. No network differences were observed among children. In adolescents, higher long-range integration was tentatively associated with higher RMET scores; in those with high autistic traits, higher long-range integration related to fewer parent-reported empathic behaviors. No brain-behavior relationships were observed in adults. Our findings suggest subtle differences in network topology between autistic and non-autistic individuals, with less efficient long-range efficiency during adolescence, and less local and overall network efficiency in adulthood. Furthermore, long-range integration may play a role in complex emotion recognition and empathy difficulties associated with autism in adolescence.