Imaging Screening Research Articles

Interferometric image reorganization and screening method based on phase-shift convergence criterion

To address the challenge of obtaining high-quality interferometric images in the presence of environmental disturbances, this paper proposes a method for interferometric image reorganization and screening based on the phase-shift convergence criterion. The method begins with the acquisition of multiple sets of interferometric images, which are subsequently classified and reorganized according to the amount of phase shift. A mean absolute deviation metric is then employed to systematically screen the classified images, allowing for the selection of a subset that exhibits accurate phase shifts. Finally, the screened and combined interferometric images are evaluated for phase shift centralization through the calculation of the coefficient of variation, ensuring that the screened images conform to the expected criteria. The experimental results indicate that the average coefficient of variation of the screened interferometric images obtained using the proposed method is significantly reduced by 0.1236, 0.0968, and 0.1042 when compared to the nine sets of directly acquired interferometric images subjected to high-frequency vibration, high-amplitude vibration, and transverse distortion vibration, respectively. These data demonstrate that the proposed method can effectively adapt to various vibration environments while screening interferometric images of higher quality. Consequently, this method offers a novel approach to enhancing the vibration resistance of interferometers.

A machine learning model revealed that exosome small RNAs may participate in the development of breast cancer through the chemokine signaling pathway

BackgroundExosome small RNAs are believed to be involved in the pathogenesis of cancer, but their role in breast cancer is still unclear. This study utilized machine learning models to screen for key exosome small RNAs and analyzed and validated them.MethodPeripheral blood samples from breast cancer screening positive and negative people were used for small RNA sequencing of plasma exosomes. The differences in the expression of small RNAs between the two groups were compared. We used machine learning algorithms to analyze small RNAs with significant differences between the two groups, fit the model through training sets, and optimize the model through testing sets. We recruited new research subjects as validation samples and used PCR-based quantitative detection to validate the key small RNAs screened by the machine learning model. Finally, target gene prediction and functional enrichment analysis were performed on these key RNAs.ResultsThe machine learning model incorporates six small RNAs: piR-36,340, piR-33,161, miR-484, miR-548ah-5p, miR-4282, and miR-6853-3p. The area under the ROC curve (AUC) of the machine learning model in the training set was 0.985 (95% CI = 0.948-1), while the AUC in the test set was 0.972 (95% CI = 0.882–0.995). RT-qPCR was used to detect the expression levels of these key small RNAs in the validation samples, and the results revealed that their expression levels were significantly different between the two groups (P < 0.05). Through target gene prediction and functional enrichment analysis, it was found that the functions of the target genes were enriched mainly in the chemokine signaling pathway.ConclusionThe combination of six plasma exosome small RNAs has good prognostic value for women with positive breast cancer by imaging screening. The chemokine signaling pathway may be involved in the early stage of breast cancer. It is worth further exploring whether small RNAs mediate chemokine signaling pathways in the pathogenesis of breast cancer through the delivery of exosomes.

0.23-Tesla MRI to differentiate between ischaemic and haemorrhagic strokes within 24 hours of onset: a combined experimental–clinical study

Background and purposeThe low-field MRI is a promising tool to accurately diagnose strokes. We here report our study on the accuracy of a 0.23-Tesla (0.23-T) MRI using the haematoma enhanced...

Screening for Fabry disease in patients with hypertrophic cardiomyopathy using cardiac magnetic resonance imaging.

Fabry disease (FD) usually mimics hypertrophic cardiomyopathy (HCM). Decreased native T1 mapping and a unique late gadolinium enhancement (LGE) pattern by cardiac magnetic resonance (CMR) imaging are specific imaging markers for FD. Explore the performance of multiparametric CMR imaging in screening for FD in patients with a HCM phenotype. A prospective cohort of 602 patients with a HCM phenotype was assessed from April 2012 to December 2022. Participants underwent CMR imaging and genetic testing. FD diagnosis was according to genetic testing and enzyme-activity test of α-galactosidase A. Multiparameter CMR imaging included cardiac function, native T1 mapping, extracellular volume (ECV), T2 mapping, LGE, and myocardial strains. Diagnostic performance of CMR parameters in identifying FD from HCM was done by analysis of receiver operating characteristic (ROC) curves. FD prevalence was 1.8% (11 cases) in this cohort with HCM. Native T1 mapping was significantly lower in FD compared with HCM (FD vs. HCM: native T1 mapping: 1174.08 ± 60.60 vs. 1293.94 ± 55.86, p < 0.001). Ventricular function, mass, ventricular wall thickness, and strains did not show significant differences between the two groups. Binary logistic regression and analysis of ROC curves demonstrated myocardial native T1 mapping of the left ventricular basal slice had the best performance in screening for FD in patients with a HCM phenotype (cutoff: 1216 ms; AUC: 0.947; sensitivity: 91%; specificity: 90%). Native T1 mapping is the best parameter for screening FD in a Chinese population with a HCM phenotype. Question The prevalence of Fabry Disease (FD) in the study population is unknown and the efficacy of cardiac MRI (CMR) parameter screening for FD needs validating. Findings We report the prevalence of FD among a Chinese hypertrophic cardiomyopathy (HCM) cohort and found T1 mapping is the best CMR parameter for screening FD. Clinical relevance Native T1 mapping is the best CMR parameter for screening FD in the HCM cohort, providing an effective method for rapid screening of FD in clinic, which may help identify patients for early treatment of FD.

Application of magnetic resonance imaging and artificial intelligence algorithms in cancer screening

In this society with a high incidence of cancer, cancer screening has become an important method to reduce the incidence and mortality of cancer. Traditional cancer screening methods such as CT have certain limitations and are difficult to adapt to large-scale and periodic cancer screening scenarios. Magnetic resonance imaging technology is an effective auxiliary method in CT methods, which can achieve high image resolution at lower doses and lower costs. Therefore, magnetic resonance imaging has become the most popular imaging method in clinical practice and a key research direction in the field of medical imaging. Therefore, this article intends to conduct in-depth research on the application of image feature extraction based on magnetic resonance imaging and artificial intelligence algorithms in cancer screening. This article introduces particle swarm optimization algorithm into the learning of artificial intelligence models and further improves it. And compared multiple algorithms, such as Chaos Particle Swarm Optimization, Genetic Particle Swarm Optimization, and Grey Wolf Algorithm, in order to verify the effectiveness and feasibility of the algorithm proposed in this paper. On this basis, the intelligent optimization algorithm was further improved and validated. Experimental results have shown that the new method proposed in this article has strong fault tolerance, and various functional modules of the cancer screening management system have been optimized and designed from five aspects: front-end, back-end, external, database, and infrastructure.

Two-Stage Recruitment Design to Reduce Magnetic Resonance Imaging Screening Cost for a Theoretical Clinical Trial of White Matter Hyperintensity Progression.

White matter hyperintensities (WMH) and their progression are associated with risk of dementia and stroke, so are an important target for clinical trials. The cost of broad magnetic resonance imaging (MRI) screening to identify eligible individuals, however, limits the feasibility of designing clinical trials targeting WMH. A low-cost retinal or clinical screening measure before MRI could reduce recruitment costs versus an MRI-only screening design in a hypothetical clinical trial. Data from the Atherosclerosis Risk in Communities study with valid retinal and WMH measurements (N=1311) were used. To identify a population at greater likelihood of significant WMH on MRI and thus reduce the number of screening MRIs required, we evaluated 3 theoretical prescreening measures: (1) retinal, (2) clinical, (3) combined clinical-retinal. Given a target sample for clinical trials (N=646), we calculated screening sample sizes based on the proportion within the population having an elevated score for each prescreening measure (separately) multiplied by the proportion of significant WMH among those with that prescreening feature. Recruitment costs were calculated using estimated retinal and MRI cost estimates. Compared with the estimated cost of MRI-only screening (>$4.24 million, requiring MRI on 6526 participants), prescreening for a high clinical score resulted in total cost of $2.47 million, with an initial screening group of 52 778 participants, with MRI in 3801. A high clinical-retinal score cutoff resulted in costs of $2.9 million while requiring 13 572 participants, with 3801 completing MRI. A 2-stage design with low-cost prescreening measures is a promising approach, resulting in reduced theoretical recruitment costs compared with an MRI-only design.

Low background dual-ligand Cu-MOF nanoprobe for plant tissue imaging and fast screening as well as sensitive detection of glyphosate in environmental samples

The monitoring of glyphosate residue in environmental samples is critically important due to its high environmental risk. Here, we reported a low background dual-ligand and fast response copper-based metal organic framework (Cu-MOF) nanoprobe for imaging glyphosate in plant tissue, rapid screening of glyphosate-contaminated samples, and sensitive detection of glyphosate in environmental samples. The Cu-MOF nanoprobe was prepared with 2-Aminoisophthalic Acid (AIA) and trimesic acid (H3BTC) as ligands, and Cu2+ as a metal node. Thanking to both ligand-to-metal charge transfer (LMCT) and photoinduced electron transfer (PET) effects, the fluorescence of ligand AIA could be fully quenched in Cu-AIA/BTC probe. Upon the addition of glyphosate, it competed with the ligands in Cu-AIA/BTC probe, causing the collapse of MOF structure and the release of ligand AIA with obvious fluorescence recovery. This nanoprobe exhibited a desirable linear response for glyphosate in the concentration range of 0.1–80 μM, with a low detection limit of 33 nM, much lower than the maximum contaminant level (4.1 μM) set by the U.S. Environmental Protection Agency (EPA). Furthermore, it was also successfully applied for plant tissue imaging, fast screening of glyphosate-contaminated samples and monitoring of the degradation of glyphosate on tea leaves and in soil, indicating the broad application prospect of the nanoprobe.

Nicotinamide and pyridoxine stimulate muscle stem cell expansion and enhance regenerative capacity during aging.

Skeletal muscle relies on resident muscle stem cells (MuSCs) for growth and repair. Aging and muscle diseases impair MuSC function, leading to stem cell exhaustion and regenerative decline that contribute to the progressive loss of skeletal muscle mass and strength. In the absence of clinically available nutritional solutions specifically targeting MuSCs, we used a human myogenic progenitor (hMP) high-content imaging screen of natural molecules from food to identify nicotinamide (NAM) and pyridoxine (PN) as bioactive nutrients that stimulate MuSCs and have history of safe human use. NAM and PN synergize via CK1-mediated cytoplasmic β-catenin activation and AKT signaling to promote amplification and differentiation of MuSCs. Oral treatment with a combination of NAM/PN accelerates muscle regeneration in vivo by stimulating MuSCs, increases muscle strength during recovery, and overcomes MuSC dysfunction and regenerative failure during aging. Levels of NAM and bioactive PN spontaneously decline during aging in model organisms and inter-independently associate with muscle mass and walking speed in a human cohort of 186 aged people. Collectively, our results establish NAM/PN as a new nutritional intervention that stimulates MuSCs, enhances muscle regeneration, and alleviates age-related muscle decline with a direct opportunity for clinical translation.

Prospective Study of Supplemental Screening With Contrast-Enhanced Mammography in Women With Elevated Risk of Breast Cancer: Results of the Prevalence Round.

Contrast-enhanced mammography (CEM) and magnetic resonance imaging (MRI) have shown similar diagnostic performance in detection of breast cancer. Limited CEM data are available for high-risk breast cancer screening. The purpose of the study was to prospectively investigate the efficacy of supplemental screening CEM in elevated risk patients. A prospective, single-institution, institutional review board-approved observational study was conducted in asymptomatic elevated risk women age 35 years or older who had a negative conventional two-dimensional digital breast tomosynthesis screening mammography (MG) and no additional supplemental screening within the prior 12 months. Four hundred sixty women were enrolled from February 2019 to April 2021. The median age was 56.8 (range, 35.0-79.2) years; 408 of 460 (88.7%) were mammographically dense. Biopsy revealed benign changes in 22 women (22/37, 59%), high-risk lesions in four women (4/37, 11%), and breast cancer in 11 women (11/37, 30%). Fourteen cancers (10 invasive, tumor size range 4-15 mm, median 9 mm) were diagnosed in 11 women. The overall supplemental cancer detection rate was 23.9 per 1,000 patients, 95% CI (12.0 to 42.4). All cancers were grade 1 or 2, ER+ ERBB2-, and node negative. CEM imaging screening offered high specificity (0.875 [95% CI, 0.844 to 0.906]), high NPV (0.998 [95% CI, 0.993 to 1.000), moderate PPV1 (0.164 [95% CI, 0.076 to 0.253), moderate PPV3 (0.275 [95% CI, 0.137 to 0.413]), and high sensitivity (0.917 [95% CI, 0.760 to 1.000]). At least 1 year of imaging follow-up was available on all patients, and one interval cancer was detected on breast MRI 4 months after negative screening CEM. A pilot trial demonstrates a supplemental cancer detection rate of 23.9 per 1,000 in women at an elevated risk for breast cancer. Larger, multi-institutional, multiyear CEM trials in patients at elevated risk are needed for validation.

An innovative fluorescent probe for monitoring of ONOO− in multiple liver-injury models

The liver plays a pivotal role in numerous critical physiological processes, functioning as the body's metabolic and detoxification center. Chronic liver disease can precipitate more severe health complications. The onset and progression of liver disease are often characterized by abnormal concentrations of ONOO−, a highly reactive species whose direct capture and detection in physiological environments pose significant challenges. This work presents an innovative fluorescent probe NAP-ONOO derived from 1,8-naphthalimide, specifically engineered to dynamically monitor fluctuations of ONOO− levels during liver injury. Due to its high biocompatibility, NAP-ONOO enabled to observe varying degrees of ONOO− up-regulation across models of liver inflammatory injury, alcohol-induced damage, and drug-induced hepatotoxicity in cellular systems as well as in zebrafish and mice models. These findings highlight the potential of NAP-ONOO for identifying and detecting the liver injury biomarker ONOO−. Furthermore, NAP-ONOO serves as potent tool for the identification of liver injuries, drug screening, and cellular imaging analyses, thereby promising avenues for future research endeavors.

High Content Screening Assay of Inhibitors of the Legionella Pneumophila Histone Methyltransferase RomA in Infected Cells.

Resistance to anti-microbial agents is a world-wide health threat. Thus, there is an urgent need for new treatments. An alternative approach to disarm pathogens consists in developing drugs targeting epigenetic modifiers. Bacterial pathogens can manipulate epigenetic regulatory systems of the host to bypass defences to proliferate and survive. One example is Legionella pneumophila, a Gram-negative intracellular pathogen that targets host chromatin with a specific, secreted bacterial SET-domain methyltransferase named RomA. This histone methyltransferase specifically methylates H3 K14 during infection and is responsible for changing the host epigenetic landscape upon L. pneumophila infection. To inhibit RomA activity during infection, we developed a reliable high-content imaging screening assay, which we used to screen an in-house chemical library developed to inhibit DNA and histone methyltransferases. This assay was optimised using monocytic leukemic THP-1 cells differentiated into macrophages infected with L. pneumophila in a 96- or 384-well plate format using the Opera Phenix (Perkin Elmer) confocal microscope, combined with Columbus software for automated image acquisition and analysis. H3 K14 methylation was followed in infected, single cells and cytotoxicity was assessed in parallel. A first pilot screening of 477 compounds identified a potential starting point for inhibitors of H3 K14 methylation.

Health technology assessment of nailfold capillaroscopy and digital retina imaging in diabetes screening

AimThis study aims to identify the criteria for the evaluation of medical technologies in diabetes screening and then perform health technology assessment (HTA) for two technologies: digital retina imaging (DRI) and Nailfold Capillaroscopy (NFC). MethodsA multicriteria decision-making tool measuring attractiveness using a categorical-based evaluation technique (MACBETH) was used to calculate and compare alternatives. A focus group of ten decision-makers with more than ten years of experience in health technology was used for the decision-making. The Delphi Method was used to get a consensus about comparing criteria and alternatives to these criteria. The sampling method used in the study was the nonprobability purposive sampling method. ResultsThe study concluded that clinical efficacy (0.42) is the most important criterion for evaluating medical technologies, followed by cost-effectiveness (0.27). At the same time, ethical considerations were found to be less important (0.03). The sensitivity analysis concluded that the decision is sensitive to clinical efficacy and cost-effectiveness changes. The study found that none of the alternatives dominates others as an option for early detection of diabetes through vascular changes. LimitationsThis study primarily uses systemic literature review methods to identify criteria for evaluating alternatives. There may be additional criteria for evaluation. ConclusionThis study provides an approach for a multicriteria comparison of medical devices. The study findings are useful for public health professionals and health policymakers. Plain language summaryThis study assesses nailfold capillaroscopy (NFC) as a substitute for digital retina imaging (DRI) in diabetes screening using the MACBETH multicriteria decision-making tool. The research identifies five key evaluation criteria: clinical efficacy, cost-effectiveness, patient needs, user safety, and ethical considerations. A focus group of experienced health technology decision-makers was used to compare NFC and DRI. Results indicate that clinical efficacy and cost-effectiveness are the most critical factors, with NFC showing potential but not significantly outperforming DRI. Sensitivity analyses highlight the need for further research to validate NFC as an effective diabetes screening tool.

Imaging Yield and Surgical Outcomes of Nasal, Medial Brow, Forehead, and Scalp Dermoid Cysts.

Dermoid cyst (DC) is a congenital cyst with the potential to have intracranial extension (ICE). This study aims to evaluate the imaging yield and surgical outcomes of nasal, medial brow, forehead, and scalp DCs. Retrospective review of craniofacial DCs treated at our institution between 1992 and 2024. A total of 117 patients (57 females) were included. The median age at cyst detection and removal were 4.8 months (IQR 3.6-9.6) and 1.8 years (IQR 0.9-5.3), respectively. In 42 patients, parents have noticed the presence of the cyst immediately after birth. Cyst wall rupture during surgical removal was reported in 15.4%. The median follow-up time was 1.3 months (IQR 0.5-12.2). Three patients experienced recurrence. No postoperative complication was reported. The regions with the highest prevalence of ICE were the forehead, frontotemporal scalp, and nasal region. The lateral frontal/temporal scalp had a 33.3% rate of ICE. Midline forehead/scalp lesions demonstrated a higher risk of ICE compared to their lateral equivalents (54.5% vs 17.5%, P = .03). The sensitivity and specificity of magnetic resonance imaging (MRI) were 100% and 95.7%, while for computed tomography (CT scans) were 72.7% and 96.5%. The Area Under the Curve for MRI was 0.978, and for CT was 0.846. The sensitivity and specificity of ultrasound were 50% and 100%. Midline forehead/scalp DCs are more prone to extend intracranially than lateral DCs. MRI had a higher sensitivity and specificity than CT scans in detecting ICE. Routine screening imaging should be considered in midline forehead/scalp, lateral frontal/temporal, and nasal DCs.

Appropriateness Criteria for Radiation Therapy in the Setting of Presumed Early-Stage Lung Cancer

Low-dose chest computed tomography imaging for lung cancer screening is revealing increasing numbers of radiographic early-stage lung cancers. This topic discussion describes when a clinical scenario merits radiation therapy without a histologic diagnosis, with an emphasis on pragmatic algorithms in settings without readily available advanced biopsy techniques.

Microwave imaging for breast cancer screening: protocol for an open, multicentric, interventional, prospective, non-randomised clinical investigation to evaluate cancer detection capabilities of MammoWave system on an asymptomatic population across multiple European countries.

Microwave imaging presents several potential advantages including its non-ionising and harmless nature. This open, multicentric, interventional, prospective, non-randomised trial aims to validate MammoWave's artificial intelligence (AI)-based classification algorithm, leveraging microwave imaging, to achieve a sensitivity exceeding 75% and a specificity exceeding 90% in breast screening. 10 000 volunteers undergoing regular mammographic breast cancer screening will be recruited across 9 European centres and invited to participate in the clinical study, involving MammoWave testing on both breasts. MammoWave results will be checked against the reference standard, to be intended as the output of conventional breast examination path (with histological confirmation of cancer cases) with 2 years follow-up. Anonymised clinical and MammoWave's results, including microwave images, associated features and a label provided by the AI-based classification algorithm, will be collected and stored in a dedicated electronic case report form. The prospective study will involve a comparative analysis between the output of the conventional breast examination path (control intervention) and the labels provided by MammoWave's AI system (experimental intervention). These labels will categorise breasts into two groups: breast With Suspicious Finding, indicating the presence of a suspicious lesion or No Suspicious Finding, indicating the absence of a lesion or the presence of a low-suspicion lesion. This trial aims to provide evidence regarding the novel MammoWave's AI system for detecting breast cancer in asymptomatic populations during screening. This study was approved by the Research Ethics Committee of the Liguria Region (CET), Italy (CET-Liguria: 524/2023-DB id 13399), the Research Ethics Committee of Complejo Hospitalario de Toledo (CEIC), Spain (CEIC-1094), the National Ethics Committee for Clinical Research (CEIC), Portugal (CEIC-2311KC814), the Bioethical Committee of Pomeranian Medical University in Szczecin, Poland (KB-006/23/2024) and the Zurich Cantonal Ethics Commission, Switzerland (BASEC 2023-D0101). The findings of this study will be disseminated through academic and scientific conferences as well as peer-reviewed journals. NCT06291896.

Deep Imbalanced Regression Model for Predicting Refractive Error from Retinal Photos

PurposeRecent studies utilized ocular images and deep learning (DL) to predict refractive error and yielded notable results. However, most studies did not address biases from imbalanced datasets or conduct external validations. To address these gaps, this study aimed to integrate Deep Imbalanced Regression (DIR) technique into ResNet and Vision Transformer models to predict refractive error from retinal photographs. Design and SubjectsWe developed the DL models using 103,865 images from the Singapore Epidemiology of Eye Diseases (SEED) Study and the United Kingdom Biobank (UKBB), with internal testing on 8,067images. External testing was conducted on 7,043 images from the Singapore Prospective Study (SP2) and 5,539 images from the Beijing Eye Study (BES). Retinal images and corresponding refractive error data were extracted. MethodsThis retrospective study developed regression-based models, including ResNet34 with DIR, and SwinV2 (Swin Transformer) with DIR, incorporating Label Distribution Smoothing (LDS) and Feature Distribution Smoothing (FDS). These models were compared against their baseline versions, ResNet34 and SwinV2, in predicting spherical and spherical equivalent (SE) power. Main Outcome MeasuresMean absolute error (MAE) and coefficient of determination (R2) were used to evaluate the models’ performances. The Wilcoxon signed-rank test was performed to assess statistical significance between DIR-integrated models and their baseline versions. ResultsFor prediction of the spherical power, ResNet34 with DIR (MAE: 0.84D) and SwinV2 with DIR (MAE: 0.77D) significantly outperformed their baseline – ResNet34 (MAE: 0.88D, P<0.001) and SwinV2 (MAE: 0.87D, P<0.001) in internal test. For prediction of the SE power, ResNet34 with DIR (MAE: 0.78D) and SwinV2 with DIR (MAE: 0.75D) consistently significantly outperformed its baseline – ResNet34 (MAE: 0.81D, P<0.001) and SwinV2 (MAE: 0.78D, P<0.05) in internal test. Similar trends were observed in external test sets for both spherical and SE power prediction. ConclusionDIR-integrated DL models showed potential in addressing data imbalances and improving the prediction of refractive error. These findings highlight the potential utility of combining DL models with retinal imaging for opportunistic screening of refractive errors, particularly in settings where retinal cameras are already in use.

Vocal cord anomaly detection based on Local Fine-Grained Contour Features

Laryngoscopy is a popular examination for vocal cord disease diagnosis. The conventional screening of laryngoscopic images is labor-intensive and depends heavily on the experience of the medical specialists. Automatic detection of vocal cord diseases from laryngoscopic images is highly sought to assist regular image reading. In laryngoscopic images, the symptoms of vocal cord diseases are concentrated in the inner vocal cord contour, which is often characterized as vegetation and small protuberances. The existing classification methods pay little, if any, attention to the role of vocal cord contour in the diagnosis of vocal cord diseases and fail to effectively capture the fine-grained features. In this paper, we propose a novel Local Fine-grained Contour Feature extraction method for vocal cord anomaly detection. Our proposed method consists of four stages: image segmentation to obtain the overall vocal cord contour, inner vocal cord contour isolation to obtain the inner contour curve by comparing the changes of adjacent pixel values, extraction of the latent feature in the inner vocal cord contour by taking the tangent inclination angle of each point on the contour as the latent feature, and the classification module. Our experimental results demonstrate that the proposed method improves the detection performance of vocal cord anomaly and achieves an accuracy of 97.21%.

Incidence, diagnostics, therapeutic management and outcomes of paediatric intestinal pseudo-obstruction in the Netherlands: A 20-year retrospective cohort study.

To describe incidence, clinical course, diagnostic and therapeutic management and long-term follow-up of paediatric intestinal pseudo-obstruction (PIPO) in the Netherlands between 2000 and 2020. Multicenter, national, retrospective, observational study including patients aged <18 years diagnosed with PIPO and treated between 2000 and 2020 in Dutch academic medical centres. Outcomes included demographics, incidence, symptoms, diagnostic- and treatment methods used during follow-up, number of hospital admissions and mortality. Between 2000 and 2020, 43 children (median age 120 months, range 13 - 301, 54% female) were diagnosed with PIPO in the Netherlands. Mean incidence was 0.008/100,000/years (range 0/100 000-0.029/100 000). Twenty-six patients developed PIPO in the neonatal period. Initial symptoms were vomiting (n = 21/35, 60%) and abdominal distension (n = 14/35, 40%). Diagnostic strategies included imaging, manometry, histopathology, metabolic- and genetic screening, endoscopy and exploratory surgery. Treatment was divided in nutritional support, pharmacotherapy, colonic irrigation and surgical interventions, of which nutrition and surgery were the cornerstones for care. During the observed study period, the median number of hospital admissions was 22.5 (range 1-176) with a median of 157.5 days (range 3-840) during 20-year follow-up. Two patients (6%) died: one from sepsis and one due to a severe underlying neurological disease. Heterogeneity in diagnostic- en treatment methods existed between patients. PIPO is a rare, long-lasting complex disease requiring a high number of diagnostic and therapeutic interventions and hospital admissions. However, mortality rate is relatively low. Based on our results, we recommend centralization and standardization of care for this complex rare disease.

A Highly-Efficient, Scalable Pipeline for Fixed Feature Extraction from Large-Scale High-Content Imaging Screens

A Highly-Efficient, Scalable Pipeline for Fixed Feature Extraction from Large-Scale High-Content Imaging Screens

Ependymoma Alongside Hereditary Paraganglioma-Phaeochromocytoma Syndrome Due to a SDHB Mutation: A Case Report

Mutations in the SDHB gene cause a characteristic syndrome that includes paragangliomas (PGL) and phaeochromocytomas (PCC). Herein we present a rare case of an ependymoma in a patient with a germline SDHB mutation. A 41-year-old man with a positive family history of PGL/PCC syndrome was found to have the familial SDHB mutation. Screening imaging for paragangliomas revealed an incidental presumed ependymoma originating from the fourth ventricle. He was followed with serial imaging to assess for progression of the lesion. Due to slow, substantial growth of the tumour, and increasing symptoms which included diplopia, unsteadiness, and wide-based gait, he underwent a resection 5 years after the lesion’s identification. Following resection of the tumour, the pathology confirmed the tumour as a posterior fossa type B (PFB) ependymoma of the fourth ventricle. Unfortunately, on Day 26 post-operatively, the patient had a pulmonary embolism and died. His family consented to an autopsy, which revealed the presence of a clinically-silent pituitary neuroendocrine tumour (PitNET). Though ependymomas are not commonly seen in PGL/PCC syndrome, they can occur. This case represents the first molecularly-characterised ependymal tumour described in this tumour predisposition syndrome. Clinicians ought to be aware of the risk of ependymoma in patients with PGL/PCC syndrome and consider this tumour when conducting their screening and follow-up of asymptomatic SDHx mutation carriers.