At the occasion of the European Society of Paediatric Radiology (ESPR) annual meeting 2015 in Graz, Austria, the newly termed ESPR abdominal (gastrointestinal and genitourinary) imaging task force set out to complete the suggestions for paediatric urogenital imaging and procedural recommendations. Some of the last missing topics were addressed and proposals on imaging of children with anorectal and cloacal malformations and suspected ovarian torsion were issued after intense discussions and a consensus finding process that considered all evidence. Additionally, the terminology was adapted to fit new developments introducing the term pelvicalyceal dilatation/distension (PCD) instead of the sometimes misunderstood hydronephrosis. The present state of paediatric urogenital radiology was discussed in a dedicated minisymposium, including an attempt to adapt terminology to create a standardised glossary.

At the European Society of Paediatric Radiology (ESPR) annual meeting 2017 in Davos, Switzerland, the ESPR Abdominal (gastrointestinal and genitourinary) Imaging Task Force set out to complete the suggestions for paediatric abdominal imaging and its procedural recommendations. Some final topics were addressed including how to perform paediatric gastrointestinal ultrasonography. Based on the recent approval of ultrasound (US) contrast agents for paediatric use, important aspects of paediatric contrast-enhanced US were revisited. Additionally, the recent developments concerning the use and possible brain deposition of gadolinium as a magnetic resonance imaging contrast agent were presented. The recommendations for paediatric use were reissued after considering all available evidence. Recent insights on the incidence of neoplastic lesions in children with testicular microlithiasis were discussed and led to a slightly altered recommendation.

Anorectal and cloacal malformations are a broad mix of congenital abnormalities related to the distal rectum and anus. Confusion exists between all the forms in this large and heterogeneous group. The spectrum includes everything from anal stenosis, ventral anus, anal atresia (with and without fistula) and the full spectrum of cloacal malformations. Imaging in these conditions is done through the whole armamentarium of radiologic modalities, with very different imaging strategies seen across the centres where these conditions are managed. In 2017, the European Society of Paediatric Radiology (ESPR) abdominal imaging task force issued recommendations on the imaging algorithm and standards for imaging anorectal malformations. This was followed by further letters and clarifications together with an active multispecialty session on the different imaging modalities for anorectal malformations at the 2018 ESPR meeting in Berlin. Through this paper, the abdominal task force updates its guidelines and recommended imaging algorithm for anorectal malformations.

Background Whole-body magnetic resonance imaging (MRI) is increasingly being used in children, however, to date there are no studies addressing the reliability of the findings. Objective To examine intra- and interobserver reliability of a scoring system for assessment of high signal areas within the bone marrow, as visualized on T2-weighted, fat-saturated images. Materials and methods Ninety-six whole-body MRIs (1.5 T) in 78 healthy volunteers (mean age: 11.5 years) and 18 children with chronic nonbacterial osteomyelitis (mean age: 12.4 years) were included. Coronal water-only Dixon T2-weighted images were used to score the left lower extremity/pelvis for high signal intensity areas, intensity (0–2 scale), extension (0–4 scale) and shape and contour in a blinded fashion by two pairs of radiologists. Results For the pelvis, grading of bone marrow signal showed moderate to good intra- and interobserver agreement with kappa values of 0.51–0.94 and 0.41–0.87, respectively. Corresponding figures for the femur were 0.61–0.68 within and 0.32–0.61 between observers, and for the tibia 0.60–0.72 and 0.51–0.73. Agreement for assessing extension was moderate to good both within and between observers for the pelvis (k = 0.52–0.85 and 0.35–0.80), for the femur (0.52–0.67 and 0.51–0.60) and for the tibia (k = 0.59–0.69 and 0.47–0.63) except for the femur metaphysis/diaphysis, with interobserver kappa values of 0.29–0.30. Scoring of shape was moderate to good within observers, but in general poorer between observers, with kappa values of 0.40–0.73 and 0.18–0.69, respectively. For contour, the corresponding figures were 0.35–0.62 and 0.09–0.54, respectively. Conclusion MRI grading of intensity and extension of high signal intensity areas within the bone marrow of pelvis and lower limb performs well and thus can be used interchangeably by different observers, while assessment of shape and contour is reliable for the same observer but is less reliable between observers. This should be considered when performing clinical trials.

Background Manual assessment of bone marrow signal is time-consuming and requires meticulous standardisation to secure adequate precision of findings. Objective We examined the feasibility of using deep learning for automated segmentation of bone marrow signal in children and adolescents. Materials and methods We selected knee images from 95 whole-body MRI examinations of healthy individuals and of children with chronic non-bacterial osteomyelitis, ages 6–18 years, in a longitudinal prospective multi-centre study cohort. Bone marrow signal on T2-weighted Dixon water-only images was divided into three color-coded intensity-levels: 1 = slightly increased; 2 = mildly increased; 3 = moderately to highly increased, up to fluid-like signal. We trained a convolutional neural network on 85 examinations to perform bone marrow segmentation. Four readers manually segmented a test set of 10 examinations and calculated ground truth using simultaneous truth and performance level estimation (STAPLE). We evaluated model and rater performance through Dice similarity coefficient and in consensus. Results Consensus score of model performance showed acceptable results for all but one examination. Model performance and reader agreement had highest scores for level-1 signal (median Dice 0.68) and lowest scores for level-3 signal (median Dice 0.40), particularly in examinations where this signal was sparse. Conclusion It is feasible to develop a deep-learning-based model for automated segmentation of bone marrow signal in children and adolescents. Our model performed poorest for the highest signal intensity in examinations where this signal was sparse. Further improvement requires training on larger and more balanced datasets and validation against ground truth, which should be established by radiologists from several institutions in consensus.