Review of whole-body magnetic resonance imaging in multiple myeloma.

Multiple Myeloma (MM) is a hematological malignancy affecting bone marrow, most frequently in elderly men. Imaging has a crucial role in this disease. Recently, whole-body MRI has been introduced and it has gained growing interest due to is high sensitivity and specificity in evaluating bone marrow involvement in MM. Diffusion-weighted sequences (DWI) with apparent diffusion coefficient (ADC) maps have emerged as the most sensitive technique to evaluate patients with MM, both in the pre- and post-treatment setting. Aim of this review is to provide an overview of the role and main imaging findings of whole-body MRI in MM.

Fat Fraction Extracted from Whole-Body Magnetic Resonance (WB-MR) in Bone Metastatic Prostate Cancer: Intra- and Inter-Reader Agreement of Single-Slice and Volumetric Measurements.

BACKGROUND: This study evaluates the repeatability and reproducibility of fat-fraction percentage (FF%) in whole-body magnetic resonance imaging (WB-MRI) of prostate cancer patients with bone metastatic hormone naive disease. METHODS: Patients were selected from the database of a prospective phase-II trial. The treatment response was assessed using the METastasis Reporting and Data System for Prostate (MET-RADS-P). Two operators identified a Small Active Lesion (SAL, <10 mm) and a Large Active Lesion (LAL, ≥10 mm) per patient, performing manual segmentation of lesion volume and the largest cross-sectional area. Measurements were repeated by one operator after two weeks. Intra- and inter-reader agreements were assessed via Interclass Correlation Coefficient (ICC) on first-order radiomics features. RESULTS: Intra-reader ICC showed high repeatability for both SAL and LAL in a single slice (SS) and volumetric (VS) measurements with values ranging from 0.897 to 0.971. Inter-reader ICC ranged from 0.641 to 0.883, indicating moderate to good reproducibility. Spearman's rho analysis confirmed a strong correlation between SS and VS measurements for SAL (0.817) and a moderate correlation for LAL (0.649). Both intra- and inter-rater agreement exceeded 0.75 for multiple first-order features across lesion sizes. CONCLUSION: This study suggests that FF% measurements are reproducible, particularly for larger lesions in both SS and VS assessments.

Total Tumor ADC Histogram Analysis: A New Tool for Predicting High-Risk Cytogenetic Abnormalities in Multiple Myeloma Patients.

RATIONALE AND OBJECTIVES: We explored the feasibility of using total tumor apparent diffusion coefficient (ttADC) histogram parameters to predict high-risk cytogenetic abnormalities (HRCA) in patients with multiple myeloma (MM) and compared the performance of an image prediction model based on these parameters with that of a combined prediction model based on these parameters and clinical indicators. METHODS: We retrospectively analyzed the parameters of the ttADC histogram based on whole-body diffusion-weighted images(WB-DWI) and clinical indicators in 92 patients with MM. The patients were divided into HRCA and non-HRCA groups according to the results of the fluorescence in situ hybridization. Logistic regression analysis was used to construct the image prediction and combined prediction models. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate the performance of the models to identify HRCA. The DeLong test was used to compare the AUC differences of each prediction model. RESULTS: Logistic regression analysis results revealed that the ttADC histogram parameter, ttADC entropy < 7.959 (OR: 39.167; 95% confidence interval [CI]: 3.891-394.208; P < 0.05), was an independent risk factor for HRCA. The image prediction model consisted of ttADC entropy and ttADC SD. The combined prediction model included ttADC entropy along with patient clinical indicators such as biological sex and M protein percentage. The AUCs of the image prediction and combined prediction models were 0.739 and 0.811, respectively (P < .05). The image prediction model showed a sensitivity of 73.9% and a specificity of 68.1%. The combined prediction model showed 82.6% sensitivity and 72.5% specificity. CONCLUSIONS: Using ttADC histogram parameters based on WB-DWI images to predict HRCA in patients with MM is feasible, and combining ttADC parameters with clinical indicators can achieve better predictive performance.

Applying ONCO-RADS to whole-body MRI cancer screening in a retrospective cohort of asymptomatic individuals.

BACKGROUND: Whole-body magnetic resonance imaging (WB-MRI) has emerged as a valuable tool for cancer detection. This study evaluated the prevalence rates of cancer in asymptomatic individuals undergoing WB-MRI according to the Oncologically Relevant Findings Reporting and Data System (ONCO-RADS) classifications in order to assess the reliability of the classification method. METHODS: We retrospectively enrolled 2064 asymptomatic individuals who participated in a WB-MRI cancer screening program between 2017 and 2022. WB-MRI was acquired on a 3-T system with a standard protocol, including regional multisequence and gadolinium-based contrast agent-enhanced oncologic MRI. Results of further examinations, including additional imaging and histopathology examinations, performed at our institute were used to validate the WB-MRI findings. Two radiologists blinded to the clinical outcome classified the WB-MRI findings according to the ONCO-RADS categories as follows: 1 (normal), 2 (benign finding highly likely), 3 (benign finding likely), 4 (malignant finding likely), and 5 (malignant finding highly likely). Firth logistic regression analysis was performed to determine the associations between participant characteristics and findings of ONCO-RADS category ≥ 4. RESULTS: Of the 2064 participants with median age of 55 years, 1120 (54.3%) were men, 43 (2.1%) had findings of ONCO-RADS category ≥ 4, and 24 (1.2%) had confirmed cancer. The cancer prevalence rates were 0.1%, 5.4%, 42.9%, and 75% for ONCO-RADS categories 2, 3, 4, and 5, respectively. In the multivariable model, older age (OR: 1.035, p = 0.029) and history of hypertension (OR: 2.051, p = 0.026), hepatitis B carrier (OR: 2.584, p = 0.013), or prior surgery (OR: 3.787, p < 0.001) were independently associated with the findings for ONCO-RADS category ≥ 4. CONCLUSIONS: The ONCO-RADS categories for cancer risk stratification were validated and found to be positively correlated with cancer risk. The application of ONCO-RADS facilitates risk-based management after WB-MRI for cancer screening.

Whole-Body MRI at Initial Presentation of Chronic Recurrent Multifocal Osteomyelitis, Juvenile Idiopathic Arthritis, Their Overlapping Syndrome, and Non-Specific Arthropathy.

(1) Background: Whole-body magnetic resonance imaging (WB-MRI) is central to defining total inflammatory burden in juveniles with arthritis. Our aim was to determine and compare the initial distribution of lesions in the WB-MRI in patients with chronic recurrent multifocal osteomyelitis (CRMO), juvenile idiopathic arthritis (JIA), their overlapping syndrome (OS), and with Non-specific Arthropathy (NA). (2) Methods: This retrospective single center study was performed on an Avanto 1.5-T MRI scanner with a dedicated multichannel surface coil system. A total of 173 pediatric patients were included with the following final diagnoses: CRMO (15.0%), JIA (29.5%), OS (4.6%), and NA (50.9%). (3) Results: Bone marrow edema (BME) was the most common abnormality, being seen in 100% patients with CRMO, 88% with OS, 55% with JIA, and 11% with NA. The bones of the lower extremities were the most affected in all compared entities. Effusion was seen in 62.5% children with OS, and in 52.9% with JIA, and in CRMO and NA, the exudate was sporadic. Enthesitis was found in 7.8% of patients with JIA and 3.8% with CRMO, and myositis was seen in 12.5% of patients with OS and in 3.9% with JIA. (4) Conclusions: The most frequent indication for WB-MRI in our center was JIA. The most common pathology in all rheumatic entities was BME, followed by effusion mainly seen in in OS and JIA. Enthesitis and myositis were less common; no case was observed in NA.

Differential Diagnosis of Generalized Cystic Lymphangiomatosis: A Literature Review.

BACKGROUND: Generalized cystic lymphangiomatosis (GCL) is a rare disease characterized by the widespread proliferation of lymphatic vessels, often seen in the pediatric patient group. Imaging techniques are instrumental in revealing the extent and morphological features of the disease. OBJECTIVE: The objective of this study is to interpret the radiological findings of GCL and address the differential diagnosis between GCL and other lymphatic malformations in light of the relevant literature data. METHODS: The sample of this retrospective study consisted of six pediatric patients, four males and two females, diagnosed with GCL based on clinical, radiological, and histopathological findings between 2015 and 2022. The age of the patients at the time of diagnosis and their symptoms at admission were obtained from the hospital database. Radiological imaging findings were evaluated in detail based on the involved systems (thorax, abdomen, and musculoskeletal). RESULTS: The median age of the sample, 4/6 were male, was 9 years at admission (min. 3, max. 12). The most common symptom at admission was dyspnea, often accompanied by pleural effusion. Bone involvement was the most common extrathoracic finding. Abdominal involvement was primarily asymptomatic, and the spleen was the most frequently involved organ in the abdomen. CONCLUSION: The diagnosis of GCL is challenging because of its rarity and overlapping diseases. Whole-body magnetic resonance imaging is a valuable tool as it reveals the typical radiological features of GCL and how far it has spread throughout the body.

Whole-body magnetic resonance imaging in two minutes: cross-sectional real-time coverage of multiple volumes.

This work describes a novel technique for rapid and motion-robust whole-body magnetic resonance imaging (MRI). The method employs highly undersampled radial fast low angle shot (FLASH) sequences to cover large volumes by cross-sectional real-time MRI with automatic slice advancement after each frame. The slice shift typically amounts to a fraction of the slice thickness (e.g., 10% to 50%) in order to generate a successive series of partially overlapping sections. Joint reconstructions of these serial images and their respective coil sensitivity maps rely on nonlinear inversion (NLINV) with regularization to the image and sensitivity maps of a preceding frame. The procedure exploits the spatial similarity of neighboring sections. Whole-body scanning is accomplished by measuring multiple volumes at predefined locations, i.e., at fixed table positions, in combination with intermediate automatic movements of the patient table. Individual volumes may take advantage of different field-of-views, image orientations, spatial and temporal resolutions as well as contrasts. Preliminary proof-of-principle applications to healthy subjects at 3 T without cardiac gating and during free breathing yield high-quality anatomic images with acquisition times of less than 100 ms. Spin-density and T1 contrasts are obtained by spoiled FLASH sequences, while T2-type (i.e., T2/T1) contrast results from refocused FLASH sequences that generate a steady state free precession (SSFP) free induction decay (FID) signal. Total measuring times excluding vendor-controlled adjustment procedures are less than two minutes for a 100 cm scan that, for example, covers the body from head to thigh by three optimized volumes and more than 1,300 images. In conclusion, after demonstrating technical feasibility the proposed method awaits clinical trials.

Deep learning for the prediction of type 2 diabetes mellitus from neck-to-knee Dixon MRI in the UK biobank.

Rationale and objectives: We evaluate the automatic identification of type 2 diabetes from neck-to-knee, two-point Dixon MRI scans with 3D convolutional neural networks on a large, population-based dataset. To this end, we assess the best combination of MRI contrasts and stations for diabetes prediction, and the benefit of integrating risk factors. Materials and methods: Subjects with type 2 diabetes mellitus have been identified in the prospective UK Biobank Imaging study, and a matched control sample has been created to avoid confounding bias. Five-fold cross-validation is used for the evaluation. All scans from the two-point Dixon neck-to-knee sequence have been standardized. A neural network that considers multi-channel MRI input was developed and integrates clinical information in tabular format. An ensemble strategy is used to combine multi-station MRI predictions. A subset with quantitative fat measurements is identified for comparison to prior approaches. Results: MRI scans from 3406 subjects (mean age, 66.2 years ± 7.1 [standard deviation]; 1128 women) were analyzed with 1703 diabetics. A balanced accuracy of 78.7 %, AUC ROC of 0.872, and an average precision of 0.878 was obtained for the classification of diabetes. The ensemble over multiple Dixon MRI stations yields better performance than selecting the individually best station. Moreover, combining fat and water scans as multi-channel inputs to the networks improves upon just using single contrasts as input. Integrating clinical information about known risk factors of diabetes in the network boosts the performance across all stations and the ensemble. The neural network achieved superior results compared to the prediction based on quantitative MRI measurements. Conclusions: The developed deep learning model accurately predicted type 2 diabetes from neck-to-knee two-point Dixon MRI scans.

Diffusion-weighted whole-body magnetic resonance imaging with background body signal suppression was useful in a patient with isolated myocardial abscess confined to the right atrial wall: a case report.

BACKGROUND: Myocardial abscess is often associated with infective endocarditis (IE), and isolated myocardial abscess without IE is rare. Echocardiography and computed tomography (CT) are often used to diagnose myocardial abscess; however, to the best of our knowledge, diffusion-weighted whole-body magnetic resonance imaging with background body signal suppression (DWIBS) has not been used. Here, we present a case of myocardial abscess without IE that was diagnosed using DWIBS. CASE PRESENTATION: A 72-year-old Japanese man with a history of hypertension, dyslipidemia, and retinitis pigmentosa presented to our hospital with malaise and a fever lasting 10 days. Blood test results showed elevated inflammatory marker levels (white blood cell count 18,700/µL and C-reactive protein level 23.0 mg/dL). Infection was suspected; however, the source of the infection could not be identified. DWIBS, which was performed on day 7 of admission to determine the source of infection, showed a high signal surrounding the right wall, suggesting inflammation. Contrast-enhanced CT performed on day 1 of hospitalization revealed a low-density area in the same region; however, the pathological implications of this finding could not be determined. Based on DWIBS findings, we concluded that the condition presented as a myocardial abscess that was confined specifically to the right atrial wall. Three sets of blood cultures revealed negative findings, and echocardiography showed no vegetation or valve regurgitation. Therefore, the patient was diagnosed with an isolated myocardial abscess uncomplicated with IE. An electrocardiogram on admission showed no P waves, and the patient had a junctional rhythm. However, on day 20 of hospitalization, he developed a complete atrioventricular block. After complete myocardial abscess healing following antibiotic treatment was confirmed, the patient underwent pacemaker implantation. Ten months after surgery, the patient had no signs of infection recurrence. CONCLUSIONS: Based on history and physical examination alone, diagnosis of an isolated myocardial abscess can be challenging. In addition to CT and echocardiography, DWIBS might be helpful for the diagnosis of myocardial abscesses.

Whole body MRI with Diffusion Weighted Imaging versus 18F-fluorodeoxyglucose-PET/CT in the staging of lymphomas.

PURPOSE: To assess the diagnostic performance of Whole Body (WB)-MRI in comparison with 18F-Fluorodeoxyglucose-PET/CT (18F-FDG-PET/CT) in lymphoma staging and to assess whether quantitative metabolic parameters from 18F-FDG-PET/CT and Apparent Diffusion Coefficient (ADC) values are related. MATERIALS AND METHODS: We prospectively enrolled patients with a histologically proven primary nodal lymphoma to  undergo 18F-FDG-PET/CT and WB-MRI, both performed within 15 days one from the other, either before starting treatment (baseline) or during treatment (interim). Positive and negative predictive values of WB-MRI for the identification of nodal and extra-nodal disease were measured. The agreement between WB-MRI and 18F-FDG-PET/CT for the identification of lesions and staging was assessed through Cohen's coefficient k and observed agreement. Quantitative parameters of nodal lesions derived from 18F-FDG-PET/CT and WB-MRI (ADC) were measured and the Pearson or Spearman correlation coefficient was used to assess the correlation between them. The specified level of significance was p ≤ 0.05. RESULTS: Among the 91 identified patients, 8 refused to participate and 22 met exclusion criteria, thus images from 61 patients (37 men, mean age 30.7 years) were evaluated. The agreement between 18F-FDG-PET/CT and WB-MRI for the identification of nodal and extra-nodal lesions was 0.95 (95% CI 0.92 to 0.98) and 1.00 (95% CI NA), respectively; for staging it was 1.00 (95% CI NA). A strong negative correlation was found between ADCmean and SUVmean of nodal lesions in patients evaluated at baseline (Spearman coefficient rs = - 0.61, p = 0.001). CONCLUSION: WB-MRI has a good diagnostic performance for staging of patients with lymphoma in comparison with 18F-FDG-PET/CT and is a promising technique for the quantitative assessment of disease burden in these patients.

Semiautomated segmentation of lower extremity MRI reveals distinctive subcutaneous adipose tissue in lipedema: a pilot study.

Purpose: Lipedema is a painful subcutaneous adipose tissue (SAT) disease involving disproportionate SAT accumulation in the lower extremities that is frequently misdiagnosed as obesity. We developed a semiautomatic segmentation pipeline to quantify the unique lower-extremity SAT quantity in lipedema from multislice chemical-shift-encoded (CSE) magnetic resonance imaging (MRI). Approach: Patients with lipedema (n=15) and controls (n=13) matched for age and body mass index (BMI) underwent CSE-MRI acquired from the thighs to ankles. Images were segmented to partition SAT and skeletal muscle with a semiautomated algorithm incorporating classical image processing techniques (thresholding, active contours, Boolean operations, and morphological operations). The Dice similarity coefficient (DSC) was computed for SAT and muscle automated versus ground truth segmentations in the calf and thigh. SAT and muscle volumes and the SAT-to-muscle volume ratio were calculated across slices for decades containing 10% of total slices per participant. The effect size was calculated, and Mann-Whitney U test applied to compare metrics in each decade between groups (significance: two-sided P<0.05). Results: Mean DSC for SAT segmentations was 0.96 in the calf and 0.98 in the thigh, and for muscle was 0.97 in the calf and 0.97 in the thigh. In all decades, mean SAT volume was significantly elevated in participants with versus without lipedema (P<0.01), whereas muscle volume did not differ. Mean SAT-to-muscle volume ratio was significantly elevated (P<0.001) in all decades, where the greatest effect size for distinguishing lipedema was in the seventh decade approximately midthigh (r=0.76). Conclusions: The semiautomated segmentation of lower-extremity SAT and muscle from CSE-MRI could enable fast multislice analysis of SAT deposition throughout the legs relevant to distinguishing patients with lipedema from females with similar BMI but without SAT disease.

Free-breathing accelerated whole-body MRI using an automated workflow: Comparison with conventional breath-hold sequences.

Whole-body magnetic resonance imaging (MRI) has become increasingly popular in oncology. However, the long acquisition time might hamper its widespread application. We sought to assess and compare free-breathing sequences with conventional breath-hold examinations in whole-body MRI using an automated workflow process. This prospective study consisted of 20 volunteers and six patients with a variety of pathologies who had undergone whole-body 1.5-T MRI that included T1-weighted radial and Dixon volumetric interpolated breath-hold examination sequences. Free-breathing sequences were operated by using an automated user interface. Image quality, diagnostic confidence, and image noise were evaluated by two experienced radiologists. Additionally, signal-to-noise ratio was measured. Diagnostic performance for the overall detection of pathologies was assessed using the area under the receiver operating characteristics curve (AUC). Study participants were asked to rate their examination experiences in a satisfaction survey. MR free-breathing scans were rated as at least equivalent to conventional MR scans in more than 92% of cases, showing high overall diagnostic accuracy (95% [95% CI 92-100]) and performance (AUC 0.971, 95% CI 0.942-0.988; p < 0.0001) for the assessment of pathologies at simultaneously reduced examination times (25 ± 2 vs. 32 ± 3 min; p < 0.0001). Interrater agreement was excellent for both free-breathing (Ï° = 0.96 [95% CI 0.88-1.00]) and conventional scans (Ï° = 0.93 [95% CI 0.84-1.00]). Qualitative and quantitative assessment for image quality, image noise, and diagnostic confidence did not differ between the two types of MR image acquisition (all p > 0.05). Scores for patient satisfaction were significantly better for free-breathing compared with breath-hold examinations (p = 0.0145), including significant correlations for the grade of noise (r = 0.79, p < 0.0001), tightness (r = 0.71, p < 0.0001), and physical fatigue (r = 0.52, p = 0.0065). In summary, free-breathing whole-body MRI in tandem with an automated user interface yielded similar diagnostic performance at equivalent image quality and shorter acquisition times compared to conventional breath-hold sequences.

Analysis of whole-body MRI artifacts in a pediatric population with a special emphasis on the effect of hands position.

PURPOSE: The purpose of this retrospective study was to determine the prevalence of artifacts on whole-body (WB) magnetic resonance imaging (MRI) examination in pediatric patients and identify their causes. MATERIALS AND METHODS: A total of 107 pediatric patients who underwent a total of 107 WB-MRI examinations, including short-tau inversion recovery (STIR) and T1-weighted sequences, were included. There were 62 girls and 45 boys with a mean age of 11 ± 3 (SD) years (age range: 2-16 years). WB-MRI examinations were analyzed for the presence of artifacts on STIR and T1-weighted sequences. Artifacts were further assigned to one of eight categories (motion, partial volume, cross-talk, phase sampling, susceptibility, equipment, noise, and "other") and 19 anatomical sites by a 4-year resident. Prevalence of artifacts were analyzed especially according to hands position during the examination for the upper limbs and patients' age. Age was expressed as a binary variable using median age (10 years) as the cut-off value. All qualitative variables were compared using chi-square test. RESULTS: A total of 3436 artifacts were found. The STIR sequences showed more "noise" artifacts (93/1038; 8.96%) and more "cross-talk" (102/1038; 9.83%) artifacts than T1-weighted sequences (12/1038 [1.16%] and 7/1038 [0.67%], respectively) (P < 0.001 for both). T1-weighted sequences showed more "equipment" (84/1038; 8.09%) and "stair-step" (a subset of "other") (41/1038; 3.95%) artifacts than the STIR sequences (39/1038 [3.76%] and 21/1038 [2.02%], respectively) (P < 0.001 and P = 0.01, respectively). T1-weighted sequences showed fewer artifacts on the wrists when the hands were under the bottom (P = 0.001). T1-weighted sequences showed less "equipment" artifacts when the hands were alongside the body (22/296; 7%) than on the abdomen (48/432; 11%) or under the bottom (14/128; 11%) (P < 0.001). STIR sequences showed more "motion" artifacts when the hands were on the abdomen (54/432; 13%) than alongside the body (30/296; 10%) or under the bottom (15/128; 12%) (P < 0.001). WB-MRI examinations had more "susceptibility" artifacts (38/960; 4%) and more "equipment" artifacts (81/960; 8.4%) in patients older than 10 years than in those under 10 years (23/752 [3.1%] and 42/752 [5.6%]) (P = 0.01 and P < 0.001, respectively). CONCLUSION: Artifacts on WB-MRI do not affect coronal STIR and T1-weighted sequences equally, so the use of both sequence types appears useful. Hands position should be considered with respect to both diagnostic benefit and safety.

Machine Learning Algorithm: Texture Analysis in CNO and Application in Distinguishing CNO and Bone Marrow Growth-Related Changes on Whole-Body MRI.

OBJECTIVE: The purpose of this study is to analyze the texture characteristics of chronic non-bacterial osteomyelitis (CNO) bone lesions, identified as areas of altered signal intensity on short tau inversion recovery (STIR) sequences, and to distinguish them from bone marrow growth-related changes through Machine Learning (ML) and Deep Learning (DL) analysis. MATERIALS AND METHODS: We included a group of 66 patients with confirmed diagnosis of CNO and a group of 28 patients with suspected extra-skeletal systemic disease. All examinations were performed on a 1.5 T MRI scanner. Using the opensource 3D Slicer software version 4.10.2, the ROIs on CNO lesions and on the red bone marrow were sampled. Texture analysis (TA) was carried out using Pyradiomics. We applied an optimization search grid algorithm on nine classic ML classifiers and a Deep Learning (DL) Neural Network (NN). The model's performance was evaluated using Accuracy (ACC), AUC-ROC curves, F1-score, Positive Predictive Value (PPV), Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE). Furthermore, we used Shapley additive explanations to gain insight into the behavior of the prediction model. RESULTS: Most predictive characteristics were selected by Boruta algorithm for each combination of ROI sequences for the characterization and classification of the two types of signal hyperintensity. The overall best classification result was obtained by the NN with ACC = 0.91, AUC = 0.93 with 95% CI 0.91-0.94, F1-score = 0.94 and PPV = 93.8%. Between classic ML methods, ensemble learners showed high model performance; specifically, the best-performing classifier was the Stack (ST) with ACC = 0.85, AUC = 0.81 with 95% CI 0.8-0.84, F1-score = 0.9, PPV = 90%. CONCLUSIONS: Our results show the potential of ML methods in discerning edema-like lesions, in particular by distinguishing CNO lesions from hematopoietic bone marrow changes in a pediatric population. The Neural Network showed the overall best results, while a Stacking classifier, based on Gradient Boosting and Random Forest as principal estimators and Logistic Regressor as final estimator, achieved the best results between the other ML methods.

Two phenotypes of chronic recurrent multifocal osteomyelitis with different patterns of bone involvement.

INTRODUCTION: Chronic Recurrent Multifocal Osteomyelitis (CRMO) is an autoinflammatory bone disorder with predominantly paediatric onset. Children present with multifocal osteolytic lesions accompanied by bone pain and soft tissue swelling. Patients often exhibit extraosseous co-morbidities such as psoriasis, inflammatory bowel disease, and arthritis. OBJECTIVES: Comparison of children with two different phenotypes of CRMO defined by presence or absence of extraosseous co-morbidities. METHODS: Children diagnosed with CRMO at the Motol University Hospital between 2010 and 2020 were retrospectively reviewed, and according to the absence or presence of extraosseous manifestations divided into two cohorts - bone limited CRMO and complex CRMO. The two groups were compared in terms of demographic data, age at disease onset, number and site of bone lesions, laboratory biomarker values, and need of escalation to a second-line therapy. RESULTS: Thirty-seven children (30 female, 7 male) with confirmed CRMO were included in the analysis. The mean age at disease onset was 10 years. All but 3 patients presented with multifocal disease. Twenty-three children (62%) had at least one extraosseous manifestation (13 sacroiliitis, 8 inflammatory bowel disease, 6 skin disease [acne, pustulosis, or psoriasis], 7 arthritis). Complex CRMO was associated with a significantly higher ESR rate (p = 0.0064) and CRP level (p = 0.018). The groups did not differ in number of foci or in age at disease onset. Bone lesion distribution differed between the two groups with significantly more frequent involvement of clavicle (p = 0.011) and pelvis (p = 0.038) in patients with complex CRMO. Children with complex CRMO more often needed escalation of therapy to DMARDs and biologic agents. CONCLUSION: Our data suggest that CRMO affecting solely the skeleton has milder course compared to complex CRMO with extraskeletal features. Further studies are needed to explore the clinical as well as the patient reported outcomes and promote individually tailored therapeutic strategies in both CRMO phenotypes.

Population Based Average Parotid Gland Volume and Prevalence of Incidental Tumors in T1-MRI.

Representative epidemiologic data on the average volume of the parotid gland in a large population-based MRI survey is non-existent. Within the Study of Health in Pomerania (SHIP), we examined the parotid gland in 1725 non-contrast MRI-scans in T1 weighted sequence of axial layers. Thus, a reliable standard operating procedure (Intraclass Correlation Coefficient > 0.8) could be established. In this study, we found an average, single sided parotid gland volume of 27.82 cm3 (95% confidence interval (CI) 27.15 to 28.50) in male and 21.60 cm3 (95% CI 21.16 to 22.05) in female subjects. We observed positive associations for age, body mass index (BMI), as well as male sex with parotid gland size in a multivariate model. The prevalence of incidental tumors within the parotid gland regardless of dignity was 3.94% in the Northeast German population, slightly higher than assumed. Further epidemiologic investigations regarding primary salivary gland diseases are necessary.

Chronic non-bacterial osteomyelitis in children: Outcomes, quality of life.

BACKGROUND: Chronic non-bacterial osteomyelitis is a chronic sterile inflammatory bone condition. We aimed to describe patients' clinical and radiographic findings and to evaluate their response to therapy and their quality of life. METHODS: This cross-sectional study included 18 patients from a single center in Turkey whose clinical, radiological features, and outcomes were reviewed retrospectively. The quality of the patients' lives after treatment was compared with healthy controls using the Pediatric Quality of Life Inventory 4.0. RESULTS: The median age of disease onset was 12 years (IQR 10-14 years) and 11 (61.1%) patients were male. The median follow-up duration was 15 months (IQR 12-22 months). The persistent form of chronic non-bacterial osteomyelitis was the most common pattern in 15 (83.3%) patients and a recurrent pattern was defined in three (16.7%) patients. The lesions were multifocal in all patients and 15 (83.3%) patients had symmetric distribution in whole-body magnetic resonance imaging. The most common sites of arthritis were the knee and sacroiliac joints. Methotrexate was used in 16 (88.9%) patients as first-line therapy. However, some patients were unresponsive to the first-line therapy and needed tumor necrosis factor-α inhibitors (55.6%) and bisphosphonates (16.7%). We observed remission in only four (22.2%) patients, and three (16.7%) patients were unresponsive. The patients had a significantly poorer quality of life than controls (P = 0.005). CONCLUSIONS: Chronic non-bacterial osteomyelitis is an insidious disease that requires detailed analysis for diagnosis and whole-body magnetic resonance imaging is an effective tool for its diagnosis. Despite the advanced treatment, patients with chronic non-bacterial osteomyelitis have a poor quality of life.

Standardized reporting and quantification of whole-body MRI findings in children with chronic non-bacterial osteomyelitis treated with pamidronate.

OBJECTIVES: The objectives were to assess changes in radiological disease activity in children with chronic non-bacterial osteomyelitis (CNO) receiving pamidronate therapy and to test a modified radiological index for non-bacterial osteitis (mRINBO) in CNO. mRINBO was used for standardized reporting and quantification of whole-body MRI (WBMRI) findings resulting in an individual summary patient score. METHODS: WBMRI was retrospectively assessed in 18 children with CNO at baseline and after receiving pamidronate therapy for one year. Parameters of interest were: number and anatomic site of radiologically active bone lesions (RAL), size of RAL, extramedullary affection, spinal involvement and changes in mRINBO, which includes both the number and maximal size of RAL (RALmax) in addition to extramedullary and chronic changes. RESULTS: At the time of diagnosis, the mean age of the children was 9.8 (sd, 8.7-10.9) years and 11/18 were females. The number of RALs per patient decreased from median [interquartile range] 4.5 [3-8] to 3 [2-5] RALs per patient (p = 0.02) and extramedullary inflammatory changes regressed. Sixty-one percent of all RALs occurring at baseline resolved and three children became without active inflammatory lesions by WBMRI. The median size of RALs did not change when taking new lesions occurring in 7/18 children into account, but RALmax decreased significantly from 39 [29-45] mm at baseline to 28 [20-40] mm (p < 0.01) at year-one with a concomitant decrease of mRINBO from a median of 5 [4-7] to 4 [3-5] (p = 0.05). CONCLUSIONS: Pamidronate therapy resulted in a decrease of mRINBO from baseline to year one. mRINBO may be a potential scoring method to quantify changes in radiological disease activity in children with CNO. However, further studies are needed to test feasibility and validity of mRINBO.

[Li-Fraumeni syndrome]. / Li-Fraumeni-Syndrom.

BACKGROUND: The autosomal dominant inherited Li-Fraumeni syndrome (LFS) increases the lifetime risk of developing a malignancy to almost 100%. Although breast cancer, central nervous system (CNS) tumors and sarcomas are particularly common, tumors can ultimately occur almost anywhere in the body. As causal therapy is not available, the primary focus for improving the prognosis is early cancer detection. To this end, current cancer surveillance recommendations include a series of examinations including regular imaging beginning at birth. CHALLENGES IN IMAGING IN LFS: Due to the wide range of tumor entities that can occur in individuals affected by LFS, a sensitive detection requires imaging of various tissue contrasts; however, because life-long screening is potentially initiated at a young age, this requirement for comprehensiveness must be balanced against the presumed high psychological burden associated with frequent or invasive examinations. As radiation exposure may lead to an increased (secondary) tumor risk, computed tomography (CT) and X­ray examinations should be avoided as far as possible. CURRENT STATUS AND PERSPECTIVES: Because annual whole-body magnetic resonance imaging (MRI) has no radiation exposure and yet a high sensitivity for many tumors, it forms the basis of the recommended imaging; however, due to the rarity of the syndrome, expertise is sometimes lacking and whole-body MRI examinations are performed heterogeneously and sometimes with limited diagnostic quality. Optimization and standardization of MRI protocols should therefore be pursued. In addition, the need for an intravenously administered contrast agent has not been conclusively clarified despite its high relevance.