Using diffusion models to generate synthetic labeled data for medical image segmentation.

PURPOSE: Medical image analysis has become a prominent area where machine learning has been applied. However, high-quality, publicly available data are limited either due to patient privacy laws or the time and cost required for experts to annotate images. In this retrospective study, we designed and evaluated a pipeline to generate synthetic labeled polyp images for augmenting medical image segmentation models with the aim of reducing this data scarcity. METHODS: We trained diffusion models on the HyperKvasir dataset, comprising 1000 images of polyps in the human GI tract from 2008 to 2016. Qualitative expert review, Fréchet Inception Distance (FID), and Multi-Scale Structural Similarity (MS-SSIM) were tested for evaluation. Additionally, various segmentation models were trained with the generated data and evaluated using Dice score (DS) and Intersection over Union (IoU). RESULTS: Our pipeline produced images more akin to real polyp images based on FID scores. Segmentation model performance also showed improvements over GAN methods when trained entirely, or partially, with synthetic data, despite requiring less compute for training. Moreover, the improvement persists when tested on different datasets, showcasing the transferability of the generated images. CONCLUSIONS: The proposed pipeline produced realistic image and mask pairs which could reduce the need for manual data annotation when performing a machine learning task. We support this use case by showing that the methods proposed in this study enhanced segmentation model performance, as measured by Dice and IoU scores, when trained fully or partially on synthetic data.

Automated screening of computed tomography using weakly supervised anomaly detection.

BACKGROUND: Current artificial intelligence studies for supporting CT screening tasks depend on either supervised learning or detecting anomalies. However, the former involves a heavy annotation workload owing to requiring many slice-wise annotations (ground truth labels); the latter is promising, but while it reduces the annotation workload, it often suffers from lower performance. This study presents a novel weakly supervised anomaly detection (WSAD) algorithm trained based on scan-wise normal and anomalous annotations to provide better performance than conventional methods while reducing annotation workload. METHODS: Based on surveillance video anomaly detection methodology, feature vectors representing each CT slice were trained on an AR-Net-based convolutional network using a dynamic multiple-instance learning loss and a center loss function. The following two publicly available CT datasets were retrospectively analyzed: the RSNA brain hemorrhage dataset (normal scans: 12,862; scans with intracranial hematoma: 8882) and COVID-CT set (normal scans: 282; scans with COVID-19: 95). RESULTS: Anomaly scores of each slice were successfully predicted despite inaccessibility to any slice-wise annotations. Slice-level area under the curve (AUC), sensitivity, specificity, and accuracy from the brain CT dataset were 0.89, 0.85, 0.78, and 0.79, respectively. The proposed method reduced the number of annotations in the brain dataset by 97.1% compared to an ordinary slice-level supervised learning method. CONCLUSION: This study demonstrated a significant annotation reduction in identifying anomalous CT slices compared to a supervised learning approach. The effectiveness of the proposed WSAD algorithm was verified through higher AUC than existing anomaly detection techniques.

Detection of solid and subsolid pulmonary nodules with lung MRI: performance of UTE, T1 gradient-echo, and single-shot T2 fast spin echo.

BACKGROUND: Although MRI is a radiation-free imaging modality, it has historically been limited in lung imaging due to inherent technical restrictions. The aim of this study is to explore the performance of lung MRI in detecting solid and subsolid pulmonary nodules using T1 gradient-echo (GRE) (VIBE, Volumetric interpolated breath-hold examination), ultrashort time echo (UTE) and T2 Fast Spin Echo (HASTE, Half fourier Single-shot Turbo spin-Echo). METHODS: Patients underwent a lung MRI in a 3 T scanner as part of a prospective research project. A baseline Chest CT was obtained as part of their standard of care. Nodules were identified and measured on the baseline CT and categorized according to their density (solid and subsolid) and size (> 4 mm/ ≤ 4 mm). Nodules seen on the baseline CT were classified as present or absent on the different MRI sequences by two thoracic radiologists independently. Interobserver agreement was determined using the simple Kappa coefficient. Paired differences were compared using nonparametric Mann-Whitney U tests. The McNemar test was used to evaluate paired differences in nodule detection between MRI sequences. RESULTS: Thirty-six patients were prospectively enrolled. One hundred forty-nine nodules (100 solid/49 subsolid) with mean size 10.8 mm (SD = 9.4) were included in the analysis. There was substantial interobserver agreement (k = 0.7, p = 0.05). Detection for all nodules, solid and subsolid nodules was respectively; UTE: 71.8%/71.0%/73.5%; VIBE: 61.6%/65%/55.1%; HASTE 72.4%/72.2%/72.7%. Detection rate was higher for nodules > 4 mm in all groups: UTE 90.2%/93.4%/85.4%, VIBE 78.4%/88.5%/63.4%, HASTE 89.4%/93.8%/83.8%. Detection of lesions ≤4 mm was low for all sequences. UTE and HASTE performed significantly better than VIBE for detection of all nodules and subsolid nodules (diff = 18.4 and 17.6%, p = < 0.01 and p = 0.03, respectively). There was no significant difference between UTE and HASTE. There were no significant differences amongst MRI sequences for solid nodules. CONCLUSIONS: Lung MRI shows adequate performance for the detection of solid and subsolid pulmonary nodules larger than 4 mm and can serve as a promising radiation-free alternative to CT.

Magnetic Resonance Elastography-derived Stiffness Predicts Renal Function Loss and Is Associated With Microvascular Inflammation in Kidney Transplant Recipients.

Background: Organ stiffening can be caused by inflammation and fibrosis, processes that are common causes of transplant kidney dysfunction. Magnetic resonance elastography (MRE) is a contrast-free, noninvasive imaging modality that measures kidney stiffness. The objective of this study was to assess the ability of MRE to serve as a prognostic factor for renal outcomes. Methods: Patients were recruited from the St Michael's Hospital Kidney Transplant Clinic. Relevant baseline demographic, clinical, and Banff histologic information, along with follow-up estimated glomerular filtration rate (eGFR) data, were recorded. Two-dimensional gradient-echo MRE imaging was performed to obtain kidney "stiffness" maps. Binary logistic regression analyses were performed to examine for relationships between stiffness and microvascular inflammation score. Linear mixed-effects modeling was used to assess the relationship between stiffness and eGFR change over time controlling for other baseline variables. A G2-likelihood ratio Chi-squared test was performed to compare between the baseline models with and without "stiffness." Results: Sixty-eight transplant kidneys were scanned in 66 patients (mean age 56 ± 12 y, 24 females), with 38 allografts undergoing a contemporaneous biopsy. Mean transplant vintage was 7.0 ± 6.8 y. In biopsied allografts, MRE-derived allograft stiffness was associated only with microvascular inflammation (Banff g + ptc score, Spearman ρ = 0.43, P = 0.01), but no other histologic parameters. Stiffness was negatively associated with eGFR change over time (Stiffness × Time interaction ß = -0.80, P < 0.0001), a finding that remained significant even when adjusted for biopsy status and baseline variables (Stiffness × Time interaction ß = -0.46, P = 0.04). Conversely, the clinical models including "stiffness" showed significantly better fit (P = 0.04) compared with the baseline clinical models without "stiffness." Conclusions: MRE-derived renal stiffness provides important prognostic information regarding renal function loss for patients with allograft dysfunction, over and above what is provided by current clinical variables.

External Validation of O-RADS US Risk Stratification and Management System.

Background The Ovarian-Adnexal Reporting and Data System (O-RADS) US risk stratification and management system (O-RADS US) was designed to improve risk assessment and management of ovarian and adnexal lesions. Validation studies including both surgical and nonsurgical treatment as the reference standard remain lacking. Purpose To externally validate O-RADS US in women who underwent either surgical or nonsurgical treatment and to determine if incorporating acoustic shadowing as a benign finding improves diagnostic performance. Materials and Methods This retrospective study included consecutive women who underwent pelvic US between August 2015 and April 2017 at a tertiary referral oncology center. Two independent readers blinded to clinical and histologic outcome assigned an O-RADS risk category and an International Ovarian Tumor Analysis (IOTA) Assessment of Different NEoplasias in the adneXa (ADNEX) model risk of malignancy score to assessable lesions. Reference standards were surgical histopathology or 2-year imaging follow-up. Receiver operating characteristic (ROC) curve analysis was used to evaluate performance of the O-RADS US, ADNEX, and modified O-RADS models incorporating acoustic shadowing. Results In total, 227 women (mean age, 52 years ± 16 [SD]) with 262 ovarian or adnexal lesions were evaluated. Of these lesions, 187 (71%) were benign and 75 (29%) were malignant. The proportion of malignancy was 0% (0 of 100) for O-RADS 2, 3% (one of 32) for O-RADS 3, 35% (22 of 63) for O-RADS 4, and 78% (52 of 67) for O-RADS 5. The area under the ROC curve (AUC) for O-RADS and ADNEX was 0.91 (95% CI: 0.88, 0.94) and 0.95 (95% CI: 0.92, 0.97; P = .01), respectively. The addition of acoustic shadowing as a benign finding improved O-RADS AUC to 0.94 (95% CI: 0.91, 0.96; P = .01). Use of O-RADS 4 as a threshold yielded a sensitivity of 99% (74 of 75; 95% CI: 96, 100) and a specificity of 70% (131 of 187; 95% CI: 64, 77). Conclusion In a tertiary referral oncology center, the Ovarian-Adnexal Reporting and Data System US risk stratification and management system enabled accurate distinction of benign from malignant ovarian and adnexal lesions. Adding acoustic shadowing as a benign finding improved its diagnostic performance. © RSNA, 2022 See also the editorial by Levine in this issue.

Quantifying uncertainty in machine learning classifiers for medical imaging.

PURPOSE: Machine learning (ML) models in medical imaging (MI) can be of great value in computer aided diagnostic systems, but little attention is given to the confidence (alternatively, uncertainty) of such models, which may have significant clinical implications. This paper applied, validated, and explored a technique for assessing uncertainty in convolutional neural networks (CNNs) in the context of MI. MATERIALS AND METHODS: We used two publicly accessible imaging datasets: a chest x-ray dataset (pneumonia vs. control) and a skin cancer imaging dataset (malignant vs. benign) to explore the proposed measure of uncertainty based on experiments with different class imbalance-sample sizes, and experiments with images close to the classification boundary. We also further verified our hypothesis by examining the relationship with other performance metrics and cross-checking CNN predictions and confidence scores with an expert radiologist (available in the Supplementary Information). Additionally, bounds were derived on the uncertainty metric, and recommendations for interpretability were made. RESULTS: With respect to training set class imbalance for the pneumonia MI dataset, the uncertainty metric was minimized when both classes were nearly equal in size (regardless of training set size) and was approximately 17% smaller than the maximum uncertainty resulting from greater imbalance. We found that less-obvious test images (those closer to the classification boundary) produced higher classification uncertainty, about 10-15 times greater than images further from the boundary. Relevant MI performance metrics like accuracy, sensitivity, and sensibility showed seemingly negative linear correlations, though none were statistically significant (p [Formula: see text] 0.05). The expert radiologist and CNN expressed agreement on a small sample of test images, though this finding is only preliminary. CONCLUSIONS: This paper demonstrated the importance of uncertainty reporting alongside predictions in medical imaging. Results demonstrate considerable potential from automatically assessing classifier reliability on each prediction with the proposed uncertainty metric.

Missed Breast Cancers on MRI in High-Risk Patients: A Retrospective Case-Control Study.

Purpose: To determine if MRI features and molecular subtype influence the detectability of breast cancers on MRI in high-risk patients. Methods and Materials: Breast cancers in a high-risk population of 104 patients were diagnosed following MRI describing a BI-RADS 4-5 lesion. MRI characteristics at the time of diagnosis were compared with previous MRI, where a BI-RADS 1-2-3 lesion was described. Results: There were 77 false-negative MRIs. A total of 51 cancers were overlooked and 26 were misinterpreted. There was no association found between MRI characteristics, the receptor type and the frequency of missed cancers. The main factors for misinterpreted lesions were multiple breast lesions, prior biopsy/surgery and long-term stability. Lesions were mostly overlooked because of their small size and high background parenchymal enhancement. Among missed lesions, 50% of those with plateau kinetics on initial MRI changed for washout kinetics, and 65% of initially progressively enhancing lesions then showed plateau or washout kinetics. There were more basal-like tumours in BRCA1 carriers (50%) than in non-carriers (13%), p = 0.0001, OR = 6.714, 95% CI = [2.058-21.910]. The proportion of missed cancers was lower in BRCA carriers (59%) versus non-carriers (79%), p < 0.05, OR = 2.621, 95% CI = [1.02-6.74]. Conclusions: MRI characteristics or molecular subtype do not influence breast cancer detectability. Lesions in a post-surgical breast should be assessed with caution. Long-term stability does not rule out malignancy and multimodality evaluation is of added value. Lowering the biopsy threshold for lesions with an interval change in kinetics for a type 2 or 3 curve should be considered. There was a higher rate of interval cancers in BRCA 1 patients attributed to lesions more aggressive in nature.

Survival analysis in lung cancer patients with interstitial lung disease.

OBJECTIVE: Lung cancer patients with interstitial lung disease (ILD) are prone for higher morbidity and mortality and their treatment is challenging. The purpose of this study is to investigate whether the survival of lung cancer patients is affected by the presence of ILD documented on CT. MATERIALS AND METHODS: 146 patients with ILD at initial chest CT were retrospectively included in the study. 146 lung cancer controls without ILD were selected. Chest CTs were evaluated for the presence of pulmonary fibrosis which was classified in 4 categories. Presence and type of emphysema, extent of ILD and emphysema, location and histologic type of cancer, clinical staging and treatment were evaluated. Kaplan-Meier estimates and Cox regression models were used to assess survival probability and hazard of death of different groups. P value < 0.05 was considered significant. RESULTS: 5-year survival for the study group was 41% versus 48% for the control group (log-rank test p = 0.0092). No significant difference in survival rate was found between the four different categories of ILD (log-rank test, p = 0.195) and the different histologic types (log-rank test, p = 0.4005). A cox proportional hazard model was used including presence of ILD, clinical stage and age. The hazard of death among patients with ILD was 1.522 times that among patients without ILD (95%CI, p = 0.029). CONCLUSION: Patients with lung cancer and CT evidence of ILD have a significantly shorter survival compared to patients with lung cancer only. Documenting the type and grading the severity of ILD in lung cancer patients will significantly contribute to their challenging management.

Distribution of Iliac Artery Calcification on Unenhanced Computed Tomography Scans Performed on Potential Recipients Prior to Renal Transplantation.

PURPOSE: To investigate whether a significant difference exists between the calcification of the common iliac arteries (CIAs) and the external iliac arteries (EIAs) and test for associations between clinical factors and the distribution of calcification. METHODS: A retrospective review of renal transplant candidates who underwent a routine preoperative unenhanced computed tomography yielded 214 patients. Agatston scores for the patients' left CIA, left EIA, right CIA, and right EIA were assigned. A retrospective search of patient records screened for 5 clinical factors (diabetes, hypertension, coronary artery disease [CAD], smoking, and dialysis). Data were assessed using a 2-sided t test, odds ratio, and a multivariate linear regression calculated through generalized estimating equation (GEE). RESULTS: The log-transformed Agatston scores in the CIA were found to be significantly greater than that in the EIA (t = 9.57, P < .0001), with a mean difference of 1.5078 (95% confidence interval: 1.1962-1.8194), indicating relative EIA sparing. There were no significant differences in calcification between the right and left sides. Generalized estimating equation found that CAD and smoking demonstrated independent positive associations with EIA sparing (GEE = 2.6464 [P = .0197] and 1.9092 [P = .0470], respectively). Age was also significantly associated and indicated that EIA sparing remained relatively constant throughout patients' lives (GEE = 1.0711 [P < .0001]). CONCLUSION: This study has demonstrated statistically significant EIA sparing in end-stage renal disease patients and identified CAD and smoking as associated factors. This phenomenon warrants further investigation into its biological mechanisms and the impact of EIA sparing on outcomes following transplants.

Assessing and mitigating the effects of class imbalance in machine learning with application to X-ray imaging.

PURPOSE: Machine learning (ML) algorithms are well known to exhibit variations in prediction accuracy when provided with imbalanced training sets typically seen in medical imaging (MI) due to the imbalanced ratio of pathological and normal cases. This paper presents a thorough investigation of the effects of class imbalance and methods for mitigating class imbalance in ML algorithms applied to MI. METHODS: We first selected five classes from the Image Retrieval in Medical Applications (IRMA) dataset, performed multiclass classification using the random forest model (RFM), and then performed binary classification using convolutional neural network (CNN) on a chest X-ray dataset. An imbalanced class was created in the training set by varying the number of images in that class. Methods tested to mitigate class imbalance included oversampling, undersampling, and changing class weights of the RFM. Model performance was assessed by overall classification accuracy, overall F1 score, and specificity, recall, and precision of the imbalanced class. RESULTS: A close-to-balanced training set resulted in the best model performance, and a large imbalance with overrepresentation was more detrimental to model performance than underrepresentation. Oversampling and undersampling methods were both effective in mitigating class imbalance, and efficacy of oversampling techniques was class specific. CONCLUSION: This study systematically demonstrates the effect of class imbalance on two public X-ray datasets on RFM and CNN, making these findings widely applicable as a reference. Furthermore, the methods employed here can guide researchers in assessing and addressing the effects of class imbalance, while considering the data-specific characteristics to optimize imbalance mitigating methods.

[Formula: see text]: deep learning-based radiomics for the time-to-event outcome prediction in lung cancer.

Hand-crafted radiomics has been used for developing models in order to predict time-to-event clinical outcomes in patients with lung cancer. Hand-crafted features, however, are pre-defined and extracted without taking the desired target into account. Furthermore, accurate segmentation of the tumor is required for development of a reliable predictive model, which may be objective and a time-consuming task. To address these drawbacks, we propose a deep learning-based radiomics model for the time-to-event outcome prediction, referred to as DRTOP that takes raw images as inputs, and calculates the image-based risk of death or recurrence, for each patient. Our experiments on an in-house dataset of 132 lung cancer patients show that the obtained image-based risks are significant predictors of the time-to-event outcomes. Computed Tomography (CT)-based features are predictors of the overall survival (OS), with the hazard ratio (HR) of 1.35, distant control (DC), with HR of 1.06, and local control (LC), with HR of 2.66. The Positron Emission Tomography (PET)-based features are predictors of OS and recurrence free survival (RFS), with hazard ratios of 1.67 and 1.18, respectively. The concordance indices of [Formula: see text], [Formula: see text], and [Formula: see text] for predicting the OS, DC, and RFS show that the deep learning-based radiomics model is as accurate or better in predicting predefined clinical outcomes compared to hand-crafted radiomics, with concordance indices of [Formula: see text], [Formula: see text], and [Formula: see text], for predicting the OS, DC, and RFS, respectively. Deep learning-based radiomics has the potential to offer complimentary predictive information in the personalized management of lung cancer patients.

3D-MCN: A 3D Multi-scale Capsule Network for Lung Nodule Malignancy Prediction.

Despite the advances in automatic lung cancer malignancy prediction, achieving high accuracy remains challenging. Existing solutions are mostly based on Convolutional Neural Networks (CNNs), which require a large amount of training data. Most of the developed CNN models are based only on the main nodule region, without considering the surrounding tissues. Obtaining high sensitivity is challenging with lung nodule malignancy prediction. Moreover, the interpretability of the proposed techniques should be a consideration when the end goal is to utilize the model in a clinical setting. Capsule networks (CapsNets) are new and revolutionary machine learning architectures proposed to overcome shortcomings of CNNs. Capitalizing on the success of CapsNet in biomedical domains, we propose a novel model for lung tumor malignancy prediction. The proposed framework, referred to as the 3D Multi-scale Capsule Network (3D-MCN), is uniquely designed to benefit from: (i) 3D inputs, providing information about the nodule in 3D; (ii) Multi-scale input, capturing the nodule's local features, as well as the characteristics of the surrounding tissues, and; (iii) CapsNet-based design, being capable of dealing with a small number of training samples. The proposed 3D-MCN architecture predicted lung nodule malignancy with a high accuracy of 93.12%, sensitivity of 94.94%, area under the curve (AUC) of 0.9641, and specificity of 90% when tested on the LIDC-IDRI dataset. When classifying patients as having a malignant condition (i.e., at least one malignant nodule is detected) or not, the proposed model achieved an accuracy of 83%, and a sensitivity and specificity of 84% and 81% respectively.

Effectiveness of Electronic Medication Packaging Devices on Medication Adherence: A Scoping Review.

Poor adherence to complex medication regimens is a global problem that affects the treatment of chronic diseases, which involves polypharmacy and requires long-term administration of medications. The most significant barrier to medication adherence in older adults is patient-related factors. The purpose of this study was to find evidence from the current literature to evaluate the effectiveness of electronic medication packaging (EMP) devices on improving medication adherence in older patients. MEDLINE and EMBASE databases were searched based on inclusion/exclusion criteria, focusing on medication adherence and EMP devices with specific technological features. Search results included studies with experiences of patients with four different devices and various medical conditions. Study results indicated that EMP devices may improve medication adherence in older patients. However, due to insufficient evidence that supports their effectiveness specifically in the aging population, further clinical validation in older adults is recommended to draw strong conclusions. [Journal of Gerontological Nursing, 46(3), 27-36.].

The effects of physical photostimulable phosphor plate artifacts on the radiologic interpretation of periapical inflammatory disease.

OBJECTIVE: To evaluate how physical photostimulable phosphor (PSP) plate artifacts, such as those created by scratches, phosphor degradation, and surface peeling, affect the radiologic interpretation of periapical inflammatory disease. STUDY DESIGN: A novel technique was developed to digitally superimpose 25 real PSP artifact masks over 100 clinical complementary metal oxide semiconductor (CMOS) periapical images with known radiologic interpretations. These images were presented to 25 general dentists, who were asked to state their radiologic interpretations, their confidence in their interpretations, and their opinions on whether the plates should be discarded. Statistical analyses were conducted by using random intercept mixed models for repeated measures and χ2 tests of the pooled data. RESULTS: No statistically significant adverse effect on interpretation was seen, even at severe artifact levels. There was a statistically significant decrease in the clinicians' confidence and an increase in discard proportions when interpreting images with severe PSP plate artifacts (P < .05). CONCLUSIONS: Although diagnostic efficacy was unaffected, clinicians' confidence decreased and proportionally more clinicians opted to discard sensors when interpreting images with severe artifacts. Future studies on the effects of artifacts on the efficacy of diagnosis of other dental diseases are recommended. Ultimately, these results can guide recommendations for PSP plate quality assurance.

Serum cytokine and chemokine levels in patients with eosinophilic granulomatosis with polyangiitis, hypereosinophilic syndrome, or eosinophilic asthma.

OBJECTIVES: The pathogenesis of eosinophilic granulomatosis with polyangiitis (EGPA) remains poorly understood, and may overlap with eosinophilic asthma and primary hypereosinophilic syndrome (HES). The aim of this study was to analyse a panel of serum cytokines and chemokines as markers of disease activity in patients with these conditions. METHODS: The levels of 54 cytokines and chemokines were measured in the sera of 40 patients with active EGPA, 10 of these patients during inactive disease, 6 patients with HES, 8 with asthma, and 10 healthy controls. Serum cytokine/chemokines measured included interleukin (IL)-1α, 1ß, 3, 4, 5, 6, 8, 13, 15, 17A, 17E(25), 18, 23 and 33, soluble IL-2 receptor alpha, eotaxin-1 (CCL11), -2 (CCL24) and -3 (CCL26), macrophage-derived chemokine (MDC/CCL22), macrophage inflammatory protein (MIP)-1a and -1b, and tumour necrosis factor (TNF)-α. Results were compared between disease and control groups using regression analysis, with Bonferroni correction for multiple comparisons (significant p value ≤0.00093). RESULTS: Significant differences were observed only in serum levels of MDC, IL-8, MIP-1a and -1b, TNF-α, each of which were lower in patients with active EGPA than in healthy controls (p<0.0001). Differences between patients with active disease and other disease groups did not reach significance. Paired comparisons between sera from patients with active or inactive EGPA showed no significant difference for any of the studied cytokines or chemokines. CONCLUSIONS: No clear difference in the serum levels of measured cytokines and chemokines helped distinguish between active EGPA or inactive EGPA, or other disease or control groups.

Radiomics analysis at PET/CT contributes to prognosis of recurrence and survival in lung cancer treated with stereotactic body radiotherapy.

We sought to quantify contribution of radiomics and SUVmax at PET/CT to predict clinical outcome in lung cancer patients treated with stereotactic body radiotherapy (SBRT). 150 patients with 172 lung cancers, who underwent SBRT were retrospectively included. Radiomics were applied on PET/CT. Principal components (PC) for 42 CT and PET-derived features were examined to determine which ones accounted for most of variability. Survival analysis quantified ability of radiomics and SUVmax to predict outcome. PCs including homogeneity, size, maximum intensity, mean and median gray level, standard deviation, entropy, kurtosis, skewness, morphology and asymmetry were included in prediction models for regional control (RC) [PC4-HR:0.38, p = 0.02], distant control (DC) [PC4-HR:0.51, p = 0.02 and PC1-HR:1.12, p = 0.01], recurrence free probability (RFP) [PC1-HR:1.08, p = 0.04], disease specific survival (DSS) [PC2-HR:1.34, p = 0.03 and PC3-HR:0.64, p = 0.02] and overall survival (OS) [PC4-HR:0.45, p = 0.004 and PC3-HR:0.74, p = 0.02]. In combined analysis with SUVmax, PC1 lost predictive ability over SUVmax for RFP [HR:1.1, p = 0.04] and DC [HR:1.13, p = 0.002], while PC4 remained predictive of DC independent of SUVmax [HR:0.5, p = 0.02]. Radiomics remained the only predictors of OS, DSS and RC. Neither SUVmax nor radiomics predicted recurrence free survival. Radiomics on PET/CT provided complementary information for prediction of control and survival in SBRT-treated lung cancer patients.

Diagnostic criteria for cryopyrin-associated periodic syndrome (CAPS).

Cryopyrin-associated periodic syndrome (CAPS) is a rare, heterogeneous disease entity associated with NLRP3 gene mutations and increased interleukin-1 (IL-1) secretion. Early diagnosis and rapid initiation of IL-1 inhibition prevent organ damage. The aim of the study was to develop and validate diagnostic criteria for CAPS. An innovative process was followed including interdisciplinary team building, item generation: review of CAPS registries, systematic literature review, expert surveys, consensus conferences for item refinement, item reduction and weighting using 1000Minds decision software. Resulting CAPS criteria were tested in large cohorts of CAPS cases and controls using correspondence analysis. Diagnostic models were explored using sensitivity analyses. The international team included 16 experts. Systematic literature and registry review identified 33 CAPS-typical items; the consensus conferences reduced these to 14. 1000Minds exercises ranked variables based on importance for the diagnosis. Correspondence analysis determined variables consistently associated with the diagnosis of CAPS using 284 cases and 837 controls. Seven variables were significantly associated with CAPS (p<0.001). The best diagnosis model included: Raised inflammatory markers (C-reactive protein/serum amyloid A) plus ≥two of six CAPS-typical symptoms: urticaria-like rash, cold-triggered episodes, sensorineural hearing loss, musculoskeletal symptoms, chronic aseptic meningitis and skeletal abnormalities. Sensitivity was 81%, specificity 94%. It performed well for all CAPS subtypes and regardless of NLRP3 mutation. The novel approach integrated traditional methods of evidence synthesis with expert consensus, web-based decision tools and innovative statistical methods and may serve as model for other rare diseases. These criteria will enable a rapid diagnosis for children and adults with CAPS.

Early detection of sensorineural hearing loss in Muckle-Wells-syndrome.

BACKGROUND: Muckle-Wells-syndrome (MWS) is an autoinflammatory disease characterized by systemic and organ-specific inflammation due to excessive interleukin (IL)-1 release. Inner ear inflammation results in irreversible sensorineural hearing loss, if untreated. Early recognition and therapy may prevent deafness. The aims of the study were to characterize the spectrum of hearing loss, optimize the otologic assessment for early disease and determine responsiveness to anti-IL-1-therapy regarding hearing. METHODS: A single center prospective cohort study of children and adults with MWS was performed. Standardized clinical, laboratory and otologic assessments including standard pure tone audiometry, additional high tone thresholds, vestibular organ testing, tinnitus evaluation and functional disability classes were determined serially. Pure-tone-average models were developed and evaluated. Risk factors for hearing loss and the impact of anti-IL-1 treatment were determined. RESULTS: A total of 23 patients with genetically confirmed MWS were included, of whom 63 % were females; 52 % were children. At baseline all patients had active MWS; 91 % reported clinically impaired hearing with 74 % having an abnormal standard assessment (0.5-4 kHz). In contrast, high frequency pure tone averages (HF-PTA) were abnormal in all symptomatic patients including those with early hearing loss (sensitivity 100 %). Females were at highest risk for hearing loss even after adjustment for age (p = 0.008). Treatment with IL-1 blockade resulted in improved or stable hearing in 91 % of patients. CONCLUSIONS: Early inner ear inflammation in MWS primarily affects the high frequencies, beyond the range of standard otologic assessment tools. The HF-PTA is a sensitive tool to detect imminent hearing loss and monitor treatment response.

Lower socioeconomic status, adiposity and negative health behaviours in youth: a cross-sectional observational study.

OBJECTIVE: Understanding obesity and its modifiable risk factors in youth is key to addressing the burden of cardiovascular disease later in life. Our aim was to examine the associations among adiposity, negative health behaviours and socioeconomic status in youth from the Niagara Region. DESIGN, SETTING AND PARTICIPANTS: Cross-sectional observational study of 3467 grade 9 students during their mandatory health and physical education class to investigate the association between socioeconomic status (postal code), self-reported health behaviour and adiposity in the Niagara Region, Ontario, Canada. RESULTS: Median household income was $63,696 and overall percentage below the after-tax low-income cut-off was 4.2%. Negative health behaviours (especially skipped meals, lower fruit and vegetable consumption, higher screen time) were associated with lower income neighbourhoods, however, the absolute effect was small. Those participants in the lowest income quintile had a significantly greater body mass index z-score than those in the highest (0.72±1.19 vs 0.53±1.12), but the overall trend across quintiles was not statistically significant. A similar trend was noted for waist-to-height ratio. The lowest income neighbourhoods according to after-tax low-income cut-off had small but statistically significant associations with higher adiposity compared with the middle or highest income neighbourhoods. CONCLUSIONS: Obesity prevention efforts should target modifiable behaviours, with particular attention to adolescents from lower income families and neighbourhoods.

Presentation and outcome of paediatric membranous non-proliferative lupus nephritis.

BACKGROUND: Studies of paediatric patients with membranous lupus nephritis (MLN) have yielded variable results, mostly due to the inclusion of mixed, i.e. proliferative nephritis. The aim of this study was to describe clinical and laboratory findings at the diagnosis of paediatric non-proliferative MLN, report the outcome and identify predictors of remission. METHODS: A single-center cohort study of consecutive children diagnosed with non-proliferative MLN was performed. Clinical and laboratory measures and treatment regimens were obtained in prospective standardized assessments. Renal outcome was measured by renal parameters and steroid requirement. Predictors for remission and time to remission were determined. RESULTS: A total of 30 children were identified with a median follow-up time 4.1 years. Of 21 patients followed for more than 2 years, 19 (90 %) achieved clinical remission, and 16 (76 %) achieved a state of maintained clinical remission on low-dose prednisone. Three patients developed proliferative nephritis on subsequent renal biopsy. Lower albumin at the time of biopsy was correlated with a lower rate of remission and longer time to remission. CONCLUSIONS: Among our paediatric patient cohort the outcome of non-proliferative MLN in systemic lupus erythematosus was good. The majority of patients did not require aggressive immunosuppressive treatment to reach a stable disease state on low-dose steroid treatment.