Background: Positron emission tomography (PET) images of head and neck squamous cell carcinoma (HNSCC) patients can assess the functional and biochemical processes at cellular levels. Therefore, PET radiomics‐based prediction and prognostic models have the potentials to understand tumour heterogeneity and assist clinicians with diagnosis, prognosis and management of the disease. We conducted a systematic review of published modelling information to evaluate the usefulness of PET radiomics in the prediction and prognosis of HNSCC patients.Methods: We searched bibliographic databases (MEDLINE, Embase, Web of Science) from 2010 to 2021 and considered 31 studies with pre‐defined inclusion criteria. We followed the CHARMS checklist for data extraction and performed quality assessment using the PROBAST tool. We conducted a meta‐analysis to estimate the accuracy of the prediction and prognostic models using the diagnostic odds ratio (DOR) and average C‐statistic, respectively.Results: Manual segmentation method followed by 40% of the maximum standardised uptake value (SUV_max) thresholding is a commonly used approach. The area under the receiver operating curves of externally validated prediction models ranged between 0.60–0.87, 0.65–0.86 and 0.62–0.75 for overall survival, distant metastasis and recurrence, respectively. Most studies highlighted an overall high risk of bias (outcome definition, statistical methodologies and external validation of models) and high unclear concern in terms of applicability. The meta‐analysis showed the estimated pooled DOR of 6.75 (95% CI: 4.45, 10.23) for prediction models and the C‐statistic of 0.71 (95% CI: 0.67, 0.74) for prognostic models.Conclusions: Both prediction and prognostic models using clinical variables and PET radiomics demonstrated reliable accuracy for detecting adverse outcomes in HNSCC, suggesting the prospect of PET radiomics in clinical settings for diagnosis, prognosis and management of HNSCC patients. Future studies of prediction and prognostic models should emphasise the quality of reporting, external model validation, generalisability to real clinical scenarios and enhanced reproducibility of results.

BACKGROUND AND PURPOSE: The detection of new subtle brain pathology on MR imaging is a time-consuming and error-prone task for the radiologist. This article introduces and evaluates an image-registration and subtraction method for highlighting small changes in the brain with a view to minimizing the risk of missed pathology and reducing fatigue. MATERIALS AND METHODS: We present a fully automated algorithm for highlighting subtle changes between multiple serially acquired brain MR images with a novel approach to registration and MR imaging bias field correction. The method was evaluated for the detection of new lesions in 77 patients undergoing cardiac surgery, by using pairs of fluid-attenuated inversion recovery MR images acquired 1–2 weeks before the operation and 6–8 weeks postoperatively. Three radiologists reviewed the images. RESULTS: On the basis of qualitative comparison of pre- and postsurgery FLAIR images, radiologists identified 37 new ischemic lesions in 22 patients. When these images were accompanied by a subtraction image, 46 new ischemic lesions were identified in 26 patients. After we accounted for interpatient and interradiologist variability using a multilevel statistical model, the likelihood of detecting a lesion was 2.59 (95% CI, 1.18–5.67) times greater when aided by the subtraction algorithm (P = .017). Radiologists also reviewed the images significantly faster (P < .001) by using the subtraction image (mean, 42 seconds; 95% CI, 29–60 seconds) than through qualitative assessment alone (mean, 66 seconds; 95% CI, 46–96 seconds). CONCLUSIONS: Use of this new subtraction algorithm would result in considerable savings in the time required to review images and in improved sensitivity to subtle focal pathology.

Background and Aims: Mental health condition among adolescents is a leading cause of health‐related disability in Sri Lanka. The study aims to estimate the prevalence and evaluate the associated risk factors in three major mental health domains—loneliness, anxiety and suicidal ideation—among Sri Lankan adolescents.Methods: We conducted a secondary analysis of cross‐sectional data of 3262 adolescents from the Global School‐based Health Survey (GSHS) conducted by the WHO in 2016. We modeled the binary outcome variables using multivariable logistic regression models with exposures representing demography, food habits, personal hygiene, behavior, substance abuse, parental and social engagement of the respondents.Results: We estimated the prevalence of loneliness, anxiety and suicidal ideation as 30.8% (95% CI: 29.3, 32.5), 20.2% (95% CI: 18.8, 21.6) and 3.7% (95% CI: 3.1, 4.4), respectively, and the overall prevalence as 40.3% (95% CI: 38.6, 42.0). Mental health problems were more prevalent among females than males. Engagement with parents and close friends, adequate nutritional intake and physically active lifestyles reduced the risk of common mental health problems. Exposure variables like food insecurity, truancy, second‐hand smoking, physical fight, and being bullied increased adolescents’ risk of reported psychological problems.Conclusions: We conclude that the prevalence of mental health problems in the Sri Lankan adolescent population was higher than the global average. Results suggest that future policy decisions to mitigate mental health problems among Sri Lankan adolescents should incorporate an integrated approach involving the individual, family and community to promote positive home and school environments combined with an active and healthy lifestyle.

Aims: Chronic heart failure (CHF) is a systemic syndrome with a poor prognosis and a need for novel therapies. We investigated whether whole blood transcriptomic profiling can provide new mechanistic insights into cardiovascular (CV) mortality in CHF. Methods and results: Transcriptome profiles were generated at baseline from 944 CHF patients from the BIOSTAT‐CHF study, of whom 626 survived and 318 died from a CV cause during a follow‐up of 21 months. Multivariable analysis, including adjustment for cell count, identified 1153 genes (6.5%) that were differentially expressed between those that survived or died and strongly related to a validated clinical risk score for adverse prognosis. The differentially expressed genes mainly belonged to five non‐redundant pathways: adaptive immune response, proteasome‐mediated ubiquitin‐dependent protein catabolic process, T‐cell co‐stimulation, positive regulation of T‐cell proliferation, and erythrocyte development. These five pathways were selectively related (RV coefficients >0.20) with seven circulating protein biomarkers of CV mortality (fibroblast growth factor 23, soluble ST2, adrenomedullin, hepcidin, pentraxin‐3, WAP 4‐disulfide core domain 2, and interleukin‐6) revealing an intricate relationship between immune and iron homeostasis. The pattern of survival‐associated gene expression matched with 29 perturbagen‐induced transcriptome signatures in the iLINCS drug‐repurposing database, identifying drugs, approved for other clinical indications, that were able to reverse in vitro the molecular changes associated with adverse prognosis in CHF. Conclusion: Systematic modelling of the whole blood protein‐coding transcriptome defined molecular pathways that provide a link between clinical risk factors and adverse CV prognosis in CHF, identifying both established and new potential therapeutic targets.

Objective . Accurate and reproducible tumor delineation on positron emission tomography (PET) images is required to validate predictive and prognostic models based on PET radiomic features. Manual segmentation of tumors is time-consuming whereas semi-automatic methods are easily implementable and inexpensive. This study assessed the reliability of semi-automatic segmentation methods over manual segmentation for tumor delineation in head and neck squamous cell carcinoma (HNSCC) PET images. Approach . We employed manual and six semi-automatic segmentation methods (just enough interaction (JEI), watershed, grow from seeds (GfS), flood filling (FF), 30% SUVmax and 40%SUVmax threshold) using 3D slicer software to extract 128 radiomic features from FDG-PET images of 100 HNSCC patients independently by three operators. We assessed the distributional properties of all features and considered 92 log-transformed features for subsequent analysis. For each paired comparison of a feature, we fitted a separate linear mixed effect model using the method (two levels; manual versus one semi-automatic method) as a fixed effect and the subject and the operator as the random effects. We estimated different statistics—the intraclass correlation coefficient agreement (aICC), limits of agreement (LoA), total deviation index (TDI), coverage probability (CP) and coefficient of individual agreement (CIA)—to evaluate the agreement between the manual and semi-automatic methods. Main results . Accounting for all statistics across 92 features, the JEI method consistently demonstrated acceptable agreement with the manual method, with median values of aICC = 0.86, TDI = 0.94, CP = 0.66, and CIA = 0.91. Significance . This study demonstrated that JEI method is a reliable semi-automatic method for tumor delineation on HNSCC PET images.