Immunotherapy-induced pneumonitis (IIP) is a serious side-effect which requires accurate diagnosis and management with high-dose corticosteroids. The differential diagnosis between IIP and other types of pneumonitis (OTP) remains challenging due to similar radiological patterns. This study was aimed to develop a prediction model to differentiate IIP from OTP in patients with stage IV non-small cell lung cancer (NSCLC) who developed pneumonitis during immunotherapy. Consecutive patients with metastatic NSCLC treated with immunotherapy in six centres in the Netherlands and Belgium from 2017 to 2020 were reviewed and cause-specific pneumonitis events were identified. Seven regions of interest (segmented lungs and spheroidal/cubical regions surrounding the inflammation) were examined to extract the most predictive radiomic features from the chest computed tomography images obtained at pneumonitis manifestation. Models were internally tested regarding discrimination, calibration and decisional benefit. To evaluate the clinical application of the models, predicted labels were compared with the separate clinical and radiological judgements. A total of 556 patients were reviewed; 31 patients (5.6%) developed IIP and 41 patients developed OTP (7.4%). The line of immunotherapy was the only predictive factor in the clinical model (2nd versus 1st odds ratio = 0.08, 95% confidence interval:0.01-0.77). The best radiomic model was achieved using a 75-mm spheroidal region of interest which showed an optimism-corrected area under the receiver operating characteristic curve of 0.83 (95% confidence interval:0.77-0.95) with negative and positive predictive values of 80% and 79%, respectively. Good calibration and net benefits were achieved for the radiomic model across the entire range of probabilities. A correct diagnosis was provided by the radiomic model in 10 out of 12 cases with non-conclusive radiological judgements. Radiomic biomarkers applied to computed tomography imaging may support clinicians making the differential diagnosis of pneumonitis in patients with NSCLC receiving immunotherapy, especially when the radiologic assessment is non-conclusive.

Background: Socioeconomic-related inequality in oral hygiene behaviors in Iran is poorly understood. This study aims to measure and decompose socioeconomic-related inequalities in oral hygiene behaviors among middle-aged and elderly adults in Iran. Methods: A cross-sectional analysis was performed using data from the Prospective Epidemiological Research Studies in IrAN (PERSIAN), a large national cohort study. A total of 130,016 individuals aged 35 years and above from 17 cohort centers in Iran were included in the study. The normalized concentration index (C_n) was used to measure the magnitude of inequality in oral hygiene behaviors, i.e. brushing at least twice and flossing once daily, among middle-aged and elderly Iranian adults included in the cohort centers. Decomposition analysis was performed to quantify the contribution of each determinant to the observed inequality in oral hygiene behaviors. Results: Totally, 65.5% of middle-aged and elderly adults brushed their teeth twice a day or more, 7.6% flossed at least once a day and 3.48% had both habits. The estimated C_n of the two habits combined, i.e. tooth brushing and dental flossing, for all provinces taken part in the PERSIAN cohort study was 0.399 (95% confidence interval [CI]: 0.383 to 0.417), indicating that the prevalence of the two habits combined is more concentrated among individuals with higher socioeconomic status. Inequality in oral hygiene behaviors was pro-rich in all cohort centers. The decomposition results suggested socioeconomic status as the main factor contributing to the overall inequality, followed by the level of education, and the province of residence. Conclusion: A low prevalence of oral hygiene behaviors among middle-aged and elderly Iranian adults was observed. There was also a pro-rich inequality in oral hygiene behaviors among middle-aged and elderly adults in all cohort centers. These results suggest an urgent need for targeted policy interventions to increase the prevalence of preventive oral hygiene behaviors among the poor and less-educated middle-aged and elderly adults in Iran.

Purpose: Although an increasing body of literature suggests a relationship between brain irradiation and deterioration of neurocognitive function, it remains as the standard therapeutic and prophylactic modality in patients with brain tumors. This review was aimed to abstract and evaluate the prediction models for radiation-induced neurocognitive decline in patients with primary or secondary brain tumors.Methods: MEDLINE was searched on October 31, 2021 for publications containing relevant truncation and MeSH terms related to “radiotherapy,” “brain,” “prediction model,” and “neurocognitive impairments.” Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool.Results: Of 3,580 studies reviewed, 23 prediction models were identified. Age, tumor location, education level, baseline neurocognitive score, and radiation dose to the hippocampus were the most common predictors in the models. The Hopkins verbal learning (n = 7) and the trail making tests (n = 4) were the most frequent outcome assessment tools. All studies used regression (n = 14 linear, n = 8 logistic, and n = 4 Cox) as machine learning method. All models were judged to have a high risk of bias mainly due to issues in the analysis.Conclusion: Existing models have limited quality and are at high risk of bias. Following recommendations are outlined in this review to improve future models: developing cognitive assessment instruments taking into account the peculiar traits of the different brain tumors and radiation modalities; adherence to model development and validation guidelines; careful choice of candidate predictors according to the literature and domain expert consensus; and considering radiation dose to brain substructures as they can provide important information on specific neurocognitive impairments.

To maximize the likelihood of positive outcome in non-small-cell lung cancer (NSCLC) survivors, potential benefits of treatment modalities have to be weighed against the possibilities of damage to normal tissues, such as the heart. High-quality data-driven evidence regarding appropriate risk stratification strategies is still scarce. The aim of this review is to summarize and appraise available prediction models for treatment-induced cardiac events in patients with NSCLC. A systematic search of MEDLINE was performed using a Boolean combination of appropriate truncation and indexing terms related to “NSCLC”, “prediction models”, “cardiac toxicity”, and “treatment modalities”. The following exclusion criteria were applied: sample-size of less than 100, no significant predictors in multivariate analysis, lack of model specifications, and case-mix studies. The generic inverse variance method was used to pool the summary effect estimate for each predictor. The quality of the papers was assessed using the Prediction model Risk Of Bias Assessment Tool. Of the 3,056 papers retrieved, 28 prediction models were identified, including seven for (chemo-)radiotherapy, one for immunotherapy, and 20 for surgical resection. Forty-one distinct predictors were entered in the prediction models. The pooled effect estimate of the mean heart dose (HR = 1.06, 95%CI:1.04–1.08) and history of cardiovascular diseases (HR = 3.1, 95%CI:1.8–5.36) were shown to significantly increase the risk of developing late cardiac toxicity after (chemo-)radiotherapy. Summary estimates of age (OR = 1.17, 95%CI:1.06–1.29), male gender (OR = 1.61, 95%CI:1.4–1.85), and advanced stage (OR = 1.34, 95%CI:1.06–1.69) were significantly associated with higher risk of acute cardiac events after surgery. Risk of bias varied across studies, but analysis was the most concerning domain where none of the studies were judged to be low risk. This review highlights the need for a robust prediction model which can inform patients and clinicians about expected treatment-induced heart damage. Identified clues suggest incorporation of detailed cardiac metrics (substructures’ volumes and doses).

Deterioration of neurocognitive function in adult patients with a primary brain tumor is the most concerning side effect of radiotherapy. This study aimed to develop and evaluate normal-tissue complication probability (NTCP) models using clinical and dose–volume measures for 6-month, 1-year, and 2-year Neurocognitive Decline (ND) postradiotherapy. A total of 219 patients with a primary brain tumor treated with radical photon and/or proton radiotherapy (RT) between 2019 and 2022 were included. Controlled oral word association test, Hopkins verbal learning test-revised, and trail making test were used to objectively measure ND. A comprehensive set of potential clinical and dose–volume measures on several brain structures were considered for statistical modeling. Clinical, dose–volume and combined models were constructed and internally tested in terms of discrimination (area under the curve, AUC), calibration (mean absolute error, MAE), and net benefit. Fifty percent, 44.5%, and 42.7% of the patients developed ND at 6-month, 1-year, and 2-year time points, respectively. The following predictors were included in the combined model for 6-month ND: age at radiotherapy > 56 years (OR = 5.71), overweight (OR = 0.49), obesity (OR = 0.35), chemotherapy (OR = 2.23), brain V_{20 Gy} ≥ 20% (OR = 3.53), brainstem volume ≥ 26 cc (OR = 0.39), and hypothalamus volume ≥ 0.5 cc (OR = 0.4). Decision curve analysis showed that the combined models had the highest net benefits at 6-month (AUC = 0.79, MAE = 0.021), 1-year (AUC = 0.72, MAE = 0.027), and 2-year (AUC = 0.69, MAE = 0.038) time points. The proposed NTCP models use easy-to-obtain predictors to identify patients at high risk of ND after brain RT. These models can potentially provide a base for RT-related decisions and post-therapy neurocognitive rehabilitation interventions.

Purpose Rectal toxicity remains a major threat to quality of life of patients, who receive brachytherapy to the abdominal pelvic area. Estimating the risk of toxicity development is essential to maximize therapeutic benefit without impairing rectal function. This study aimed to abstract and...