Histopathology imaging and clinical data including remission status in pediatric inflammatory bowel disease.

The incidence of inflammatory bowel disease (IBD) is increasing annually. Children with IBD often suffer significant morbidity due to physical and emotional effects of the disease and treatment. Corticosteroids, often a component of therapy, carry undesirable side effects with long term use. Steroid-free remission has become a standard for care-quality improvement. Anticipating therapeutic outcomes is difficult, with treatments often leveraged in a trial-and-error fashion. Artificial intelligence (AI) has demonstrated success in medical imaging classification tasks. Predicting patients who will attain remission will help inform treatment decisions. The provided dataset comprises 951 tissue section scans (167 whole-slides) obtained from 18 pediatric IBD patients. Patient level structured data include IBD diagnosis, 12- and 52-week steroid use and name, and remission status. Each slide is labelled with biopsy site and normal or abnormal classification per the surgical pathology report. Each tissue section scan from an abnormal slide is further classified by an experienced pathologist. Researchers utilizing this dataset may select from the provided outcomes or add labels and annotations from their own institutions.

Adults With Spina Bifida Fare Worse Than Young Adults: A Systemic Vulnerability in Urinary Tract Infection-Related Hospital Care.

PURPOSE: We aim to estimate the odds of UTI-related hospital care in spina bifida (SB) patients aged 18 to 25 years as compared with patients with SB in adolescence (11-17 years) or adulthood (26-35 years). We hypothesize that patients with SB in the typical transitional age, 18 to 25 years, will have higher odds of UTI-related hospital care as compared to adolescent SB patients or adult SB patients. MATERIALS AND METHODS: Using Cerner Real-World Data, we performed a retrospective cohort analysis comparing SB patients to age- and gender-matched controls. SB cases between 2015 and 2021 were identified and compared in 3 cohorts: 11 to 17 years (adolescents), 18 to 25 years (young adults [YA]), and 26 to 35 years (adults). Logistic regression analysis was used to characterize the odds of health care utilization. RESULTS: Of the 5497 patients with SB and 77,466 controls identified, 1839 SB patients (34%) and 3275 controls (4.2%) had at least 1 UTI encounter. UTI-related encounters as a proportion of all encounters significantly increased with age in SB patients (adolescents 8%, YA 12%, adult 15%; P < .0001). Adjusting for race, sex, insurance, and comorbidities, the odds of a UTI-related encounter in YA with SB were significantly higher than for adolescents with SB (adolescent odds ratio = 0.65, 95% CI: 0.57-0.75, P < .001). YA had lower odds of a UTI-related encounter as compared with adults with SB (adult odds ratio = 1.31, 95% CI: 1.16-1.49, P < .001). CONCLUSIONS: YA with SB have higher odds of UTI-related hospital care than adolescents, but lower odds of UTI-related hospital care when compared with adults.

Artificial intelligence for nailfold capillaroscopy analyses - a proof of concept application in juvenile dermatomyositis.

BACKGROUND: Biomarkers for idiopathic inflammatory myopathies are difficult to identify and may involve expensive laboratory tests. We assess the potential for artificial intelligence (AI) to differentiate children with juvenile dermatomyositis (JDM) from healthy controls using nailfold capillaroscopy (NFC) images. We also assessed the potential of NFC images to reflect the range of disease activity with JDM. METHODS: A total of 1,120 NFC images from 111 children with active JDM, diagnosed between 1990 and 2020, and 321 NFC images from 31 healthy controls were retrieved from the CureJM JDM Registry. We built a lightweight and explainable deep neural network model called NFC-Net. Images were downscaled by interpolation techniques to reduce the computational cost. RESULTS: NFC-Net achieved high performance in differentiating patients with JDM from controls, with an area under the ROC curve (AUROC) of 0.93 (0.84, 0.99) and accuracy of 0.91 (0.82, 0.92). With sensitivity (0.85) and specificity (0.90) resulted in model precision of 0.95. The AUROC and accuracy for predicting clinical disease activity from inactivity were 0.75 (0.61, 0.81) and 0.74 (0.65, 0.79). CONCLUSION: The good performance of the NFC-Net demonstrates that NFC images are sufficient for detecting often unrecognized JDM disease activity, providing a reliable indicator of disease status. IMPACT: Proposed NFC-Net can accurately predict children with JDM from healthy controls using nailfold capillaroscopy (NFC) images. Additionally, it predicts the scores to JDM disease activity versus no activity. Equipped with gradients, NFC-Net is explainable and gives visual information beside the reported accuracies. NFC-Net is computationally efficient since it is applied to substantially downscaled NFC images. Furthermore, the model can be wrapped within an edge-based device like a mobile application that is accessible to both clinicians and patients.

Radiomics quality score in renal masses: a systematic assessment on current literature.

OBJECTIVE: To perform a systematic assessment and analyze the quality of radiomics methodology in current literature in the evaluation of renal masses using the Radiomics Quality Score (RQS) approach. METHODS: We systematically reviewed recent radiomics literature in renal masses published in PubMed, EMBASE, Elsevier, and Web of Science. Two reviewers blinded by each other's scores evaluated the quality of radiomics methodology in studies published from 2015 to August 2021 using the RQS approach. Owing to the diversity in the imaging modalities and radiomics applications, a meta-analysis could not be performed. RESULTS: Based on our inclusion/exclusion criteria, a total of 87 published studies were included in our study. The highest RQS was noted in three categories: reporting of clinical utility, gold standard, and feature reduction. The average RQS of the two reviewers ranged from 5 ≤ RQS≤19, with the maximum attainable RQS being 36. Very few (7/87 i.e., 8%) studies received an average RQS that ranged from 17 < RQS≤19, which represents studies with the highest RQS in our study. Many (39/87 i.e., 45%) studies received an average RQS that ranged from 13 < RQS≤15. No significant interreviewer scoring differences were observed. CONCLUSIONS: We report that the overall scientific quality and reporting of radiomics studies in renal masses is suboptimal, and subsequent studies should bolster current deficiencies to improve reporting of radiomics methodologies. ADVANCES IN KNOWLEDGE: The RQS approach is a meaningful quantitative scoring system to assess radiomics methodology quality and supports a comprehensive evaluation of the radiomics approach before its incorporation into clinical practice.