The Salford Nature Environments Database (SNED): an open-access database of standardized high-quality pictures from natural environments.

The growing interest in harnessing natural environments to enhance mental health, including cognitive functioning and mood, has yielded encouraging results in initial studies. Given that images of nature have demonstrated similar benefits, they are frequently employed as proxies for real-world environments. To ensure precision and control, researchers often manipulate images of natural environments.

Canadian Association of Radiologists Pediatric Imaging Referral Guideline.

The Canadian Association of Radiologists (CAR) Pediatric Expert Panel is made up of pediatric physicians from the disciplines of radiology, emergency medicine, endocrinology, gastroenterology, general surgery, neurology, neurosurgery, respirology, orthopaedic surgery, otolaryngology, urology, a patient advisor, and an epidemiologist/guideline methodologist. After developing a list of 50 clinical/diagnostic scenarios, a rapid scoping review was undertaken to identify systematically produced referral guidelines that provide recommendations for one or more of these clinical/diagnostic scenarios. Recommendations from 32 guidelines and contextualization criteria in the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) for guidelines framework were used to develop 133 recommendation statements across the 50 scenarios.

Potential anatomical triggers for plan adaptation of cervical cancer external beam radiotherapy.

This study aimed to identify potential anatomical variation triggers using magnetic resonance imaging for plan adaption of cervical cancer patients to ensure dose requirements were met over an external beam radiotherapy course. Magnetic resonance images (MRIs) acquired before and during treatment were rigidly registered to a pre-treatment computerised tomography (CT) image for 11 retrospective cervix cancer datasets. Target volumes (TVs) and organs at risk (OARs) were delineated on both MRIs and propagated onto the CT.

Towards a survival risk prediction model for metastatic NSCLC patients on durvalumab using whole-lung CT radiomics.

Background: Existing criteria for predicting patient survival from immunotherapy are primarily centered on the PD-L1 status of patients. We tested the hypothesis that noninvasively captured baseline whole-lung radiomics features from CT images, baseline clinical parameters, combined with advanced machine learning approaches, can help to build models of patient survival that compare favorably with PD-L1 status for predicting 'less-than-median-survival risk' in the metastatic NSCLC setting for patients on durvalumab. With a total of 1062 patients, inclusive of model training and validation, this is the largest such study yet.