‘It felt like there was always someone there for us’: Supporting children affected by domestic violence and abuse who are identified by general practice

One in five children in the UK are affected by domestic violence and abuse. However, primary care clinicians (GPs and nurses) struggle to effectively identify and support children and young people living in homes where it is present. The IRIS+ (Enhanced Identification and Referral to Improve Safety) training and advocacy support intervention aimed to improve how clinicians respond to children and young people affected by domestic violence and abuse. IRIS+ training was delivered as part of a feasibility study to four general practices in an urban area in England (UK). Our mixed method design included interviews and questionnaires about the IRIS+ intervention with general practice patients, including children and young people as well as with clinicians and advocacy service providers. We collected the number of identifications and referrals by clinicians of children experiencing domestic violence and abuse through a retrospective search of medical and agency records 10 months after the intervention. Forty-nine children exposed to domestic violence and abuse were recorded in medical records. Thirty-five children were referred to a specialist domestic violence and abuse support service over a period of 10 months. Of these, 22 received direct or indirect support. The qualitative findings indicated that children benefitted from being referred by clinicians to the service. However, several barriers at the patient and professional level prevented children and young people from being identified and supported. Some of these barriers can be addressed through modifications to professional training and guidance, but others require systematic and structural changes to the way health and social care services work with children affected by domestic violence and abuse.     

Mesenchymal neoplasms with NTRK and other kinase gene alterations

Kinase alterations are increasingly recognised as oncogenic drivers in mesenchymal tumours. Infantile fibrosarcoma and the related renal tumour, congenital mesoblastic nephroma, were among the first solid tumours shown to harbour recurrent tyrosine kinase fusions, with the canonical ETV6::NTRK3 fusion identified more than 20 years ago. Although targeted testing has long been used in diagnosis, the advent of more robust sequencing techniques has driven the discovery of kinase alterations in an array of mesenchymal tumours. As our ability to identify these genetic alterations has improved, as has our recognition and understanding of the tumours that harbour these alterations. Specifically, this study will focus upon mesenchymal tumours harbouring NTRK or other kinase alterations, including tumours with an infantile fibrosarcoma-like appearance, spindle cell tumours resembling lipofibromatosis or peripheral nerve sheath tumours and those occurring in adults with a fibrosarcoma-like appearance. As publications describing the histology of these tumours increase so, too, do the variety kinase alterations reported, now including NTRK1/2/3, RET, MET, RAF1, BRAF, ALK, EGFR and ABL1 fusions or alterations. To date, these tumours appear locally aggressive and rarely metastatic, without a clear link between traditional features used in histological grading (e.g. mitotic activity, necrosis) and outcome. However, most of these tumours are amenable to new targeted therapies, making their recognition of both diagnostic and therapeutic import. The goal of this study is to review the clinicopathological features of tumours with NTRK and other tyrosine kinase alterations, discuss the most common differential diagnoses and provide recommendations for molecular confirmation with associated treatment implications.     

Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring of Bladder and Rectum for Prostate Radiation Therapy

PurposeAuto-contouring may reduce workload, interobserver variation, and time associated with manual contouring of organs at risk. Manual contouring remains the standard due in part to uncertainty around the time and workload savings after accounting for the review and editing of auto-contours. This preliminary study compares a standard manual contouring workflow with 2 auto-contouring workflows (atlas and deep learning) for contouring the bladder and rectum in patients with prostate cancer.Methods and MaterialsThree contouring workflows were defined based on the initial contour-generation method including manual (MAN), atlas-based auto-contour (ATLAS), and deep-learning auto-contour (DEEP). For each workflow, initial contour generation was retrospectively performed on 15 patients with prostate cancer. Then, radiation oncologists (ROs) edited each contour while blinded to the manner in which the initial contour was generated. Workflows were compared by time (both in initial contour generation and in RO editing), contour similarity, and dosimetric evaluation.ResultsMean durations for initial contour generation were 10.9 min, 1.4 min, and 1.2 min for MAN, DEEP, and ATLAS, respectively. Initial DEEP contours were more geometrically similar to initial MAN contours. Mean durations of the RO editing steps for MAN, DEEP, and ATLAS contours were 4.1 min, 4.7 min, and 10.2 min, respectively. The geometric extent of RO edits was consistently larger for ATLAS contours compared with MAN and DEEP. No differences in clinically relevant dose-volume metrics were observed between workflows.ConclusionAuto-contouring software affords time savings for initial contour generation; however, it is important to also quantify workload changes at the RO editing step. Using deep-learning auto-contouring for bladder and rectum contour generation reduced contouring time without negatively affecting RO editing times, contour geometry, or clinically relevant dose–volume metrics. This work contributes to growing evidence that deep-learning methods are a clinically viable solution for organ-at-risk contouring in radiation therapy.