Evaluating the Role of Surgical Resection and Reconstruction in the Management of Ewing sarcoma of the Chest Wall in the Paediatric Population: A Systematic Review.

Background And Objectives: Ewing sarcoma is the most common malignant chest wall tumour in the paediatric population. Following neoadjuvant chemotherapy regimens, the role and extent of surgical resection and reconstruction of the chest wall remain unclear.

Methods: A systematic review was conducted in accordance with PRISMA guidelines across four major literature databases.

Needle in the heart. Management of penetrating foreign body in an intracardiac chamber: A case report and scoping literature review.

Introduction: Penetrating thoracic trauma with retained foreign bodies, such as needles, is rare. These injuries present management challenges due to diverse aetiologies and potential complications, including tension pneumothorax, cardiac tamponade, and major haemorrhage. Cardiac penetration can result in arrhythmias, ischaemia, valvular and septal defects.

Use of a Smartphone Application to Promote Adherence to Oral Medications in Patients With Breast Cancer.

Purpose: Medication nonadherence is common among patients with breast cancer (BC) and increases BC mortality and complications from comorbidities. There is growing interest in mobile health interventions such as smartphone applications (apps) to promote adherence.

Methods: Use of Medisafe, a medication reminder and tracking app, was tested over 12 weeks among patients on BC treatment and at least one oral medication.

Effects of vitamin D supplementation on a deep learning-based mammographic evaluation in SWOG S0812.

Deep learning-based mammographic evaluations could noninvasively assess response to breast cancer chemoprevention. We evaluated change in a convolutional neural network-based breast cancer risk model applied to mammograms among women enrolled in SWOG S0812, which randomly assigned 208 premenopausal high-risk women to receive oral vitamin D3 20 000 IU weekly or placebo for 12 months. We applied the convolutional neural network model to mammograms collected at baseline (n = 109), 12 months (n = 97), and 24 months (n = 67) and compared changes in convolutional neural network-based risk score between treatment groups.