Background: Choosing Wisely (CW) Canada is a national campaign to identify unnecessary or harmful services that are frequently used in Canada. The original CW Oncology Canada Cancer list was developed in 2014. A CW Oncology Canada working group was established to review new evidence and guidelines and to update the current CW Oncology Canada Cancer List.
Methods: Between January and March 2022, we conducted a survey of members of the Canadian Association of Medical Oncology (CAMO), Canadian Association of Radiation Oncology (CARO) and the Canadian Society of Surgical Oncology (CSSO). We took the feedback from the survey, including potential new recommendations as well as those that were thought to be no longer relevant and up to date, and conducted a literature review with the assistance of the Canadian Agency for Drugs and Technology in Health (CADTH). The final updated list of recommendations was made by the CW Oncology Canada working group based on a consensus process.
Results: We reviewed two potential recommendations to add and two potential recommendations to remove from the existing CW Oncology Canada Cancer List. The recommendation "Do not prescribe whole brain radiation over stereotactic radiosurgery for patient with limited brain metastases (≤4 lesions)" was supported by several evidence-based guidelines with the strength of recommendations ranging from strong to moderate and the quality of evidence ranging from level 1 to level 3. After reviewing the evidence, the working group felt that the other potential recommendation to add and the two potential recommendations to remove did not have sufficient strength and quality of evidence at this time to be added or removed from the list.
Conclusion: The updated Choosing Wisely Oncology Canada Cancer List consists of 11 items that oncologists should question in the treatment of patients with cancer. This list can be used to design specific interventions to reduce low value care.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.jcpo.2023.100431 | DOI Listing |
Ann Intern Med
January 2025
Durham VA Health Care System, Durham; and Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina (K.M.G.).
Background: Tissue-based genomic classifiers (GCs) have been developed to improve prostate cancer (PCa) risk assessment and treatment recommendations.
Purpose: To summarize the impact of the Decipher, Oncotype DX Genomic Prostate Score (GPS), and Prolaris GCs on risk stratification and patient-clinician decisions on treatment choice among patients with localized PCa considering first-line treatment.
Data Sources: MEDLINE, EMBASE, and Web of Science published from January 2010 to August 2024.
JMIR Res Protoc
January 2025
Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC, Canada.
Background: Telehomecare monitoring (TM) in patients with cancer is a complex intervention. Research shows variations in the benefits and challenges TM brings to equitable access to care, the therapeutic relationship, self-management, and practice transformation. Further investigation into these variations factors will improve implementation processes and produce effective outcomes.
View Article and Find Full Text PDFOncologist
January 2025
Department of Medical Oncology, Princess Margaret Hospital, Toronto, ON M5G 2M9, Canada.
Background: Metastatic castration-resistant prostate cancer (mCRPC) has a poor prognosis, necessitating the investigation of novel treatments and targets. This study evaluated JNJ-70218902 (JNJ-902), a T-cell redirector targeting transmembrane protein with epidermal growth factor-like and 2 follistatin-like domains 2 (TMEFF2) and cluster of differentiation 3, in mCRPC.
Patients And Methods: Patients who had measurable/evaluable mCRPC after at least one novel androgen receptor-targeted therapy or chemotherapy were eligible.
Support Care Cancer
January 2025
Department of Community Health Sciences, Cumming, School of Medicine, University of Calgary, 2500 University, Drive NW, Calgary, AB, T2N 1N4, Canada.
Biomed Phys Eng Express
January 2025
Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA, United States of America.
This study aimed to develop and evaluate an efficient method to automatically segment T1- and T2-weighted brain magnetic resonance imaging (MRI) images. We specifically compared the segmentation performance of individual convolutional neural network (CNN) models against an ensemble approach to advance the accuracy of MRI-guided radiotherapy (RT) planning..
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!