Background: The systemic treatment of epithelial ovarian cancer (OC) is one of the cornerstones in the multimodal management of advanced OC in both primary and recurrent stages of this disease. In most situations various treatment options are available but only few data exists about the treatment decision-making process. Therefore, we conducted a review of the current literature regarding the decision-making process concerning the systemic therapy in patients with advanced ovarian cancer.
Materials And Methods: The electronic database MEDLINE (PubMed) was systematically reviewed for studies that evaluate the treatment decision-making processes in patients with advanced OC.
Results: The PubMed database was searched in detail for all titles and abstracts of potentially relevant studies published between 1995 and 2011. An initial search identified 15 potentially relevant studies, but only seven met all inclusion criteria. Factors that influence treatment decisions in patients with OC include not only rational arguments and medical reasons, but also individual attitudes, fears, existential questions, various projections resulting from the physician patient relationship and the social environment. The physician's personal experience with OC treatment seems to be an important factor, followed by previous personal experience with medical issues, and the fear of side-effects and future metastases. Family and self-support organisations also seem to play a significant role in the treatment decision-making process.
Conclusion: This review underlines the need for more research activities to explore the treatment decision-making process to enable the best individual support for patients in treatment decision-making. It is a challenge for clinicians to determine the individual information needs of women with OC and to involve them during the decision-making process to the extent they wish.
Download full-text PDF |
Source |
---|
J Med Internet Res
January 2025
School of Business, Innovation and Sustainability, Halmstad University, Halmstad, Sweden.
Background: Recent advancements in artificial intelligence (AI) have changed the care processes in mental health, particularly in decision-making support for health care professionals and individuals with mental health problems. AI systems provide support in several domains of mental health, including early detection, diagnostics, treatment, and self-care. The use of AI systems in care flows faces several challenges in relation to decision-making support, stemming from technology, end-user, and organizational perspectives with the AI disruption of care processes.
View Article and Find Full Text PDFJ Clin Oncol
January 2025
INSERM, IMRBU955, Univ Paris Est Créteil, Créteil, France.
Purpose: Establishing an accurate prognosis remains challenging in older patients with cancer because of the population's heterogeneity and the current predictive models' reduced ability to capture the complex interactions between oncologic and geriatric predictors. We aim to develop and externally validate a new predictive score (the Geriatric Cancer Scoring System [GCSS]) to refine individualized prognosis for older patients with cancer during the first year after a geriatric assessment (GA).
Materials And Methods: Data were collected from two French prospective multicenter cohorts of patients with cancer 70 years and older, referred for GA: ELCAPA (training set January 2007-March 2016) and ONCODAGE (validation set August 2008-March 2010).
Background: With the increasing availability and use of digital tools such as virtual reality in medical education, there is a need to evaluate their impact on clinical performance and decision-making among healthcare professionals. The Trauma SimVR study is investigating the efficacy of virtual reality training in the context of traumatic in-hospital cardiac arrest.
Methods And Analysis: This study protocol (clinicaltrials.
PLoS One
January 2025
School of Physical Therapy, Western University, London, Ontario, Canada.
Background: Spinal pain is prevalent and burdensome worldwide. A large proportion of patients with neck and thoracic pain experience chronic symptoms, which can significantly impact their physical functioning. Therefore, it is important to understand factors predicting outcome to inform effective examination and treatment.
View Article and Find Full Text PDFJ Eur Acad Dermatol Venereol
January 2025
Pathology Department, IHP Group, Nantes, France.
Background: There is a need to improve risk stratification of primary cutaneous melanomas to better guide adjuvant therapy. Taking into account that haematoxylin and eosin (HE)-stained tumour tissue contains a huge amount of clinically unexploited morphological informations, we developed a weakly-supervised deep-learning approach, SmartProg-MEL, to predict survival outcomes in stages I to III melanoma patients from HE-stained whole slide image (WSI).
Methods: We designed a deep neural network that extracts morphological features from WSI to predict 5-y overall survival (OS), and assign a survival risk score to each patient.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!