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Cancer is a disease associated with ageing. Managing cancer in older adults may prove challenging owing to pre-existing frailty, comorbidity, and wider holistic needs, as well as the unclear benefits and harms of standard treatment options. With the ongoing advances in oncology and the increasing complexity of treating older adults with cancer, the geriatric oncology field must be a priority for healthcare systems in education, research, and clinical practice. However, geriatric oncology is currently not formally taught in undergraduate education or postgraduate training programmes in the United Kingdom (UK). In this commentary, we outline the landscape of geriatric oncology undergraduate education and postgraduate training for UK doctors. We highlight current challenges and opportunities and provide practical recommendations for better preparing the medical workforce to meet the needs of the growing population of older adults with cancer. This includes key outcomes to be considered for inclusion within undergraduate and postgraduate curricula.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10571920 | PMC |
http://dx.doi.org/10.3390/cancers15194782 | DOI Listing |
Palliat Med Rep
June 2025
Department of Palliative Medicine, Tan Tock Seng Hospital, Singapore, Singapore.
Background: Goals of care (GOC) discussions align medical care with patients' wishes. Many physician-associated barriers to GOC discussions have been identified, but there is little understanding of the lived experiences of patients and their nominated health care spokespersons (NHSs) who have participated in the discussion.
Objectives: We aimed to describe the lived experience of participants of GOC discussions conducted during acute inpatient care and identify the features of well-conducted GOC discussions.
3 Biotech
October 2025
Department of Oncology, The Affiliated Qingyuan Hospital (Qingyuan People's Hospital), Guangzhou Medical University, Qingyuan, China.
Unlabelled: By integrating single-cell and bulk RNA-sequencing data for esophageal cancer (ESCA), we developed and validated a seven-macrophage-gene prognostic signature (FCN1, SCARB2, ATF5, PHLDA2, GLIPR1, CHORDC1, and BCKDK). This signature effectively stratified patients into high- and low-risk groups with significantly different overall survival, achieving area under the curve (AUC) values greater than 0.7 for 1-, 2-, and 3-year survival prediction.
View Article and Find Full Text PDFFront Immunol
September 2025
Department of Geriatrics, Jilin Geriatrics Clinical Research Center, The First Hospital of Jilin University, Changchun, Jilin, China.
Kaempferol (KMF) is a dietary flavonoid exhibiting profound immunomodulatory effects across multiple immune cell populations. This review synthesizes current insights into how KMF regulates diverse immune cell populations and its therapeutic potential in inflammatory and immune-related disorders. KMF exhibits multifaceted effects on T cells.
View Article and Find Full Text PDFFront Nutr
August 2025
Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Background: Cancer survivors have a heightened risk of cardiovascular disease (CVD), partly associated with high rates of malnutrition, which is linked to poor cardiovascular outcomes. Changes in aortic morphology affect vascular hemodynamics and cardiovascular health. However, the relationship between malnutrition and aortic morphology in cancer patients remains unreported.
View Article and Find Full Text PDFCancer Med
September 2025
Geriatric Medicine Center, Department of Endocrinology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
Background: The pathological response to neoadjuvant chemotherapy (NAC) has become a vital prognostic indicator for patients with breast cancer (BC). The newly generated models depended on rather basic imaging and pathology characteristics and did not sufficiently elucidate the importance of the incorporated data. The purpose of this study is to establish and authenticate a machine learning model for predicting the pathological complete response to NAC using baseline clinical and pathological features in BC patients.
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