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Enregistrement W4411075579 · doi:10.3389/fmed.2025.1631044

Editorial: Clinical management of older persons with cancer: current status and future directions

2025· editorial· en· W4411075579 sur OpenAlexaboutno aff
Aziz Karaoğlu, Gülistan Bahat

Notice bibliographique

RevueFrontiers in Medicine · 2025
Typeeditorial
Langueen
DomaineMedicine
ThématiqueFrailty in Older Adults
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCurrent (fluid)CancerGerontologyMedicinePsychologyEngineeringInternal medicineElectrical engineering

Résumé

récupéré en direct d'OpenAlex

Globally, populations are aging. Cancer is essentially a disease of old age, and the number of older patients with cancer will increase with the aging of the population (1). Cancer management in older patients presents unique challenges that extend beyond traditional oncology paradigms. A comprehensive approach that incorporates the principles of geriatric oncology is crucial to improving both treatment outcomes and quality of life in this population. Furthermore, there is a need for more clinical trials that focus on older patients with cancer and new clinical trial designs that incorporate geriatric oncology concepts (new endpoints, expansion cohorts, frailty classifications, etc.). This special issue aims to present research focused on developing effective care strategies in the clinical management of older adults with cancer. Chronological age, the Eastern Cooperative Oncology Group (ECOG) performance status, and the Karnofsky performance scale do not adequately reflect the functional diversity of older patients with cancer (2). Therefore, leading cancer organizations such as the International Society of Geriatric Oncology (SIOG), the American Society of Clinical Oncology (ASCO), the European Society For Medical Oncology (ESMO), and the National Comprehensive Cancer Network (NCCN) recommend geriatric assessment. The Comprehensive Geriatric Assessment (CGA) is the gold standard for assessing this patient population (3). However, CGA is not suitable for routine use in all older patients with cancer. Geriatric screening tools such as the the Geriatric 8 (G8), the Vulnerable Elders Survey-13 (VES-13), and Practical Geriatric Assessment (PGA) have been developed (4-6). In this special issue, De Schrevel et al. report that the Edmonton Frailty Scale reliably predicts CGA-identified frailty and one-year mortality in older cancer patients pre-selected as potentially frail by the G8. Since frailty is a known risk factor for poor outcomes, including increased mortality, identification of frail patients is crucial for the development of treatment plans. Sarcopenia in cancer patients has received increasing attention due to its high prevalence and association with adverse outcomes (7,8). Sarcopenia is an independent prognostic factor for complications and survival following surgical resection of malignancy (9). Increasing evidence shows that sarcopenia is related to the risk of adverse postoperative outcomes, including morbidity, prolonged length of hospital stay, and mortality. The study by Tirnova et al. underscores the association between low skeletal muscle mass (-as a proxy marker of sarcopenia) and increased risk of postoperative complications in older patients undergoing colon cancer surgery. This finding emphasizes the need for preoperative assessments that evaluate nutritional status and muscle mass to predict surgical outcomes. Inadequate nutrition in older adults with cancer can reduce treatment tolerance and lead to poor treatment outcomes (10,11). A significant relationship has been demonstrated between low prognostic nutritional index (PNI) and poor overall survival (OS) in bladder cancer. Balcik et al. demonstrated the prognostic value of the Geriatric Nutritional Risk Index (GNRI), Controlling Nutritional Status (CONUT) score, and Prognostic Nutritional Index (PNI) in patients with bladder cancer. They reported that both GNRI and CONUT scores may serve as useful predictors of survival in metastatic bladder cancer patients over 70 years of age. Breast cancer is a heterogeneous disease, with patients who have similar prognostic features experiencing diverse outcomes. This highlights the need for further research on new prognostic factors, particularly for older patients. Yu et al. proposed a dynamic-effect Restricted Mean Time Lost (RMTL) regression model to investigate the time-varying effects of prognostic factors in the context of competing risk survival data. This study is the first to consider both competing risks and time-varying effects, with the real-time effects differing from previous one-tailed analyses. Applying this model to an older early-stage breast cancer cohort, they showed that protective factors like positive estrogen receptor status and chemotherapy lost impact over time, while the benefit of breast-conserving surgery and the negative effects of advanced tumor stage and grade increased. This new framework may support personalized decision making by reflecting how risks change over time and may provide clinicians with a more accurate and personalized understanding of prognosis in the geriatric oncology setting. Telemedicine is playing an increasingly important role in geriatric oncology (12). The Cancer and Aging Interdisciplinary Team (CAIT) clinic at Memorial Sloan Kettering Cancer Center presents the findings of a study examining the role of telemedicine in older adults undergoing cancer treatment (Alexander et al.). They found that 77% of 288 patients (aged 67 100) preferred telemedicine visits. Factors such as advanced age, lower educational level, abnormal cognitive screening results, impaired performance status, instrumental activities of daily living (IADL) dependency, and poor social support were associated with preferring in-person visits. The study emphasizes the significant potential of telemedicine to optimize cancer care in older adults, improve access to care, reduce the burden of in-person visits, and enhance quality of life. Supportive care provides psychological and social support, helping older patients with cancer cope with the emotional and social challenges they encounter during treatment. Lian et al. examined the changes in supportive care needs, quality of life, and social support among older patients with colorectal cancer patients undergoing chemotherapy. In this longitudinal study, 155 patients were followed over multiple chemotherapy cycles, with the results showing a significant increase in the need for supportive care, particularly psychological support and patient care, as treatment progressed. Meanwhile, the patients' quality of life and social support gradually deteriorated throughout the chemotherapy cycles. In older adults with cancer, suicide risk is a critically important concern, with studies demonstrating a higher incidence of suicide in this group (13). Older patients with prostate cancer have also been shown to be at elevated risk for suicidal ideation. In their study, Yang et al. developed a predictive model for suicide risk in prostate cancer survivors using the Surveillance, Epidemiology, and End Results (SEER) data from over 238,000 patients. Their model, based on seven accessible clinical variables (age, race, marital status, income, prostatic specific antigen (PSA) levels, metastatic stage, and surgical status), demonstrated good predictive accuracy and identified high-risk individuals as having a 3.5-fold greater suicide risk than their low-risk counterparts. These findings underscore the importance of integrating routine psychosocial screening and psycho-oncological interventions into the care of older patients with prostate cancer to mitigate suicide risk and support overall well being. The articles in this special issue reflect the growing body of knowledge and evolving clinical paradigms in geriatric oncology. Optimizing care for older adults with cancer requires ongoing advancements in education, clinical research, and healthcare systems. Integrating geriatric principles into oncology training, designing inclusive clinical trials for older and frail patients, and implementing geriatric assessment are essential steps. Additionally, supportive health policy frameworks, including reimbursement models, are needed. Collaborative, interdisciplinary efforts are essential for older cancer patients.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,023
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,049

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,023
Méta-épidémiologie (sens strict)0,0040,001
Méta-épidémiologie (sens large)0,0050,003
Bibliométrie0,0030,001
Études des sciences et des technologies0,0020,002
Communication savante0,0060,007
Science ouverte0,0050,001
Intégrité de la recherche0,0150,017
Charge utile insuffisante (le modèle a refusé de juger)0,0150,013

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,010
Tête enseignante GPT0,338
Écart entre enseignants0,328 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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