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The integration of digital health technologies in geriatric oncology has the potential to enhance patient care and self-management. This review article discusses the applications of these technologies, including teleassessment, telemonitoring, and teleintervention, within geriatric oncology, and evaluates their potential to improve cancer care and patient outcomes. We also review challenges to the implementation of digital health technologies among populations of older patients with cancer. The article provides a perspective for clinicians, researchers, policymakers, and patients on the integration and utilisation of digital health technologies in current geriatric oncology practice., Competing Interests: Declaration of Competing Interest Colm Mac Eochagain: None. Nicolás María González Senac: None. Mukul Roy: None. Andrea Ciccone: None. Beatriz Contreras: None. Rogelio Velasco: None. Melody Schiaffino: None. Giuseppe Dario Testa: None. Joana Marinho: None. Adolfo Gonzales Serrano: None. Mercedes Cavanagh: None. Fabio Gomes: Dr. Gomes reports grants from Pfizer, personal fees from Roche, personal fees from BMS, personal fees from AstraZeneca, personal fees from Takeda, outside the submitted work., (Copyright © 2023. Published by Elsevier Ltd.)

The importance of neurons and nerve fibers in the tumor microenvironment (TME) of solid tumors is now acknowledged after being unexplored for a long time; this is possible due to the development of new technologies that allow in situ characterization of the TME. Recent studies have shown that the density and types of nerves that innervate tumors can predict a patient's clinical outcome and drive several processes of tumor biology. Nowadays, several efforts in cancer research and neuroscience are taking place to elucidate the mechanisms that drive tumor-associated innervation and nerve-tumor and nerve-immune interaction. Assessment of neurons and nerves within the context of the TME can be performed in situ, in tumor tissue, using several pathology-based strategies that utilize histochemical and immunohistochemistry principles, hi-plex technologies, and computational pathology approaches to identify measurable histopathological characteristics of nerves. These features include the number and type of tumor associated nerves, topographical location and microenvironment of neural invasion of malignant cells, and investigation of neuro-related biomarker expression in nerves, tumor cells, and cells of the TME. A deeper understanding of these complex interactions and the impact of nerves in tumor biology will guide the design of better strategies for targeted therapy in clinical trials., (© 2022 The Authors. Advanced Biology published by Wiley-VCH GmbH.)

Background & Aims: Predicting the risk of early limiting toxicity (ELT) is major challenge for the clinician seeking an effective, safe treatment for older patients with cancer. The Cancer and Aging Research Group (CARG) and CRASH (Chemotherapy Risk Assessment Scale for High-Age Patients) toxicity scores were designed to predict chemotherapy-related toxicity. Elevated resting energy expenditure (REE) may predispose to cachexia and increase ELT and mortality in older patients with cancer. The primary objective was to assess the association between elevated REE and ELT in older patients with cancer. The secondary objectives were to assess the discriminant ability of a predictive model including REE (relative to the CARG and CRASH scores) and the prognostic value of elevated REE., Methods: We assessed patients aged 70 or over included in the prospective ELCAPA cohort between 2014 and 2018. The inclusion criteria were a solid tumour, a measurement of REE at baseline (mREE, by indirect calorimetry), and a geriatric assessment prior to cancer treatment in a teaching hospital (Paris, France). The mREE was compared with the predicted REE (pREE), as defined by the Harris-Benedict equation. Depending on the mREE/pREE ratio, study participants were classified as hypermetabolic, hypometabolic or normometabolic. The primary endpoint was 3-month ELT, defined as any unplanned hospitalization or any event leading to dose reduction, a treatment delay of more than 7 days, or treatment discontinuation within 3 months of initiation. The secondary endpoint was the 3-month mortality rate., Results: A total of 179 patients were included. The median age was 80 [interquartile range: 76-84] years, 37% of the patients were female, 81.8% had metastatic disease, 67.6% received chemotherapy, 20.7% received hormone therapy, and 11.7% received targeted therapies. According to the mREE/pREE ratio, 85 patients (47%) were hypermetabolic, 63 (35%) were normometabolic, and 31 (18%) were hypometabolic. Sixty patients (33.5%; 95% confidence interval (CI): 26.7-40.9) experienced ELT. The discriminant ability (as assessed by the C-index) of a multivariate model including REE and adjustment factors was 0.82 [95%CI: 0.73-0.91]. In comparison, the discriminant ability of the CARG and CRASH models was 0.57 [0.45-0.68] and 0.51 [0.40-0.62], respectively. In our model, hypermetabolism was an independent risk factor for ELT (adjusted odds ratio = 2.44; 95%CI: 1.02-5.80). Other risk factors were the cancer type and stage, the treatment protocol, a clinical diagnosis of depression, the presence of grade 3 or 4 comorbidities, and the serum lactate dehydrogenase level., Conclusion: Hypermetabolism status is an independent predictor of ELT in older patients with cancer, relative to normometabolic status. Baseline REE measurement might improve the ELT risk assessment and decision-making process., Competing Interests: Conflict of interest The authors declare no conflicts of interest., (Copyright © 2022. Published by Elsevier Ltd.)