Person: Armisen, Ricardo
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Publication Advances in machine learning for tumour classification in cancer of unknown primary: A mini-review(2024) Oróstica, Karen; Mardones, Felipe; Bernal, Yanara; Molina, Samuel; Orchard, Marcos; Verdugo, Ricardo; Carvajal-Hausdorf, Daniel; Marcelain, Katherine; Contreras, Seba; Armisen, RicardoCancers of unknown primary (CUP) are a heterogeneous group of aggressive metastatic cancers where standardised diagnostic techniques fail to identify the organ where it originated, resulting in a poor prognosis and resistance to treatment. Recent advances in large-scale sequencing techniques have enabled the identification of mutational signatures specific to particular tumour subtypes, even from liquid biopsy samples such as blood. This breakthrough paves the way for the development of new cost-effective diagnostic strategies. This mini-review explores recent advancements in Machine Learning (ML) and its application to tumour classification methods for CUP patients, identifying its weaknesses and strengths when classifying the tumour type. In the era of multi-omics, integrating several sources of information (e.g., imaging, molecular biomarkers, and family history) requires important theoretical advancements: increasing the dimensionality of the problem can result in lowering the predictive accuracy and robustness when data is scarce. Here, we review and discuss different architectures and strategies for incorporating cutting-edge machine learning into CUP diagnosis, aiming to bridge the gap between theory and clinical practice.Publication Multimorbidity profile among cancer-related hospitalization events in younger and older patients: a large-scale nationwide cross-sectional study(2025) Bernal, Yanara; Campaña, Carla; Sanhueza, Cristobal; Apablaza, Mauricio; Armisen, Ricardo; Delgado, IrisBackground Multimorbidity, the coexistence of two or more chronic diseases, among cancer patients offers critical insights into shared risk factors, while posing increasing challenges for healthcare systems due to the complexity of care required. Despite its relevance, research in multimorbidity across different age groups is limited in middle income countries. Methods We analyzed cancer-related hospitalizations between 2019 and 2023, using a nationwide Diagnosis-Related Groups database covering 68 Chilean health institutions. We examined the distribution of 40 chronic conditions, multimorbidity prevalence, comorbidity profile, and their distribution across age group, sex, and cancer diagnosis. Findings We identified 4,722,723 hospitalization events, including 149,270 unique adult patients hospitalized with cancer (mean of 63 ± 15.17 years old). Multimorbidity was present in 47.9% of all cancer-related hospitalizations, increasing steeply with age: 14% in patients aged 18–35, 24.9% in those 36–50, and 55.5% in patients >50 years. Obesity and diabetes were among the most common comorbid conditions across age groups, with significant variations by sex. Notably, obesity was more prevalent in younger patients, particularly those aged 18–35, whereas hypertension showed an inverse profile, increasing markedly with age. Interpretation Multimorbidity profile reflect both the clinical complexity of cancer care and potential shared biological and environmental pathways in carcinogenesis. These findings highlight the need to transition from diseasecentered to person-centered care models. In Chile, understanding multimorbidity in younger and middle-aged adults may inform precision prevention, integrated service delivery, and equitable planning for both oncologic and non-oncologic care. Funding This study was conducted without external funding.