Publications by authors named "M Barlund"

Introduction: Treatment decisions are challenging in older adults with solid tumors. Geriatric 8 (G8)-screening and comprehensive geriatric assessment (CGA) are important but additional methods are needed. We examined the association of computed tomography (CT)-derived high visceral adipose tissue index (VATI) with or without low skeletal muscle index (SMI) on three-month and overall survival (OS).

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Background And Purpose: The CardioSwitch-study demonstrated that patients with solid tumors who develop cardiotoxicity on capecitabine or 5-fluorouracil (5-FU) treatment can be safely switched to S-1, an alternative fluoropyrimidine (FP). In light of the European Medicines Agency approval of S-1 in metastatic colorectal cancer (mCRC), this analysis provides more detailed safety and efficacy information, and data regarding metastasectomy and/or local ablative therapy (LAT), on the mCRC patients from the original study.

Materials And Methods: This retrospective cohort study was conducted at 12 European centers.

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As patients with solid (non-hematological) cancers and a life expectancy of <3 months rarely benefit from oncological treatment, we examined whether the CT-determined loss of muscle mass is associated with an impaired 3-month overall survival (OS) in frail ≥75-year-old patients with cancer. Frailty was assessed with G8-screening and comprehensive geriatric assessment in older adults at risk of frailty. The L3-level skeletal (SMI) and psoas (PMI) muscle indexes were determined from routine CT scans.

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Background: Capecitabine- or 5-fluorouracil (5-FU)-based chemotherapy is widely used in many solid tumours, but is associated with cardiotoxicity. S-1 is a fluoropyrimidine with low rates of cardiotoxicity, but evidence regarding the safety of switching to S-1 after 5-FU- or capecitabine-associated cardiotoxicity is scarce.

Patients And Methods: This retrospective study (NCT04260269) was conducted at 13 centres in 6 countries.

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Background: The existing risk prediction models for chemotherapy-induced febrile neutropenia (FN) do not necessarily apply to real-life patients in different healthcare systems and the external validation of these models are often lacking. Our study evaluates whether a machine learning-based risk prediction model could outperform the previously introduced models, especially when validated against real-world patient data from another institution not used for model training.

Methods: Using Turku University Hospital electronic medical records, we identified all patients who received chemotherapy for non-hematological cancer between the years 2010 and 2017 (N = 5879).

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