Publications by authors named "K Panageas"

Exercise and mindfulness-based interventions have growing evidence for managing fatigue and comorbid symptoms; however, packaging them in a cohesive digital way for patients undergoing cancer treatment has not been evaluated. We conducted a randomized controlled trial to assess the impact of a 12 week digital integrative medicine program, Integrative Medicine at Home (IM@Home), versus enhanced usual care on fatigue severity (primary outcome), comorbid symptoms and acute healthcare utilization (secondary outcomes), in 200 patients with solid tumors experiencing fatigue during treatment. Fatigue severity decreased more in IM@Home than in the control (1.

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Cancer is a complex disease driven by genomic alterations, and tumor sequencing is becoming a mainstay of clinical care for cancer patients. The emergence of multi-institution sequencing data presents a powerful resource for learning real-world evidence to enhance precision oncology. GENIE BPC, led by American Association for Cancer Research, establishes a unique database linking genomic data with clinical information for patients treated at multiple cancer centers.

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Anxiety and depression are common in many cancers but have not been systematically studied in patients with histiocytic neoplasms (HN). We sought to estimate rates of anxiety and depression and identify clinical features and patient-reported outcomes (PROs) associated with anxiety and depression in patients with HN. A registry-based cohort of patients with HN completing PROs including the Hospital Anxiety and Depression Scale (HADS) from 2018-2023 was identified.

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Article Synopsis
  • Cancer is driven by genomic changes, and tumor sequencing is now a key part of treating cancer patients, with initiatives like GENIE BPC creating a database that connects genomic and clinical data from multiple centers.
  • However, using data from different institutions presents challenges such as differences in gene panels, sequencing methods, and patient diversity, making it hard to analyze the information effectively.
  • To address these issues, the Bridge model has been developed to improve data integration by using advanced statistical techniques that help preserve important information and enhance predictions about patient outcomes across various cancer types.
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