Personalizing Treatment in Cancer Patients: Improving Patient Outcomes

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Nursing".

Deadline for manuscript submissions: closed (1 February 2024) | Viewed by 1414

Special Issue Editor


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Guest Editor
Department of Symptom Reseach, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
Interests: patient-reported outcomes; quality of life; percision medicine; personalized cancer care; toxicity risk models

Special Issue Information

Dear Colleagues,

Cancer patients still experience treatment-induced toxicities despite treatment advancement. Thus, shifting from current reactive supportive care strategies to more proactive ones to optimize patient-centered care. For more effective personalized cancer care, we need to determine the individualized risk of treatment-induced toxicities by incorporating patient perspectives via patient-reported outcomes, biomarkers, and imaging features in toxicity prediction models.

In the era of personalized cancer care, advanced imaging, genomic techniques, and biomarkers could be used for toxicity risk stratification to choose between multimodal therapies.

This Special Issue aims to provide evidence-based data to support incorporating patient-reported outcomes, image features, and biomarkers in toxicity risk prediction models to improve patient outcomes during and after cancer treatment.

Dr. Mona K. Jomaa
Guest Editor

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Keywords

  • adaptive cancer treatment
  • precision cancer care
  • personalized cancer care
  • patient-reported outcomes in cancer settings
  • risk-based cancer management
  • quality of life after cancer therapy
  • computational models for risk reduction in cancer setting
  • survival outcomes in oncologic setting
  • qualitative imaging and biological markers for risk assessment in oncologic setting
  • patient-centred oncologic approaches

Published Papers (1 paper)

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14 pages, 4448 KiB  
Systematic Review
Characteristics and Outcomes of Stem Cell Transplant Patients during the COVID-19 Era: A Systematic Review and Meta-Analysis
by Mona Kamal, Massimo Baudo, Jacinth Joseph, Yimin Geng, Omnia Mohamed, Mohamed Rahouma and Uri Greenbaum
Healthcare 2024, 12(5), 530; https://doi.org/10.3390/healthcare12050530 - 23 Feb 2024
Viewed by 968
Abstract
This systematic review and meta-analysis aims to identify the outcomes of stem cell transplant (SCT) patients during the COVID-19 era. Pooled event rates (PER) were calculated, and meta-regression was performed. A random effects model was utilized. In total, 36 eligible studies were included [...] Read more.
This systematic review and meta-analysis aims to identify the outcomes of stem cell transplant (SCT) patients during the COVID-19 era. Pooled event rates (PER) were calculated, and meta-regression was performed. A random effects model was utilized. In total, 36 eligible studies were included out of 290. The PER of COVID-19-related deaths and COVID-19-related hospital admissions were 21.1% and 55.2%, respectively. The PER of the use of hydroxychloroquine was 53.27%, of the receipt of immunosuppression it was 39.4%, and of the use of antivirals, antibiotics, and steroids it was 71.61%, 37.94%, and 18.46%, respectively. The PER of the time elapsed until COVID-19 infection after SCT of more than 6 months was 85.3%. The PER of fever, respiratory symptoms, and gastrointestinal symptoms were 70.9, 76.1, and 19.3%, respectively. The PER of acute and chronic GvHD were 40.2% and 60.9%, respectively. SCT patients are at a higher risk of severe COVID-19 infection and mortality. The use of dexamethasone improves the survival of hospitalized SCT patients with moderate to severe COVID-19 requiring supplemental oxygen or ventilation. The SCT patient group is a heterogeneous group with varying characteristics. The quality of reporting on these patients when infected with COVID-19 is not uniform and further prospective or registry studies are needed to better guide clinical care in this unique setting. Full article
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