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Amal Al Omari

Publications and source records attributed to Amal Al Omari.

3 recordsLinked to original sources

Development of an age specific EORTC questionnaire to assess the health-related quality of life (HRQOL) of children with cancer aged 8-14 years - a mixed-methods content elicitation phase I & II EORTC QLG study.

BACKGROUND: Experiencing cancer can be physically and psychosocially challenging in the short- and long-term. Integrating Patient-Reported Outcome Measurements (PROMs) to capture these challenges can support patient-centered care and may reduce morbidity and mortality. However, age-appropriate tools remain scarce for children and adolescents. We report on the development phases I/II of the EORTC QLQ-CHI questionnaire tailored for children 8-14 years. METHODS: Following the EORTC QLG module development guidelines, qualitative semi-structured interviews were conducted in children aged 8-14 years undergoing active cancer treatment, parents and healthcare professionals (HCPs). Participants rated the relevance of issues identified in a previous systematic review (Phase Ia) and provided preferences on response format and recall period (Phase Ib). During expert round tables, key issues were converted into items (Phase II). RESULTS: Interviews were completed with 47 children, 45 parents, and 22 HCPs from six countries. Among all children (mean age=10.8&#x202f;&#xb1;&#x202f;1.9 years; 57.4% female), 55.3% had haematological cancers. Of the initially identified issues, 57 were identified as key issues by comparing children's, parents' and HCPs feedback. Children preferred four response options (70.0%) and a shorter recall period (43.3%). Through expert round tables (Phase II), items were assigned to a more symptom-oriented core scale and an additional scale for psychosocial concerns. The provisional questionnaire consists of 50 items. CONCLUSION: This study represents the first step in developing the EORTC QLQ-CHI (8-14 years). Next steps include pilot testing, validation (Phase III and IV), co-development of a corresponding measure for children <&#x202f;8 years and an observer-rating version.

Humans

Worldwide Innovative Network Consortium: Building a Common Global Cancer Database.

This review shares the ongoing work of the global Worldwide Innovative Network (WIN) Consortium for Precision Medicine to synthesize emerging cancer treatment data and to define the requirements for a common global cancer database that can truly support precision oncology. We performed a narrative review of emerging cancer treatment data, molecular profiling technologies, and existing clinicogenomic databases, focusing on how tumors are characterized, how subgroups are defined, and how demographic, lifestyle, and environmental factors are captured. The growth in molecular profiling technologies and the development of new targeted therapies are transforming cancer care. Tumors, regardless of tissue origin, are increasingly defined as composites of multiple, often rare, subgroups, each with distinct biology and likely response to specific therapies, based on multidimensional profiling of the tumor and its microenvironment. The solution lies in building vast databases that capture racial and ethnic diversity, reflected in genomic data, as well as diet and lifestyle factors that may have epigenetic impact on gene expression and post-translational modifications. A truly inclusive and informative data set must reflect global diversity, and there are multiple examples of demography-dependent differences in genomic signals. With members caring for and studying patients with cancer across five continents, WIN is actively exploring pathways to create a global cancer database, rich in clinical and molecular detail, granular enough for precise analysis, and large enough to power artificial intelligence-driven insights, provided appropriate data quality, validation, and governance frameworks are in place. This review surveys the current landscape and outlines practical paths forward to achieve this goal.

Humans

Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine: Bringing next-generation precision oncology to patients.

The human genome project ushered in a genomic medicine era that was largely unimaginable three decades ago. Discoveries of druggable cancer drivers enabled biomarker-driven gene- and immune-targeted therapy and transformed cancer treatment. Minimizing treatment not expected to benefit, and toxicity-including financial and time-are important goals of modern oncology. The Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine founded by Drs. John Mendelsohn and Thomas Tursz provided a vision for innovation, collaboration and global impact in precision oncology. Through pursuit of transcriptomic signatures, artificial intelligence (AI) algorithms, global precision cancer medicine clinical trials and input from an international Molecular Tumor Board (MTB), WIN has led the way in demonstrating patient benefit from precision-therapeutics through N-of-1 molecularly-driven studies. WIN Next-Generation Precision Oncology (WINGPO) trials are being developed in the neoadjuvant, adjuvant or metastatic settings, incorporate real-world data, digital pathology, and advanced algorithms to guide MTB prioritization of therapy combinations for a diverse global population. WIN has pursued combinations that target multiple drivers/hallmarks of cancer in individual patients. WIN continues to be impactful through collaboration with industry, government, sponsors, funders, academic and community centers, patient advocates, and other stakeholders to tackle challenges including drug access, costs, regulatory barriers, and patient support. WIN's collaborative next generation of precision oncology trials will guide treatment selection for patients with advanced cancers through MTB and AI algorithms based on serial liquid and tissue biopsies and exploratory omics including transcriptomics, proteomics, metabolomics and functional precision medicine. Our vision is to accelerate the future of precision oncology care.

Humans