Aug 2026· Journal of Neuro-Oncology· Vol 179· 0 citations· 24 references
Medicine
TL;DR
An overview of the landscape of major U.S. neuro-oncology data resources is provided and how these datasets are used in contemporary research is evaluated, including population registries, clinical data networks, federal and consortium research cohorts, institutional datasets, specialized resources, and artificial intelligence benchmarking resources.
This narrative review aims to summarize the contribution of population-based cancer registries, hospital-based clinical registries, and administrative healthcare databases to the understanding and management of prostate, bladder, renal, and other genitourinary malignancies.
Ali Bourgi, Emmanuel Rusch, P. Bigot et al.· Current Urology Reports· 0 citations
Big data in cancer genomics offer substantial potential to advance precision oncology by enabling more accurate and personalized treatment strategies, however, overcoming technical, ethical, and infrastructural barriers is essential to ensure effective translation into clinical practice and equitable healthcare outcomes.
Nur Vanu, Nur Mohammad, Fahad Ahmed et al.· Computational and Systems On...· 0 citations
This review synthesizes key developments in ML for oncology, covering foundational algorithms alongside emerging approaches, and describes future directions, including federated learning, graph neural networks, longitudinal modeling, and integration of real-world and wearable data to support precision oncology.
Kanishk Yadav, Taneesha Gupta· Journal of the Egyptian Nati...· 0 citations
Overall, AI research in neurodegenerative diseases suffers from significant limitations in reproducibility, data inclusivity, and clinical translatability, and a set of recommendations can be adopted to address these issues and improve reliability and downstream clinical utility.
W. Endrizzi, F. Ragni, S. Bovo et al.· Communications Medicine· 0 citations
Adequately powered analyses in precision oncology often require combining cohorts across institutions. Yet integration is constrained by the least granular source and may become infeasible when data elements are too heterogeneous to harmonize and map to a common data model. This challenge is acute in multi-institutional precision oncology research, where real-world evidence requires harmonized clinico-omic data integration. Existing models often lack sufficient treatment patterns, outcomes, and genomic data, limiting interoperability and scalability. To address these gaps, AACR Project GENIE™ (Genomics Evidence Neoplasia Information Exchange) developed the GENIE Data Model (GDM), a comprehensive, open-source, oncology data model for scalable, consistent, and interoperable data collection across solid tumors designed to effectively capture the patient's journey with cancer. Through iterative consensus-building, four working groups comprising 13 subject matter experts defined data elements across multiple clinical domains: patient characteristics, imaging, diagnosis, surgery, histopathology, biomarkers, systemic therapy, radiation, clinical trial history, disease response and outcomes, and social determinants of health. Elements were defined using standardized terminologies and permissible values to support mapping to HL7 FHIR, OMOP, and other existing oncology standards. The model architecture distinguishes manually abstracted elements from computationally collected elements, enabling parallel workflows. The GDM provides an extensible framework that addresses critical gaps and enables scalable, harmonized data collection essential for precision oncology and real-world evidence generation.
J. Hoppe, Jocelyn Lee, Tomi F. Akinyemiju et al.· Cancer Research Communicatio...· 0 citations
The discussion on precision oncology integrates multiomics technologies and artificial intelligence, specifically addressing biomarker discovery and personalized therapeutic strategies. In this way, clinical translation and multiomics biomarkers are reconstructed challenges such as heterogeneity, validate, algorithmic bias, regulatory complexities, and ethical issues. This review critically evaluates how advanced technologies operating in an integrated manner facilitate precision oncology by supporting fields such as genomics, transcriptomics, proteomics, metabolomics, and radiomics. To conduct the study, we performed literature search using standard databases such as PubMed, Web of Science, and Scopus, focusing on papers published between 2020 and 2025. We address the all aspects of biomarker identification and clinical applications; we employed a five-stage framework comprising multiparametric data generation, integration, biomarker discovery, rigorous validation, and regulatory implementation. To further examine this review, we have employed emerging computational approaches, including machine learning and deep learning and graph neural networks alongside regulatory frameworks and ethical, legal, and social considerations. Discussing translation barriers, we consider factors such as limited reproducibility, validation, and critical discussion particularly studies. Ultimately, a future model based on standardized, validated, and transparent learning strategies accelerates and fosters the development of clinically reliable standards. Our review provides an integrative roadmap for modern, multiomics-driven biomarker approaches in precision oncology practice.
Ujwal Havelikar, Atharva A. Shinde, Hrushikesh Mhaismale et al.· Omics· 0 citations
