Download PDFOpen PDF in browserIntegrative Approaches in Cancer Diagnostics: Advancing Precision Medicine Through Data Integration and Analytical TechniquesEasyChair Preprint 1281615 pages•Date: March 28, 2024AbstractCancer diagnosis and treatment decision-making have significantly evolved with the emergence of integrative approaches that combine diverse data types and advanced analytical techniques. This abstract explores the integration of genomic data, clinical data, imaging data, and biomarker information to develop comprehensive and integrative diagnostic strategies in cancer care. Integrative cancer diagnostics involves the integration of heterogeneous data sources, enabling a holistic understanding of the complex molecular and clinical characteristics of tumors. Genomic data, such as DNA sequencing and gene expression profiling, provide insights into the genetic alterations and molecular subtypes of cancer. Clinical data, including patient demographics, medical history, and treatment outcomes, offer valuable information for personalized treatment planning. Imaging data, such as radiographic images and functional imaging modalities, provide visual representations of tumor location, size, and metabolic activity. Biomarker information, encompassing molecular markers and circulating tumor cells, can offer additional insights into disease progression and treatment response. The integration of these diverse data types is facilitated by advanced analytical techniques, particularly machine learning algorithms and data fusion methods. Machine learning algorithms can identify complex patterns and relationships within large-scale datasets, enabling the development of predictive models for cancer diagnosis and prognosis. Data fusion methods integrate data from multiple sources to enhance the accuracy and reliability of diagnostic assessments. Keyphrases: Cancer diagnosis, Lung Cancer Treatment, cancer awareness
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