Multimodal Data Integration for Precision Oncology: Challenges and Future Directions
arxiv(2024)
摘要
The essence of precision oncology lies in its commitment to tailor targeted
treatments and care measures to each patient based on the individual
characteristics of the tumor. The inherent heterogeneity of tumors necessitates
gathering information from diverse data sources to provide valuable insights
from various perspectives, fostering a holistic comprehension of the tumor.
Over the past decade, multimodal data integration technology for precision
oncology has made significant strides, showcasing remarkable progress in
understanding the intricate details within heterogeneous data modalities. These
strides have exhibited tremendous potential for improving clinical
decision-making and model interpretation, contributing to the advancement of
cancer care and treatment. Given the rapid progress that has been achieved, we
provide a comprehensive overview of about 300 papers detailing cutting-edge
multimodal data integration techniques in precision oncology. In addition, we
conclude the primary clinical applications that have reaped significant
benefits, including early assessment, diagnosis, prognosis, and biomarker
discovery. Finally, derived from the findings of this survey, we present an
in-depth analysis that explores the pivotal challenges and reveals essential
pathways for future research in the field of multimodal data integration for
precision oncology.
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