Building Flexible, Scalable, and Machine Learning-ready Multimodal Oncology Datasets
The advancements in data acquisition, storage, and processing techniques have
resulted in the rapid growth of heterogeneous medical data. Integrating
radiological scans, histopathology images, and molecular information with
clinical data is essential for developing a holistic understanding of the
disease and optimizing treatment. The need for integrating data from multiple
sources is further pronounced in complex diseases such as cancer for enabling
precision medicine and personalized treatments. This work proposes Multimodal
Integration of Oncology Data System (MINDS) - a flexible, scalable, and
cost-effective metadata framework for efficiently fusing disparate data from
public sources such as the Cancer Research Data Commons (CRDC) into an
interconnected, patient-centric framework. MINDS offers an interface for
exploring relationships across data types and building cohorts for developing
large-scale multimodal machine learning models. By harmonizing multimodal data,
MINDS aims to potentially empower researchers with greater analytical ability
to uncover diagnostic and prognostic insights and enable evidence-based
personalized care. MINDS tracks granular end-to-end data provenance, ensuring
reproducibility and transparency. The cloud-native architecture of MINDS can
handle exponential data growth in a secure, cost-optimized manner while
ensuring substantial storage optimization, replication avoidance, and dynamic
access capabilities. Auto-scaling, access controls, and other mechanisms
guarantee pipelines' scalability and security. MINDS overcomes the limitations
of existing biomedical data silos via an interoperable metadata-driven approach
that represents a pivotal step toward the future of oncology data integration.