Modern healthcare relies upon a variety of information and diagnostic sources to empower clinicians to make decisions. Cancer diagnosis, treatment, and prognosis is a complex task involving the acquisition of complementary data streams including imaging scans (e.g. CT or MRI images), molecular data (e.g., genome sequencing, gene-expression, epigenomics, also known as multi-omics), cellular-scale (e.g., histological images), as well as clinical data (such as age, gender, cognitive scores). Detailed, comprehensive assessment of the deluge of data is often infeasible for clinicians alone. The recent increase in the performance, costs, and access to sophisticated computational resources has presented an opportunity to supplement the analysis of information by the clinician with algorithmic or deep learning information synthesis.
While multi-dataset machine-learning systems have demonstrated improvements over comparable single modality models, incorporation of different or lager diversity in datasets or modalities, or otherwise deviating from a well-understood process of data analysis conducted by clinicians, is often impractical for a number of reasons.
UW-Madison researchers have developed a system and methods that incorporate data from a plurality of modalities into a hierarchal structure of machine learning models to construct one or more prognostic signatures of a disease or therapeutic prospects. For example, a hierarchal structure of deep learning networks is provided that provides a multi-scale approach to information analysis across multiple, distinct datasets, such as cellular (genomic), phenotypic (pathology), and structural and functional (MRI) datasets. The systems and methods provided herein can produce a report that includes an integrated marker that is diagnostic of a disease or prognostic of disease outcome, for example, relative to a therapy or treatment.
One implementation is applied in the context of developing integrated prognostic biomarkers for glioblastoma across radiology, pathology, genomics and patient demographics. However, the proposed framework is applicable to building prognostic and predictive biomarkers across different types of cancers.