Seeing the primary tumor because of all the trees: Cancer type prediction on low-dimensional data.

ABSTRACT: The Cancer of Unknown Primary (CUP) syndrome is characterized by identifiable metastases while the primary tumor remains hidden. In recent years, various data-driven approaches have been suggested to predict the location of the primary tumor (LOP) in CUP patients promising improved diagnosis and outcome. These LOP prediction approaches use high-dimensional input data like images or genetic data. However, leveraging such data is challenging, resource-intensive and therefore a potential translational barrier. Instead of using high-dimensional data, we analyzed the LOP prediction performance of low-dimensional data from routine medical care. With our findings, we show that such low-dimensional routine clinical information suffices as input data for tree-based LOP prediction models. The best m

SUBMITTER: Gehrmann J 

PROVIDER: S-EPMC11385615 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature