Realizing the promise of machine learning in precision oncology: expert perspectives on opportunities and challenges.

ABSTRACT:

Background

The ability of machine learning (ML) to process and learn from large quantities of heterogeneous patient data is gaining attention in the precision oncology community. Some remarkable developments have taken place in the domain of image classification tasks in areas such as digital pathology and diagnostic radiology. The application of ML approaches to the analysis of DNA data, including tumor-derived genomic profiles, microRNAs, and cancer epigenetic signatures, while relatively more recent, has demonstrated some utility in identifying driver variants and molecular signatures with possible prognostic and therapeutic applications.

Methods

We conducted semi-structured interviews with academic and clinical experts to capture the status quo, challenges, opportunities

SUBMITTER: Nittas V 

PROVIDER: S-EPMC11834663 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature