Work

Creating strong predictive models in oncology

Year: 2026

Type: article

Abstract: Many oncology predictive models fail to improve care. Issues include risks of bias, underpowered radiomics studies, and limited clinical impact. A path forward involves an emphasis on clinically actionable questions, rigor, and generalizability.

Source: Patterns

DOI: 10.1016/j.patter.2026.101492

Author: Michael F. Gensheimer

Institutions: Stanford Medicine, Stanford University

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $4,900