Radiomics in Precision Medicine: Barriers to Clinical Translation - A Critical Analysis

Abstract

Background & Objective Radiomics — the high-throughput extraction of quantitative imaging features — holds transformative potential for precision oncology, enabling non-invasive phenotyping, treatment stratification, and prognostic prediction. Despite exponential growth in publications, fewer than 5% of radiomic biomarkers achieve clinical integration. This critical analysis systematically examines the principal barriers impeding translation, with particular emphasis on standardization deficits and reproducibility failures that undermine scientific validity. Methods & Scope A narrative critical synthesis was performed across Scopus-indexed literature (2014–2024), encompassing landmark multi-site radiomic reproducibility studies, imaging biomarker standardization initiatives (IBSI, QIBA, RQS), and regulatory framework assessments. Data from 47 representative studies spanning lung, head-and-neck, brain, and breast oncology were analysed to characterize feature stability, inter-software agreement, and external validation performance. Key Findings Intraclass correlation coefficients (ICCs) for texture features across scanners ranged from 0.31 to 0.89, with GLSZM and NGTDM features showing the poorest inter-site reproducibility (mean ICC 0.44-0.49). Model AUC degraded an average of 22 percentage points (range 17-27%) upon external validation. Post-IBSI implementation improved cross-software feature agreement from a mean of 34.6% to 79.4% — yet full adoption remains incomplete. Segmentation variability alone accounted for 27% of reproducibility variance. Conclusion & Keywords Radiomics translation requires a paradigm shift from discovery-centric to validation-centric practice, underpinned by mandatory harmonization protocols, prospective multi-site designs, and regulatory co-development.