Radiomics in Lung Cancer: Current Evidence and Translational Challenges

A narrative review of 27 systematic reviews/meta-analyses finds radiomics consistently aids lung cancer diagnosis, prognostication, and treatment response assessment, but clinical translation is hampered by lack of standardization, limited external validation, and reproducibilit…

• Machine learning and deep learning approaches frequently enhanced predictive performance.
• Key barriers include heterogeneity in imaging protocols, small single-centre datasets, and limited reproducibility of radiomic features.
• Future progress requires standardization, external validation, multimodal data integration, and improved interpretability.

Automated summary

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