What question did this study set out to answer?

To evaluate the accuracy of radiomics-based machine learning models in predicting recurrence risk in non-small cell lung cancer (NSCLC).

April 8, 2026Open Access

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Accuracy of Radiomics-Based Machine Learning for Predicting Risk of Recurrence in Non–Small Cell Lung Cancer: Systematic Review and Meta-Analysis

Key Points

To evaluate the accuracy of radiomics-based machine learning models in predicting recurrence risk in non-small cell lung cancer (NSCLC).
Systematic review of existing radiomics-based ML studies
Meta-analysis of the predictive performance for recurrence in NSCLC
Assessment of methodological approaches across studies
Radiomics-based ML models show effective prediction of recurrence in NSCLC.
Current methodologies require standardization for improved accuracy and applicability.
Incorporating multicenter imaging data is necessary for enhancing model generalizability.

Abstract

This study is the first to confirm that radiomics-based ML models effectively predict the risk of recurrence in NSCLC. This study provides evidence-based support for the subsequent development or updating of radiomics-based ML models. However, the current methodological application of radiomics remains concerning. Therefore, in the future, research should standardize the workflow for implementing radiomics-based ML and incorporate multicenter imaging data to enhance its generalizability.

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Cite This Study

Wu et al. (Thu,) studied this question.

synapsesocial.com/papers/69d5f0ee74eaea4b11a7a70a https://doi.org/https://doi.org/10.2196/84223

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