Work

RaFiST: a radiomics model for non-invasive stratification of tumor fibrosis and prognostic prediction in non-small cell lung cancer

Year: 2026

Type: article

Abstract: OBJECTIVES: Tumor fibrosis plays a critical role in driving therapeutic heterogeneity and drug resistance. However, relevant research in non-small cell lung cancer (NSCLC) remains limited. This study aimed to determine the prognostic value of tumor fibrosis and develop a novel radiomics fibrosis stratification tool (RaFiST) for non-invasive patient stratification. MATERIALS AND METHODS: In this multicenter retrospective study of 532 patients with resected NSCLC, tumor fibrosis was histopathologically quantified via collagen fraction. RaFiST was developed using pre-treatment contrast-enhanced CT scans from training and external test cohorts. Prognostic performance was subsequently compared among clinical, direct radiomics, and combined models. Transcriptomic analysis investigated the model's underlying molecular mechanisms. RESULTS: Multivariable Cox regression revealed that the fibrosis score was an independent risk factor for disease-free survival (DFS) and overall survival (OS) at both centers. An optimal cutoff of 11.12% stratified patients into high- and low-fibrosis groups. RaFiST demonstrated excellent performance in predicting tumor fibrosis, achieving an area under the curve (AUC) of 0.879 in the training cohort and 0.813 in the test cohort. RaFiST-High patients exhibited significantly worse survival across both cohorts (all p < 0.01). Furthermore, the combined Clinical-RaFiST model outperformed the baseline clinical and direct radiomics models, yielding a 5-year DFS AUC of 0.827. Transcriptomic analysis associated the RaFiST-High group with pathways for extracellular matrix remodeling, hypoxia, and immune suppression. CONCLUSION: Tumor fibrosis is an independent prognostic risk factor in NSCLC. RaFiST provides a robust, non-invasive imaging biomarker for tumor fibrosis stratification and prognostic prediction. KEY POINTS: Question Tumor fibrosis plays a critical role in tumor therapeutic heterogeneity and drug resistance. However, relevant research in non-small cell lung cancer (NSCLC) remains relatively limited. Findings Tumor fibrosis is an independent prognostic factor in NSCLC, and radiomics fibrosis stratification tool (RaFiST), a CT-based radiomics model provides a robust non-invasive biomarker for fibrosis stratification and outcome prediction. Clinical relevance RaFiST offers a non-invasive, reproducible framework to evaluate tumor fibrosis in NSCLC, thereby enabling the earlier identification of postoperative patients with poor prognoses for prompt adjuvant therapy.

Source: European Radiology

DOI: 10.1007/s00330-026-12764-5