ArticleEuropean journal of radiology open2026
Standardized contrast-enhanced CT-based radiomics for non-invasive prediction of TTF-1 status in lung adenocarcinoma: A cross-validated single-center study.
Article in European journal of radiology open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: Thyroid transcription factor-1 (TTF-1) is a clinically relevant immunohistochemical marker in lung adenocarcinoma with diagnostic, prognostic, and therapeutic implications. As tissue sampling is invasive and often limited by small biopsy specimens, non-invasive imaging biomarkers are of increasing interest. This study aimed to develop and internally validate a standardized contrast-enhanced CT-based radiomics workflow for prediction of TTF-1 expression, with emphasis on reproducibility and methodological robustness. Methods: This retrospective single-center study included 164 treatment-naive patients with histologically confirmed lung adenocarcinoma and available TTF-1 immunohistochemistry. TTF-1 expression was assessed using routine immunohistochemical staining on biopsy or resection specimens, with binary classification based on nuclear staining. Primary tumors were semi-automatically segmented on contrast-enhanced CT using IBSI-compliant preprocessing. A total of 107 radiomic features were extracted. Interobserver reproducibility was evaluated in a subset of 55 tumors using intraclass correlation coefficients. Logistic regression, random forest, and XGBoost models were trained using five-fold stratified cross-validation. Performance was evaluated using AUC. Results: Among 164 patients, 115 tumors (70.1%) were TTF-1 positive. XGBoost achieved the highest performance (mean AUC = 0.83 ± 0.04), significantly outperforming other models (p < 0.01). Radiomic features showed good interobserver reproducibility, particularly for shape and first-order features. SHAP analysis identified sphericity, entropy, and mean CT attenuation as key predictors. Conclusion: A standardized CT-based radiomics approach shows promising performance for non-invasive prediction of TTF-1 status in lung adenocarcinoma. Given the retrospective single-center design and lack of external validation, further multicenter studies are required.
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