ArticleFrontiers in pharmacology2025
Prediction of early mucosal healing of Crohn's disease after treatment with biologics- a novel nomogram based on radiomics and clinical risk factors.
Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Development and validation of a nomogram for predicting the probability of medication adherence to adjuvant endocrine therapy in breast cancer patients: a predictive modeling study.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Risk prediction model for severe potential drug-drug interactions in colorectal cancer patients: a real-world data study.Frontiers in pharmacology · 2025Article
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4 authors.
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Abstract
Background: Predicting endoscopic remission is crucial for optimizing clinical treatment strategies and switching biologics in Crohn's disease (CD). Mucosal healing (MH) is a key therapeutic target. This study aimed to develop a clinically applicable prediction model for early MH in CD patients receiving biological therapy. Methods: This study retrospectively analyzed 120 CD patients diagnosed between 2018 and 2023, randomly divided into a training cohort and an internal validation cohort 1. Additionally, 34 prospectively enrolled CD patients diagnosed between 2024 and 2025 formed an internal validation cohort 2. Clinical indicators and conventional imaging features were evaluated to establish a clinical model. Radiomics features were extracted from computed tomography enterography (CTE) images, with regions of interest (ROIs) manually delineated to align with ulcerated intestinal segments identified through colonoscopy. A radiomics model was constructed, and a radiomics score (Rad-score) was derived. A clinical-radiomics nomogram was then developed by integrating Rad-score with clinical risk factors. Model performance was assessed using discrimination, calibration, decision curve analysis (DCA), and clinical impact curves. Results: The clinical-radiomics nomogram demonstrated strong predictive performance, with AUC values of 0.948 (95% CI: 0.902-0.995) in the training cohort, 0.925 (95% CI: 0.805-1.0) in the internal validation cohort 1, and 0.940 (95% CI: 0.802-0.993) in the internal validation cohort 2. The nomogram outperformed standalone clinical and radiomics models, with DCA confirming its clinical utility. Conclusion: The developed nomogram effectively predicts early MH in CD patients undergoing biological therapy, providing a practical tool for clinicians to optimize treatment strategies and improve outcomes.
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