ArticleInsights into imaging2024
CT-based pancreatic radiomics predicts secondary loss of response to infliximab in biologically naïve patients with Crohn's disease.
Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The predictive value of machine learning for the response to anti-TNF-α therapy in patients with Crohn's disease: a systematic review and meta-analysis.Frontiers in immunology · 2026Pooled it
- Gradient Boosting Machine-Based Prognostic Model with SurvSHAP Analysis for Predicting Secondary Loss of Response to Infliximab Therapy in Crohn's Disease.Drug design, development and therapy · 2026Article
- Psoas muscle CT radiomics-based machine learning models to predict response to infliximab in patients with Crohn's disease.Annals of medicine · 2025Article
- Expanding the role of radiomics and artificial intelligence in the management of inflammatory bowel disease: Insights, opportunities, and challenges.World journal of gastroenterology · 2025Article
- Novel endoluminal parameters for predicting primary loss of response in Crohn's disease: a multi-center study.Insights into imaging · 2025Article
- Development and Validation of a Machine Learning Model to Predict Anti-Drug Antibody Formation During Infliximab Induction in Crohn's Disease.Biomedicines · 2025Article
- Robustness of radiomics within photon-counting detector CT: impact of acquisition and reconstruction factors.European radiology · 2025Article
- Longitudinal computed tomography-based delta-radiomics of visceral adipose tissue predicts infliximab secondary loss of response in Crohn's disease patients.World journal of gastroenterology · 2025Article
- ChatGPT as an effective tool for quality evaluation of radiomics research.European radiology · 2025Article
- Development and validation of a CT-based radiomic nomogram for predicting surgical resection risk in patients with adhesive small bowel obstruction.BMC medical imaging · 2025Article
- Deep-Learning, Radiomics and Clinic Based Fusion Models for Predicting Response to Infliximab in Crohn's Disease Patients: A Multicentre, Retrospective Study.Journal of inflammation research · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectivesPredicting secondary loss of response (SLR) to infliximab (IFX) is paramount for tailoring personalized management regimens. Concurrent pancreatic manifestations in patients with Crohn's disease (CD) may correlate with SLR to anti-tumor necrosis factor treatment. This work aimed to evaluate the potential of pancreatic radiomics to predict SLR to IFX in biologic-naive individuals with CD.
methodsThree models were developed by logistic regression analyses to identify high-risk subgroup prone to SLR. The area under the curve (AUC), calibration curve, decision curve analysis (DCA), and integrated discrimination improvement (IDI) were applied for the verification of model performance. A quantitative nomogram was proposed based on the optimal prediction model, and its reliability was substantiated by 10-fold cross-validation.
resultsIn total, 184 CD patients were enrolled in the period January 2016 to February 2022. The clinical model incorporated age of onset, disease duration, disease location, and disease behavior, whereas the radiomics model consisted of five texture features. These clinical parameters and the radiomics score calculated by selected texture features were applied to build the combined model. Compared to other two models, combined model achieved favorable, significantly improved discrimination power (AUC
conclusionsThe first proposed pancreatic-related nomogram represents a credible, noninvasive predictive instrument to assist clinicians in accurately identifying SLR and non-SLR in CD patients. CRITICAL RELEVANCE STATEMENT: This study first built a visual nomogram incorporating pancreatic texture features and clinical factors, which could facilitate clinicians to make personalized treatment decisions and optimize cost-effectiveness ratio for patients with CD. KEY POINTS: • The first proposed pancreatic-related model predicts secondary loss of response for infliximab in Crohn's disease. • The model achieved satisfactory predictive accuracy, calibration ability, and clinical value. • The model-based nomogram has the potential to identify long-term failure in advance and tailor personalized management regimens.
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