ArticleInsights into imaging2023
Determining rib fracture age from CT scans with a radiomics-based combined model: a multicenter retrospective study.
Article in Insights into imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A multi-task learning-based deep learning model for precise estimation of rib fracture age on chest CT.International journal of legal medicine · 2026Article
- Evaluating the age of rib fractures throughInternational journal of legal medicine · 2026Article
- Multifaceted Preprocessing Optimization for Post-Mortem Radiomics Analysis: A Pilot Study on Forensic Age Estimation Using Proximal Femur Radiomics.Journal of imaging informatics in medicine · 2026Article
- Ultrasound-Based Deep Learning Radiomics Models for Predicting Primary and Secondary Salivary Gland Malignancies: A Multicenter Retrospective Study.Bioengineering (Basel, Switzerland) · 2025Article
- ChatGPT as an effective tool for quality evaluation of radiomics research.European radiology · 2025Article
- YOLOv8 Algorithm-aided Detection of Rib Fracture on Multiplane Reconstruction ImagesCurrent medical imaging · 2025Article
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7 authors.
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Abstract
objectivesWe aimed to develop a combined model based on clinical and radiomic features to classify fracture age.
methodsWe included 1219 rib fractures from 239 patients from our center between March 2016 and September 2022. We created an external dataset using 120 rib fractures from 32 patients from another center between October 2019 and August 2023. According to tasks (fracture age between < 3 and ≥ 3 weeks, 3-12, and > 12 weeks), the internal dataset was randomly divided into training and internal test sets. A radiomic model was built using radiomic features. A combined model was constructed using clinical features and radiomic signatures by multivariate logistic regression, visualized as a nomogram. Internal and external test sets were used to validate model performance.
resultsFor classifying fracture age between < 3 and ≥ 3 weeks, the combined model had higher areas under the curve (AUCs) than the radiomic model in the training set (0.915 vs 0.900, p = 0.009), internal test (0.897 vs 0.854, p < 0.001), and external test sets (0.881 vs 0.811, p = 0.003). For classifying fracture age between 3-12 and > 12 weeks, the combined model had higher AUCs than the radiomic model in the training model (0.848 vs 0.837, p = 0.12) and internal test sets (0.818 vs 0.793, p < 0.003). In the external test set, the AUC of the nomogram-assisted radiologist was 0.966.
conclusionThe combined radiomic and clinical model showed good performance and has the potential to assist in the classification of rib fracture age. This will be beneficial for clinical practice and forensic decision-making. CRITICAL RELEVANCE STATEMENT: This study describes the development of a combined radiomic and clinical model with good performance in the classification of the age of rib fractures, with potential clinical and forensic applications. KEY POINTS: • Complex factors make it difficult to determine the age of a fracture. • Our model based on radiomic features performed well in classifying fracture age. • Associating the radiomic features with clinical features improved the model's performance.
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