ArticleScientific reports2024
Combination of clinical information and radiomics models for the differentiation of acute simple appendicitis and non simple appendicitis on CT images.
Article in Scientific reports, 2024. 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.
- Path beyond the blind end-unravel the imaging spectrum of appendiceal pathologies.Abdominal radiology (New York) · 2026Review
- Artificial intelligence in acute appendicitis: A comprehensive review of machine learning and deep learning applications.World journal of gastroenterology · 2025Review
- Progress in the application of machine learning in CT diagnosis of acute appendicitis.Abdominal radiology (New York) · 2025Review
- Article
- A Systematic Integration of Artificial Intelligence Models in Appendicitis Management: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Retrospective Development of an AI Model Combining Ultrasound and Clinical Data for Pediatric Appendicitis Differentiation.Emergency medicine international · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
To investigate the radiomics models for the differentiation of simple and non-simple acute appendicitis. This study retrospectively included 334 appendectomy cases (76 simple and 258 non-simple cases) for acute appendicitis. These cases were divided into training (n = 106) and test cohorts (n = 228). A radiomics model was developed using the radiomic features of the appendix area on CT images as the input variables. A CT model was developed using the clinical and CT features as the input variables. A combined model was developed by combining the radiomics model and clinical information. These models were tested, and their performance was evaluated by receiver operating characteristic curves and decision curve analysis (DCA). The variables independently associated with non-simple appendicitis in the combined model were body temperature, age, percentage of neutrophils and Rad-score. The AUC of the combined model was significantly higher than that of the CT model (P = 0.041). The AUC of the radiomics model was also higher than that of the CT model but did not reach a level of statistical significance (P = 0.053). DCA showed that all three models had a higher net benefit (NB) than the default strategies, and the combined model presented the highest NB. A nomogram of the combined model was developed as the graphical representation of the final model. It is feasible to use the combined information of clinical and CT radiomics models for the differentiation of simple and non-simple acute appendicitis.
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