ArticleRadiology. Imaging cancer2026
AI-assisted Radiomic Model for Cervical Cancer Recurrence Prediction: A Multicenter Retrospective Study with Experimental Validation.
Article in Radiology. Imaging cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Peritumoral ultrasound-driven multimodal AI model for predicting lymphovascular space invasion and prognosis in cervical cancer.Abdominal radiology (New York) · 2026Article
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6 authors.
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Abstract
Purpose To evaluate the value of a nomogram model incorporating clinical parameters, hematologic inflammatory biomarkers, and MRI radiomic features in predicting postoperative disease-free survival (DFS) in patients with cervical cancer and to explore the biologic mechanism underlying the radiomic signature. Materials and Methods This multicenter retrospective study enrolled 804 patients with cervical cancer (2016-2023), with a median follow-up of 43.7 months. Three-dimensional radiomic features were extracted from pretreatment MRI tumor and 5-mm peritumoral regions. Five machine learning algorithms were tested to construct the optimal radiomics score (Radscore). Multivariable Cox regression was used to develop the nomogram model, with bioinformatic analysis and in vitro experiments for biologic mechanism validation. Results In the clinical prediction cohort (
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