ArticleFrontiers in pediatrics2022
Machine Learning-Based CT Radiomics Method for Identifying the Stage of Wilms Tumor in Children.
Article in Frontiers in pediatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Comparison of random survival forest and Cox regression for long-term prognosis of pediatric Wilms tumor: based on the SEER database and external validation cohort from China.Discover oncology · 2025Article
- Article
- Artificial intelligence can help individualize Wilms tumor treatment by predicting tumor response to preoperative chemotherapy.Investigative and clinical urology · 2025Article
- Applications of Artificial Intelligence for Pediatric Cancer Imaging.AJR. American journal of roentgenology · 2024Review
- Radiomics in differential diagnosis of Wilms tumor and neuroblastoma with adrenal location in children.European radiology · 2024Article
- Insights into Personalized Care Strategies for Wilms Tumor: A Narrative Literature Review.Biomedicines · 2024Review
- Automated segmentation and volume prediction in pediatric Wilms' tumor CT using nnu-net.BMC pediatrics · 2024Article
- The application of machine learning based on computed tomography images in the identification of renal tumors in children.Translational pediatrics · 2024Article
- CT radiomics to differentiate between Wilms tumor and clear cell sarcoma of the kidney in children.BMC medical imaging · 2024Article
- Review
- Promoting the application of pediatric radiomics via an integrated medical engineering approach.Cancer innovation · 2023Review
- Radiologic versus Segmentation Measurements to Quantify Wilms Tumor Volume on MRI in Pediatric Patients.Cancers · 2023Article
- Prediction of Wilms' Tumor Susceptibility to Preoperative Chemotherapy Using a Novel Computer-Aided Prediction System.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
9 authors.
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Abstract
Purpose: To develop and validate a machine learning-based CT radiomics method for preoperatively predicting the stages (stage I and non-stage I) of Wilms tumor (WT) in pediatric patients. Methods: A total of 118 patients with WT, who underwent contrast-enhanced computed tomography (CT) scans in our center between 2014 and 2021, were studied retrospectively and divided into two groups: stage I and non-stage I disease. Patients were randomly divided into training cohorts ( Results: The SVM model was fitted with 15 radiomic features obtained by Conclusions: The machine learning model of SVM based on radiomic features extracted from CT images accurately predicted WT stage I and non-stage I disease in pediatric patients preoperatively, which provided a rapid and non-invasive way for investigation of WT stages.
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