ArticleWorld journal of gastrointestinal oncology2025
Predicting esophageal cancer response to neoadjuvant therapy with magnetic resonance imaging radiomics.
Article in World journal of gastrointestinal oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Machine learning applications in the detection and treatment of esophageal cancer.Discover oncology · 2026Review
- Radiomics-based model for predicting neoadjuvant therapy response in esophageal cancer: Limitations and suggestions.World journal of gastrointestinal oncology · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
backgroundPredicting the pathological response of esophageal cancer (EC) to neoadjuvant therapy (NAT) is of significant clinical importance.
aimTo evaluate the pathological response of NAT in EC patients using multiple machine learning algorithms based on magnetic resonance imaging (MRI) radiomics.
methodsThis retrospective study included 132 patients with pathologically confirmed EC, were randomly divided into a training cohort (
resultsA total of 1834 features were extracted. Following feature dimension reduction, ten radiomics features were selected to construct radiomics signatures. Among the nine classification algorithms, the ExtraTrees algorithm demonstrated the best diagnostic performance in both the training (AUC: 0.932; SEN: 0.906; SPE: 0.817) and validation cohorts (AUC: 0.900; SEN: 0.667; SPE: 0.700). The Delong test proved no significance in the diagnostic efficiency within these models (
conclusionT2WI radiomics may aid in determining the pathological response to NAT in EC patients, serving as a noninvasive and quantitative tool to assist personalized treatment planning.
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