ArticleDiagnostics (Basel, Switzerland)2022
Prediction of Non-Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer Patients with
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning applications in predicting pharmacological treatment outcomes and responses in esophageal cancer.Frontiers in pharmacology · 2026Pooled it
- Radiomics: Current Applications and Future Directions.MedComm · 2026Review
- Building an interpretable machine learning prognosis prediction model-based on baseline examinations of patients with esophageal cancer undergoing surgery.Frontiers in oncology · 2026Article
- Application of peritumoral radiomics based on simulated positioning CT images in the prognosis of intermediate-advanced esophageal cancer.Scientific reports · 2025Article
- Review
- The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review.Health data science · 2023Review
- The application of radiomics in esophageal cancer: Predicting the response after neoadjuvant therapy.Frontiers in oncology · 2023Review
- Role of radiomics in the diagnosis and treatment of gastrointestinal cancer.World journal of gastroenterology · 2022Review
- Could [Diagnostics (Basel, Switzerland) · 2022Article
- Frontiers and hotspots ofFrontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundApproximately 26% of esophageal cancer (EC) patients do not respond to neoadjuvant chemoradiotherapy (nCRT), emphasizing the need for pre-treatment selection. The aim of this study was to predict non-response using a radiomic model on baseline
methodsRetrospectively, 143
resultsThe best performing model had an AP (mean ± SD) of 0.47 ± 0.06 on the training subset, achieved by a support vector machine classifier trained on five principal components of relevant clinical and radiomic features. The model was externally validated with an AP of 0.66 and an AUC of 0.67.
conclusionIn the present study, the best-performing model on pre-treatment
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Registered trials
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