ArticleFrontiers in immunology2024
A machine learning approach using
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Machine learning applications in predicting pharmacological treatment outcomes and responses in esophageal cancer.Frontiers in pharmacology · 2026Pooled it
- Machine learning applications in the detection and treatment of esophageal cancer.Discover oncology · 2026Review
- AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health.Journal of translational medicine · 2026Review
- Article
- Integration of habitat radiomics and traditional radiomic features for predicting pathological complete response in esophageal squamous cell carcinoma following neoadjuvant immunotherapy and chemotherapy: a multicenter comparative study.Journal of translational medicine · 2026Article
- Genomic alterations and dynamic molecular residual disease monitoring predict pathological response to neoadjuvant chemoimmunotherapy in esophageal squamous cell carcinoma.Frontiers in immunology · 2026Article
- Intratumoral and peritumoral heterogeneity based on CT to predict the pathological response after neoadjuvant chemoimmunotherapy in esophageal squamous cell carcinoma.International journal of surgery (London, England) · 2026Article
- Multimodal CT and MRI Radiomics Integrated with Clinical Models Predict Pathological Complete Response in ESCC Following Neoadjuvant Immunochemotherapy.Tomography (Ann Arbor, Mich.) · 2025Article
- Radiomics applications in the modern management of esophageal squamous cell carcinoma.Medical oncology (Northwood, London, England) · 2025Review
- Multi-modal radiomics features to predict overall survival of locally advanced esophageal cancer after definitive chemoradiotherapy.BMC cancer · 2025Article
- Current Role of Artificial Intelligence in the Management of Esophageal Cancer.Journal of clinical medicine · 2025Review
- Gastrointestinal tumor personalized immunotherapy: an integrated analysis from molecular genetics to imaging biomarkers.Therapeutic advances in gastroenterology · 2025Review
- CT-based habitat radiomics for predicting treatment response to neoadjuvant chemoimmunotherapy in esophageal cancer patients.Frontiers in oncology · 2024Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: We aim to evaluate the value of an integrated multimodal radiomics with machine learning model to predict the pathological complete response (pCR) of primary tumor in a prospective cohort of esophageal squamous cell carcinoma (ESCC) treated with neoadjuvant chemoradiotherapy (nCRT) and anti-PD-1 inhibitors. Materials and methods: Clinical information of 126 ESCC patients were included for analysis. Radiomics features were extracted from Results: A total of 842 features were extracted. Among the four machine learning algorithms, SVM achieved the most promising performance on the test set for PET(AUC:0.775), CT (AUC:0.710) and clinical model (AUC:0.722). For all combinations of various modalities-based models, the combination model of Conclusion: Machine learning models utilizing radiomics features from
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.