ArticleCancer imaging : the official publication of the International Cancer Imaging Society2024
MR radiomics predicts pathological complete response of esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy: a multicenter study.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07181850 (Integration of Clinical, Radiomics, and 2.5D Deep Learning-Based Multiple Instance Learning Features for Predicting Pathological Complete Response in Esophageal Squamous Cell Carcinoma Following Neoadjuvant Immunotherapy and Chemotherapy), which is not on this map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Integration of Clinical, Radiomics, and 2.5D Deep Learning-Based Multiple Instance Learning Features for Predicting Pathological Complete Response in Esophageal Squamous Cell Carcinoma Following Neoadjuvant Immunotherapy and Chemotherapy: A Multicenter Comparative Study
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Machine learning applications in predicting pharmacological treatment outcomes and responses in esophageal cancer.Frontiers in pharmacology · 2026Pooled it
- Prognostic value of IVIM-DWI parameters for overall and recurrence-free survival in resected esophageal squamous cell carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Article
- The geriatric nutritional risk index predicts pathological complete response and anastomotic leakage in esophageal cancer patients undergoing neoadjuvant therapy.Journal of thoracic disease · 2026Article
- Article
- Unlocking the potential of radiomics in predicting the response of neoadjuvant immunochemotherapy for operable locally advanced esophageal squamous cell carcinoma: a narrative review.Journal of thoracic disease · 2026Review
- 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
- Category-ComBat with distribution coefficient: a scale-adaptive harmonization algorithm for multicenter radiomic feature standardization.Frontiers in oncology · 2026Article
- Multimodal CT and MRI Radiomics Integrated with Clinical Models Predict Pathological Complete Response in ESCC Following Neoadjuvant Immunochemotherapy.Tomography (Ann Arbor, Mich.) · 2025Article
- Predicting pathological complete response to chemoradiotherapy using artificial intelligence-based magnetic resonance imaging radiomics in esophageal squamous cell carcinoma.World journal of gastroenterology · 2025Article
- Radiomics applications in the modern management of esophageal squamous cell carcinoma.Medical oncology (Northwood, London, England) · 2025Review
- A preoperative predictive model based on multi-modal features to predict pathological complete response after neoadjuvant chemoimmunotherapy in esophageal cancer patients.Frontiers in immunology · 2025Article
- Organ-Sparing Approach after Neoadjuvant Treatment in Oesophageal Cancer.Digestive surgery · 2025Review
- Sexual Dimorphism of Radiomic Features in the Brain: An Exploratory Study Using 700 μm MP2RAGE MRI at 7 T.Investigative radiology · 2024Article
- Review
- Dynamic radiological features predict pathological response after neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma.Journal of translational medicine · 2024Article
- Preoperative CT-based radiomic prognostic index to predict the benefit of postoperative radiotherapy in patients with non-small cell lung cancer: a multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- CT-based habitat radiomics for predicting treatment response to neoadjuvant chemoimmunotherapy in esophageal cancer patients.Frontiers in oncology · 2024Article
- Prediction of Neoadjuvant Chemoradiotherapy Sensitivity in Patients With Esophageal Squamous Cell Carcinoma Using CT-Based Radiomics Combined With Clinical Features.Dose-response : a publication of International Hormesis SocietyArticle
- Predicting the Efficacy of Neoadjuvant Chemotherapy Combined with Immunotherapy for Esophageal Squamous Cell Carcinoma via Enhanced CT Radiomics Combined with Clinical Features.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
16 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundMore than 40% of patients with resectable esophageal squamous cell cancer (ESCC) achieve pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT), who have favorable prognosis and may benefit from an organ-preservation strategy. Our study aims to develop and validate a machine learning model based on MR radiomics to accurately predict the pCR of ESCC patients after nCRT.
methodsIn this retrospective multicenter study, eligible patients with ESCC who underwent baseline MR (T2-weighted imaging) and nCRT plus surgery were enrolled between September 2014 and September 2022 at institution 1 (training set) and between December 2017 and August 2021 at institution 2 (testing set). Models were constructed using machine learning algorithms based on clinical factors and MR radiomics to predict pCR after nCRT. The area under the curve (AUC) and cutoff analysis were used to evaluate model performance.
resultsA total of 155 patients were enrolled in this study, 82 in the training set and 73 in the testing set. The radiomics model was constructed based on two radiomics features, achieving AUCs of 0.968 (95%CI 0.933-0.992) in the training set and 0.885 (95%CI 0.800-0.958) in the testing set. The cutoff analysis resulted in an accuracy of 82.2% (95%CI 72.6-90.4%), a sensitivity of 75.0% (95%CI 58.3-91.7%), and a specificity of 85.7% (95%CI 75.5-96.0%) in the testing set.
conclusionA machine learning model based on MR radiomics was developed and validated to accurately predict pCR after nCRT in patients with ESCC.
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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.