ArticleBMC cancer2021
Predicting response to immunotherapy plus chemotherapy in patients with esophageal squamous cell carcinoma using non-invasive Radiomic biomarkers.
Article in BMC cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06579768 (Preoperative Differentiation of Jaw Cystic Lesions Based on Radiomics From Computed Tomography Images), which is not on this map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Preoperative Differentiation of Jaw Cystic Lesions Based on Radiomics From Computed Tomography Images: A Multicenter, Prospective Machine Learning Study
Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of radiomics-based artificial intelligence systems in the diagnosis and prediction of treatment response and survival in esophageal cancer: a systematic review and meta-analysis of diagnostic accuracy.Diseases of the esophagus : official journal of the International Society for Diseases of the Esophagus · 2023Pooled it
- Prognostic Value of the C-PLAN Index in Advanced Esophageal Squamous Cell Carcinoma Patients Treated With Immune Checkpoint Inhibitors.The Kaohsiung journal of medical sciences · 2026Article
- A computed tomography-based deep learning radiomics for predicting the response to neoadjuvant chemotherapy combined with immunotherapy in patients with locally advanced esophageal cancer: a multicenter cohort study.Journal of thoracic disease · 2025Article
- Article
- Review
- Sub-regional radiomics combining multichannel 2-dimensional or 3-dimensional deep learning for predicting neoadjuvant chemo-immunotherapy response in esophageal squamous cell carcinoma: a multicenter study.NPJ precision oncology · 2025Article
- Radiomics applications in the modern management of esophageal squamous cell carcinoma.Medical oncology (Northwood, London, England) · 2025Review
- Development and validation a radiomics combined clinical model predicts treatment response for esophageal squamous cell carcinoma patients.BMC gastroenterology · 2025Article
- Development and validation of a prediction model based on two-dimensional dose distribution maps fused with computed tomography images for noninvasive prediction of radiochemotherapy resistance in non-small cell lung cancer.Translational cancer research · 2025Article
- Current Role of Artificial Intelligence in the Management of Esophageal Cancer.Journal of clinical medicine · 2025Review
- CT radiomics to predict pathologic complete response after neoadjuvant immunotherapy plus chemoradiotherapy in locally advanced esophageal squamous cell carcinoma.European radiology · 2025Article
- 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
- Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver Metastases.Journal of the anus, rectum and colon · 2025Article
- Delta/pre-radiomics based on enhanced CT predicts complete response in locally advanced esophageal squamous cell carcinoma.American journal of translational research · 2025Article
- Prognostic factors in locally advanced oesophageal squamous cell carcinoma: a clinical and radiomic analysis of neoadjuvant immunochemotherapy before surgery.Frontiers in oncology · 2025Article
- Gastrointestinal tumor personalized immunotherapy: an integrated analysis from molecular genetics to imaging biomarkers.Therapeutic advances in gastroenterology · 2025Review
- Radiomic Model Associated with Tumor Microenvironment Predicts Immunotherapy Response and Prognosis in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma.Research (Washington, D.C.) · 2025Article
- Integration of Delta-Radiomics Features and Inflammatory Biomarkers for Neoadjuvant Chemo-Immunotherapy Efficacy in Locally Advanced Esophageal Squamous Cell Carcinoma.Journal of inflammation research · 2025Article
- CT-based delta-radiomics for predicting pathological response to neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma: a multicenter study.BMC medical imaging · 2024Article
- Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo develop and validate a radiomics model for evaluating treatment response to immune-checkpoint inhibitor plus chemotherapy (ICI + CT) in patients with advanced esophageal squamous cell carcinoma (ESCC).
methodsA total of 64 patients with advance ESCC receiving first-line ICI + CT at two centers between January 2019 and June 2020 were enrolled in this study. Both 2D ROIs and 3D ROIs were segmented. ComBat correction was applied to minimize the potential bias on the results due to different scan protocols. A total of 788 features were extracted and radiomics models were built on corrected/uncorrected 2D and 3D features by using 5-fold cross-validation. The performance of the radiomics models was assessed by its discrimination, calibration and clinical usefulness with independent validation.
resultsFive features and support vector machine algorithm were selected to build the 2D uncorrected, 2D corrected, 3D uncorrected and 3D corrected radiomics models. The 2D radiomics models significantly outperformed the 3D radiomics models in both primary and validation cohorts. When ComBat correction was used, the performance of 2D models was better (p = 0.0059) in the training cohort, and significantly better (p < 0.0001) in the validation cohort. The 2D corrected radiomics model yielded the optimal performance and was used to build the nomogram. The calibration curve of the radiomics model demonstrated good agreement between prediction and observation and the decision curve analysis confirmed the clinical utility.
conclusionsThe easy-to-use 2D corrected radiomics model could facilitate noninvasive preselection of ESCC patients who would benefit from ICI + CT.
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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.