ArticleOncoTargets and therapy2020
Pretreatment CT-Based Radiomics Signature as a Potential Imaging Biomarker for Predicting the Expression of PD-L1 and CD8+TILs in ESCC.
Article in OncoTargets and therapy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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36 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Prediction of PD-L1 expression in nonsmall cell lung cancer using artificial intelligence models based on radiomics: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Radiomics nomogram for preoperative differentiation of pulmonary mucinous adenocarcinoma from tuberculoma in solitary pulmonary solid nodules.BMC cancer · 2023Trial
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- Multivariable Radiomics Model for Predicting Programmed Death-Ligand 1 Expression After Neoadjuvant Chemoradiotherapy in Esophageal Squamous Cell Carcinoma.Cancer medicine · 2026Article
- AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health.Journal of translational medicine · 2026Review
- Deep learning and radiomics models in patients with advanced non-small cell lung cancer treated with immunotherapy combined with stereotactic radiotherapy.Scientific reports · 2026Article
- Deep learning-based non-invasive prediction of PD-L1 status and immunotherapy survival stratification in esophageal cancer using [European journal of nuclear medicine and molecular imaging · 2026Article
- Radiomics and prognostic nutritional index for predicting postoperative survival in esophageal carcinoma.European journal of medical research · 2025Article
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- A CT-based interpretable deep learning signature for predicting PD-L1 expression in bladder cancer: a two-center study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Molecular markers for the efficacy of neoadjuvant immunotherapy for head and neck squamous cell carcinoma.Frontiers in oncology · 2025Review
- Delta/pre-radiomics based on enhanced CT predicts complete response in locally advanced esophageal squamous cell carcinoma.American journal of translational research · 2025Article
- Multi-sequence MRI based radiomics nomogram for prediction expression of programmed death ligand 1 in thymic epithelial tumor.Frontiers in immunology · 2025Article
- Predicting response to immunotherapy in oral squamous cell carcinoma via a CT-based radiomics model.BMC medical imaging · 2024Article
- CT-based deep learning radiomics biomarker for programmed cell death ligand 1 expression in non-small cell lung cancer.BMC medical imaging · 2024Article
- CT-based delta-radiomics nomogram to predict pathological complete response after neoadjuvant chemoradiotherapy in esophageal squamous cell carcinoma patients.Journal of translational medicine · 2024Article
- The value of CT shape quantification in predicting pathological classification of lung adenocarcinoma.BMC cancer · 2024Article
- Cancer Immunotherapy and Medical Imaging Research Trends from 2003 to 2023: A Bibliometric Analysis.Journal of multidisciplinary healthcare · 2024Review
- A computed tomography-based nomogram for neoadjuvant chemotherapy plus immunotherapy response prediction in patients with advanced esophageal squamous cell carcinoma.Frontiers in oncology · 2024Article
- Biomarkers for Early Detection, Prognosis, and Therapeutics of Esophageal Cancers.International journal of molecular sciences · 2023Review
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe present study constructed and validated models to predict PD-L1 and CD8+TILs expression levels in esophageal squamous cell carcinoma (ESCC) patients using radiomics features and clinical factors. PATIENTS AND
methodsThis retrospective study randomly assigned 220 ESCC patients to a discovery dataset (n= 160) and validation dataset (n= 60). A total of 462 radiomics features were extracted from the segmentation of regions of interest (ROIs) based on pretreatment CT images of each patient. The LASSO algorithm was applied to reduce the dimensionality of the data and select features. A multivariable logistic regression analysis was adopted to build radiomics signatures. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive accuracy of these models.
resultsThere was no significant difference between the training and validation datasets for any clinical factors in patients with ESCC. The PD-L1 expression level correlated with the differentiation degree (
conclusionCT-based radiomics features have a potential value for classifying patients according to PD-L1 and CD8+TILs expression levels. The combination of clinical factors and radiomics signatures significantly improved the predictive performance in ESCC.
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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.