ArticleJournal of radiation research2019
CT-based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy.
Article in Journal of radiation research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 2 of them syntheses that pooled it.
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Who cites it
52 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy.European journal of nuclear medicine and molecular imaging · 2022Pooled it
- A Meta-Analysis for Using Radiomics to Predict Complete Pathological Response in Esophageal Cancer Patients Receiving Neoadjuvant Chemoradiation.In vivo (Athens, Greece)Pooled it
- Multivariable Radiomics Model for Predicting Programmed Death-Ligand 1 Expression After Neoadjuvant Chemoradiotherapy in Esophageal Squamous Cell Carcinoma.Cancer medicine · 2026Article
- CT-based deep learning predicts immunotherapy response in esophageal squamous cell cancer patients.Journal of thoracic disease · 2026Article
- CT imaging is a feasible method for the preoperative prediction of the main pathologic response after neoadjuvant therapy for locally advanced esophageal squamous cell carcinoma.Updates in surgery · 2026Article
- Predicting esophageal cancer response to neoadjuvant therapy with magnetic resonance imaging radiomics.World journal of gastrointestinal oncology · 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
- CT-based radiomics for prediction of response to neoadjuvant immunochemotherapy in patients with esophageal carcinoma.Frontiers in oncology · 2025Article
- Neoadjuvant therapy-associated malignant phenotype score predicts prognosis and highlights the roles of MIF signaling and DUXAP8 in ESCC.Frontiers in immunology · 2025Article
- Preoperative inflammatory burden index for prognostication in esophageal squamous cell carcinoma undergoing radical resection.Scientific reports · 2024Article
- Multimodality deep learning radiomics predicts pathological response after neoadjuvant chemoradiotherapy for esophageal squamous cell carcinoma.Insights into 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
- Article
- Radiomics and dosiomics for predicting complete response to definitive chemoradiotherapy patients with oesophageal squamous cell cancer using the hybrid institution model.European radiology · 2024Article
- A novel CT-based radiomics model for predicting response and prognosis of chemoradiotherapy in esophageal squamous cell carcinoma.Scientific reports · 2024Article
- MR radiomics predicts pathological complete response of esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy: a multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- A machine learning radiomics based on enhanced computed tomography to predict neoadjuvant immunotherapy for resectable esophageal squamous cell carcinoma.Frontiers in immunology · 2024Article
- The MRI radiomics signature can predict the pathologic response to neoadjuvant chemotherapy in locally advanced esophageal squamous cell carcinoma.European radiology · 2024Article
- Radiomics nomogram combined with clinical factors for predicting pathological complete response in resectable esophageal squamous cell carcinoma.Frontiers in oncology · 2024Article
- Prediction of pCR based on clinical-radiomic model in patients with locally advanced ESCC treated with neoadjuvant immunotherapy plus chemoradiotherapy.Frontiers in oncology · 2024Article
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8 authors.
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Abstract
The objective of this study was to build models to predict complete pathologic response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC) patients using radiomic features. A total of 55 consecutive patients pathologically diagnosed as having ESCC were included in this study. Patients were divided into a training cohort (44 patients) and a testing cohort (11 patients). The logistic regression analysis using likelihood ratio forward selection was performed to select the predictive clinical parameters for pCR, and the least absolute shrinkage and selection operator (LASSO) with logistic regression to select radiomic predictors in the training cohort. Model performance in the training and testing groups was evaluated using the area under the receiver operating characteristic curves (AUC). The multivariate logistic regression analysis identified no clinical predictors for pCR. Thus, only radiomic features selected by LASSO were used to build prediction models. Three logistic regression models for pCR prediction were developed in the training cohort, and they were able to predict pCR well in both the training (AUC, 0.84-0.86) and the testing cohorts (AUC, 0.71-0.79). There were no differences between these AUCs. We developed three predictive models for pCR after nCRT using radiomic parameters and they demonstrated good model performance.
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