ArticleInsights into imaging2022
Enhanced CT-based radiomics predicts pathological complete response after neoadjuvant chemotherapy for advanced adenocarcinoma of the esophagogastric junction: a two-center study.
Article in Insights into imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 3 syntheses or guidelines pooled it, 17 citations in OpenAlex.
- Pooled it
- Radiomics and artificial intelligence-based prediction of tumor response in digestive system neoplasm: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Deep learning or radiomics based on CT for predicting the response of gastric cancer to neoadjuvant chemotherapy: a meta-analysis and systematic review.Frontiers in oncology · 2024Pooled it
- Neoadjuvant immunotherapy followed by surgery versus non-operative management in dMMR/MSI gastroesophageal adenocarcinomas: a case series and review of the literature.ESMO gastrointestinal oncology · 2026Article
- Noninvasive Characterization of Tumor Heterogeneity in HNSCC: From Clinical Utility to Biological Correlates.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The added value of intratumoral and peritumoral radiomic scores based on iodine density map for preoperative prediction of lymphovascular invasion in patients with gastric adenocarcinoma: a two-center study.Abdominal radiology (New York) · 2026Article
- Article
- CT-based radiomics nomogram to predict response of advanced adenocarcinoma of esophagogastric junction to neoadjuvant chemotherapy.Insights into imaging · 2025Article
- Automatic future remnant segmentation in liver resection planning.International journal of computer assisted radiology and surgery · 2025Article
- Predictive value of baseline CT imaging features combined with serum biomarkers for neoadjuvant chemotherapy response in adenocarcinoma of the gastroesophageal junction.American journal of cancer research · 2025Article
- IntegratingBMC cancer · 2024Article
- Intratumoral and peritumoral radiomics predict pathological response after neoadjuvant chemotherapy against advanced gastric cancer.Insights into imaging · 2024Article
- Predicting Extrathyroidal Extension in Papillary Thyroid Carcinoma Using a Clinical-Radiomics Nomogram Based on B-Mode and Contrast-Enhanced Ultrasound.Diagnostics (Basel, Switzerland) · 2023Article
- A Predictive Clinical-Radiomics Nomogram for Differentiating Tuberculous Spondylitis from Pyogenic Spondylitis Using CT and Clinical Risk Factors.Infection and drug resistance · 2022Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
purposeThis study aimed to develop and validate CT-based models to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for advanced adenocarcinoma of the esophagogastric junction (AEG).
methodsPre-NAC clinical and imaging data of AEG patients who underwent surgical resection after preoperative-NAC at two centers were retrospectively collected from November 2014 to September 2020. The dataset included training (n = 60) and external validation groups (n = 32). Three models, including CT-based radiomics, clinical and radiomics-clinical combined models, were established to differentiate pCR (tumor regression grade (TRG) = grade 0) and nonpCR (TRG = grade 1-3) patients. For the radiomics model, tumor-region-based radiomics features in the arterial and venous phases were extracted and selected. The naïve Bayes classifier was used to establish arterial- and venous-phase radiomics models. The selected candidate clinical factors were used to establish a clinical model, which was further incorporated into the radiomics-clinical combined model. ROC analysis, calibration and decision curves were used to assess the model performance.
resultsFor the radiomics model, the AUC values obtained using the venous data were higher than those obtained using the arterial data (training: 0.751 vs. 0.736; validation: 0.768 vs. 0.750). Borrmann typing, tumor thickness and degree of differentiation were utilized to establish the clinical model (AUC-training: 0.753; AUC-validation: 0.848). The combination of arterial- and venous-phase radiomics and clinical factors further improved the discriminatory performance of the model (AUC-training: 0.838; AUC-validation: 0.902). The decision curve reflects the higher net benefit of the combined model.
conclusionThe combination of CT imaging and clinical factors pre-NAC for advanced AEG could help stratify potential responsiveness to NAC.
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