ArticleFrontiers in immunology2025
A preoperative predictive model based on multi-modal features to predict pathological complete response after neoadjuvant chemoimmunotherapy in esophageal cancer patients.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Research trends in neoadjuvant therapy for esophageal cancer: a bibliometric and meta-analysis.Frontiers in immunology · 2025Pooled it
- Intratumoral and peritumoral radiomics for preoperative prediction of pathological complete response to neoadjuvant immunochemotherapy in patients with esophageal squamous cell carcinoma.European journal of radiology open · 2026Article
- A neutrophil-to-lymphocyte ratio-centered machine learning model for predicting immunotherapy response in esophageal squamous cell carcinoma patients.Journal of thoracic disease · 2026Article
- Clinical Applications of Multimodal Artificial Intelligence in Otolaryngology: A State-of-the-Art Review.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026Review
- CT-based deep learning predicts immunotherapy response in esophageal squamous cell cancer patients.Journal of thoracic disease · 2026Article
- Machine learning applications in the detection and treatment of esophageal cancer.Discover oncology · 2026Review
- AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health.Journal of translational medicine · 2026Review
- Spatially defined danger zone shapes gastric cancer progression through CCDC80Apoptosis : an international journal on programmed cell death · 2026Article
- Interpretable multimodal radiopathomics model predicting pathological complete response to neoadjuvant chemoimmunotherapy in esophageal squamous cell carcinoma.Journal for immunotherapy of cancer · 2025Article
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Treatment decision support for esophageal cancer based on PET/CT data using deep learning.BMC medical informatics and decision making · 2025Article
- Artificial neural networks as a prognostic tool using hyperspectral imaging on pretherapeutic histopathological specimens of esophageal adenocarcinoma.Journal of cancer research and clinical oncology · 2025Article
- Multimodal artificial intelligence in medicine: a task-oriented framework for clinical translation.Frontiers in medicine · 2025Review
- Nomogram-based risk stratification to analyze the value of receiving postoperative adjuvant therapy after neoadjuvant immunochemotherapy for patients with locally advanced esophageal squamous carcinoma.Frontiers in immunology · 2025Article
- Artificial intelligence-assisted noninvasive preoperative prediction of lymph nodes metastasis in cervical cancer through a clinical-imaging feature combined strategy.Frontiers in oncology · 2025Article
- 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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6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study aimed to develop a multi-modality model by incorporating pretreatment computed tomography (CT) radiomics and pathomics features along with clinical variables to predict pathologic complete response (pCR) to neoadjuvant chemoimmunotherapy in patients with locally advanced esophageal cancer (EC). Method: A total of 223 EC patients who underwent neoadjuvant chemoimmunotherapy followed by surgical intervention between August 2021 and December 2023 were included in this study. Radiomics features were extracted from contrast-enhanced CT images using PyrRadiomics, while pathomics features were derived from whole-slide images (WSIs) of pathological specimens using a fine-tuned deep learning model (ResNet-50). After feature selection, three single-modality prediction models and a combined multi-modality model integrating two radiomics features, 11 pathomics features, and two clinicopathological features were constructed using the support vector machine (SVM) algorithm. The performance of the models were evaluated using receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analysis (DCA). Shapley values were also utilized to explain the prediction model. Results: The predictive capability of the multi-modality model in predicting pCR yielded an area under the curve (AUC) of 0.89 (95% confidence interval [CI], 0.75-1.00), outperforming the radiomics model (AUC 0.70 [95% CI 0.54-0.85]), pathomics model (AUC 0.77 [95% CI 0.53-1.00]), and clinical model (AUC 0.63 [95% CI 0.46-0.80]). Additionally, both the calibration plot and DCA curves support the clinical utility of the integrated multi-modality model. Conclusions: The combined multi-modality model we propose can better predict the pCR status of esophageal cancer and help inform clinical treatment decisions.
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