ArticleBiomedicines2023
A Radiotherapy Dose Map-Guided Deep Learning Method for Predicting Pathological Complete Response in Esophageal Cancer Patients after Neoadjuvant Chemoradiotherapy Followed by Surgery.
Article in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 3 syntheses or guidelines pooled it, 16 citations in OpenAlex.
- Machine learning applications in predicting pharmacological treatment outcomes and responses in esophageal cancer.Frontiers in pharmacology · 2026Pooled it
- Pooled 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
- Trial
- Radiation dose and pathological complete response rate of neoadjuvant chemoradiotherapy for esophageal squamous cell carcinoma: a model-based dose-response meta-analysis.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026Article
- Response-adapted perioperative therapy for resectable esophageal cancer in the immunotherapy era: a narrative review.Journal of thoracic disease · 2026Review
- Development and validation of a radiomics-dosiomics model for predicting radiotherapy response in locally advanced, unresectable non-small cell lung cancer: a multi-center study.Scientific reports · 2026Article
- AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health.Journal of translational medicine · 2026Review
- Post-neoadjuvant Metabolic Response Assessed by ¹⁸F-FDG PET/CT Predicts Pathologic Complete Response and Survival in Esophageal Squamous Cell Carcinoma Treated with the CROSS Regimen.Journal of gastrointestinal cancer · 2026Article
- Development and validation of a nomogram using transaminase levels, cycle cost, and hair loss as predictive factors for the response to neoadjuvant chemotherapy in esophageal cancer.Journal of cardiothoracic surgery · 2026Article
- Improving prediction accuracy of radiation-induced temporal lobe injury in nasopharyngeal carcinoma using ADC-based deep learning and dosiomics.BMC cancer · 2026Article
- Investigations on the Prospects of Using Lactiplantibacillus plantarum to Combat Depression through Gut Microbiota-Brain Axis.Molecular neurobiology · 2025Review
- Multi-class brain tumor MRI segmentation and classification using deep learning and machine learning approaches.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- A CT-based radiomics nomogram incorporating adipose tissue to differentiate invasive adenocarcinomas among part-solid pulmonary nodules.BMC cancer · 2025Article
- Enhanced prognosis prediction model in esophageal cancer via lymph node assessment post-neoadjuvant.BMC cancer · 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
- Mesenchymal stem cell-derived exosomes in renal ischemia-reperfusion injury: a new therapeutic strategy.International urology and nephrology · 2025Review
- Article
- Non-invasive multimodal CT deep learning biomarker to predict pathological complete response of non-small cell lung cancer following neoadjuvant immunochemotherapy: a multicenter study.Journal for immunotherapy of cancer · 2024Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Esophageal cancer is a deadly disease, and neoadjuvant chemoradiotherapy can improve patient survival, particularly for patients achieving a pathological complete response (ypCR). However, existing imaging methods struggle to accurately predict ypCR. This study explores computer-aided detection methods, considering both imaging data and radiotherapy dose variations to enhance prediction accuracy. It involved patients with node-positive esophageal squamous cell carcinoma undergoing neoadjuvant chemoradiotherapy and surgery, with data collected from 2014 to 2017, randomly split into five subsets for 5-fold cross-validation. The algorithm DCRNet, an advanced version of OCRNet, integrates RT dose distribution into dose contextual representations (DCR), combining dose and pixel representation with ten soft regions. Among the 80 enrolled patients (mean age 55.68 years, primarily male, with stage III disease and middle-part lesions), the ypCR rate was 28.75%, showing no significant demographic or disease differences between the ypCR and non-ypCR groups. Among the three summarization methods, the maximum value across the CTV method produced the best results with an AUC of 0.928. The HRNetV2p model with DCR performed the best among the four backbone models tested, with an AUC of 0.928 (95% CI, 0.884-0.972) based on 5-fold cross-validation, showing significant improvement compared to other models. This underscores DCR-equipped models' superior AUC outcomes. The study highlights the potential of dose-guided deep learning in ypCR prediction, necessitating larger, multicenter studies to validate the results.
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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.