ArticleJournal of healthcare engineering2022
A Deep Learning Radiomics Analysis for Survival Prediction in Esophageal Cancer.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of radiomics-based artificial intelligence systems in the diagnosis and prediction of treatment response and survival in esophageal cancer: a systematic review and meta-analysis of diagnostic accuracy.Diseases of the esophagus : official journal of the International Society for Diseases of the Esophagus · 2023Pooled it
- AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health.Journal of translational medicine · 2026Review
- DentoMorph-LDMs: diffusion models based on novel adaptive 8-connected gum tissue and deciduous teeth loss for dental image augmentation.Scientific reports · 2025Article
- Advanced feature fusion of radiomics and deep learning for accurate detection of wrist fractures on X-ray images.BMC musculoskeletal disorders · 2025Article
- A novel framework for esophageal cancer grading: combining CT imaging, radiomics, reproducibility, and deep learning insights.BMC gastroenterology · 2025Article
- Current Role of Artificial Intelligence in the Management of Esophageal Cancer.Journal of clinical medicine · 2025Review
- Deep Learning Radiomics for Survival Prediction in Non-Small-Cell Lung Cancer Patients from CT Images.Journal of medical systems · 2025Article
- Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation.Frontiers in digital health · 2025Review
- Tumor-lymph cross-plane projection reveals spatial relationship features: a ResNet-CBAM model for prognostic prediction in esophageal cancer.Frontiers in oncology · 2025Article
- Development of a flexible feature selection framework in radiomics-based prediction modeling: Assessment with four real-world datasets.Scientific reports · 2024Article
- Deep Learning for Image Analysis in the Diagnosis and Management of Esophageal Cancer.Cancers · 2024Review
- Automatic segmentation of esophageal cancer, metastatic lymph nodes and their adjacent structures in CTA images based on the UperNet Swin network.Cancer medicine · 2024Article
- Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers.Frontiers in artificial intelligence · 2024Review
- A semi-automatic deep learning model based on biparametric MRI scanning strategy to predict bone metastases in newly diagnosed prostate cancer patients.Frontiers in oncology · 2024Article
- Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling.Military Medical Research · 2023Review
- The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review.Health data science · 2023Review
- Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview.World journal of gastroenterology · 2022Review
- Ultrasound Radiomics Nomogram to Diagnose Sub-Centimeter Thyroid Nodules Based on ACR TI-RADS.Cancers · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this study was to explore the deep learning radiomics (DLR) nomogram to predict the overall 3-year survival after chemoradiotherapy in patients with esophageal cancer. The 154 patients' data were used in this study, which was randomly split into training (116) and validation (38) data. Deep learning and handcrafted features were obtained via the preprocessing diagnostic computed tomography images. The selected features were used to construct radiomics signatures through the least absolute shrinkage and selection operator (LASSO) regression, maximizing relevance while minimizing redundancy. The DLR signature, handcrafted features' radiomics (HCR) signature, and clinical factors were incorporated to develop a DLR nomogram. The DLR nomogram was evaluated in terms of discrimination and calibration with comparison to the HCR signature-based radiomics model. The experimental results showed the outperforming discrimination ability of the proposed DLR over the HCR model in terms of Harrel's concordance index, 0.76 and 0.784, for training and validation sets, respectively. Also, the proposed DLR nomogram calibrates and classifies better than the HCR model in terms of AUC, 0.984 (vs. 0.797) and 0.942 (vs. 0.665) for training and validation sets, respectively. Furthermore, the nomogram-predicted Kaplan-Meier survival (KMS) curves differed significantly from the nonsurvival groups in the log-rank test (
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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.