ArticleJournal of oncology2022
Radiomics Analysis of Lymph Nodes with Esophageal Squamous Cell Carcinoma Based on Deep Learning.
Article in Journal of oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 11 citations in OpenAlex.
- Radiomics diagnostic performance for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis.BMC medical imaging · 2024Pooled it
- CT-based radiomics for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Integration of CT radiomics and machine learning for preoperative T staging of esophageal squamous cell carcinoma.Journal of thoracic disease · 2026Article
- Reproducible meningioma grading across multi-center MRI protocols via hybrid radiomic and deep learning features.Neuroradiology · 2025Article
- Deep learning-based prediction of enhanced CT scans for lymph node metastasis in esophageal squamous cell carcinoma.Japanese journal of radiology · 2025Article
- Unraveling the power of radiomics: prediction and exploration of lymph node metastasis in stage T1/2 esophageal squamous cell carcinoma.NPJ precision oncology · 2025Article
- Future prospects of deep learning in esophageal cancer diagnosis and clinical decision support (Review).Oncology letters · 2025Review
- A novel framework for esophageal cancer grading: combining CT imaging, radiomics, reproducibility, and deep learning insights.BMC gastroenterology · 2025Article
- Tumor-lymph cross-plane projection reveals spatial relationship features: a ResNet-CBAM model for prognostic prediction in esophageal cancer.Frontiers in oncology · 2025Article
- Development and Validation of PET/CT-Based Nomogram for Preoperative Prediction of Lymph Node Status in Esophageal Squamous Cell Carcinoma.Annals of surgical oncology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To assess the role of multiple radiomic features of lymph nodes in the preoperative prediction of lymph node metastasis (LNM) in patients with esophageal squamous cell carcinoma (ESCC). Methods: Three hundred eight patients with pathologically confirmed ESCC were retrospectively enrolled (training cohort, Results: No significant differences were observed between cohorts. Ten handcrafted and 12 deep radiomic features were selected from the extracted features ( Conclusion: The combination of handcrafted radiomic features, deep radiomic features, and clinical features could be used clinically to assess lymph node status in patients with ESCC.
Identifiers
What OpenQuestion holds
Registered trials
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.