ArticleQuantitative imaging in medicine and surgery2024
Value of dual-layer spectral detector CT in predicting lymph node metastasis of non-small cell lung cancer.
Article in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 5 citations in OpenAlex.
- Application and Development of Spectral CT in Target Delineation for Lung Cancer Radiotherapy.Diagnostics (Basel, Switzerland) · 2026Review
- Clinical Utility of Dual-Energy CT for Detection, Characterization, and Staging of Lung Tumors: A Rapid Review.Diagnostics (Basel, Switzerland) · 2026Review
- Dual-layer spectral detector computed tomography multiparameter machine learning model for prediction of lymph node metastases in esophageal squamous cell carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- The diagnostic efficacy of venous-phase spectral CT combined with Node-RADS for differentiating enlarged lymph nodes in common solid tumors: a preliminary exploratory study.Frontiers in oncology · 2026Article
- Mapping the research trends and hotspots on solid pulmonary nodules from 2000-2024: insights from bibliometric analysis.Journal of thoracic disease · 2025Article
- A novel computed tomography-based multi-parameter decision tree algorithm model for preoperatively predicting the risk of lymph node metastasis in surgically resectable synchronous multiple primary lung cancer.Quantitative imaging in medicine and surgery · 2025Article
- Utilizing spectral detector computed tomography quantitative parameters via multimodality tumor tracking to predict occult lymph node metastasis in clinical stage 1 pure solid lung adenocarcinoma.Journal of thoracic disease · 2025Article
- The preoperative prediction of lymph node metastasis of resectable pancreatic ductal adenocarcinoma using dual-layer spectral computed tomography.European radiology · 2025Article
- Dual-layer detector spectral CT-based machine learning models in the differential diagnosis of solitary pulmonary nodules.Scientific reports · 2024Article
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The accurate assessment of lymph node metastasis (LNM) is crucial for the staging, treatment, and prognosis of lung cancer. In this study, we explored the potential value of dual-layer spectral detector computed tomography (SDCT) quantitative parameters in the prediction of LNM in non-small cell lung cancer (NSCLC). Methods: In total, 91 patients presenting with solid solitary pulmonary nodules (8 mm < diameter ≤30 mm) with pathologically confirmed NSCLC (57 without LNM, and 34 with LNM) were enrolled in the study. The patients' basic clinical data and the SDCT morphological features were analyzed using the chi-square test or Fisher's exact test. The Mann-Whitney Results: There were no significant differences between the non-LNM and LNM groups in terms of age, sex, and smoking history. Lesion size and vascular convergence sign differed significantly between the two groups (P<0.05), but there were no significant differences in the six tumor markers. The SDCT quantitative parameters [SAR Conclusions: The SDCT quantitative parameters combined with the clinical data have potential value in predicting LNM in NSCLC. The size + λ + NIC combined parameter model could further improve the prediction efficacy of LNM.
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