ArticleEuropean journal of nuclear medicine and molecular imaging2022
Deep learning radiomics of dual-energy computed tomography for predicting lymph node metastases of pancreatic ductal adenocarcinoma.
Article in European journal of nuclear medicine and molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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42 citing papers in PubMed, 71 citations in OpenAlex.
- Imaging Biomarkers in Radiotherapy.Cancers · 2026Review
- Multi-DECT image-based radiomics with interpretable machine learning for preoperative prediction of tumor budding grade and prognosis in colorectal cancer: a dual-center study.Abdominal radiology (New York) · 2026Article
- Radiomics analysis of dual-energy CT-derived iodine maps for predicting histopathologic grading of pancreatic ductal adenocarcinoma: a two-center study.Frontiers in medicine · 2026Article
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- Lymphatic metastasis in pancreatic cancer: from bedside to bench and back.Cancer metastasis reviews · 2025Review
- The preoperative prediction of lymph node metastasis of resectable pancreatic ductal adenocarcinoma using dual-layer spectral computed tomography.European radiology · 2025Article
- A deep learning model based on self-supervised learning for identifying subtypes of proliferative hepatocellular carcinoma from dynamic contrast-enhanced MRI.Insights into imaging · 2025Article
- Radiomics analysis of dual-layer detector spectral CT-derived iodine maps for predicting Ki-67 PI in pancreatic ductal adenocarcinoma.BMC medical imaging · 2025Article
- Preoperative assessment in lymph node metastasis of pancreatic ductal adenocarcinoma: a transformer model based on dual-energy CT.World journal of surgical oncology · 2025Article
- Contrast-enhanced MRI-based intratumoral heterogeneity assessment for predicting lymph node metastasis in resectable pancreatic ductal adenocarcinoma.Insights into imaging · 2025Article
- Applications of Artificial Intelligence for Metastatic Gastrointestinal Cancer: A Systematic Literature Review.Cancers · 2025Review
- The clinical application of artificial intelligence in cancer precision treatment.Journal of translational medicine · 2025Review
- Construction of feature selection and efficacy prediction model for transformation therapy of locally advanced pancreatic cancer based on CT,Cancer cell international · 2025Article
- A multimodal deep learning radiomics model for predicting degenerative meniscus tear after arthroscopy.PloS one · 2025Article
- A CT-based deep learning model to predict local recurrence-free survival in primary retroperitoneal sarcoma.Frontiers in medicine · 2025Article
- One novel transfer learning-based CLIP model combined with self-attention mechanism for differentiating the tumor-stroma ratio in pancreatic ductal adenocarcinoma.La Radiologia medica · 2024Article
- Dual-Energy Computed Tomography in Urological Diseases: A Narrative Review.Journal of clinical medicine · 2024Review
- A clinical-radiomics nomogram based on dual-layer spectral detector CT to predict cancer stage in pancreatic ductal adenocarcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Development of a CT radiomics nomogram for preoperative prediction of Ki-67 index in pancreatic ductal adenocarcinoma: a two-center retrospective study.European radiology · 2024Article
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Authors and funding
14 authors at 5 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeDiagnosis of lymph node metastasis (LNM) is critical for patients with pancreatic ductal adenocarcinoma (PDAC). We aimed to build deep learning radiomics (DLR) models of dual-energy computed tomography (DECT) to classify LNM status of PDAC and to stratify the overall survival before treatment.
methodsFrom August 2016 to October 2020, 148 PDAC patients underwent regional lymph node dissection and scanned preoperatively DECT were enrolled. The virtual monoenergetic image at 40 keV was reconstructed from 100 and 150 keV of DECT. By setting January 1, 2021, as the cut-off date, 113 patients were assigned into the primary set, and 35 were in the test set. DLR models using VMI 40 keV, 100 keV, 150 keV, and 100 + 150 keV images were developed and compared. The best model was integrated with key clinical features selected by multivariate Cox regression analysis to achieve the most accurate prediction.
resultsDLR based on 100 + 150 keV DECT yields the best performance in predicting LNM status with the AUC of 0.87 (95% confidence interval [CI]: 0.85-0.89) in the test cohort. After integrating key clinical features (CT-reported T stage, LN status, glutamyl transpeptadase, and glucose), the AUC was improved to 0.92 (95% CI: 0.91-0.94). Patients at high risk of LNM portended significantly worse overall survival than those at low risk after surgery (P = 0.012).
conclusionsThe DLR model showed outstanding performance for predicting LNM in PADC and hold promise of improving clinical decision-making.
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