ArticleCancer imaging : the official publication of the International Cancer Imaging Society2020
Contrast-enhanced CT radiomics for predicting lymph node metastasis in pancreatic ductal adenocarcinoma: a pilot study.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it, 68 citations in OpenAlex.
- Predictive role of radiomics features extracted from preoperative cross-sectional imaging of pancreatic ductal adenocarcinoma in detecting lymph node metastasis: a systemic review and meta-analysis.Abdominal radiology (New York) · 2023Pooled it
- Pancreas image mining: a systematic review of radiomics.European radiology · 2021Pooled it
- Federated CT foundation models for multi-center detection of lymph node metastasis in pancreatic cancer.Scientific reports · 2026Article
- Dual-layer spectral detector CT: a non-invasive tool for simultaneously predicting Ki-67 proliferation status and p53 mutations in pancreatic ductal adenocarcinoma.Abdominal radiology (New York) · 2026Article
- Magnetic resonance imaging radiomics-based machine learning model for preoperative prediction of pathological grade in pancreatic cancer: A preliminary study.European journal of radiology open · 2025Article
- A radiomics-based model for predicting lymph nodes metastasis of pancreatic ductal adenocarcinoma: a multicenter study.Insights into imaging · 2025Article
- The preoperative prediction of lymph node metastasis of resectable pancreatic ductal adenocarcinoma using dual-layer spectral computed tomography.European radiology · 2025Article
- Personalized Medical Approach in Gastrointestinal Surgical Oncology: Current Trends and Future Perspectives.Journal of personalized medicine · 2025Review
- A CT-based radiomics nomogram for the preoperative prediction of perineural invasion in pancreatic ductal adenocarcinoma.Frontiers in oncology · 2025Article
- Delta-Radiomics Approach Using Contrast-Enhanced and Noncontrast-Enhanced Computed Tomography Images for Predicting Distant Metastasis in Patients With Borderline Resectable Pancreatic Carcinoma.Advances in radiation oncology · 2025Article
- Predicting and Monitoring Immune Checkpoint Inhibitor Therapy Using Artificial Intelligence in Pancreatic Cancer.International journal of molecular sciences · 2024Review
- Dendritic cell vaccination combined with irreversible electroporation for treating pancreatic cancer-a narrative review.Annals of translational medicine · 2024Review
- Review
- Dual-energy computed tomography in a multiparametric regression model for diagnosing lymph node metastases in pancreatic ductal adenocarcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Review
- Contrast-enhanced CT radiomics combined with multiple machine learning algorithms for preoperative identification of lymph node metastasis in pancreatic ductal adenocarcinoma.Frontiers in oncology · 2024Article
- The Utility of Artificial Intelligence in the Diagnosis and Management of Pancreatic Cancer.Cureus · 2023Review
- [Advanced Faster RCNN: a non-contrast CT-based algorithm for detecting pancreatic lesions in multiple disease stages].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2023Article
- CT-measured body composition radiomics predict lymph node metastasis in localized pancreatic ductal adenocarcinoma.Discover oncology · 2023Article
- Artificial Intelligence Outperforms Radiologists for Pancreatic Cancer Lymph Node Metastasis Prediction at CT.Radiology · 2023Article
Corrections and comments
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Authors and funding
16 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundWe developed a computational model integrating clinical data and imaging features extracted from contrast-enhanced computed tomography (CECT) images, to predict lymph node (LN) metastasis in patients with pancreatic ductal adenocarcinoma (PDAC).
methodsThis retrospective study included 159 patients with PDAC (118 in the primary cohort and 41 in the validation cohort) who underwent preoperative contrast-enhanced computed tomography examination between 2012 and 2015. All patients underwent surgery and lymph node status was determined. A total of 2041 radiomics features were extracted from venous phase images in the primary cohort, and optimal features were extracted to construct a radiomics signature. A combined prediction model was built by incorporating the radiomics signature and clinical characteristics selected by using multivariable logistic regression. Clinical prediction models were generated and used to evaluate both cohorts.
resultsFifteen features were selected for constructing the radiomics signature based on the primary cohort. The combined prediction model for identifying preoperative lymph node metastasis reached a better discrimination power than the clinical prediction model, with an area under the curve of 0.944 vs. 0.666 in the primary cohort, and 0.912 vs. 0.713 in the validation cohort.
conclusionsThis pilot study demonstrated that a noninvasive radiomics signature extracted from contrast-enhanced computed tomography imaging can be conveniently used for preoperative prediction of lymph node metastasis in patients with PDAC.
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