ArticleWorld journal of radiology2025
Developing and validating a computed tomography radiomics strategy to predict lymph node metastasis in pancreatic cancer.
Article in World journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
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Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic value of the systemic immune-inflammation index in gastrointestinal cancers treated with immune checkpoint inhibitors: a systematic review and meta-analysis.BMC gastroenterology · 2026Pooled it
- Clinical and imaging differences between pancreatic carcinoma in situ and T1a/T1b pancreatic ductal adenocarcinoma.Abdominal radiology (New York) · 2026Article
- Detection of Pancreatic Cancer via Specific Metabolite Markers: A Metabolomics Approach.Health science reports · 2026Article
- Artificial intelligence-enabled MRI surveillance of prostate cancer: integrating PRECISE v2, PI-QUAL, and longitudinal biomarkers-a narrative review.Abdominal radiology (New York) · 2026Review
- Radiomics using contrast-enhanced T1-weighted imaging and clinical features for predicting response to EGFR-TKIs in EGFR-mutated non-small cell lung cancer patients with brain metastases.BMC medical imaging · 2026Article
- Multimodal AI for early prediction of adverse clinical outcomes in acute pancreatitis.Abdominal radiology (New York) · 2026Article
- Assessing Textbook Oncologic Outcomes in Distal Pancreatectomy for Pancreatic Adenocarcinoma: A National Cancer Database Study.Journal of clinical medicine · 2026Article
- Parametric mapping of dynamicCancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Preoperative prediction of perineural invasion in pancreatic ductal adenocarcinoma using dual-layer spectral CT.Abdominal radiology (New York) · 2026Article
- Predicting the biological behavior of cervical squamous cell carcinoma: a machine learning approach using apparent transverse relaxation rate (R2* maps) radiomics nomogram.BMC medical imaging · 2026Article
- Advanced Pancreatic Cancer Complicated by Nasogastric Tube Syndrome: A Case Report.Clinical case reports · 2026Article
- Review
- Multimodal MRI and machine learning for identifying depression in cerebral small vessel disease: a multicenter study.BMC medical imaging · 2026Article
- A nomogram based on dual-layer spectral detector CT-derived 40-KeV virtual monoenergetic images and iodine maps for preoperative prediction of regional lymph node metastasis in colorectal cancer.BMC medical imaging · 2026Article
- A nomogram for predicting the disease-free survival of patients with pancreatic ductal adenocarcinoma.BMC medical imaging · 2026Article
- Impact of good glycemic control on survival of pancreatic ductal adenocarcinoma with diabetes mellitus.BMC gastroenterology · 2026Article
- An interpretable machine learning model based on habitat radiomics combined with deep learning for predicting the WHO/ISUP grade of patients with clear cell renal cell carcinoma.BMC medical imaging · 2026Article
- Integrated analysis of metabolic gene features and the immune microenvironment: identification of DPYD-mediated prognostic model and therapeutic targets in pancreatic cancer.BMC gastroenterology · 2026Article
- Article
- Dual-center validation of a multi-sequence MRI radiomics nomogram for noninvasive assessment of renal fibrosis in chronic kidney disease.BMC medical imaging · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLymph node metastasis (LNM) is a key prognostic factor in pancreatic cancer (PC). Accurate preoperative prediction of LNM remains challenging. Radiomics offers a noninvasive method to extract quantitative imaging features that may aid in predicting LNM.
aimTo investigate the potential value of a computed tomography (CT)-based radiomics model in prediction of LNM in PC.
methodsA retrospective analysis was performed on 168 pathologically confirmed PC patients who underwent contrast-enhanced-CT. Among them, 107 cases had no LNM, while 61 cases had confirmed LNM. These patients were randomly divided into a training cohort (
resultsSix radiomics features from the arterial phase and nine from the portal venous phase were selected. The Radscore model demonstrated strong predictive performance for LNM in both the training and test cohorts, with AUC values ranging from 0.86 to 0.94, sensitivity between 66.7% and 91.7%, specificity from 71.4% to 100.0%, accuracy between 78.8% and 91.1%, PPV ranging from 64.7% to 100.0%, and negative predictive value between 84.0% and 93.8%. No significant differences in AUC values were observed between the arterial and portal venous phases in either the training or test set.
conclusionThe preoperative CT-based radiomics model exhibited robust predictive capability for identifying LNM in PC.
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