ArticleBMC medical imaging2017
CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma - a quantitative analysis.
Article in BMC medical imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
80 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of prognosis predictive role of radiomics in pancreatic cancer: heterogeneity markers or statistical tricks?European radiology · 2022Pooled it
- Pancreas image mining: a systematic review of radiomics.European radiology · 2021Pooled it
- Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images.Scientific reports · 2021Trial
- Assessment of Response to Neoadjuvant Therapy Using CT Texture Analysis in Patients With Resectable and Borderline Resectable Pancreatic Ductal Adenocarcinoma.AJR. American journal of roentgenology · 2020Trial
- Advancements in Radiomics-Based AI for Pancreatic Ductal Adenocarcinoma.Bioengineering (Basel, Switzerland) · 2025Review
- Preoperative arterial and venous CT radiomics for survival prediction after pylorus preserving pancreatoduodenectomy in pancreatic head cancer.Scientific reports · 2025Article
- Preoperative Prognostic Factors in Resectable Pancreatic Cancer: State of the Art and Prospects.Annals of surgical oncology · 2025Review
- Development of a CT-based radiomics-clinical model to diagnose acute pancreatitis on nonobvious findings on CT in children with pancreaticobiliary maljunction.The British journal of radiology · 2024Article
- An ultrasound-based nomogram model in the assessment of pathological complete response of neoadjuvant chemotherapy in breast cancer.Frontiers in oncology · 2024Article
- Comparison of multiple machine learning models for predicting prognosis of pancreatic ductal adenocarcinoma based on contrast-enhanced CT radiomics and clinical features.Frontiers in oncology · 2024Article
- Exploratory analysis of mesenteric-portal axis CT radiomic features for survival prediction of patients with pancreatic ductal adenocarcinoma.European radiology · 2023Article
- Pancreatic Cancer Biomarkers: Oncogenic Mutations, Tissue and Liquid Biopsies, and Radiomics-A Review.Digestive diseases and sciences · 2023Review
- Multiparametric detection and outcome prediction of pancreatic cancer involving dual-energy CT, diffusion-weighted MRI, and radiomics.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rays.IEEE transactions on medical imaging · 2023Article
- TextureWGAN: texture preserving WGAN with multitask regularizer for computed tomography inverse problems.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Differentiation between Pancreatic Ductal Adenocarcinoma and Normal Pancreatic Tissue for Treatment Response Assessment using Multi-Scale Texture Analysis of CT Images.Journal of biomedical physics & engineering · 2022Article
- Liver metastases in pancreatic ductal adenocarcinoma: a predictive model based on CT texture analysis.La Radiologia medica · 2022Article
- Multimodal survival prediction in advanced pancreatic cancer using machine learning.ESMO open · 2022Article
- Update on quantitative radiomics of pancreatic tumors.Abdominal radiology (New York) · 2022Review
- Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review.Healthcare (Basel, Switzerland) · 2022Review
20 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo assess whether CT-derived texture features predict survival in patients undergoing resection for pancreatic ductal adenocarcinoma (PDAC).
methodsThirty patients with pre-operative CT from 2007 to 2012 for PDAC were included. Tumor size and five texture features namely uniformity, entropy, dissimilarity, correlation, and inverse difference normalized were calculated. Mann-Whitney rank sum test was used to compare tumor with normal pancreas. Receiver operating characteristics (ROC) analysis, Cox regression and Kaplan-Meier tests were used to assess association of texture features with overall survival (OS).
resultsUniformity (p < 0.001), entropy (p = 0.009), correlation (p < 0.001), and mean intensity (p < 0.001) were significantly different in tumor regions compared to normal pancreas. Tumor dissimilarity (p = 0.045) and inverse difference normalized (p = 0.046) were associated with OS whereas tumor intensity (p = 0.366), tumor size (p = 0.611) and other textural features including uniformity (p = 0.334), entropy (p = 0.330) and correlation (p = 0.068) were not associated with OS.
conclusionCT-derived PDAC texture features of dissimilarity and inverse difference normalized are promising prognostic imaging biomarkers of OS for patients undergoing curative intent surgical resection.
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