ReviewPancreas2020
Texture Analysis: An Emerging Clinical Tool for Pancreatic Lesions.
Review in Pancreas, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 19 citations in OpenAlex.
- Quantitative CT-Derived Imaging Features Improve Preoperative Risk Stratification of Intraductal Papillary Mucinous Neoplasms.Annals of surgical oncology · 2026Article
- Human biomarker navigator.iMeta · 2026Review
- Review
- Radiomics in differential diagnosis of pancreatic tumors.European journal of radiology open · 2025Article
- Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography.Diagnostics (Basel, Switzerland) · 2024Article
- Assessment of chemotherapy resistance changes in human colorectal cancer xenografts in rats based on MRI histogram features.Frontiers in oncology · 2024Article
- Liver metastases in pancreatic ductal adenocarcinoma: a predictive model based on CT texture analysis.La Radiologia medica · 2022Article
- Quantitative MRI of Pancreatic Cystic Lesions: A New Diagnostic Approach.Healthcare (Basel, Switzerland) · 2022Article
- Review
- A novel nomogram model combining CT texture features and urine energy metabolism to differentiate single benign from malignant pulmonary nodule.Frontiers in oncology · 2022Article
- Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential.Frontiers in oncology · 2022Review
- CT Simplified Radiomic Approach to Assess the Metastatic Ductal Adenocarcinoma of the Pancreas.Cancers · 2021Article
- Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions.Diagnostics (Basel, Switzerland) · 2020Review
- Machine learning-based prediction of long-term new-onset diabetes mellitus risk after pancreaticoduodenectomy using radiomics.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Radiologic characterization of pancreatic lesions is currently limited. Computed tomography is insensitive in detecting and characterizing small pancreatic lesions. Moreover, heterogeneity of many pancreatic lesions makes determination of malignancy challenging. As a result, invasive diagnostic testing is frequently used to characterize pancreatic lesions but often yields indeterminate results. Computed tomography texture analysis (CTTA) is an emerging noninvasive computational tool that quantifies gray-scale pixels/voxels and their spatial relationships within a region of interest. In nonpancreatic lesions, CTTA has shown promise in diagnosis, lesion characterization, and risk stratification, and more recently, pancreatic applications of CTTA have been explored. This review outlines the emerging role of CTTA in identifying, characterizing, and risk stratifying pancreatic lesions. Although recent studies show the clinical potential of CTTA of the pancreas, a clear understanding of which specific texture features correlate with high-grade dysplasia and predict survival has not yet been achieved. Further multidisciplinary investigations using strong radiologic-pathologic correlation are needed to establish a role for this noninvasive diagnostic tool.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.