ArticleQuantitative imaging in medicine and surgery2020
Development and multicenter validation of a CT-based radiomics signature for discriminating histological grades of pancreatic ductal adenocarcinoma.
Article in Quantitative imaging in medicine and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pancreas image mining: a systematic review of radiomics.European radiology · 2021Pooled it
- Development of a CT radiomics nomogram for preoperative prediction of differentiation grading in pancreatic ductal adenocarcinoma: a two-center retrospective study.BMC gastroenterology · 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
- 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
- Clinical Applications of Quantitative Imaging and Artificial Intelligence for Pancreatic Cance.Seminars in radiation oncology · 2025Review
- Advancements in Radiomics-Based AI for Pancreatic Ductal Adenocarcinoma.Bioengineering (Basel, Switzerland) · 2025Review
- The development of a multimodal prediction model based on CT and MRI for the prognosis of pancreatic cancer.BMC gastroenterology · 2025Article
- Spectral CT for non-invasive evaluation of bladder cancer grade.Abdominal radiology (New York) · 2025Article
- Dual-energy computed tomography for predicting histological grading and survival in patients with pancreatic ductal adenocarcinoma.European radiology · 2025Article
- Radiomics Beyond the Hype: A Critical Evaluation Toward Oncologic Clinical Use.Radiology. Artificial intelligence · 2024Review
- Avoid non-diagnostic EUS-FNA: a DNN model as a possible gatekeeper to distinguish pancreatic lesions prone to inconclusive biopsy.The British journal of radiology · 2023Article
- Current state of radiomic research in pancreatic cancer: focusing on study design and reproducibility of findings.European radiology · 2023Article
- Article
- Prognostic predictors of radical resection of stage I-IIIB non-small cell lung cancer: the role of preoperative CT texture features, conventional imaging features, and clinical features in a retrospectively analyzed.BMC pulmonary medicine · 2023Article
- Clinical-radiomics nomogram using contrast-enhanced CT to predict histological grade and survival in pancreatic ductal adenocarcinoma.Frontiers in oncology · 2023Article
- Research on automatic classification technology of kidney tumor and normal kidney tissue based on computed tomography radiomics.Frontiers in oncology · 2023Article
- Radiomics models based on multi-sequence MRI for preoperative evaluation of MUC4 status in pancreatic ductal adenocarcinoma: a preliminary study.Quantitative imaging in medicine and surgery · 2022Article
- Pancreatic cancer, radiomics and artificial intelligence.The British journal of radiology · 2022Review
- Comparison of Multiple Radiomics Models for Identifying Histological Grade of Pancreatic Ductal Adenocarcinoma Preoperatively Based on Multiphasic Contrast-Enhanced Computed Tomography: A Two-Center Study in Southwest China.Diagnostics (Basel, Switzerland) · 2022Article
- Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging.Cancers · 2022Review
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6 authors.
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Abstract
backgroundThe histological grade of pancreatic cancer is an important independent predictor of outcome. However, we lack a method for safely and accurately obtaining the pathological grade before surgery. Radiomics has been used to discriminate between histological grades in tumors. We aimed to develop and validate a radiomics signature for the preoperative prediction of histological grades of pancreatic ductal adenocarcinoma (PDAC) that was based on contrast-enhanced computed tomography (CE-CT).
methodsThis study comprised 301 patients with pathologically confirmed PDAC who were randomly divided into a training (n=151) and test group (n=150). Radiomics features were selected by a support vector machine (SVM) model, and a radiomics signature was generated by the least absolute shrinkage and selection operator (LASSO) model. An additional 100 patients from 2 other medical centers were used for external validation. Receiver operating characteristic (ROC) curve analysis was used to assess the model and to identify the optimal cutoff value.
resultsThe radiomics signatures between high-grade and low-grade PDACs in the training and test groups were significantly different (P<0.05). The areas under the curve (AUCs) of the training and test datasets were 0.961 and 0.910, respectively. The optimal cutoff value of the radiomics score was 0.426. In the external validation dataset, the difference between the radiomics signatures of high-grade versus low-grade PDACs was also significant (P<0.05). The radiomics signature for the external validation data had an AUC of 0.770.
conclusionsThe CE-CT-based radiomics signature showed moderate predictive accuracy for differentiating low-grade from high-grade PDAC and should become a new noninvasive method for the preoperative prediction of histological grades of PDAC.
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