ArticleCurrent medical science2022
Application of Unenhanced Computed Tomography Texture Analysis to Differentiate Pancreatic Adenosquamous Carcinoma from Pancreatic Ductal Adenocarcinoma.
Article in Current medical science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Clinical and imaging differences between pancreatic carcinoma in situ and T1a/T1b pancreatic ductal adenocarcinoma.Abdominal radiology (New York) · 2026Article
- Identifying a better-prognosis pancreatic cancer from its benign inflammatory mimic: a machine learning approach with contrast-enhanced ultrasound for early intervention.Abdominal radiology (New York) · 2026Article
- Integrating CT radiomics and morphologic features for preoperative risk stratification of gastrointestinal stromal tumours.Scientific reports · 2026Article
- 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
- 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
- Multimodal MRI and machine learning for identifying depression in cerebral small vessel disease: a multicenter study.BMC medical imaging · 2026Article
- A nomogram for predicting the disease-free survival of patients with pancreatic ductal adenocarcinoma.BMC medical imaging · 2026Article
- Predictive imaging in abdominal oncology: current trends and future directions.Japanese journal of radiology · 2026Review
- Pancreatic cancer diagnosis on unenhanced CT with deep learning for opportunistic diagnosis.Radiology advances · 2026Article
- Delta radiomics-based nomogram for preoperative prediction vessels encapsulating tumor clusters (VETC) and prognosis in hepatocellular carcinoma using dynamic contrast-enhanced CT.BMC medical imaging · 2026Article
- Contrast-enhanced CT-based radiomics model explained by the Shapley Additive exPlanations (SHAP) method for predicting preoperative diagnosis of pheochromocytoma and adrenal adenoma.BMC medical imaging · 2026Article
- Radiomic features of CECT and SUVmax of dual-tracer PET/CT reveal PD-L1 spatial heterogeneity in PDAC.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Machine-learning CT radiomics for prognostication in unresectable pancreatic cancer.Frontiers in pharmacology · 2026Article
- Efficacy of a large language model in classifying branch-duct intraductal papillary mucinous neoplasms.Abdominal radiology (New York) · 2026Article
- Study on the prediction of postoperative metastasis in renal cancer using perirenal fat CT radiomics combined with clinical features.Frontiers in oncology · 2026Article
- A CT-based radiomics model for predicting pain relief after radiotherapy in patients with bone metastases: a dual-center study.Frontiers in oncology · 2026Article
- Ultrasound-based radiomics and habitat analysis for noninvasive assessment of Ki-67 overexpression in breast cancer.Frontiers in oncology · 2026Article
- Habitat and peritumoral CT radiomics accurately predict early treatment response to hepatic arterial infusion chemotherapy combined with tyrosine kinase inhibitors and programmed death-1 inhibitors in unresectable hepatocellular carcinoma.Frontiers in oncology · 2026Article
- Predicting symptomatic carotid artery plaques with radiomics-based carotid perivascular adipose tissue characteristics: a multicenter, multiclassifier study.BMC medical imaging · 2025Article
- The development of a multimodal prediction model based on CT and MRI for the prognosis of pancreatic cancer.BMC gastroenterology · 2025Article
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6 authors.
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Abstract
objectiveThe objective of this study was to investigate the application of unenhanced computed tomography (CT) texture analysis in differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC).
methodsPreoperative CT images of 112 patients (31 with PASC, 81 with PDAC) were retrospectively reviewed. A total of 396 texture parameters were extracted from AnalysisKit software for further texture analysis. Texture features were selected for the differentiation of PASC and PDAC by the Mann-Whitney U test, univariate logistic regression analysis, and the minimum redundancy maximum relevance algorithm. Furthermore, receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the texture feature-based model by the random forest (RF) method. Finally, the robustness and reproducibility of the predictive model were assessed by the 10-times leave-group-out cross-validation (LGOCV) method.
resultsIn the present study, 10 texture features to differentiate PASC from PDAC were eventually retained for RF model construction after feature selection. The predictive model had a good classification performance in differentiating PASC from PDAC, with the following characteristics: sensitivity, 95.7%; specificity, 92.5%; accuracy, 94.3%; positive predictive value (PPV), 94.3%; negative predictive value (NPV), 94.3%; and area under the ROC curve (AUC), 0.98. Moreover, the predictive model was proved to be robust and reproducible using the 10-times LGOCV algorithm (sensitivity, 90.0%; specificity, 71.3%; accuracy, 76.8%; PPV, 59.0%; NPV, 95.2%; and AUC, 0.80).
conclusionThe unenhanced CT texture analysis has great potential for differentiating PASC from PDAC.
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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.