ArticleAbdominal radiology (New York)2021
Preoperative differentiation of serous cystic neoplasms from mucin-producing pancreatic cystic neoplasms using a CT-based radiomics nomogram.
Article in Abdominal radiology (New York), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed, 29 citations in OpenAlex.
- Determining malignant potential of pancreatic intraductal papillary mucinous neoplasm with CT: interobserver variability and diagnostic performance.Abdominal radiology (New York) · 2026Article
- Predicting placenta accreta spectrum antenatally: a deep learning-radiomics dual-model based on intraoperative clinical and histopathologic criteria.BMC medical imaging · 2026Article
- MRI-based radiomics interpretable model predicts microsatellite instability status in endometrioid adenocarcinoma.BMC medical imaging · 2026Article
- Radiomics-based unsupervised clustering and deep learning nomogram for response prediction after neoadjuvant chemotherapy in locally advanced laryngeal cancer.BMC medical imaging · 2026Article
- Multimodal AI for early prediction of adverse clinical outcomes in acute pancreatitis.Abdominal radiology (New York) · 2026Article
- Mass Spectrometry-Driven Proteomic Biomarkers for Serum-Based Detection of Pancreatic Ductal Adenocarcinoma and Intraductal Papillary Mucinous Neoplasm-Associated Invasive Carcinoma.Cancer medicine · 2026Article
- Differentiating walled-off pancreatic necrosis with no documented pancreatitis from mucinous cystic neoplasms: a multicenter LASSO-based nomogram.BMC gastroenterology · 2026Article
- A nomogram for malignancy prediction of pancreatic cystic lesions based on trans-abdominal ultrasound features.BMC medical imaging · 2026Article
- Comparative Diagnostic Accuracy of EUS-Guided Fine-Needle Biopsy Versus Aspiration for Pancreatic Serous Cystic Neoplasms: A Retrospective Cohort Study.Journal of clinical medicine · 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
- CT and MRI findings of intraductal tubulopapillary neoplasm of the pancreas.BMC medical imaging · 2026Article
- Dual-center validation of a multi-sequence MRI radiomics nomogram for noninvasive assessment of renal fibrosis in chronic kidney disease.BMC medical imaging · 2026Article
- 3D intratumoral heterogeneity-based quantitative score from chest CT for preoperative prediction of visceral pleural invasion in lung adenocarcinoma: a multicenter study.Frontiers in oncology · 2026Article
- Predicting overall survival in synchronous metastatic nasopharyngeal carcinoma using a stacking ensemble machine learning model: a multicenter retrospective study.Frontiers in oncology · 2026Article
- Differentiation of intraductal papillary mucinous neoplasms and pancreatic ductal adenocarcinoma using arterial-phase CT radiomics combined with clinical features.Frontiers in oncology · 2026Article
- Interpretable intratumoral and peritumoral radiomics using SHAP for predicting microsatellite status in gastric adenocarcinoma: a dual-center study.Frontiers in oncology · 2026Article
- Integrating MR dynamic radiomics and clinical parameters for machine learning-based prediction of short-term response to induction chemotherapy in nasopharyngeal carcinoma.Frontiers in oncology · 2026Article
- Study on the prediction of postoperative metastasis in renal cancer using perirenal fat CT radiomics combined with clinical features.Frontiers in oncology · 2026Article
- Applying Radiomics to Predict Outcomes in Patients with High-Grade Retroperitoneal Sarcoma Treated with Preoperative Radiotherapy.Journal of imaging · 2025Article
- Risk Factor and Prediction Model for Malignant Transformation in Pancreatic Intraductal Papillary Mucinous Neoplasm.Cancer medicine · 2025Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop and validate a CT-based radiomics nomogram in preoperative differential diagnosis of SCNs from mucin-producing PCNs. MATERIAL AND
methodsA total of 89 patients consisting of 31 SCNs, 30 IPMNs, and 28 MCNs who underwent preoperative CT were analyzed. A total of 710 radiomics features were extracted from each case. Patients were divided into training (n = 63) and validation cohorts (n = 26) with a ratio of 7:3. Least absolute shrinkage and selection operator (LASSO) method and logistic regression analysis were used for feature selection and model construction. A nomogram was created from a comprehensive model consisting of clinical features and the fusion radiomics signature. A decision curve analysis was used for clinical decisions.
resultsThe radiomics features extracted from CT could assist with the differentiation of SCNs from mucin-producing PCNs in both the training and validation cohorts. The signature of the combination of the plain, late arterial, and venous phases had the largest areas under the curve (AUCs) of 0.960 (95% CI 0.910-1) in the training cohort and 0.817 (95% CI 0.651-0.983) in the validation cohort with good calibration. The value and efficacy of the nomogram was verified using decision curve analysis.
conclusionA comprehensive nomogram incorporating clinical features and fusion radiomics signature can differentiate SCNs from mucin-producing PCNs.
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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.