ArticleCancer imaging : the official publication of the International Cancer Imaging Society2024
Preoperative prediction of early recurrence in resectable pancreatic cancer integrating clinical, radiologic, and CT radiomics features.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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15 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Image-based artificial intelligence for preoperative differentiation of pancreatic cancer from pancreatitis: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Spatiotemporal heterogeneity of recurrence and predictors after radical surgery for pancreatic ductal adenocarcinoma: a retrospective observational study.Surgical endoscopy · 2026Observational
- Article
- Preoperative CT and inflammatory index-based risk scores for predicting short-term recurrence following resection of pancreatic ductal adenocarcinoma.BMC surgery · 2026Article
- CT-based habitat analysis combined with multi-channel deep learning for predicting early recurrence after pancreatic cancer resection: a multicenter study.Journal of translational medicine · 2026Article
- Integrating Radiomics and Computational Pathology to Predict Early Recurrence of Pancreatic Ductal Adenocarcinoma and Uncover Its Biological Basis in Tumor Microenvironment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Combination of imaging features on pancreatic CT for predicting early recurrence after upfront pancreatoduodenectomy of pancreatic ductal adenocarcinoma.European journal of radiology open · 2025Article
- Applications of artificial intelligence in abdominal imaging.Abdominal radiology (New York) · 2025Review
- Postoperative early recurrence prediction of pancreatic cancer based on CT and pathomics.BMC gastroenterology · 2025Article
- Radiomics analysis using machine learning to predict perineural invasion in pancreatic cancer.BMC cancer · 2025Article
- Radiomics-based machine-learning method to predict extrahepatic metastasis in hepatocellular carcinoma after hepatectomy: a multicenter study.Scientific reports · 2025Article
- Preoperative Prediction of Perineural Invasion in Pancreatic Ductal Adenocarcinoma Using Machine Learning Radiomics Based on Contrast-Enhanced CT Imaging.Journal of imaging informatics in medicine · 2025Article
- Reproducibility of methodological radiomics score (METRICS): an intra- and inter-rater reliability study endorsed by EuSoMII.European radiology · 2025Article
- Article
- A data-centric machine learning approach to improve prediction of glioma grades using low-imbalance TCGA data.Scientific reports · 2024Article
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9 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesTo use clinical, radiographic, and CT radiomics features to develop and validate a preoperative prediction model for the early recurrence of pancreatic cancer.
methodsWe retrospectively analyzed 190 patients (150 and 40 in the development and test cohort from different centers) with pancreatic cancer who underwent pancreatectomy between January 2018 and June 2021. Radiomics, clinical-radiologic (CR), and clinical-radiologic-radiomics (CRR) models were developed for the prediction of recurrence within 12 months after surgery. Performance was evaluated using the area under the curve (AUC), Brier score, sensitivity, and specificity.
resultsEarly recurrence occurred in 36.7% and 42.5% of the development and test cohorts, respectively (P = 0.62). The features for the CR model included carbohydrate antigen 19-9 > 500 U/mL (odds ratio [OR], 3.60; P = 0.01), abutment to the portal and/or superior mesenteric vein (OR, 2.54; P = 0.054), and adjacent organ invasion (OR, 2.91; P = 0.03). The CRR model demonstrated significantly higher AUCs than the radiomics model in the internal (0.77 vs. 0.73; P = 0.048) and external (0.83 vs. 0.69; P = 0.038) validations. Although we found no significant difference between AUCs of the CR and CRR models (0.83 vs. 0.76; P = 0.17), CRR models showed more balanced sensitivity and specificity (0.65 and 0.87) than CR model (0.41 and 0.91) in the test cohort.
conclusionsThe CRR model outperformed the radiomics and CR models in predicting the early recurrence of pancreatic cancer, providing valuable information for risk stratification and treatment guidance.
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