ArticleCancers2023
Development and Validation of a Nomogram for Preoperative Prediction of Early Recurrence after Upfront Surgery in Pancreatic Ductal Adenocarcinoma by Integrating Deep Learning and Radiological Variables.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Artificial intelligence in pancreatic cancer: applications in early detection, tumor staging, and survival prediction-a comprehensive review.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Article
- Development of an artificial intelligence-based prediction platform for early recurrence of resectable pancreatic cancer after curative surgery-toward future use as an indication for neoadjuvant treatment: a retrospective multicenter cohort study.Annals of surgical treatment and research · 2026Article
- Integrative multi-omics and radiogenomic profiling decodes NNK-related tumor remodeling and prognostic stratification in pancreatic cancer.International journal of surgery (London, England) · 2026Article
- Early postoperative recurrence prediction in pancreatic ductal adenocarcinoma: a nomogram based on dual-energy computed tomography andQuantitative imaging in medicine and surgery · 2026Article
- Clinical Applications of Quantitative Imaging and Artificial Intelligence for Pancreatic Cance.Seminars in radiation oncology · 2025Review
- MRI-based risk stratification for predicting overall survival in pancreatic ductal adenocarcinoma.Insights into imaging · 2025Article
- Recent Advances and Future Challenges in Pancreatic Cancer Care: Early Detection, Liquid Biopsies, Precision Medicine and Artificial Intelligence.Journal of clinical medicine · 2023Article
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Authors and funding
7 authors.
Funding
Abstract
Around 80% of pancreatic ductal adenocarcinoma (PDAC) patients experience recurrence after curative resection. We aimed to develop a deep-learning model based on preoperative CT images to predict early recurrence (recurrence within 12 months) in PDAC patients. The retrospective study included 435 patients with PDAC from two independent centers. A modified 3D-ResNet18 network was used for a deep learning model construction. A nomogram was constructed by incorporating deep learning model outputs and independent preoperative radiological predictors. The deep learning model provided the area under the receiver operating curve (AUC) values of 0.836, 0.736, and 0.720 in the development, internal, and external validation datasets for early recurrence prediction, respectively. Multivariate logistic analysis revealed that higher deep learning model outputs (odds ratio [OR]: 1.675; 95% CI: 1.467, 1.950;
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