ArticleAnnals of translational medicine2021
Predicting the recurrence risk of pancreatic neuroendocrine neoplasms after radical resection using deep learning radiomics with preoperative computed tomography images.
Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Are deep models in radiomics performing better than generic models? A systematic review.European radiology experimental · 2023Pooled it
- GEP-NET radiomics: a systematic review and radiomics quality score assessment.European radiology · 2022Pooled it
- Multi-omic profiles of neuroendocrine neoplasms of the pancreas: an integrated landscape.Molecular cancer · 2026Review
- A Novel Radiomics-Integrated Panel for Preoperative Stratification of Pancreatic Neuroendocrine Tumors (PNETs).Cancers · 2026Article
- Explainable deep learning and radiomics integration for differentiating insulinomas from NF-PNETs in EUS imaging.BMC medical informatics and decision making · 2026Article
- Predicting the Unpredictable: AI-Driven Prognosis in Pancreatic Neuroendocrine Neoplasms.Cancers · 2026Review
- Clinical Applications of Quantitative Imaging and Artificial Intelligence for Pancreatic Cance.Seminars in radiation oncology · 2025Review
- A nomogram to preoperatively predict the aggressiveness of pancreatic neuroendocrine tumors based on CT features and 3D CT radiomic features.Abdominal radiology (New York) · 2025Article
- Artificial Intelligence for Prognosis of Gastro-Entero-Pancreatic Neuroendocrine Neoplasms.Cancers · 2025Review
- Pancreatic neuroendocrine neoplasms coexisting with biliary intraductal papillary mucinous neoplasm: A case report and review of literature.World journal of gastrointestinal oncology · 2025Article
- An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer.Scientific reports · 2025Article
- Deep Learning Applications in Pancreatic Cancer.Cancers · 2024Review
- Weakly supervised large-scale pancreatic cancer detection using multi-instance learning.Frontiers in oncology · 2024Article
- Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics.Diagnostics (Basel, Switzerland) · 2023Review
- A deep learning model using convolutional neural networks for caries detection and recognition with endoscopes.Annals of translational medicine · 2022Article
- CT-based radiomics for prediction of therapeutic response to Everolimus in metastatic neuroendocrine tumors.La Radiologia medica · 2022Article
- Long-term effects of totalAnnals of translational medicine · 2022Article
- Artificial Intelligence and Machine Learning in the Diagnosis and Management of Gastroenteropancreatic Neuroendocrine Neoplasms-A Scoping Review.Diagnostics (Basel, Switzerland) · 2022Article
- Review
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo establish and validate a prediction model for pancreatic neuroendocrine neoplasms (pNENs) recurrence after radical surgery with preoperative computed tomography (CT) images.
methodsWe retrospectively collected data from 74 patients with pathologically confirmed pNENs (internal group: 56 patients, Hospital I; external validation group: 18 patients, Hospital II). Using the internal group, models were trained with CT findings evaluated by radiologists, radiomics, and deep learning radiomics (DLR) to predict 5-year pNEN recurrence. Radiomics and DLR models were established for arterial (A), venous (V), and arterial and venous (A&V) contrast phases. The model with the optimal performance was further combined with clinical information, and all patients were divided into high- and low-risk groups to analyze survival with the Kaplan-Meier method.
resultsIn the internal group, the areas under the curves (AUCs) of DLR-A, DLR-V, and DLR-A&V models were 0.80, 0.58, and 0.72, respectively. The corresponding radiomics AUCs were 0.74, 0.68, and 0.70. The AUC of the CT findings model was 0.53. The DLR-A model represented the optimum; added clinical information improved the AUC from 0.80 to 0.83. In the validation group, the AUCs of DLR-A, DLR-V, and DLR-A&V models were 0.77, 0.48, and 0.64, respectively, and those of radiomics-A, radiomics-V, and radiomics-A&V models were 0.56, 0.52, and 0.56, respectively. The AUC of the CT findings model was 0.52. In the validation group, the comparison between the DLR-A and the random models showed a trend of significant difference (P=0.058). Recurrence-free survival differed significantly between high- and low-risk groups (P=0.003).
conclusionsUsing DLR, we successfully established a preoperative recurrence prediction model for pNEN patients after radical surgery. This allows a risk evaluation of pNEN recurrence, optimizing clinical decision-making.
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