ArticleCancers2021
Prediction of Early Distant Recurrence in Upfront Resectable Pancreatic Adenocarcinoma: A Multidisciplinary, Machine Learning-Based Approach.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05816954 (Artificial Intelligence-based Identification of Imaging Biomarkers of Lung FRAILty in Patients With Acute Respiratory Distress Syndrome.), which is not on this map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Artificial Intelligence-based Identification of Imaging Biomarkers of Lung FRAILty in Patients With Acute Respiratory Distress Syndrome.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The role of artificial intelligence in pancreatic surgery: a systematic review.Updates in surgery · 2022Pooled it
- Early postoperative recurrence prediction in pancreatic ductal adenocarcinoma: a nomogram based on dual-energy computed tomography andQuantitative imaging in medicine and surgery · 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
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- The role of artificial intelligence in pancreatic surgery: Current and future perspectives.Annals of hepato-biliary-pancreatic surgery · 2025Article
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- Uncovering the clinicopathological features of early recurrence after surgical resection of pancreatic cancer.Scientific reports · 2024Article
- Preoperative prediction of early recurrence in resectable pancreatic cancer integrating clinical, radiologic, and CT radiomics features.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Prediction of overall survival in patients with locally advanced pancreatic cancer using longitudinal diffusion-weighted MRI.Frontiers in oncology · 2024Article
- Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers.Frontiers in artificial intelligence · 2024Review
- Pancreatic Ductal Adenocarcinoma: Update of CT-Based Radiomics Applications in the Pre-Surgical Prediction of the Risk of Post-Operative Fistula, Resectability Status and Prognosis.Journal of clinical medicine · 2023Review
- Prediction of the characteristics of aggressiveness of pancreatic neuroendocrine neoplasms (PanNENs) based on CT radiomic features.European radiology · 2023Article
- Ex-vivo investigation of radiofrequency ablation in pancreatic adenocarcinoma after neoadjuvant chemotherapy.DEN open · 2023Article
- Machine learning in pancreas surgery, what is new? literature review.Frontiers in surgery · 2023Review
- Pancreatic cancer, radiomics and artificial intelligence.The British journal of radiology · 2022Review
- Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis.Frontiers in oncology · 2022Article
- Development and Validation of a Preoperative Nomogram for Predicting Benign and Malignant Gallbladder Polypoid Lesions.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
Despite careful selection, the recurrence rate after upfront surgery for pancreatic adenocarcinoma can be very high. We aimed to construct and validate a model for the prediction of early distant recurrence (<12 months from index surgery) after upfront pancreaticoduodenectomy. After exclusions, 147 patients were retrospectively enrolled. Preoperative clinical and radiological (CT-based) data were systematically evaluated; moreover, 182 radiomics features (RFs) were extracted. Most significant RFs were selected using minimum redundancy, robustness against delineation uncertainty and an original machine learning bootstrap-based method. Patients were split into training (
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Registered trials
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.