ArticleFrontiers in oncology2023
Development and validation of an MRI-radiomics nomogram for the prognosis of pancreatic ductal adenocarcinoma.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05313854 (Diagnosis and Survival Prediction of Pancreatic Cancer by Machine Learning of Image Data), which is not on this map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Diagnosis and Survival Prediction of Pancreatic Cancer by Machine Learning of Image Data
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Global research trends and hotspots in prognostic prediction models for pancreatic cancer: a bibliometric analysis.Frontiers in oncology · 2025Pooled it
- Development of a CT radiomics nomogram for preoperative prediction of differentiation grading in pancreatic ductal adenocarcinoma: a two-center retrospective study.BMC gastroenterology · 2026Article
- A nomogram for predicting the disease-free survival of patients with pancreatic ductal adenocarcinoma.BMC medical imaging · 2026Article
- Development and validation of a deep learning radiomics model based on ultrasound and clinical features to predict prognosis in elderly patients with advanced pancreatic cancer after HIFU therapy.BMC gastroenterology · 2026Article
- From metabolic syndrome to survival: a prognostic model for PDAC incorporating insulin resistance markers and pathological variables.Frontiers in endocrinology · 2026Article
- Development and validation of a CT-measured body composition radiomics model for prognostic assessment in resected pancreatic adenocarcinoma.Scientific reports · 2025Article
- Article
- Machine Learning-Based Radiomics for Differentiating Pancreatic Lesions: A Potential Tool to Enhance Clinical Decision-Making and Nursing Management.Journal of nursing management · 2025Article
- Delta-Radiomics Approach Using Contrast-Enhanced and Noncontrast-Enhanced Computed Tomography Images for Predicting Distant Metastasis in Patients With Borderline Resectable Pancreatic Carcinoma.Advances in radiation oncology · 2025Article
- Artificial Intelligence in Pancreatic Image Analysis: A Review.Sensors (Basel, Switzerland) · 2024Review
- Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images.Diagnostics (Basel, Switzerland) · 2024Review
- Contrast-enhanced CT radiomics combined with multiple machine learning algorithms for preoperative identification of lymph node metastasis in pancreatic ductal adenocarcinoma.Frontiers in oncology · 2024Article
- Explainable machine learning for predicting neurological outcome in hemorrhagic and ischemic stroke patients in critical care.Frontiers in neurology · 2024Article
- Risk Assessment and Radiomics Analysis in Magnetic Resonance Imaging of Pancreatic Intraductal Papillary Mucinous Neoplasms (IPMN).Cancer control : journal of the Moffitt Cancer CenterReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To develop and validate an MRI-radiomics nomogram for the prognosis of pancreatic ductal adenocarcinoma (PDAC). Background: "Radiomics" enables the investigation of huge amounts of radiological features in parallel by extracting high-throughput imaging data. MRI provides better tissue contrast with no ionizing radiation for PDAC. Methods: There were 78 PDAC patients enrolled in this study. In total, there were 386 radiomics features extracted from MRI scan, which were screened by the least absolute shrinkage and selection operator algorithm to develop a risk score. Cox multivariate regression analysis was applied to develop the radiomics-based nomogram. The performance was assessed by discrimination and calibration. Results: The radiomics-based risk-score was significantly associated with PDAC overall survival (OS) (P < 0.05). With respect to survival prediction, integrating the risk score, clinical data and TNM information into the nomogram exhibited better performance than the TNM staging system, radiomics model and clinical model. In addition, the nomogram showed fine discrimination and calibration. Conclusions: The radiomics nomogram incorporating the radiomics data, clinical data and TNM information exhibited precise survival prediction for PDAC, which may help accelerate personalized precision treatment. Clinical trial registration: clinicaltrials.gov, identifier NCT05313854.
Indexed as
Identifiers
What OpenQuestion holds
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.