ArticleEJNMMI research2021
Preoperative prediction of pathological grade in pancreatic ductal adenocarcinoma based on
Article in EJNMMI research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- Diagnostic performance of ¹⁸F-FDG PET/CT radiomics-based machine learning models for pancreatic lesion characterisation: Comparison with visual assessment and evaluation of human-radiomics synergy.European journal of nuclear medicine and molecular imaging · 2026Article
- Clinical Applications of Quantitative Imaging and Artificial Intelligence for Pancreatic Cance.Seminars in radiation oncology · 2025Review
- Article
- Construction of feature selection and efficacy prediction model for transformation therapy of locally advanced pancreatic cancer based on CT,Cancer cell international · 2025Article
- Artificial Intelligence in Pancreatic Image Analysis: A Review.Sensors (Basel, Switzerland) · 2024Review
- Clinical application ofJapanese journal of radiology · 2024Review
- Computer-Aided Detection for Pancreatic Cancer Diagnosis: Radiological Challenges and Future Directions.Journal of clinical medicine · 2023Review
- Diagnostic ability of deep learning in detection of pancreatic tumour.Scientific reports · 2023Article
- Advances in the Early Diagnosis of Pancreatic Ductal Adenocarcinoma and Premalignant Pancreatic Lesions.Biomedicines · 2023Review
- Article
- A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based onJapanese journal of radiology · 2023Article
- Prognostic analysis of curatively resected pancreatic cancer using harmonized positron emission tomography radiomic features.European journal of hybrid imaging · 2023Article
- An Automated Method for Artifical Intelligence Assisted Diagnosis of Active Aortitis Using Radiomic Analysis of FDG PET-CT Images.Biomolecules · 2023Article
- Clinical-radiomics nomogram using contrast-enhanced CT to predict histological grade and survival in pancreatic ductal adenocarcinoma.Frontiers in oncology · 2023Article
- Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review.Healthcare (Basel, Switzerland) · 2022Review
- Comparison of Multiple Radiomics Models for Identifying Histological Grade of Pancreatic Ductal Adenocarcinoma Preoperatively Based on Multiphasic Contrast-Enhanced Computed Tomography: A Two-Center Study in Southwest China.Diagnostics (Basel, Switzerland) · 2022Article
- Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging.Cancers · 2022Review
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers.Diagnostics (Basel, Switzerland) · 2022Review
- Review
- Prediction for Mitosis-Karyorrhexis Index Status of Pediatric Neuroblastoma via Machine Learning BasedDiagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
purposeTo develop and validate a machine learning model based on radiomic features derived from
methodsA total of 149 patients (83 men, 66 women, mean age 61 years old) with pathologically proven PDAC and a preoperative
resultsThe prediction model based on a twelve-feature-combined radiomics signature could stratify PDAC patients into grade 1 and grade 2/3 groups with AUC of 0.994 in the training set and 0.921 in the validation set.
conclusionThe model developed is capable of predicting pathological differentiation grade of PDAC based on preoperative
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.