ArticleJournal of magnetic resonance imaging : JMRI2020
Noncontrast Radiomics Approach for Predicting Grades of Nonfunctional Pancreatic Neuroendocrine Tumors.
Article in Journal of magnetic resonance imaging : JMRI, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled 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
- Article
- Article
- Interpretable deep learning model and nomogram for predicting pathological grading of PNETs based on endoscopic ultrasound.BMC medical informatics and decision making · 2025Article
- Endoscopic ultrasonography-based intratumoral and peritumoral machine learning ultrasomics model for predicting the pathological grading of pancreatic neuroendocrine tumors.BMC medical imaging · 2025Article
- Can CT Image Reconstruction Parameters Impact the Predictive Value of Radiomics Features in Grading Pancreatic Neuroendocrine Neoplasms?Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Review
- Virtual biopsy in abdominal pathology: where do we stand?BJR open · 2023Review
- Qualitative imaging features of pancreatic neuroendocrine neoplasms predict histopathologic characteristics including tumor grade and patient outcome.Abdominal radiology (New York) · 2022Article
- Nuclear Medicine and Radiological Imaging of Pancreatic Neuroendocrine Neoplasms: A Multidisciplinary Update.Journal of clinical medicine · 2022Review
- Article
- Review
- Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance.European journal of nuclear medicine and molecular imaging · 2021Review
- Computed tomography-based radiomics approach in pancreatic tumors characterization.La Radiologia medica · 2021Review
- Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI.Cancers · 2021Review
- Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2021Article
- Application of CT-Based Radiomics in Discriminating Pancreatic Cystadenomas From Pancreatic Neuroendocrine Tumors Using Machine Learning Methods.Frontiers in oncology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundEndoscopic ultrasound-guided fine-needle aspiration is associated with the accurate determination of tumor grade. However, because it is an invasive procedure there is a need to explore alternative noninvasive procedures. PURPOSE: To develop and validate a noncontrast radiomics model for the preoperative prediction of nonfunctional pancreatic neuroendocrine tumor (NF-pNET) grade (G). STUDY TYPE: Retrospective, single-center study. SUBJECTS: Patients with pathologically confirmed PNETs (139) were included. FIELD STRENGTH/SEQUENCE: 3T/breath-hold single-shot fast-spin echo T ASSESSMENT: Tumor features on contrast MR images were evaluated by three board-certified abdominal radiologists. STATISTICAL TESTS: Multivariable logistic regression analysis was used to develop the clinical model. The least absolute shrinkage and selection operator method and linear discriminative analysis (LDA) were used to select the features and to construct a radiomics model. The performance of the models was assessed using the training cohort (97 patients) and the validation cohort (42 patients), and decision curve analysis (DCA) was applied for clinical use.
resultsThe clinical model included 14 imaging features, and the corresponding area under the curve (AUC) was 0.769 (95% confidence interval [CI], 0.675-0.863) in the training cohort and 0.729 (95% CI, 0.568-0.890) in the validation cohort. The LDA included 14 selected radiomics features that showed good discrimination-in the training cohort (AUC, 0.851; 95% CI, 0.758-0.916) and the validation cohort (AUC, 0.736; 95% CI, 0.518-0.874). In the decision curves, if the threshold probability was 0.17-0.84, using the radiomics score to distinguish NF-pNET G1 and G2/3, offered more benefit than did the use of a treat-all-patients or treat-none scheme. DATA
conclusionThe developed radiomics model using noncontrast MRI could help differentiate G1 and G2/3 tumors, to make the clinical decision, and screen pNETs grade. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1124-1136.
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.