SynthesisJournal of gastrointestinal cancer2025
MRI and PET-Based Machine Learning Radiomics for Metastasis Prediction in Pancreatic Ductal Adenocarcinoma: A Systematic Review.
Synthesis in Journal of gastrointestinal cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with poor survival, driven in part by early metastatic spread. Conventional imaging lacks sufficient precision to predict metastasis accurately. Machine learning (ML)-based radiomics, integrating quantitative imaging features from modalities such as magnetic resonance imaging (MRI) and positron emission tomography (PET), may enhance prognostic accuracy.
objectiveTo systematically review the diagnostic accuracy and clinical utility of ML-based radiomics models for predicting metastasis in PDAC.
methodsA systematic search of PubMed, Embase, Scopus, Web of Science, and Cochrane Library was conducted according to PRISMA 2020 guidelines (PROSPERO: CRD420251109941). Eligible studies applied ML-based radiomics to MRI, PET, or combined MRI/PET for metastasis prediction in histologically or clinically confirmed PDAC. Data were extracted on study design, patient characteristics, imaging protocols, feature selection, ML algorithms, performance metrics, and validation strategies. Methodological quality was assessed using QUADAS-2. Certainty of evidence was graded using the GRADE framework.
resultsSeven studies met inclusion criteria and were included in the Systematic Review. MRI-based models were the most common, with one multimodal PET/MRI study. Risk of bias was moderate overall, primarily due to retrospective designs and variable reference standards. GRADE certainty was low for pooled diagnostic accuracy and very low for PET/MRI evidence due to imprecision and suspected publication bias.
conclusionsML-based radiomics demonstrates promising accuracy for metastasis prediction in PDAC, particularly with MRI and PET/MRI modalities. Integration with clinical biomarkers further enhances predictive value. However, methodological limitations and low certainty of evidence warrant prospective, multicenter validation with standardized protocols before clinical adoption.
Indexed as
Identifiers
41428015What 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.