ReviewDiagnostics (Basel, Switzerland)2024
Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images.
Review in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Accuracy of machine learning models for pre-diagnosis and diagnosis of pancreatic ductal adenocarcinoma in contrast-CT images: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- Deep Learning Framework for Early Detection of Pancreatic Cancer Using Multi-modal Medical Imaging Analysis.Journal of imaging informatics in medicine · 2026Article
- Pancreatic tumor detection in computed tomography images through a rotary positional siamese vision transformer.Scientific reports · 2026Article
- Can Machine Learning Predict Metastatic Sites in Pancreatic Ductal Adenocarcinoma? A Radiomic Analysis.Journal of imaging informatics in medicine · 2026Article
- Radiomic features of CECT and SUVmax of dual-tracer PET/CT reveal PD-L1 spatial heterogeneity in PDAC.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Applications of artificial intelligence in abdominal imaging.Abdominal radiology (New York) · 2025Review
- Early detection of pancreatic cancer on computed tomography: advancements with deep learning.Radiology advances · 2025Review
- Article
- Optimized Spatial Transformer for Segmenting Pancreas Abnormalities.Journal of imaging informatics in medicine · 2025Article
- Article
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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pancreatic ductal adenocarcinoma (PDAC) stands out as the predominant malignant neoplasm affecting the pancreas, characterized by a poor prognosis, in most cases patients being diagnosed in a nonresectable stage. Image-based artificial intelligence (AI) models implemented in tumor detection, segmentation, and classification could improve diagnosis with better treatment options and increased survival. This review included papers published in the last five years and describes the current trends in AI algorithms used in PDAC. We analyzed the applications of AI in the detection of PDAC, segmentation of the lesion, and classification algorithms used in differential diagnosis, prognosis, and histopathological and genomic prediction. The results show a lack of multi-institutional collaboration and stresses the need for bigger datasets in order for AI models to be implemented in a clinically relevant manner.
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.