ReviewCancers2022
Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications.
Review in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 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
40 citing papers in PubMed, 1 synthesis or guideline pooled it, 68 citations in OpenAlex.
- Artificial Intelligence in Pancreatic Imaging: A Systematic Review.United European gastroenterology journal · 2025Pooled it
- SpecAtt-Net: Time-Frequency-Based Image Representations of Slice-Level Radiomic Features for Explainable Lung Cancer Classification via Slice-Wise Activation Mapping.Diagnostics (Basel, Switzerland) · 2026Article
- Computed tomography quantitative imaging features for pancreatic ductal adenocarcinoma after neoadjuvant therapy: a narrative review.Gland surgery · 2026Review
- CT-Based Radiomics in the Characterization of Solid Renal Tumors: A Systematic Review.Cancers · 2026Review
- Radiomics and artificial intelligence in pancreatic cyst characterization: future or fiction?Abdominal radiology (New York) · 2026Review
- Imaging Biomarkers in Radiotherapy.Cancers · 2026Review
- Radiomics-Driven Machine Learning Models for Diagnosis of Pancreatic Adenocarcinoma.Iranian journal of medical sciences · 2026Article
- Research Progress on the Application of Radiomics and Deep Learning in Liver Fibrosis.Journal of imaging · 2026Review
- Magnetic resonance imaging radiomics-based machine learning model for preoperative prediction of pathological grade in pancreatic cancer: A preliminary study.European journal of radiology open · 2025Article
- Integrating artificial intelligence in healthcare: applications, challenges, and future directions.Future science OA · 2025Review
- A comprehensive magnetic resonance imaging-based model for predicting lymphovascular space invasion in endometrial cancer: a retrospective observational study.Quantitative imaging in medicine and surgery · 2025Article
- Clinical Applications of Quantitative Imaging and Artificial Intelligence for Pancreatic Cance.Seminars in radiation oncology · 2025Review
- CT-based machine learning radiomics predicts Ki-67 expression level and its relationship with overall survival in resectable pancreatic ductal adenocarcinoma.Abdominal radiology (New York) · 2025Article
- Radiomics for preoperative pancreatic ductal adenocarcinoma risk stratification: Cross-population validation, multidimensional integration, challenges, and future directions.World journal of radiology · 2025Article
- Preoperative computed tomography-based risk stratification model validation for postoperative pancreatic ductal adenocarcinoma recurrence.World journal of gastrointestinal surgery · 2025Article
- Enhancing pancreatic cancer detection in CT images through secretary wolf bird optimization and deep learning.Scientific reports · 2025Article
- The inhibitory effect of Osthole on A549 lung adenocarcinoma cells and its biomarker.Scientific reports · 2025Article
- Artificial intelligence in fracture detection on radiographs: a literature review.Japanese journal of radiology · 2025Review
- Article
- Radiomics in glioma: emerging trends and challenges.Annals of clinical and translational neurology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 5 institutions in 1 country.
Funding
Abstract
As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization.
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.