ArticleBMC medicine2022
Deep learning radiomics based on contrast-enhanced ultrasound images for assisted diagnosis of pancreatic ductal adenocarcinoma and chronic pancreatitis.
Article in BMC medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 4 of them syntheses that pooled it.
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48 citing papers in PubMed, 4 syntheses or guidelines pooled it, 71 citations in OpenAlex.
- Effectiveness of Radiomics-Based Machine Learning Models in Differentiating Pancreatitis and Pancreatic Ductal Adenocarcinoma: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Image-based artificial intelligence for preoperative differentiation of pancreatic cancer from pancreatitis: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Revolutionizing the Pancreatic Tumor Diagnosis: Emerging Trends in Imaging Technologies: A Systematic Review.Medicina (Kaunas, Lithuania) · 2024Pooled it
- Diagnostic value of deep learning-assisted endoscopic ultrasound for pancreatic tumors: a systematic review and meta-analysis.Frontiers in oncology · 2023Pooled it
- Identifying a better-prognosis pancreatic cancer from its benign inflammatory mimic: a machine learning approach with contrast-enhanced ultrasound for early intervention.Abdominal radiology (New York) · 2026Article
- A stacking model for AI-assisted diagnosis of suspected pituitary microadenomas on non-contrast T1COR MRI: a multicenter reader study on bridging the experience gap.Neuroradiology · 2026Article
- End-to-end 2.5D multisequence-multichannel fusion model for preoperative survival prediction in glioma: a retrospective study.BMC medical imaging · 2026Article
- AI-assisted radiomics for classification of benign and non-benign right heart masses in 2D-echocardiography.Journal of translational medicine · 2026Article
- Deep learning ensemble models for CT-based differentiation of malignant and benign sacral bone tumors: development and evaluation.Insights into imaging · 2026Article
- Artificial intelligence in chronic and autoimmune pancreatitis: diagnosis, prognosis, and personalized management.Frontiers in medicine · 2026Review
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- Multimodal prediction models integrating radiomics and three-dimensional deep learning for acute respiratory distress syndrome in acute pancreatitis patients.The Journal of international medical research · 2026Article
- Deep learning-based non-invasive prediction of PD-L1 status and immunotherapy survival stratification in esophageal cancer using [European journal of nuclear medicine and molecular imaging · 2026Article
- Article
- Development and validation of a nomogram model based on ultrasound and contrast-enhanced ultrasound features for differentiating mass-forming pancreatitis and pancreatic ductal adenocarcinoma.Abdominal radiology (New York) · 2025Article
- Dual-modal ultrasound-based deep learning radiomics for differentiation of benign and malignant breast lesions.Gland surgery · 2025Article
- Artificial intelligence in pancreatitis: A narrative review on advancing precision diagnosis, prognosis, and therapeutic strategies.World journal of gastroenterology · 2025Review
- Artificial Intelligence and Digital Tools Across the Hepato-Pancreato-Biliary Surgical Pathway: A Systematic Review.Journal of clinical medicine · 2025Review
- The development of a multimodal prediction model based on CT and MRI for the prognosis of pancreatic cancer.BMC gastroenterology · 2025Article
- Advancements in Radiomics-Based AI for Pancreatic Ductal Adenocarcinoma.Bioengineering (Basel, Switzerland) · 2025Review
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Authors and funding
12 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate and non-invasive diagnosis of pancreatic ductal adenocarcinoma (PDAC) and chronic pancreatitis (CP) can avoid unnecessary puncture and surgery. This study aimed to develop a deep learning radiomics (DLR) model based on contrast-enhanced ultrasound (CEUS) images to assist radiologists in identifying PDAC and CP.
methodsPatients with PDAC or CP were retrospectively enrolled from three hospitals. Detailed clinicopathological data were collected for each patient. Diagnoses were confirmed pathologically using biopsy or surgery in all patients. We developed an end-to-end DLR model for diagnosing PDAC and CP using CEUS images. To verify the clinical application value of the DLR model, two rounds of reader studies were performed.
resultsA total of 558 patients with pancreatic lesions were enrolled and were split into the training cohort (n=351), internal validation cohort (n=109), and external validation cohorts 1 (n=50) and 2 (n=48). The DLR model achieved an area under curve (AUC) of 0.986 (95% CI 0.975-0.994), 0.978 (95% CI 0.950-0.996), 0.967 (95% CI 0.917-1.000), and 0.953 (95% CI 0.877-1.000) in the training, internal validation, and external validation cohorts 1 and 2, respectively. The sensitivity and specificity of the DLR model were higher than or comparable to the diagnoses of the five radiologists in the three validation cohorts. With the aid of the DLR model, the diagnostic sensitivity of all radiologists was further improved at the expense of a small or no decrease in specificity in the three validation cohorts.
conclusionsThe findings of this study suggest that our DLR model can be used as an effective tool to assist radiologists in the diagnosis of PDAC and CP.
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