ArticleBMC cancer2025
Deep learning based on ultrasound for differential diagnosis of pancreatic serous cystic neoplasm and mucinous cystic neoplasm.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Transabdominal ultrasound subclassification of pancreatic serous cystic neoplasms: correlation with MRI and characterization of subtype-specific features.Abdominal radiology (New York) · 2026Article
- A nomogram for malignancy prediction of pancreatic cystic lesions based on trans-abdominal ultrasound features.BMC medical imaging · 2026Article
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Authors and funding
13 authors.
Funding
Abstract
purposeThis study aims to develop a deep learning (DL) model based on conventional ultrasound images for differentiating pancreatic serous neoplasms (SCN) from mucinous cystic neoplasms (MCN). We seek to determine if such a model can surpass the diagnostic performance of routine sonographic evaluation and provide a valuable, cost-effective decision-support tool.
methodsData from 459 patients with histopathologically confirmed SCN and MCN from center 1 (n = 345) and center 2 (n = 114) were retrospectively collected. Five DL models (EfficientNet-B3, Resnet-101, Densenet-121, Se-resnext-50, Inception-v3) were constructed for differential diagnosis. Simultaneously compare the diagnostic performance of DL model with that of junior radiologists (JR) and senior radiologists (SR), and determine whether DL can assist radiologists in making better diagnoses.
resultsThe EfficientNet-B3 model demonstrated the optimal predictive performance, with an area under the receiver operating curve (AUC) of 0.904 (95% CI:0.814–0.973) in the internal validation set and 0.866 (95% CI:0.783–0.936) in the external test set. In the external test set, the EfficientNet-B3 model demonstrated significantly superior diagnostic performance compared to both JR in both F1-score and specificity (all P < 0.0125), while showing fully comparable performance to both SR in these precise metrics (all P > 0.0125). With the EfficientNet-B3 model assistance, both JR showed statistically significant improvements in F1-score and specificity (all P < 0.0125), whereas both SR exhibited no statistically significant changes in either performance metric (all P > 0.0125).
conclusionsThe EfficientNet-B3 model demonstrated comparable diagnostic performance to SR while significantly enhancing the diagnostic accuracy of JR.
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