Introduction

Timely detection of neoplasia in patients with Barrett’s esophagus (BE) has significant influence on patient outcome. However, endoscopic detection remains challenging due to its often subtle appearance. Computer Aided Detection (CADe) systems may assist in neoplasia detection.

Aims & Methods

We aimed to develop and externally validate a CADe system for real-time BE neoplasia detection on multiple video test sets and provide a benchmark performance by general and BE expert endoscopists.
The system underwent pretraining with ImageNet followed by domain-specific pretraining with GastroNet. GastroNet comprises >5 million endoscopic images from the gastrointestinal tract. The system was then trained and validated on a large, heterogeneous BE dataset originating from 15 international endoscopy centers, comprising 6.337 neoplastic (1.362) patients) and 7.695 non-dysplastic images (1.139 patients). All images had histopathological confirmation. Neoplastic images were delineated by expert endoscopists. The system was internally validated on a new, unseen dataset of 58 neoplasia patients (100 images and 68 videos) and 36 non-dysplastic patients (100 images and 58 videos).
For external validation, the system was tested on two independent, prospectively collected video test sets. The "all-comers test set" was representative for cases encountered in daily practice and contained all cases acquired by the 15 participating centers, from January 2022 to March 2022, comprising 71 neoplastic (45 patients) and 180 non-dysplastic (66 patients) videos. The "benchmarking test set" was artificially enriched with subtle neoplasia, representing the challenging cases in clinical practice. It comprised of 47 neoplastic (47 patients) and 141 non-dysplastic (82 patients) videos. This second test set was evaluated by 63 general endoscopists in 2 phases to provide a benchmark performance. In phase 1 endoscopists evaluated all cases without CADe assistance. After a six week wash-out period, endoscopists evaluated the same test set with CADe assistance. Finally, 14 internationally renowned BE experts evaluated this test set.

Results

Sensitivity and specificity of the CADe system were 97% and 85% for the all-comers test set and 91% and 82% for the benchmarking test set. Sensitivity of general endoscopists increased from 67% to 79% with CADe assistance, whilst specificity decreased from 96% to 94%. General endoscopists without CADe assistance were outperformed by CADe (p<0.001) and general endoscopists with CADe (p<0.001). The median sensitivity and specificity of experts were 86% and 90%. Non-inferiority of CADe to experts for neoplasia detection could be concluded.

Conclusion

CADe outperformed general endoscopist in terms of detecting subtle early BE neoplasia. Providing CADe assistance to general endoscopists significantly improves their detection of subtle neoplasia. CADe detects virtually all BE neoplasia encountered in daily practice. CADe has a detection performance which is non-inferior to international experts.

Disclosure

This study received financial and logistical support from Olympus Tokyo.

Introduction

Endoscopic ultrasound (EUS) is the gold standard for evaluating pancreatic lesions, allowing for real-time imaging and biopsy sampling. Nevertheless, the differentiation between solid pancreatic lesions — such as pancreatic ductal adenocarcinoma (PDAC) and pancreatic neuroendocrine tumors (PNETs) — as well as cystic lesions is often troublesome. Specifically, identifying intraductal papillary mucinous neoplasms (IPMNs) with varying degrees of dysplasia is crucial for guiding clinical management. However, the diagnostic accuracy of current methods remains suboptimal. This multicenter study aimed to develop and validate convolutional neural networks (CNNs) to characterize solid and cystic pancreatic lesions and stratify IPMNs into high-grade dysplasia/carcinoma (HGD/C) and low-grade dysplasia (LGD).

Aims & Methods

This multicenter study included EUS images from 12 centers in Spain, Brazil, the United Kingdom and the United States of America. Solid lesion differentiation (PDAC vs. PNET) was achieved using a CNN trained on 49034 images from 95 patients (n=69 for PDAC and n=26 for PNET), with diagnoses confirmed via EUS-guided fine-needle biopsy or surgical specimen analysis. A second CNN was trained on 79395 images from 82 patients (n=64 for mucinous cystic lesions and n=18 for non-mucinous cystic lesions). Finally, a third model used the subset of IPMNs (n=51046 frames, 30 patients) to distinguish between HGD/C (n=5 patients) and LGD (n=25 patients). Histological confirmation was used as the gold standard. Performance metrics included sensitivity, specificity, accuracy, and area under the precision-recall curve (AUC-PR). To comprehensively determine the model's performance, we evaluated multiple thresholds for the confidence score and the IoU, between 0.45 and 0.55 with a step of 0.02 ([0.45, 0.47, 0.50, 0.52, 0.55]). The datasets were split into training (70%), validation (20%), and testing (10%) subsets.

Results

For solid lesions, the model differentiated PDAC from PNETs with an accuracy of 91.8%, with sensitivities of 96.2% and 97.8%, respectively. The AUC-PR was 0.982 for PDAC and 0.977 for PNET. Regarding cystic lesions the model detected mucinous cystic lesions with a sensitivity of 97.5%, an overall accuracy of 89.7% and an AUC-PR of 0.977. When the subset of IPMNs was specifically analyzed, the model distinguished IPMNs with HGD/C from those with LGD with a sensitivity of 97.5%, specificity of 94.3%, and an overall accuracy of 89.8%. The AUC was 0.988.

Conclusion

This is the first multicenter, transatlantic AI study aiming to provide a holistic approach to both solid and cystic pancreatic lesions, focusing on those with a significantly higher malignant potential. Our models achieved high accuracy in identifying and differentiating these conditions. Furthermore, to our knowledge this is the first integrative model to include the ability to stratify IPMNs into LGD and HGD/C, which has significant clinical implications, potentially reducing unnecessary surgeries and ensuring timely intervention for high-risk patients. The inclusion of data from diverse regions reduces demographic bias and enhances model generalizability. Real-time implementation of these solutions could significantly improve decision-making in EUS and optimize patient outcomes.