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DEEP LEARNING AND AUTOMATIC DIFFERENTIATION OF PANCREATIC LESIONS IN ENDOSCOPIC ULTRASOUND – A TRANSATLANTIC STUDY

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Introduction

Accurate diagnosis of pancreatic lesions poses a considerable challenge due to the involvement of multiple examinations in the diagnostic process. Endoscopic ultrasound (EUS) stands out as a valuable tool enabling characterization and tissue sampling of both cystic and solid lesions, thereby influencing treatment decisions and follow-up strategies significantly. Pancreatic cystic neoplasms (PCNs) encompass mucinous (M-PCN) and non-mucinous lesions (NM-PCN), while pancreatic adenocarcinoma (P-ADC) remains the most prevalent cause of pancreatic solid lesions (PSL), trailed by pancreatic neuroendocrine tumors (P-NET). Despite the potential benefits of artificial intelligence, particularly deep learning algorithms, in EUS, research on the role of convolutional neural networks (CNNs) in pancreatic lesions is scarce.

Aims & Methods

This multicentric study aims to develop a CNN tailored for the detection and differentiation of PCNs (specifically M-PCN and NM-PCN) as well as PSLs (particularly P-ADC and P-NET). Our group developed a retrospective multicenter study based on patients submitted to EUS for pancreatic lesion characterization. A trinary CNN differentiated normal pancreas tissue from M-PCN and NM-PCN. A binary CNN distinguished P-ADC from P-NET. The total dataset was divided into a training and testing dataset (used for model’s evaluation) in a 90/10% ratio. The model performance was evaluated through its sensitivity, specificity, positive and negative predictive values and accuracy. The Sci-kit learn v0.22.2 was used for statistical analysis of the model.

Results

A total of 378 videos of EUS performed for evaluation of pancreatic lesions during November 2017 and December 2023 in four reference centers (Hospital Universitario Puerta de Hierro Majadahonda, n=234; Centro Hospitalar Universitário São João, n=33; New York University Langone and Manhattan Hospital, n=60; Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, n=51) were used for development of the model. A total of 126.000 frames were extracted for development of CNN: 64.286 of P-ADC, 29.153 of P-NET, 4.858 of normal pancreas, 19.528 of M-PCN and 8.175 of NM-PCN.
Pancreatic Cystic Neoplasms.
The trinary CNN was developed in order to distinguish between normal pancreas images and cystic lesions, differentiating M-PCN from NM-PCN. The deep learning model identified normal pancreas images with 98.6% sensitivity, 99.2% specificity and 99.1% accuracy. M-PCN were detected with 98.9% sensitivity, 99.1% specificity, a PPV of 99.4% and NPV of 98.3%, with a global accuracy of 99%. Finally, the testing dataset of our CNN revealed 99.3% sensitivity, 99.9% specificity, 99.9% PPV, 99.8% NPV and 99.8% accuracy for the diagnosis of NM-PCN.
Pancreatic Solid Lesions
The binary CNN was developed to differentiate between the two commoner PSL, namely P-ADC and P-NET. The testing dataset revealed 94.0% accuracy for the distinction between the lesions, with 98.7% and 83.7% sensitivities for P-ADC and PNET, respectively.

Conclusion

Our group developed a deep learning model capable of differentiating the main types of pancreatic solid lesions, as well as pancreatic cystic lesions, with high performance values. To our knowledge this is the first multicentric approach for the development of an AI algorithm designed to simultaneously characterize cystic and solid pancreatic lesions.. The development of deep learning models may help differentiate pancreatic lesions.Larger multicentric and prospective studies are needed for the validation and future clinical implementation of this technology.

References

Kuwahara T, Hara K, Mizuno N, et al. Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. DEN Open. 2024;4(1):e267.

DEEP LEARNING AND AUTOMATIC DIFFERENTIATION OF PANCREATIC LESIONS IN ENDOSCOPIC ULTRASOUND – A TRANSATLANTIC STUDY

Belén Agudo Castillo 1, Miguel Mascarenhas-Saraiva 2, mariano gonzalez haba 1, Jessica Widmer 3, Tamas Gonda 3, Marcos Eduardo Lera dos Santos 4, Filipe Vilas-Boas 2, Francisco Mendes 2, Manuel Guilherme Macedo 2

1 Hospital Universitario Puerta de Hierro Majadahonda, Majadahonda, Spain

2 Centro Hospitalar São João, Porto, Portugal

3 New York University Langone Hospital, New York, United States

4 Hospital das Clinicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil

Event

UEG Week Vienna 2024

Topics

Digestive Oncology Endoscopy Pancreas Radiology & Imaging

Submission format

Abstract

Session

Pancreatic endoscopy and patient outcome

Citation

United European Gastroenterology Journal 2024; 12 (Supplement 8)

Published

2024

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PATIENT WITH PERCUTANEOUS ENDOSCOPIC GASTROSTOMY: COLLABORATION WITH PRIMARY HEALTH CARE

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Introduction

The Percutaneous Endoscopic Gastrostomy (PEG) tube is used to provide enteral nutrition when oral intake is not adequate.
Regular follow-up of PEG patients is essential to detect complications and clarify doubts. A partnership protocol was developed with ACES Lisboa Central (ACESLC) and Loures/Odivelas (ACESLO) to improve continuity of care.

Results & Findings

We analyzed data from PEG patients followed between 2023 and 2024. Inclusion criteria for referral to ACES included:
• Clinical stabilization;
• Absence of PEG-related complications requiring hospital admission;
• Willingness and ability of the patient/caregiver to participate actively in care.
Monitoring of the protocol was based on quarterly evaluation of five performance indicators:
1st: Percentage of patients referred to ACES, out of the total seen in PEG review consultations;
2nd: Percentage of referred patients who returned to hospital before the expected date due to complications;
3rd: Percentage of cases in which complete referral documentation, as per protocol, was sent to ACES;
4th: Percentage of cases with confirmation that ACES sent back all required clinical data;
5th: Percentage of compliance with scheduled PEG follow-up appointments at ACESLO.

Indicators	2023				2024
Indicators	1st Quarterly	2nd Quarterly	3rd Quarterly	4th Quarterly	1st Quarterly	2nd Quarterly	3rd Quarterly	4th Quarterly
1st	9%	6%	6%	5%	4%	2%	2%	3%
2nd	40%	0%	29%	20%	10%	25%	0%	0%
3rd	100%	100%	100%	100%	100%	100%	0%	100%
4th	33%	75%	67%	33%	100%	100%	0%	100%
5th	100%	100%	0%	100%	100%	0%	0%	100%

Summary & Discussion

The 1st indicator shows low referral rates in both years, mainly due to out-of-area institutionalization, clinical worsening, or death.
The 2nd indicator indicates fewer unplanned hospital readmissions among referred patients; when they occurred, complications were the cause.
Results show fewer complications and emergency visits, reflecting the benefit of close ACES collaboration.
The 3rd indicator reflects 100% compliance in sending referral documentation.
The 4th indicator showed gaps in home visit records in 2023, improved in 2024 through better coordination.
One challenge was the mismatch between designated ACES nurses and those conducting home visits, which required articulation with family health teams (USF).
The 5th indicator confirms overall adherence to scheduled PEG reviews.

Conclusion

The protocol streamlined follow-up for PEG patients.
In 2024, a previous limitation was overcome, allowing referrals of institutionalized patients within the protocol’s coverage.
As a pioneering project, we aim to share these findings to support protocol expansion to other units.

References

Kurion, M., Andrews, R., Tattersall, R., McAlindon, M., Wong, E., Johnston, A., Hoeroldt, B., Dear, K. & Sanders, D. (2017). Gastrostomies preserve but do not increase quality of life for patients and caregivers. Clinical Gastroenterology and Hepatology. 15, 1047-1054.
Lee, C.,Kang, H., Lim, Y., Lee, J., Koh, M., Lee, J., Yang, C. & Kim, J. (2013). Comparison of complications between endoscopic and percutaneous replacement of percutaneous endoscopic gastrostomy tubes. JKoren Medical Sciences. 28, 1781-1787.
Lucendo, A. J. & Friginal-Ruiz, A. B. (2014). Percutaneous endoscopic gastrostomy: An update on its indications, management, complications, and care. Revista Española de Enfermedades Digestivas. 106(8), 529-539.