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.

Introduction

In Nonalcoholic steatohepatitis (NASH), several pathogenetic mechanisms occur as explained by the “multiple hit pathogenesis”. However, the characteristic inflammation of NASH is orchestrated by the innate immunity, since Kupffer cells are triggered by PAMPs and DAMPs, and recruit monocytes (Mo), which repopulate the liver with new macrophages, perpetuating inflammation.
The recent promising field of immunometabolism showed that myeloid cells undergo a metabolic reprogramming to sustain the pro-inflammatory activity, suggesting novel immunosuppressive approaches by targeting cellular metabolism. However, immune cell bioenergetic alterations in chronic diseases such as NASH, are substantially unexplored.

Aims & Methods

Here, we questioned whether circulating Mo in NASH presented immunometabolic alterations. 20 NASH patients (histologically determined) and 10 healthy controls were enrolled for isolation of circulating CD14+ Mo from peripheral blood. Mo were used for analysis of bioenergetics and mitochondrial function, and RNA/protein isolation. To do this, glycolysis and mitochondrial respiration (MR) were quantified with extracellular acidification rate and oxygen consumption rate, respectively, by using a Seahorse HS Mini analyser (Agilent). Respiratory chain (RC) complexes enzymatic activities and H₂O₂ production rate were analysed. In a second step, the effect of succinate dehydrogenase (SDH) inhibition was investigated in vitro by exposing patient Mo to dimethyl malonate (DMM), and in vivo by using a preclinical model of NASH. In particular, wild-type (C57BL/6) mice were fed with high-fat and high-cholesterol (HF-HC) diet for 8 weeks. From week 6 to week 8 mice were i.p. injected with DMM (160 mg/kg), or vehicle (PBS) every second day.

Results

NASH Mo presented a peculiar bioenergetic profile characterized by a significant increase of both glycolysis and MR compared to controls, with a preponderant change of glycolysis. Basal and maximal respirations were higher in NASH Mo, although this resulted not only in more ATP production, but also in a very high proton leak. In accordance with this, we found that NASH Mo showed a higher enzymatic activity of RC complex I and complex II. However, using pyruvate/malate and succinate as substrates we observed that complex I and II activities produced high levels of H₂O₂ with consequent mitochondrial oxidative stress. Taken together these results highlighted that Mo in NASH have a bioenergetic reprogramming with increase of glycolysis and mitochondrial dysfunction. The mRNA expression of 78 targets, included in the PrimePCR^TM array “mitochondria energy metabolism plus”, showed the significant down-regulation of 7 genes, while 32 genes resulted up-regulated. Most of these genes encoded for RC complex subunits, and in particular, 3 out of 4 SDH subunits resulted more expressed. SDH is an enzyme complex also known as RC complex II, and its role in driving the metabolic rewiring of macrophages has been recently demonstrated. Therefore, considering the higher activity and expression of RC complexes, and in particular the SDH, we tried to expose NASH Mo to DMM, a SDH inhibitor with demonstrated immunomodulatory properties. Interestingly, DMM normalized glycolysis and MR levels, abrogating Il-1β and TNF-α expression. Moreover, HF-HC fed mice were treated with DMM, improving steatohepatitis in terms of liver injury, steatosis and inflammation. The macrophage amount in the liver was reduced and the expression of M2 markers such as arginase1 and CD163 was partially restored by DMM.

Conclusion

Mo immunometabolism might be targeted in NASH.

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.

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.