Introduction

Endoscopic ultrasound-guided tissue acquisition, including both fine needle aspiration (EUS-FNA) and fine needle biopsy (EUS-FNB), has been widely performed to obtain samples from pancreatic and non-pancreatic lesions.^{1, 2} The impact of the diagnostic yield between FNA and FNB remains unclear.

Aims & Methods

We conducted this study to compare the diagnostic performance and accuracy of the 22-gauge FNA needles with the 22-gauge FNB needles in sampling solid pancreatic and non-pancreatic lesions. This is a prospective multicenter study conducted on 465 cases presented with solid pancreatic or non-pancreatic lesions.

Results

Patients were 275 male and 190 females with a mean age of 59 years. Three hundred twenty-seven patients had solid pancreatic lesions, while 138 had non-pancreatic lesions; 245 cases underwent EUS-FNA, and the remaining 211 cases underwent EUS-FNB. The presence of intact tissue core and sample adequacy were significantly higher in the FNB cases in solid pancreatic and non-pancreatic lesions. Blood contamination was significantly more in cell blocks and smears of EUS-FNA compared to that of EUS-FNB in solid pancreatic and non-pancreatic lesions. Based on histologic assessment of cell block only, EUS-FNB had more diagnostic accuracy (99%) than FNA (61%) (P-value<0.005). However, cytological diagnosis by smears only showed no significant difference. The combined cytological and histological evaluation had 100% sensitivity, specificity, and accuracy.

Conclusion

EUS-FNA and EUS-FNB have comparable accuracy in diagnosing solid pancreatic and non-pancreatic lesions without ROSE. EUS-FNB is superior to EUS-FNA in acquiring intact tissue core and adequate samples with little blood contamination. Based on histological assessment (cell block/tissue core) only, EUS-FNA has less accuracy than EUS-FNB in diagnosing solid pancreatic lesions.

References

1. Wani S, Muthusamy VR, McGrath CM, et al. AGA white paper: opti- mizing endoscopic ultrasound-guided tissue acquisition and future di- rections. Clin Gastroenterol Hepatol 2018; 16: 318-27.

1. Wong T, Pattarapuntakul T, Netinatsunton N, et al. Diagnostic performance of endoscopic ultrasound-guided tissue acquisition by EUS-FNA versus EUS-FNB for solid pancreatic mass without ROSE: a retrospective study. World J Surg Oncol. 2022; 20(1): 215. doi:10.1186/s12957-022-02682-3.

Introduction

The incidence and severity of metabolic dysfunction-associated steatotic liver disease (MASLD) are notably higher in individuals diagnosed with type 2 diabetes mellitus (T2DM). However, studies evaluating the progression of MASLD in patients with T2DM and normal serum aminotransferase (ALT) levels are limited.

Aims & Methods

This 5-year prospective cohort study aimed to evaluate the progression of MASLD in T2DM patients with normal ALT. People with type 2 diabetes who had MASLD (defined by controlled attenuation parameter (CAP) >250 dB/min) and ALT levels that stayed normal (defined by ALT ≤40 IU/L for at least two occasions spanning at least six months) at Rajavithi Hospital, Bangkok, from November 2016 to 2017 were included. We excluded patients with viral hepatitis and alcohol consumption exceeding moderate levels. Transient elastography (TE) and CAP measurements were performed at baseline and ≥5 years of follow-up. The cutoffs to define significant and advanced fibrosis were TE >8 kPa and TE >12 kPa, respectively. All patients were instructed to reduce their weight and abstain from herbal remedies; medications for comorbidities were prescribed without limitations, subject to the physician's assessment. (most patients were under endocrinologist care.)

Results

The study included 84 patients, 57% of whom were female and had a mean age of 58.5 years. Of these, 62% were obese, with a mean BMI of 28.6 kg/m2. At baseline, the median TE was 6.92±3.35 kPa; 57 (68%) had no significant fibrosis, 22 (26%) had significant fibrosis, and 5 (6%) had advanced fibrosis, and after a 5-year follow-up, the median TE was 6.54±3.57 kPa; 69 (82%) had no significant fibrosis, 15 (18%) had significant fibrosis, and 2 (2%) had advanced fibrosis. During the follow-up, fibrosis decreased in 45 (54%), remained stable in 6 (7%), and increased in 33 (39%) patients, while a small proportion of patients experienced changes in the stages of fibrosis: 1.2% progressed from significant to advanced fibrosis; 17.4% regressed from advanced to significant fibrosis or transitioned from significant to no fibrosis; 12.8% remained stable with significant and advanced fibrosis; and the majority (66.3%) remained without significant fibrosis. A significant factor associated with fibrosis progression was alcohol consumption (p=0.013), while the use of angiotensin receptor blockers (p=0.042) was associated with fibrosis regression. Significant factors associated with steatosis progression included high baseline BMI (p=0.013), an increase in BMI ≥ 5% (p=0.04), high fasting plasma glucose, and LDL cholesterol levels, while a decrease in BMI ≥ 5% (p=0.002) the use of calcium channel blockers (p=0.038) and coffee consumption >3 cups per day (p=0.031) were associated with steatosis regression. ALT elevation during the follow-up occurred in a small proportion of patients, and it was not significantly associated with fibrosis or steatosis progression.

Conclusion

The majority of MASLD patients with T2DM and normal ALT maintained a relatively stable liver disease during 5 years of follow-up. Obesity, weight gain, alcohol consumption, and inadequate metabolic control were all associated with the progression of MASLD, whereas weight loss, coffee consumption, angiotensin receptor blockers, and calcium channel blockers were all associated with the improvement of MASLD.

Disclosure

I have nothing to disclose

Introduction

Individuals with alcohol use disorder (AUD) are at high risk of liver disease. Despite international clinical practice guidelines recommend screening for liver fibrosis in at risk populations, information regarding fibrosis prevalence, predictive factors and screening strategies in patients with AUD is scarce. Recently, a novel score (LiverRisk score) has been developed to predict liver fibrosis and liver-related outcomes in the general population.

Aims & Methods

We conducted a screening program for liver disease in subjects who attended the Addiction Unit of the Hospital Clínic of Barcelona between 2019 and 2022. Data regarding socio-economic status, medical history and alcohol consumption were recorded. Blood tests and transient elastography (TE) were performed in all patients. The main aim of the study was to analyze the prevalence of significant liver fibrosis in patients with AUD. The secondary objectives were to analyze the prevalence of advanced chronic liver disease in this population, identify the factors associated with the presence of significant liver fibrosis in patients with AUD and to evaluate the diagnostic accuracy of non-invasive liver fibrosis tests (Fibrosis-4 [FIB-4], NAFLD fibrosis score [NFS], Fatty Liver Index [FLI], AST to Platelet Ratio Index [APRI] and LiverRisk score) using TE as the Gold Standard. Significant liver fibrosis was defined as a liver stiffness (LS) value ≥ 8 kPa measured by TE. Advanced liver fibrosis was defined as a LS ≥ 15 kPa.

Results

Out of 334 total patients, 40 had significant liver fibrosis (prevalence of 12%). Out of the 40 patients with significant liver fibrosis, 19 (48%) had advanced chronic liver disease. Multiple factors were associated with the presence of significant fibrosis in univariate analysis; however, on multivariate analysis BMI (p=.04) and GGT levels (p=.001) were confirmed as the only independent predictors. Regarding non-invasive tests, the LiverRisk score was the test with the best diagnostic performance for detecting significant liver fibrosis, with an area under the receiver operating characteristic (AUROC) of 0’854. All other tests had similar diagnostic performance, with AUROC of 0’799 (NFS), 0’798 (FIB-4), 0’797 (FLI) and 0’784 (APRI).

Conclusion

The prevalence of significant liver fibrosis (TE > 8 kPa) in population with at-risk alcohol consumption is high. BMI and GGT are the main factors associated with the presence of liver fibrosis and could be useful for selecting patients with higher risk. The most accurate serological test to assess liver fibrosis in our cohort is the LiverRisk score. These results demonstrate the usefulness of a screening program for early detection of liver disease in patients with AUD and support the implementation of screening programs in this population.

References

1. Bataller R, Cabezas J, Aller R et al. Alcohol-related liver disease. Clinical practice guidelines. Consensus document sponsored by AEEH. Gastroenterology and Hepatology. 2019 Dec 1;42(10):657–76.
2. Caballería L, Pera G, Arteaga I et al. High Prevalence of Liver Fibrosis Among European Adults With Unknown Liver Disease: A Population-Based Study. Clinical gastroenterology and hepatology. 2018 Jul 1;16(7):1138-1145.e5.
3. Berzigotti A, Tsochatzis E, Boursier J et al. EASL Clinical Practice Guidelines on non-invasive tests for evaluation of liver disease severity and prognosis – 2021 update. Journal of Hepatology. 2021 Sep 1;75(3):659–89.
4. Pose E, Pera G, Torán P et al. Interaction between metabolic syndrome and alcohol consumption, risk factors of liver fibrosis: A population-based study. Liver International. 2021 [2021 Jun 16];00:1–9.
5. Ginès P, Castera L , Lammert F et al. Population screening for liver fibrosis: Toward early diagnosis and intervention for chronic liver diseases. Hepatology. 2022 [2023 Oct 17];75:4.
6. Serra-Burriel M, Juanola A, Serra-Burriel F, et al. Development, validation, and prognostic evaluation of a risk score for long-term liver-related outcomes in the general population: a multicohort study [published correction appears in Lancet. 2023 Sep 16;402(10406):964]. Lancet. 2023;402(10406):988-996. doi:10.1016/S0140-6736(23)01174-1

Disclosure

Pere Ginès has received research funding from Gilead & Grifols. He has consulted or attended advisory boards for Gilead, RallyBio, SeaBeLife, Merck, Sharp and Dohme (MSD), Ocelot Bio, Behring, Roche Diagnostics International and Boehringer Ingelheim, and received speaking fees from Pfizer. Ramon Bataller consults for GlaxoSmithKline, Novo Nordisk and Boehringer Ingelheim. He is on the speakers’ bureau for AbbVie and Gilead. Pol Bruguera is on the speakers’ bureau and received grants from Lundbeck. He received grants from Camurus and Pfizer. Maria T. Pons-Cabrera is employed by Lundbeck, Pfizer, and Esteve. Hugo López-Pelayo received grants from Lundbeck. The remaining authors declare that they have no conflicts of interest.

Introduction

Capsule endoscopy (CE) is commonly used as the initial exam in situations of suspected mid-gastrointestinal bleeding, after normal upper and lower endoscopy. Although the assessment of the small bowel is the primary focus of CE, detection of upstream or downstream vascular lesions may also be clinically significant. This study aimed to develop and test a Convolutional Neural Network (CNN)-based model for panendoscopic automatic detection of vascular lesions during CE.

Aims & Methods

A multicentric retrospective study was conducted, based on 1022 CE exams. We used a total of 35655 frames, from seven types of CE devices (PillCam Colon 1, PillCam Crohn's, PillCam SB1, PillCam SB3, OMOM HD, Olympus, Mirocam), of which 11091 were considered to have vascular lesions (angiectasia or varices) after triple validation. We divided data into a training and a validation set, the latter being used to evaluate the model’s performance. At time of division, all frames from a given patient were assigned to the same dataset. Our primary outcome measures were sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV) and an area under the precision-recall curve (AUC-PR).

Results

A total of 35655 frames were used, with 11091 including vascular lesions. The validation data set contained 2433 frames with normal mucosa and 933 vascular lesions.
Sensitivity and specificity were 86.4% and 98.3%, respectively. PPV was 95.2%, while the NPV was 95.0%. Overall accuracy was 95.0%. The AUC-PR value was 0.96. The CNN processed 115 frames per second.

Conclusion

The study presents promising results for the application of AI deep learning models in panendoscopic automatic detection of vascular lesions in various gastrointestinal topographies. The methodological details, including the patient-split design and the use of frames from different types of CE devices, suggest that the model could be effectively used in routine clinical practice independently of the device brand. This study highlights the potential of panendoscopic evaluation of all gastrointestinal tract and the need for further research in this area. Overall, the high diagnostic performance of this CNN in multibrand devices addresses an important issue of technological interoperability, allowing it to be replicated in multiple technological settings. However, the study has some limitations, including a small number of frames and a retrospective design, which may introduce selection bias, therefore prospective and multicentric studies are needed before introducing this deep learning model into clinical practice. This is the first proof-of concept AI deep learning model developed for panendoscopic automatic detection of vascular lesions during CE.

References

Triester, S.L., et al., A meta-analysis of the yield of capsule endoscopy compared to other diagnostic modalities in patients with obscure gastrointestinal bleeding. Am J Gastroenterol, 2005. 100(11): p. 2407-18.
Liao, Z., et al., Indications and detection, completion, and retention rates of small-bowel capsule endoscopy: a systematic review. Gastrointest Endosc, 2010. 71(2): p. 280-6.
Rondonotti, E., et al., Small-bowel capsule endoscopy and device-assisted enteroscopy for diagnosis and treatment of small-bowel disorders: European Society of Gastrointestinal Endoscopy (ESGE) Technical Review. Endoscopy, 2018. 50(4): p. 423-446.
Ribeiro, T.F.-U.I.C.H., Colon capsule endoscopy and artificial intelligence: a perfect match por panendoscopy, in Artificial Intelligence in Capsule Endoscopy, C.H. Mascarenhas M, Macedo G, Editor. 2023, Academic Press.
Tsuboi, A., et al., Artificial intelligence using a convolutional neural network for automatic detection of small-bowel angioectasia in capsule endoscopy images. Dig Endosc, 2020. 32(3): p. 382-390.
Saurin, J.C., et al., Diagnostic value of endoscopic capsule in patients with obscure digestive bleeding: blinded comparison with video push-enteroscopy. Endoscopy, 2003. 35(7): p. 576-84.
Mascarenhas, M., et al., Deep learning and colon capsule endoscopy: automatic detection of blood and colonic mucosal lesions using a convolutional neural network. Endosc Int Open, 2022. 10(2): p. E171-e177