Introduction

Distinguishing gastrointestinal stromal tumors (GISTs) with high malignant risk is essential for the management of small submucosal tumors (SMTs).

Aims & Methods

We aimed to develop a machine learning model based on endoscopic ultrasound (EUS) images to diagnose GISTs and non-GISTs as well as distinguish their potential malignancy. EUS images of 205 small gastric SMT (<2cm) patients were retrospectively included in the model development phase. An automatically optimized radiomics modeling system (AORMS) framework was developed to carry out the model learning based on the features extracted from the retrospective cohort. Three-fold cross-validation was used to train the machine learning model and produce the optimal model for differentiating between GIST and non-GIST as well as groups 1 and 4 GIST. Images of 178 patients from different centers were prospectively enrolled in the validation phase of the model. The diagnostic performance of the AORMS was evaluated in the development set and compared with the performance of endoscopists. Then, the performance of the AORMS was evaluated in the prospective validation set.

Results

The overall area under the curve (AUC), accuracy, sensitivity, and specificity of the AORMS were 0.762, 0.708, 0.819 and 0.649 for the diagnose of GIST, and 0.734, 0.872, 0.571 and 0.908 for the risk stratification of group 1 and 4 GIST. The accuracy of five experienced endoscopists ranged from 0.449 to 0.531 in diagnosing GIST, and from 0.702 to 0.809 in distinguishing group 1 and 4 GIST. The overall performance of AORMS is better than that of experienced endoscopists. In the prospective validation set, the AORMS also achieved 0.770 and 0.750 AUC for the diagnosis and risk stratification of small GISTs (<2cm).

Table 1. Diagnostic performance of the AORMS.

	AUC	ACC	SENS	SPEC	PPV	NPV	LR+	LR-
Model development cohort
Diagnosis of GIST	0.762	0.708	0.819	0.649	0.750	0.644	2.333	0.279
Risk stratification of GIST	0.734	0.872	0.571	0.908	0.571	0.897	6.207	0.472
Independent validation cohort
Diagnosis of GIST	0.770	0.742	0.662	0.806	0.774	0.644	3.412	0.419
Risk stratification of GIST	0.750	0.872	0.667	0.757	0.667	0.795	2.745	0.440

AORMS, automatically optimized radiomics modeling system; ACC, accuracy; AUC, area under the curve; GIST, gastrointestinal stromal tumor; LR+, positive likelihood ratio; LR-, negative likelihood ratio; NPV, negative predictive value; PPV, positive predictive value; SENS, sensitivity; SPEC, specificity.

Conclusion

AORMS showed good and stable performance in diagnosis and risk stratification of GISTs, which may help endoscopists provide more accurate diagnosis for small gastric SMTs (<2cm).

Introduction

Hepatocellular carcinoma (HCC) is a global problem being fourth in terms of cancer-related deaths¹. Contrast-enhanced ultrasound (CEUS) is used as an add-on test to confirm focal liver lesions suspected as HCC at prior diagnostic tests such as ultrasound or alpha-foetoprotein, or both^2,3. According to guidelines^4,5,6, a single contrast-enhanced imaging techniques, with either computed tomography (CT) or magnetic resonance imaging (MRI) showing the typical hallmarks is sufficient to diagnose HCC in cirrhotic patients. However, a significant number of HCC show atypical imaging features, and therefore, are missed at imaging.
The advantages of CEUS over CT and MRI is the use of contrast agents not containing iodine and that are not nephrotoxic, and quick image acquisition. Despite the advantages, the use of CEUS in the diagnostic algorithm for HCC remains controversial, with disagreement on relevant guidelines.

Aims & Methods

The aim was to assess the diagnostic accuracy of CEUS for the diagnosis of HCC of any size and at any stage in adults with chronic liver disease, in a surveillance programme or in a clinical setting and to assess the diagnostic accuracy of CEUS for the diagnosis of resectable HCC in people with chronic liver disease and identify potential sources of heterogeneity in the results.
We included studies assessing the diagnostic accuracy of CEUS for the diagnosis of HCC in adults with chronic liver disease, with cross-sectional designs, using one of the acceptable reference standards, such as pathology of the explanted liver, and histology of resected or biopsied focal liver lesion with at least a six-month follow-up.
Standard Cochrane methods ⁷ were used to screen studies, extract data, and assess the risk of bias and applicability concerns, using the QUADAS-2 checklist⁸. We used the bivariate model and provided estimates of summary sensitivity and specificity. We assessed the certainty of the evidence using GRADE^9,10. We presented uncertainty-of-the-accuracy estimates using 95% confidence intervals (CIs).

Results

We included 23 studies with 6546 participants. Studies were published between 2001 and 2021. We judged all 23 studies at high-risk of bias in at least one domain, and 13/23 studies at high concern for applicability. Most studies used different reference standards to exclude the presence of the target condition. The time interval between the index test and the reference standard was rarely defined. We also had major concerns on their applicability due to the participant characteristics.
CEUS for HCC of any size and stage: sensitivity 77.8% (95% CI 69.4% to 84.4%) and specificity 93.8% (95% CI 89.1% to 96.6%) (23 studies, 6546 participants; very low-certainty evidence).
CEUS for resectable HCC: sensitivity 77.5% (95% CI 62.9% to 87.6%) and specificity 92.7% (95% CI 86.8% to 96.1%) (13 studies, 1257 participants; low-certainty evidence).
The observed heterogeneity in the results remains unexplained.

Conclusion

Using CEUS, as an add-on test following abdominal ultrasound, to diagnose HCC of any size and stage, 22% of people with the tumor would be missed, and 6% of people without would unnecessarily undergo further testing or inappropriate treatment. As to resectable HCC, we found that 23% of people with resectable tumor would incorrectly be unresected, while 8% of people without HCC would undergo further inappropriate testing or treatment. The uncertainty resulting from the high risk of bias of the included studies, heterogeneity, and imprecision of the results and concerns on their applicability limit our ability to draw confident conclusions.

References

1) Forner A et al. Hepatocellular carcinoma. Lancet 2018;391(10127):1301-14.
2) Niu Y, et al. Contrast-enhanced ultrasonography for the diagnosis of small hepatocellular carcinoma: a metaanalysis and meta-regression analysis. Tumour Biology
2013;34(6):3667-74.
3) VogelA, et al. Hepatocellular carcinoma: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Annals of Oncology2019;30(5):871-3.
4) Galle PR, et al. EASL clinical practice guidelines: management of hepatocellular carcinoma. Journal of Hepatology 2018;69(1):182-236.
5) Omata M, et al.Asia–Pacific clinical practice guidelines on the management of hepatocellular carcinoma: a 2017 update. Hepatology International 2017;11:317-70
6) HeimbachJK,, et al. AASLD guidelines for the treatment of hepatocellular carcinoma. Hepatology 2018;67:358-80.
7) Macaskill P, et al. Chapter 10: Understanding meta-analysis Draft version (13 May 2022) for inclusion in: Deeks JJ, Bossuyt PM, Leeflang MM, TakwoingiY, editor(s). Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy Version 2. London: Cochrane.
8) Whiting PF, Ret al. QUADAS-2 Group. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Annals of Internal Medicine 2011;155(8):529-36.
9) McMaster University (developed by Evidence Prime) GRADEpro GDT. Version accessed 26 January 2022. Hamilton (ON): McMaster University (developed by Evidence Prime). Available at gradepro.org.





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Disclosure

nothing to disclose

Introduction

Patients with type 2 diabetes are prone to the development of dysbiosis, which can manifest as intestinal bacterial overgrowth (SIBO). This may explain the pathomechanism of abdominal symptoms that sometimes affect this group of patients. Disturbances in the composition and quantity of intestinal microbiota translate to impaired production of short-chain fatty acids (SCFA) in the intestinal mucosa. To increase the concentration of butyrate in the lumen of the intestine, oral microencapsulated sodium butyrate supplementation can be proposed. Given the limited research conducted on the topic of the effects of sodium butyrate supplementation in alleviating abdominal symptoms in patients with type 2 diabetes, it became the topic of our interest.

Aims & Methods

The aim of the study was to evaluate the effectiveness of oral sodium butyrate supplementation in the population of patients with type 2 diabetes in alleviating abdominal symptoms and to estimate the incidence of small intestinal bacterial overgrowth in this group. In addition, the impact of the intervention on the carbohydrate metabolism was assessed.A prospective, single-centre, randomized, placebo-controlled study was conducted between October 2022 and April 2023.
52 patients with type 2 diabetes and gastrointestinal symptoms were randomly assigned to one of 2 groups - a group receiving microencapsulated sodium butyrate (Intesta Max) at a dose of 1.5 g/day and a group receiving placebo. As 8 patients (15.3%) were lost to follow-up, for the preliminary analysis 44 patients were included. The study lasted for 6 weeks. Before and after the intervention the presence of abdominal pain, diarrhoea, constipation and flatulence was assessed, laboratory tests and hydrogen breath tests were performed, the HOMA index and body mass index were calculated.

Results

At baseline SIBO was diagnosed in 73.1% of patients in the experimental group and 58.8% of patients in the control group. After 6 weeks of the intervention there was a significant drop in SIBO occurrence in sodium butyrate group – 22.2% (p=0.001). The incidence of SIBO in the control group did not change – 64.7% (p=0.21).All patients included in the study declared abdominal pain before the intervention. After 6 weeks the pain was reported by 42.3% of the patients receiving the sodium butyrate vs. 88.4% of the patients receiving placebo (p=0.002). After the intervention there was a significant difference between the groups regarding diarrhea – 18.5% and 58.8% (p=0.006) in sodium butyrate and placebo groups respectively. A decrease in the incidence of bloating in the sodium butyrate group was observed - 100% before vs. 29,6% after the intervention (p<0.00001). The study also showed a decrease in BMI level, the HOMA index and glycated hemoglobin level after consumption of the sodium butyrate vs. placebo for 6 weeks. These results need confirmation in a larger group.

Conclusion

To our knowledge this is the first study reporting the effectiveness of oral butyric sodium supplementation in relieving abdominal symptoms (abdominal pain, diarrhea, flatulence) in patients with type 2 diabetes. Furthermore, an improvement in carbohydrate metabolism parameters was noted and body weight decrease was observed after the intervention.

Disclosure

Sodium butyrate (Intesta Max) we received from the company that produces it.