Introduction

There are several reports about the diagnosis of early gastric cancers (EGCs) using artificial intelligence (AI) systems, but satisfactory data for sensitivity and specificity are not yet available. Linked color imaging (LCI) produces high color contrast between malignant lesions and surrounding mucosa compared to white light imaging (WLI). Therefore, LCI combined with AI (LCI-AI) has the potential to further improve the diagnosis of gastric cancer compared with WLI combined with AI (WLI-AI). We developed three new LCI-AI models to determine their sensitivity and specificity for the diagnosis of EGCs.

Aims & Methods

Three models including a convolutional neural network (CNN) model (high resolution net (HRNet)), a transformer model (Segformer) and a hybrid (combined CNN and transformer) model (Uniformer), were based on MMSegmentation. When each model recognized a gastric cancer lesion from test image data, it was shown with the existing position as a blue area on WLI images and green area on LCI images. Non-neoplastic mucosa was shown as a black area on both images. Endoscopic images using WLI and LCI (LASEREO 7000 system series and scopes of EG-L600ZW and EG-L600ZW7, Fujifilm Corporation, Tokyo, Japan) were collected from patients with EGCs from January 2015 to December 2018 at Jichi Medical University Hospital. A total of 1970 images including 1179 WLI and 791 LCI images of 236 EGCs were used for training the three LCI-AI models. Another set of 414 EGC images was selected for validation from 43 cases. Primary and secondary endpoints were the sensitivity and specificity of each AI model, respectively.
The image-based performance metric was based on Dice loss, calculated by subtracting the mean Dice similarity score. A Dice score >20% was defined as the correct diagnosis of cancer because EGCs sometimes have unclear demarcation lines. The correct diagnosis of a non-malignant lesion was defined as no reactive black areas in surrounding mucosae. Each case had from four to 21 images. Therefore, the median Dice score was used as a representative value in each case to evaluate performance. All three models’ programming and data analysis were supported by AI Medical Service Inc. (Tokyo, Japan).

Results

Analysis of lesions in all EGC images showed both sensitivity (82.8%) and specificity (83.4%) with LCI-AI to be high and similar to those with WLI-AI (83.1% and 84.3%, respectively). Sensitivities of LCI-AI/ WLI-AI among HRNet, SegFormer and UniFormer were 80.9%/81.6% (p=0.625), 83.8%/83.8% (p=0.224) and 83.8%/83.8% (p=0.450), respectively and specificities were 80.0%/86.0% (p=0.120), 86.4%/84.8% (p=0.672) and 86.8%82.0%/ (p=0.214), respectively. Representative Dice values among cases with EGCs showed sensitivities for LCI-AI using HRNet, SegFormer and UniFormer to be extremely high, at 92.7% 90.9% and 93.0%, respectively, although not significantly different when compared to those using WLI-AI, 84.6% (p=0.67), 85.4% (p=0.15) and 80.5% (p=0.19), respectively. Higher sensitivities were found regardless of lesion size, morphology, and Helicobacter pylori status. All sensitivities using LCI-AI were higher than 80% whereas those using WLI-AI varied from 64% to 89%.

Conclusion

LCI-AI showed high sensitivity and specificity for the diagnosis of EGCs using all three models tested, suggesting potential for practical clinical use in the near future.

Disclosure

Hiroyuki Osawa and Hironori Yamamoto have consultant relationships with Fujifilm Corporation and have received honoraria, grants and royalties from the company. Hiroyuki Osawa has a consultant relationship with AI Medical survice inc. and has recieved honoraria.This study received no funding from Fujifilm Corporation and AI Medical survice inc. nor any other funding source.

Introduction

Psychiatric comorbidity, most commonly measured as severity of anxiety and depression symptoms, has been found to be associated with IBS symptom severity. However, available studies have rarely assessed the importance of formal psychiatric diagnoses on disease burden in IBS. In this analysis from a pan-European project (DISCOvERIE) focusing on the relevance of comorbid mental (anxiety, depression) and/or somatic (fibromyalgia, chronic fatigue syndrome) diagnoses in patients with IBS, we aimed to assess whether presence of psychiatric diagnoses according to the DSM-5 criteria has an impact on overall symptom severity in IBS.

Aims & Methods

We included patients with IBS according to the Rome IV criteria from nine different European centers. Exclusion criteria included the presence of severe psychiatric disease (i.e. bipolar disorder, schizophrenia, autism spectrum disorder, schizoaffective disorder, or organic psychiatric disorder demanding psychopharmacotherapy, psychiatric day-care or inpatient treatment). The patients underwent The Mini International Neuropsychiatric Interview (MINI) to diagnose major psychiatric diagnoses (a total of 17). Fibromyalgia and chronic fatigue were diagnosed by current diagnostic criteria. They also filled in questionnaires assessing Severity of IBS symptoms (IBS-SSS), depression (PHQ-9), anxiety (GAD-7), somatic symptoms (PHQ-15), and fibromyalgia symptoms (FIQ). Severity of symptoms were compared between groups with increasing number of psychiatric diagnoses using One-Way Between-Groups ANOVA with linear trend analysis.

Results

In total, 617 patients with IBS (median age 44 years, 80% females) were included. Among the IBS patients, 269 (44%) had none of the mental (anxiety, depression) or somatic (fibromyalgia, chronic fatigue syndrome) comorbidities (median age 44 y, 75% females), whereas 174 (28%) had comorbid mental disorder only (median age 38 y, 82% females), 49 (8%) had comorbid somatic disorder only (median age 50 y, 86% females) and 124 patients (20%) had mental and somatic comorbidities (median age 47 y, 86% females). The most common psychiatric diagnoses among the IBS patients were depression (n=284), generalized anxiety disorder (n=145) and panic disorder (n=38) followed by agoraphobia (n=23) and social anxiety (n=19). Participants were divided into four groups according to the number of psychiatric diagnoses (0, 1, 2, and ≥3). The severity of IBS, depression, anxiety, somatic, and fibromyalgia symptoms all increased gradually with increasing number of psychiatric diagnoses. The effect sizes (partial eta squared) were small for IBS symptoms, medium for somatic symptoms and large for the other severity measures (Table 1).

Table 1: Symptom severity in groups with increasing number of psychiatric diagnoses

Conclusion

This multicenter pan-European study shows that psychiatric diagnoses are common among IBS patients and that the overall disease burden in IBS is associated with psychiatric comorbidity. These findings highlight the importance of taking psychiatric comorbidity into consideration and to acknowledge the relevance of gut-brain axis interactions when managing patients with IBS.

Disclosure

Hans Törnblom: Consultant or advisory board member for Allergan, Cinclus Pharma, Medifactia, and VIPUN and as a speaker for Tillotts, Takeda, and Shire.
Daisy Jonkers: Part of the work of DJ, outside the submitted work has been financed by public-private partnership grants of Top Knowledge Institute (TKI) Agri&Food and Health Holland, by the Carbokinetics program as part of the NWO-CCC Partnership Program, by Organic A2BV/Mothersfinest BV.

Introduction

The LiverRisk score has been developed as blood-based tool to estimate liver stiffness measurement (LSM) in the general population (i.e., in subjects without known chronic liver disease (CLD), and thus, detect compensated advanced chronic liver disease (cACLD; LSM ≥10kPa) and stratify their risk of liver-related events. Notably, its utility in other settings is unclear as a one-size-fits-all blood-based test. We aimed to evaluate its diagnostic/prognostic performance in patients referred to specialized hepatology outpatient clinics (i.e., tertiary care).

Aims & Methods

Subjects with known/suspected CLD at first LSM (cohort-I, n=5897) or first referral & LSM (cohort-II, n=1558) were retrospectively included. Calibration/agreement of the LiverRisk score with LSM was assessed as well as its diagnostic accuracy for LSM-defined cACLD compared to FIB-4/APRI. Finally, its predictive performance for hepatic decompensation was evaluated.

Results

In cohort-I/II, mean age was 48.3/51.8years, 44.2%/44.7% were female, predominant etiologies were viral hepatitis (51.8%)/MASLD (63.7%), median LSM was 6.9 [5.1-10.9]/ 5.8 [4.5-8.8]kPa, and 1690 (28.7%)/ 322 (20.7%) had cACLD.
Despite a moderate correlation (Pearson’s r: 0.325/0.422), the LiverRisk score systematically underestimated LSM (mean difference according to the Bland-Altman-method: -2.9/-1.8 points≙kPa). Variation of observed differences was considerable (95%CI of agreement: 39.4 and 35.2 points≙kPa), and showed a clear pattern towards larger disagreement at higher values with lower LiverRisk score than LSM.
The diagnostic accuracy of the LiverRisk for cACLD (AUROC: 0.757 [95%CI: 0.744-0.770]/ 0.790 [95%CI: 0.762-0.819]) was comparable to FIB-4 (0.769/0.813) and APRI (0.747/0.765). The cut-off of 10points≙kPa yielded an overall accuracy of 74.2%/81.2%, high specificity (91.9/93.4%), but low negative predictive value (76.6/84.5%) with false-negative results in 20.0%/13.5% in cohort-I/II, respectively.
In cohort-I, 208 (3.5%) patients developed hepatic decompensation (median follow-up: 4.7years). The LiverRisk score showed a reasonable accuracy for predicting hepatic decompensation within 2 years (AUROC: 0.817); however, it was inferior to LSM (0.891, p<0.001) and FIB-4 (0.912, p<0.001). The strata of LiverRisk score (and of LSM/FIB-4) discriminated patient groups of distinct risks for hepatic decompensation.

Conclusion

While being a major step forward for screening subjects without known liver disease in primary care, the LiverRisk score was not superior (vs. FIB-4) for diagnosis of cACLD or (vs. FIB-4/LSM) for prediction of hepatic decompensation in a tertiary care setting, i.e., in cohorts with a higher pre-test probability of cACLD. Notably, FIB-4 even outperformed the LiverRisk score in terms of prognostic utility.