Introduction

Early gastric cancers after Helicobacter pylori (HP) eradication (HP eradicated EGC), which has become mainstream in Asian, is difficult to determine by even magnifying endoscopy with narrow band imaging (ME-NBI) due to the appearance of low atypical epithelium mimicking with noncancerous epithelial structure. We found that scattered Capillaries Pushed up by Cloudy Mucosa (CPCM), which pathologically influence blood vessels pushed up by cancerous gland ducts, appeared in HP eradicated EGC.

Aims & Methods

Our objective is to investigate whether CPCM detected in ME-NBI can be a supportive marker to diagnosis HP eradicated EGC. This study was a retrospective observational study conducted by at 2 facilities in between 2020 and 2022 under ethical approval. Of 274 lesions confirmed by biopsy after ME-NBI, 238 lesions were included, excluding HP-uninfected lesions and lesions with unknown infection status. HP-infected lesion was designated as group A (42 cancers, 30 non-cancers), and HP eradicated lesion as group B (103 cancers, 63 non-cancers). Sensitivity, specificity, positive predictive value, and negative predictive value of CPCM were calculated for each group, and differences between two groups were also examined by Fisher's exact test. The presence of CPCM was defined as when there were three or more cloudy mucosa in one image captured by ME-NBI with maximum magnification.

Results

CPCM were seen in 45.8% of group A and 33.7% of group B. The group A and B for CPCM diagnostic accuracy was as follows 66.7% and 53.4% for sensitivity, 83.3% and 98.5% for specificity, was 84.8% and 98.2% for positive predictive value, and 64.1% and 56.3% for negative predictive value, respectively. The p-values were 0.195, 0.0126, 0.0248, and 0.4528, respectively.

Conclusion

CPCM showed a higher specificity and positive predictive value for determining post-HP EGC compared with HP-infected EGC, suggesting the potential indicator.

Introduction

Linked color imaging (LCI), a unique image-enhanced endoscopy technique, expands the color range to enhance the recognition of subtle mucosal differences, facilitating early lesion detection. Recently, the advantage of LCI in cancer visibility and detection has been reported especially in Helicobacter pylori (H. pylori)-negative gastric cancer including cases post-eradication.¹ However, the correlations between the color of LCI’s visualized mucosa and histological findings have not been thoroughly investigated.

Aims & Methods

This study aimed to investigate relationships between color values and histological findings visualized by LCI in H. pylori-negative gastric cancer.
Thirty-two patients with H. pylori-negative, differentiated-type intramucosal gastric cancer (≤20 mm) who underwent endoscopic submucosal dissection were retrospectively reviewed. Images captured with LCI and white light imaging (WLI) were analyzed using the CIE1976 (L^*a^*b^*) color space, as defined by the International Commission on Illumination (CIE).² Three regions of interest, each measuring 10 × 10 pixels, were set on the malignant tumor and adjacent mucosa to the border. The values of a^*(red-green) and b^* (yellow-blue) and the color difference (ΔE), which represents the overall difference between the two colors, were calculated. Histopathological evaluation using Hematoxylin-Eosin stained specimens was performed to highlight the demarcation between cancerous and noncancerous mucosa in a width of 1.0 mm inside and outside the lesion. Vascular density, glandular density, mucosal thickness, and the degrees of intestinal metaplasia (IM) were measured. Vascular density was calculated as the percentage of the occupied blood vessels in an area up to 200 μm from the surface. And the proportion of IM relative to the glands was measured and classified as the severe IM (≥60%), moderate IM (≥30% and <60%), or mild IM (<30%) groups.

Results

LCI significantly increased ΔE between the cancer and surrounding mucosa compared with WLI (P < 0.001); however, no significant differences in Δa^* and Δb^* were observed between LCI and WLI. Moderate correlations were observed between LCI a^* values and vascular density (ρ = 0.39, P < 0.001) and between LCI b^* values and the proportion of IM (ρ = −0.49, P < 0.001). Furthermore, the vascular density of cancer in the 0−100 μm depth showed a moderate correlation with the LCI a^* values (ρ = 0.41, P < 0.001). Severe IM significantly decreased the LCI b^* values compared with mild and moderate IM (P = 0.02, P < 0.001).

Conclusion

LCI quantitatively visualizes the superficial vascularity of cancer lesions and the presence of IM of the background mucosa in H. pylori-negative patients. It is a potential noninvasive tool for gastric cancer risk stratification and early detection.

References

1. Kato M, Ono S, Kawada K et al. Diagnostic performance of linked color imaging for gastric cancer by Helicobacter pylori infection status: a subanalysis of the large-scale, multicenter randomized controlled trial LCI-FIND. Helicobacter 2024; 29: e1308.
2. Kuehni RG. Color-tolerance data and the tentative CIE 1976 L a b formula. J Opt Soc Am 1976; 66: 497–500.

Disclosure

Shoko Ono has received lecture and/or consulting fees from Fujifilm.

Introduction

There have been many reports on the usefulness of magnifying endoscopy with image-enhanced endoscopic techniques. In recent years, the magnifying endoscopy simple diagnostic algorithm for early gastric cancer (MESDA-G)^１ has been proposed as a unified diagnostic system. However, it is a “static” diagnostic algorithm that uses still images. We focused on real-time red blood cell flow in subepithelial microvascular visualized by magnifying endoscopy, and clarified that microvascular blood flow rate was significantly lower in early gastric cancer (EGC) than in benign patchy redness in the pilot study².
Moreover, we uniquely developed an automated blood flow rate analysis system (hereinafter “analysis system”) and calculated the cut-off value (1090.9um/sec) for endoscopic diagnosis of EGC using the test dataset. However, comparative studies on diagnostic accuracy between endoscopists and analysis system have not yet been analyzed.

Aims & Methods

The aim of this study was to compare the qualitative diagnostic accuracy of EGC between endoscopists and analysis system. We retrospectively reviewed magnifying endoscopy with blue laser imaging (M-BLI) videos of differentiated-type EGC and patchy redness at our hospital between December 2017 and September 2021. M-BLI videos of 31 differentiated-type EGCs and 40 patchy redness in which red blood cell flow in subepithelial microvascular was visible were included in this study. In the analysis system group, the diagnosis was made according to the above-mentioned cut-off value (1.09 mm/sec) for cancer/non-cancer. In the endoscopist group, 9 endoscopists (5 experts and 4 trainees) reviewed these M-BLI videos and made the diagnosis as cancer/non-cancer according to MESDA-G. We evaluated the diagnostic accuracy in each group, such as sensitivity/specificity/positive predictive value (PPV)/negative predictive value (NPV).

Results

In 31 EGCs, there were more men than women (22/9), the mean age was 72.2±9.5 years, the tumor location was U/M/L=2/8/21, the macroscopic type was elevated/depressed=8/23, the histological type was tub1/tub2/pap=24/6/1, and depth of invasion was M/SM=27/4. In 40 patchy redness, there were more men than women (28/12), the mean age was 69.0±9.7 years, and the location was U/M/L=3/12/25. With regard to diagnostic accuracy, sensitivity/specificity/PPV/NPV was 90.3/89.7/87.2/92.3% in the analysis system group, 85.9/87.2/80.0/85.5% in expert endoscopists, and 70.0/82.7/80.6/71.0% in trainees endoscopists, respectively.

	Analysis system	Endoscopists
		Experts (n=5)	Trainees (n=4)	All (n=9)
Sensitivity % (95%CI)	90.3 (0.68-0.95)	85.9 (57.0-87.6)	70.0 (54.8-85.8)	77.9 (55.7-86.6)
Specificity % (95%CI)	89.7 (0.79-0.98)	87.2 (65.4-90.8)	82.7 (64.3-92.4)	85.0 (64.8-91.7)
PPV % (95%CI)	87.2 (0.74-0.98)	80.0 (55.5-84.0)	80.6 (59.8-90.8)	80.3 (58.0-87.9)
NPV % (95%CI)	92.3 (0.73-0.96)	85.5 (61.3-85.5)	71.0 (60.0-87.2)	78.3 (60.8-86.4)

Conclusion

The automated blood flow rate analysis system showed higher diagnostic accuracy in the differential diagnosis between differentiated-type EGC and patchy redness compared with endoscopists. It is suggested that the analysis system may be useful as a new diagnostic modality for EGC and could potentially diagnose lesions that are difficult for endoscopists to diagnose.

References

1. Muto M et al. Dig Endosc. 2016. doi: 10.1111/den.12638.
2. Ueyama H et al. J Gastroenterol Hepatol. 2021. doi: 10.1111/jgh.15425.

Introduction

The prevalence of Helicobacter pylori (H.pylori) positive patients has gradually decreased in Japan due to eradication therapy, and H.pylori-uninfected gastric cancer (HPUGC) is expected to increase relatively. Recently, HPUGC, such as gastric adenocarcinoma of fundic-gland type (GAFG), gastric adenocarcinoma of foveolar-type (GAFV), adenocarcinoma of differentiated type (DGA) and signet ring cell carcinoma (SRCC) has been increasingly reported. Since the detection and endoscopic diagnosis of HPUGC is considered to be difficult [1]. The effective observation methods in screening endoscopy to detect HPUGC. In this context, a new image enhancement technology called Texture and Color Enhancement Imaging (TXI; Olympus Medical Systems Corporation) which optimizes the three elements: structure (texture) enhancement, color tone, and brightness correction was developed in 2020. There have been several reports on the usefulness of TXI for the detection and endoscopic diagnosis of mucosal atrophy or gastric neoplasm [2-6]. However, the usefulness of TXI for the detection of HPUGC has been investigated.

Aims & Methods

The aim of the study is to investigate whether the visibility of HPUGC improved using TXI and NBI compared with WLI as a single-center retrospective clinical study. We enrolled 40 HPUGC lesions endoscopically imaged with WLI, TXI-1, TXI-2, and NBI in our facility between October 2020 and March 2023. In addition, the visibility of HPUGCs was analyzed for each factor, such as histological type, coloration, and macroscopic type. Ten endoscopists (5 experts and 5 non-experts) evaluated the images, and visibility changes were scored by endoscopists as follows: 5, improved; 4, somewhat improved; 3, equivalent; 2, somewhat decreased; and 1, decreased. The total visibility score of ≥40 in all endoscopists and /≥20 in each of the 5 endoscopists was defined as improved visibility, 21-39/11-19 as unchanged visibility, and 20≤/10≤ as decreased visibility. The intraclass correlation coefficients (ICC) were calculated and evaluated for inter-rater agreement.

Results

The histological types were classified as GAFG (n=24), GAFV (n=9), DGA (n=4), and SRCC (n=3). There were 17 reddish lesions and 23 whitish lesions. In the macroscopic type, there were type 0-Ⅰ (n=11), type 0-Ⅱa (n=13), type 0-Ⅱb (n=11), and type 0-Ⅱc (n=5) lesions. The visibility improvement rate for all endoscopists in all HPUGCs was TXI-1: 55%, TXI-2: 29.7%, and NBI: 7.5%. The overall ICCs were TXI-1: 0.86, TXI-2: 0.84, and NBI: 0.90, which were almost perfect. GAFG (all/experts/trainees: 79/54/79%) and SRCC (all/experts/trainees: 67/67/67%) showed improved visibility with TXI-1. In contrast, GAFV and DGA did not improve the visibility. The whitish lesions (all/experts/trainees: 74/52/74%) showed improved the visibility with TXI-1, but the reddish lesions did not improve visibility. Among the macroscopic type , type 0-Ⅱa (overall/experts/trainees: 85/54/85%) and type 0-Ⅱb (overall/experts/trainees: 73/64/73%) showed improved visibility with TXI-1. The all ICCs of each factor were above 0.7, which indicated generally good correlations.

Conclusion

The usefulness of TXI-1 compared with WLI led to the improved visibility of the whitish lesions such as GAFG and SRCC for both experts and trainees when evaluated subjectively and objectively.

References

1. Ishibashi, F., et al., Exploring quality indicators for the detection of Helicobacter pylori-naïve gastric cancer: a cross-sectional nationwide survey. Clinical Endoscopy, 2023. Apr 4. doi: 10.5946/ce.2022.167.
2. Abe, S., et al., Emerging texture and color enhancement imaging in early gastric cancer. Dig Endosc, 2022. 34(4): p. 714-720.
3. Sugimoto, M., et al., Efficacy of high-vision transnasal endoscopy using texture and colour enhancement imaging and narrow-band imaging to evaluate gastritis: a randomized controlled trial. Ann Med, 2022. 54(1): p. 1004-1013.
4. Abe, S., et al., Visibility of early gastric cancer in texture and color enhancement imaging. DEN Open, 2022. 2(1): p. e46.
5. Ishikawa, T., et al., Efficacy of Texture and Color Enhancement Imaging in visualizing gastric mucosal atrophy and gastric neoplasms. Sci Rep, 2021. 11(1): p. 6910.
6. Kawasaki, A., et al., Usefulness of third-generation narrow band imaging and texture and color enhancement imaging in improving visibility of superficial early gastric cancer: A study using color difference. DEN Open, 2022. 3(1): p. e186.

Clinical Case Summary

Introduction: Endoscopic Full-thickness Resection (EFTR), full-layer resection and suture using a flexible endoscope alone, is less invasive than Laparoscopy and Endoscopy Cooperative Surgery (LECS) because it does not require stromal processing and does not involve vagotomy, and is attracting attention as a new treatment for submucosal tumors(SMT). However, suturing the gastrointestinal tract using a flexible endoscope alone is problematic because the equipment is expensive and the technique is difficult. On the other hand, Laparoscopic Intragastric Surgery (LIGS), developed by Ohashi et al. in 1995, is a highly safe procedure. Combining EFTR with suturing by LIGS (EFTR-LIGS) enables accurate resection and safe suturing, but this has not been reported.
Aim & Methods: LIGS is a procedure in which a single laparoscopic port is created directly into the stomach, the device is inserted, and surgery is performed from the lumen of the stomach as in conventional laparoscopic surgery. In this presentation, we will demonstrate the technique of EFTR-LIGS in two cases. First case was 76 years-old man who was admitted after endoscopic hemostasis for bleeding from GIST in the stomach. On Day 3 of admission, emergency surgery was necessary due to rebleeding. EFTR was selected, as the procedure, though full-layer resection was possible, suturing was not possible due to amount of clot, and the defect was sutured by LIGS. Second case was 75 years-old woman with gastric GIST. After full-layer resection by EFTR, a laceration of the pharynx occurred when retrieval through the pharynx, we performed LIGS to extract the tumor through the abdominal wall and suture the defect.
Results: The average suture time for LIGS is just 5 minutes. There were no complications and average postoperative length was 5days.
Conclusion: The collaboration of EFTR and LIGS is a minimally invasive treatment for that is safe, not difficult and inexpensive, and is very useful procedure for SMT in a stomach.

Introduction

Recently, artificial intelligence (AI)-based diagnostic support tools for early gastric cancer have been developed and reported to be superior to endoscopists (accuracy: 70% and sensitivity: 85%).¹ However, These tools could not guarantee reliable diagnostic accuracy in clinical practice. In previous reports, magnifying endoscopy has been recognized as superior to white-light imaging endoscopy for endoscopic diagnosis of early gastric cancers. Therefore, we developed an AI-based diagnostic support system using magnifying endoscopy with narrow band imaging (M-NBI) for early gastric cancers² and improved its application in clinical practice using nondegraded images obtained from selective depth inspection (SDI) output for real-time diagnosis. This study is the first to create an AI-based diagnostic support system using non-degraded M-NBI images obtained from SDI output, as several previous papers have analyzed damaged M-NBI images that were converted from various recording devices.^3,4

Aims & Methods

We built a new AI system (M-NBI AI) which was assembled using four different image recognition models with non-degraded M-NBI images (normal mucosa:1776 images, gastric cancers: 1947 images, and non-neoplastic lesions: 2451 images). We evaluated the performance of this M-NBI AI for diagnosis of early gastric cancer. The diagnostic performances of M-NBI AI and endoscopists (five specialists and five non-specialists) were compared using non-degraded M-NBI images (three images per lesion) of 50 early gastric cancer and 50 non-cancerous lesions. Early gastric cancers included histopathological types of well, moderately, and poorly differentiated adenocarcinomas and signet-ring cell adenocarcinomas. The primary outcome was the superiority of the M-NBI AI (based on accuracy) over the endoscopists. The secondary outcome was the superiority of the M-NBI AI (based on sensitivity) over the endoscopists.

Results

	M-NBI AI	Endoscopists
		Specialists	Non-specialists
Accuracy, % (95% CI)	87.0 (0.80–0.94)	80.0 (0.72–0.88)	71.0 (0.62–0.79)
Sensitivity, % (95% CI)	86.3 (0.77–0.96)	74.2 (0.63–0.85)	70.6 (0.58–0.83)
Specificity, % (95% CI)	88.0 (0.79–0.97)	89.0 (0.80–0.99)	71.0 (0.59–0.84)
PPV, % (95% CI)	88.0 (0.79–0.97)	92.0 (0.84–0.99)	72.0 (0.60–0.84)
NPV, % (95% CI)	86.0 (0.76–0.96)	68.0 (0.55–0.81)	70.0 (0.57–0.83)
PPV, positive predictive value; NPV, negative predictive value

The M-NBI AI was superior to the non-specialists based on accuracy (87.0%, 95%CI: 0.80–0.94 versus 71.0 %, 95%CI: 0.62–0.79; p<0.01), without being superior to the specialists (87.0%, 95%CI: 0.80–0.94 versus 80.0%, 95%CI: 0.72–0.88; p=0.18). The M-NBI AI was superior to the specialists and non-specialists based on sensitivity (86.3%, 95%CI: 0.77–0.96 versus 74.2%, 95%CI: 0.63–0.85 and 70.6%, 95%CI: 0.58–0.83; p<0.05, respectively).

Conclusion

The M-NBI AI was created as a new AI system that could be implemented for current endoscopy systems as a real-time diagnostic support tool for early gastric cancers. It demonstrated a robust performance and may indicate equalization of diagnostic accuracy and superadded effect over the white-light endoscopy for diagnosis of early gastric cancers in clinical practice.

References

1. Ishioka M et al. Dig Endosc. 2022. doi: 10.1111/den.14455.
2. Ueyama H et al. J Gastroenterol Hepatol. 2021;36(2):482-489.
3. Horiuchi Y et al. Dig. Dis. Sci. 2020;65:1355–1363.
4. He X et al. Gastrointest Endosc. 2022;95(4):671-678.