Integrated diagnostic strategy for the invasion depth of early gastric cancer by conventional endoscopy and EUS

An artificial intelligence system for comprehensive pathologic outcome prediction in early gastric cancer through endoscopic image analysis (with video)

BACKGROUND

Accurate prediction of pathologic results for early gastric cancer (EGC) based on endoscopic findings is essential in deciding between endoscopic and surgical resection. This study aimed to develop an artificial intelligence (AI) model to assess comprehensive pathologic characteristics of EGC using white-light endoscopic images and videos.

METHODS

To train the model, we retrospectively collected 4,336 images and prospectively included 153 videos from patients with EGC who underwent endoscopic or surgical resection. The performance of the model was tested and compared to that of 16 endoscopists (nine experts and seven novices) using a mutually exclusive set of 260 images and 10 videos. Finally, we conducted external validation using 436 images and 89 videos from another institution.

RESULTS

After training, the model achieved predictive accuracies of 89.7% for undifferentiated histology, 88.0% for submucosal invasion, 87.9% for lymphovascular invasion (LVI), and 92.7% for lymph node metastasis (LNM), using endoscopic videos. The area under the curve values of the model were 0.992 for undifferentiated histology, 0.902 for submucosal invasion, 0.706 for LVI, and 0.680 for LNM in the test. In addition, the model showed significantly higher accuracy than the experts in predicting undifferentiated histology (92.7% vs. 71.6%), submucosal invasion (87.3% vs. 72.6%), and LNM (87.7% vs. 72.3%). The external validation showed accuracies of 75.6% and 71.9% for undifferentiated histology and submucosal invasion, respectively.

CONCLUSIONS

AI may assist endoscopists with high predictive performance for differentiation status and invasion depth of EGC. Further research is needed to improve the detection of LVI and LNM.

A novel artificial intelligence-based endoscopic ultrasonography diagnostic system for diagnosing the invasion depth of early gastric cancer

BACKGROUND

We developed an artificial intelligence (AI)-based endoscopic ultrasonography (EUS) system for diagnosing the invasion depth of early gastric cancer (EGC), and we evaluated the performance of this system.

METHODS

A total of 8280 EUS images from 559 EGC cases were collected from 11 institutions. Within this dataset, 3451 images (285 cases) from one institution were used as a development dataset. The AI model consisted of segmentation and classification steps, followed by the CycleGAN method to bridge differences in EUS images captured by different equipment. AI model performance was evaluated using an internal validation dataset collected from the same institution as the development dataset (1726 images, 135 cases). External validation was conducted using images collected from the other 10 institutions (3103 images, 139 cases).

RESULTS

The area under the curve (AUC) of the AI model in the internal validation dataset was 0.870 (95% CI: 0.796-0.944). Regarding diagnostic performance, the accuracy/sensitivity/specificity values of the AI model, experts (n = 6), and nonexperts (n = 8) were 82.2/63.4/90.4%, 81.9/66.3/88.7%, and 68.3/60.9/71.5%, respectively. The AUC of the AI model in the external validation dataset was 0.815 (95% CI: 0.743-0.886). The accuracy/sensitivity/specificity values of the AI model (74.1/73.1/75.0%) and the real-time diagnoses of experts (75.5/79.1/72.2%) in the external validation dataset were comparable.

CONCLUSIONS

Our AI model demonstrated a diagnostic performance equivalent to that of experts.

Laparoscopy With Percutaneous Transgastric Endoscopy for the Assessment of Gastric Cancer in the Excluded Stomach of a Roux-en-Y Gastric Bypass Patient

Primary adenocarcinoma in the excluded stomach of Roux-en-Y gastric bypass (RYGB) patients is extremely rare. As such, the most effective diagnostic approach has not yet been determined. In typical patients, endoscopic ultrasound (EUS) is the first-line technique for evaluating suspected gastric cancer. However, RYGB patients require a more personalized approach. Endoscopic evaluation of the excluded stomach in RYGB patients, whether by EUS-directed, enteroscopy-assisted, or percutaneous means, is undoubtedly more complex than in patients with normal anatomy. In addition, gastric cancer is often diagnosed at an advanced stage due to its asymptomatic early course. With the added complexity of endoscopy in RYGB patients, a laparoscopic-assisted endoscopic approach may have a more favorable diagnostic and therapeutic utility in the case of gastric remnant malignancy in RYGB patients. The following case describes this comprehensive laparoscopic and endoscopic approach for the diagnosis of gastric cancer of the excluded stomach in an RYGB patient.

Machine learning models to predict submucosal invasion in early gastric cancer based on endoscopy features and standardized color metrics

Conventional endoscopy is widely used in the diagnosis of early gastric cancers (EGCs), but the graphical features were loosely defined and dependent on endoscopists' experience. We aim to establish a more accurate predictive model for infiltration depth of early gastric cancer including a standardized colorimetric system, which demonstrates promising clinical implication. A retrospective study of 718 EGC cases was performed. Clinical and pathological characteristics were included, and Commission Internationale de l'Eclariage (CIE) standard colorimetric system was used to evaluate the chromaticity of lesions. The predicting models were established in the derivation set using multivariate backward stepwise logistic regression, decision tree model, and random forest model. Logistic regression shows location, macroscopic type, length, marked margin elevation, WLI color difference and histological type are factors significantly independently associated with infiltration depth. In the decision tree model, margin elevation, lesion located in the lower 1/3 part, WLI a*color value, b*color value, and abnormal thickness in enhanced CT were selected, which achieved an AUROC of 0.810. A random forest model was established presenting the importance of each feature with an accuracy of 0.80, and an AUROC of 0.844. Quantified color metrics can improve the diagnostic precision in the invasion depth of EGC. We have developed a nomogram model using logistic regression and machine learning algorithms were also explored, which turned out to be helpful in decision-making progress.

Value of quantitative microsurface structure analysis for evaluating the invasion depth of type 0-II early gastric cancer

Background and Aim

The microsurface structure reflects the degree of damage to the glands, which is related to the invasion depth of early gastric cancer. To evaluate the diagnostic value of quantitative microsurface structure analysis for estimating the invasion depth of early gastric cancer.

Methods

White-light imaging and narrow-band imaging (NBI) endoscopy were used to visualize the lesions of the included patients. The area ratio and depth-predicting score (DPS) of each patient were calculated; meanwhile, each lesion was examined by endoscopic ultrasonography (EUS).

Results

Ninety-three patients were included between 2016 and 2019. Microsurface structure is related to the histological differentiation and progression of early gastric cancer. The receiver operating characteristic curve showed that when an area ratio of 80.3% was used as a cut-off value for distinguishing mucosal (M) and submucosal (SM) type 0-II gastric cancers, the sensitivity, specificity, and accuracy were 82.9%, 80.2%, and 91.6%, respectively. The accuracies for distinguishing M/SM differentiated and undifferentiated early gastric cancers were 87.4% and 84.8%, respectively. The accuracy of EUS for distinguishing M/SM early gastric cancer was 74.9%. DPS can only distinguish M-SM1 (SM infiltration <500 μm)/SM (SM infiltration ≥500 μm) with an accuracy of 83.8%. The accuracy of using area ratio for distinguishing 0-II early gastric cancers was better than those of using DPS and EUS (P < 0.05).

Conclusion

Quantitative analysis of microsurface structure can be performed to assess M/SM type 0-II gastric cancer and is expected to be effective for judging the invasion depth of gastric cancer.

Application of the convolution neural network in determining the depth of invasion of gastrointestinal cancer: a systematic review and meta-analysis

BACKGROUND

With the development of endoscopic technology, endoscopic submucosal dissection (ESD) has been widely used in the treatment of gastrointestinal tumors. It is necessary to evaluate the depth of tumor invasion before the application of ESD. The convolution neural network (CNN) is a type of artificial intelligence that has the potential to assist in the classification of the depth of invasion in endoscopic images. This meta-analysis aimed to evaluate the performance of CNN in determining the depth of invasion of gastrointestinal tumors.

METHODS

A search on PubMed, Web of Science, and SinoMed was performed to collect the original publications about the use of CNN in determining the depth of invasion of gastrointestinal neoplasms. Pooled sensitivity and specificity were calculated using an exact binominal rendition of the bivariate mixed-effects regression model. I2 was used for the evaluation of heterogeneity.

RESULTS

A total of 17 articles were included; the pooled sensitivity was 84% (95% CI, 0.81-0.88), specificity was 91% (95% CI, 0.85-0.94), and the area under the curve (AUC) was 0.93 (95% CI, 0.90-0.95). The performance of CNN was significantly better than that of endoscopists (AUC: 0.93 vs 0.83, respectively; P = .0005).

CONCLUSION

Our review revealed that CNN is one of the most effective methods of endoscopy to evaluate the depth of invasion of early gastrointestinal tumors, which has the potential to work as a remarkable tool for clinical endoscopists to make decisions on whether the lesion is feasible for endoscopic treatment.

Endoscopic Diagnosis of Superficial Gastrointestinal Cancer

White light image (WLI) findings are important for detection and characterization in the GI tract. However, magnified endoscopic examination with image enhanced endoscopy (IEE-NE) is becoming increasingly important for qualitative diagnosis of GI neoplastic lesions. IEE-ME is extremely useful for diagnosis of invasion depth in esophageal squamous cell cancer (ESCC) and colorectal cancer, whereas macroscopic findings of WLI are still useful in Barrett's adenocarcinoma (BAC) and gastric cancer. IEE-ME is also useful for diagnosis of tumor extent in BAC and gastric cancer, whereas chromoendoscopy with indigo carmine is useful in colorectal cancer and iodine staining is indispensable in ESCC.

Coexistence of early gastric cancer and benign submucosal lesions mimic invasive cancer: a retrospective multicenter experience

OBJECTIVE

To present a study to identify the characteristics of coexisting early gastric cancer (EGC) and benign submucosal lesions, with the aim of reducing the adverse consequences of overdiagnosis and overtreatment.

METHODS

In this retrospective study, we searched the endoscopic databases of three tertiary centers. We screened of patients suspected of early gastric cancer submucosal infiltration by conventional endoscopy and ultimately selected for endoscopic submucosal dissection treatment after endoscopic ultrasonography and magnifying endoscopy with narrow-band imaging examination. Patients with coexisting EGC and benign submucosal lesions in histological sections were included. Clinical data and endoscopic images were reviewed. To evaluate the precision of endoscopists' diagnoses for this type of lesion, eight endoscopists with different experiences were recruited to judge the infiltration depth of these lesions and analyze the accuracy rate.

RESULTS

We screened 520 patients and retrospectively identified 18 EGC patients with an invasive cancer-like morphology. The most common lesion site was the cardia (12/18, 66.67%). The coexisting submucosal lesions could be divided into solid (5/18, 27.78%) and cystic (13/18, 72.22%). The most common type of submucosal lesion was gastritis cystica profunda (12/18, 66.67%), whereas leiomyoma was the predominant submucosal solid lesion (3/18, 16.67%). Ten (55.56%) patients < underwent endoscopic ultrasonography; submucosal lesions were definitively diagnosed in 6 patients (60.00%). The accuracy of judgement of the infiltration depth was significantly lower in cases of coexistence of EGC with benign submucosal lesions (EGC-SML) than in EGC (38.50% versus 65.60%, P = 0.0167). The rate of over-diagnosis was significantly higher within the EGC-SML group compared to the EGC group (59.17% versus 10.83%, P < 0.0001).

CONCLUSIONS

We should be aware of the coexistence of EGC and benign submucosal lesions, the most common of which is early cardiac-differentiated cancer with gastritis cystica profunda.

Endoscopic submucosal dissection for early gastric cancer: It is time to consider the quality of its outcomes

Endoscopic resection, particularly endoscopic submucosal dissection (ESD), is widely used as a standard treatment modality for early gastric cancer (EGC) when the risk of lymph node metastasis is negligible. Compared with surgical gastrectomy, ESD is a minimally invasive procedure with additional advantages, such as preservation of the entire stomach and maintenance of the patient's quality of life. However, not all patients achieve curative resection after ESD of EGC. Several patients require surgical gastrectomy after ESD to achieve a curative treatment. Additional surgery after ESD, owing to non-curative resection, places considerable emotional and financial burdens on patients. Recently, as the number of endoscopists performing ESD has increased, the rate of non-curative resection after ESD has increased correspondingly. In order to decrease the non-curative resection rate, as well as determine the ideal rate of non-curative resection after ESD, it is time to consider quality indicators for the outcomes of ESD for EGC.

Artificial intelligence in theranostics of gastric cancer, a review

Gastric cancer (GC) is one of the commonest cancers with high morbidity and mortality in the world. How to realize precise diagnosis and therapy of GC owns great clinical requirement. In recent years, artificial intelligence (AI) has been actively explored to apply to early diagnosis and treatment and prognosis of gastric carcinoma. Herein, we review recent advance of AI in early screening, diagnosis, therapy and prognosis of stomach carcinoma. Especially AI combined with breath screening early GC system improved 97.4 % of early GC diagnosis ratio, AI model on stomach cancer diagnosis system of saliva biomarkers obtained an overall accuracy of 97.18 %, specificity of 97.44 %, and sensitivity of 96.88 %. We also discuss concept, issues, approaches and challenges of AI applied in stomach cancer. This review provides a comprehensive view and roadmap for readers working in this field, with the aim of pushing application of AI in theranostics of stomach cancer to increase the early discovery ratio and curative ratio of GC patients.

Recent advances in endoscopic management of gastric neoplasms

The development and clinical application of new diagnostic endoscopic technologies such as endoscopic ultrasonography with biopsy, magnification endoscopy, and narrow-band imaging, more recently supplemented by artificial intelligence, have enabled wider recognition and detection of various gastric neoplasms including early gastric cancer (EGC) and subepithelial tumors, such as gastrointestinal stromal tumors and neuroendocrine tumors. Over the last decade, the evolution of novel advanced therapeutic endoscopic techniques, such as endoscopic mucosal resection, endoscopic submucosal dissection, endoscopic full-thickness resection, and submucosal tunneling endoscopic resection, along with the advent of a broad array of endoscopic accessories, has provided a promising and yet less invasive strategy for treating gastric neoplasms with the advantage of a reduced need for gastric surgery. Thus, the management algorithms of various gastric tumors in a defined subset of the patient population at low risk of lymph node metastasis and amenable to endoscopic resection, may require revision considering upcoming data given the high success rate of en bloc resection by experienced endoscopists. Moreover, endoscopic surveillance protocols for precancerous gastric lesions will continue to be refined by systematic reviews and meta-analyses of further research. However, the lack of familiarity with subtle endoscopic changes associated with EGC, as well as longer procedural time, evolving resection techniques and tools, a steep learning curve of such high-risk procedures, and lack of coding are issues that do not appeal to many gastroenterologists in the field. This review summarizes recent advances in the endoscopic management of gastric neoplasms, with special emphasis on diagnostic and therapeutic methods and their future prospects.

Endoscopic Submucosal Dissection in the Stomach and Duodenum: Techniques, Indications, and Outcomes

Gastric endoscopic submucosal dissection (ESD) is established for management of early gastric cancer (EGC). Diagnosis of EGC relies on adequate endoscopic assessment involving lesion size, histopathology, presence of ulceration, and depth of invasion. Absolute indications for endoscopic resection of EGC are if patients are presumed to have a less than 1% risk of lymph node metasta endoscopic submucosal dissectionsis, and long-term outcomes are similar to those with surgical gastrectomy. Duodenal ESD is more technically difficult and requires expertise in ESD in other locations.

Diagnostic value of endoscopic ultrasonography for the depth of gastric cancer suspected of submucosal invasion: a multicenter prospective study

BACKGROUND

Although the combination of conventional endoscopy (CE) and endoscopic ultrasonography (EUS) is useful for predicting the depth of early gastric cancer (EGC), the diagnostic value of EUS for submucosal (SM) invasive cancer has not been fully investigated.

METHODS

We conducted a multicenter prospective study from May 2017 to January 2021 to evaluate the validity of a diagnostic strategy combining CE and EUS and to clarify the additional value of EUS for EGC suspected of SM invasion. In each case, the diagnosis was first made using CE, followed by EUS, and finally confirmed using a combination algorithm.

RESULTS

A total of 180 patients with EGC were enrolled from 10 institutions, of which 175 were analyzed. The histopathological depths were M, SM1, SM2, and ≥ MP in 72, 16, 64, and 23 lesions, respectively. Treatment included 92 endoscopic submucosal dissection cases and 83 surgical cases. The overall diagnostic accuracy classified by M-SM1 or SM2-MP was 58.3% for CE, 75.7% for EUS, and 78.9% for the combination of CE and EUS; the latter two were significantly higher than that of CE alone (P < 0.001). The CE, EUS, and combination accuracy rates in 108 differentiated-type lesions were 51.9%, 77.4%, and 79.6%, respectively; the latter two were significantly higher than CE alone (P < 0.001). A significant additive effect of EUS was observed in CE-SM2 low-confidence lesions but not in CE-M-SM1 lesions or in CE-SM2 high-confidence lesions. Among the nine CE findings, irregular surface, submucosal tumor-like elevation, and non-extension signs were significant independent markers of pSM2-MP. Poorly delineated EUS lesions were misdiagnosed.

CONCLUSIONS

EUS provides additional value for differentiated-type and CE-SM2 low-confidence EGCs in diagnosing invasion depth.

CLINICAL REGISTRATION NUMBER

UMIN000025862.

Current Status of Artificial Intelligence-Based Computer-Assisted Diagnosis Systems for Gastric Cancer in Endoscopy

Artificial intelligence (AI) is gradually being utilized in various fields as its performance has been improving with the development of deep learning methods, availability of big data, and the progression of computer processing units. In the field of medicine, AI is mainly implemented in image recognition, such as in radiographic and pathologic diagnoses. In the realm of gastrointestinal endoscopy, although AI-based computer-assisted detection/diagnosis (CAD) systems have been applied in some areas, such as colorectal polyp detection and diagnosis, so far, their implementation in real-world clinical settings is limited. The accurate detection or diagnosis of gastric cancer (GC) is one of the challenges in which performance varies greatly depending on the endoscopist's skill. The diagnosis of early GC is especially challenging, partly because early GC mimics atrophic gastritis in the background mucosa. Therefore, several CAD systems for GC are being actively developed. The development of a CAD system for GC is considered challenging because it requires a large number of GC images. In particular, early stage GC images are rarely available, partly because it is difficult to diagnose gastric cancer during the early stages. Additionally, the training image data should be of a sufficiently high quality to conduct proper CAD training. Recently, several AI systems for GC that exhibit a robust performance, owing to being trained on a large number of high-quality images, have been reported. This review outlines the current status and prospects of AI use in esophagogastroduodenoscopy (EGDS), focusing on the diagnosis of GC.

The feasibility and safety of endoscopic submucosal dissection of gastric lesions larger than 5 cm

BACKGROUND

As the indications for endoscopic submucosal dissection (ESD) for early gastric cancer have been revised, diagnostic ESD has increased. However, despite the technical difficulty of ESD for large lesions, the degree to which curative resection can be achieved has not been clarified. This study investigated the feasibility and safety of ESD for gastric lesions larger than 5 cm.

METHODS

This retrospective multicenter study included 3474 gastric lesions treated by ESD from April 2012 to December 2021. We compared clinicopathological characteristics and treatment outcomes between lesions ≥ 5 cm and lesions < 5 cm.

RESULTS

There were 128 lesions in the ≥ 5 cm group and 3282 lesions in the < 5 cm group. In the ≥ 5 cm group, upper location and fibrosis during ESD were more common, with a lower rate of 0-IIc type. Both en bloc resection rate and R0 resection rate were comparable, but there was a difference in curative resection rate (65.6% in the ≥ 5 cm group and 91.5% in the < 5 cm group). The frequency of adverse events (post-ESD bleeding, perforation, or stenosis) was almost similar, but delayed perforation was significantly more common (1.6% in the ≥ 5 cm group vs. 0.1% in the < 5 cm group).

CONCLUSIONS

About two-thirds of curative resections were obtained with ESD for early gastric lesions larger than 5 cm, but delayed complications should be noted (Number: UMIN000047725).

Value of EUS in determining infiltration depth of early carcinoma and associated precancerous lesions in the upper gastrointestinal tract

Objective

The objective is to evaluate the value of EUS in the determination of infiltration depth of early carcinoma and precancerous lesions in the upper gastrointestinal tract and to analyze the various factors affecting the accuracy of EUS.

Methods

One hundred and sixty-three patients diagnosed with early gastric cancer or early esophageal cancer, and associated precancerous lesions, who were seen in our hospital in the recent 10 years were selected. These patients received EUS before endoscopic submucosal dissection or surgery. With a pathological diagnosis as the gold standard, the accuracy, sensitivity, specificity, and misjudgment rate of EUS in determining the invasion depth were evaluated using the pathological stratification (mucosa, M1/2; muscularis mucosa, M3; submucosa, [SM]; and muscularis propria) or TN stratification (mucosa, T1a; SM, T1b), and the possible causes of miscalculation were analyzed.

Results

Based on the pathological stratification, the overall accuracy of EUS was 78.5%, and the overestimation and underestimation rates were 17.8% and 3.7%, respectively. Based on the TN stratification, the overall accuracy of EUS was 81%, and the overestimation and underestimation rates were 16.6% and 2.5%, respectively. There was a significant difference between the groups in terms of overestimation and underestimation rates (P < 0.05), indicating that EUS was more likely to overestimate the depth. Univariate analysis showed that the factors affecting accuracy included lesion size, macroscopic features, sunken mucosa, mucosa with granular and nodular changes, and ulceration. Multivariate logistic regression analysis revealed that larger lesions, mucosa with granular and nodular changes, and ulceration were independent risk factors for the overestimation of infiltration depth by EUS.

Conclusion

EUS is highly accurate in determining the infiltration depth of early cancer and precancerous lesions in the upper gastrointestinal tract. It also has a good reference value for treatment selection and prognostication. However, attention should be paid to its overestimation, especially accompanied by the aforementioned factors.

Endoscopic Treatment of Superficial Gastric Cancer: Present Status and Future

Although the mortality rates of gastric cancer (GC) are gradually declining, gastric cancer is still the fourth leading cause of cancer-related death worldwide. This may be due to the high rate of patients who are diagnosed with GC at advanced stages. However, in countries such as Japan with endoscopic screening systems, more than half of GCs are discovered at an early stage, enabling endoscopic resection (ER). Especially after the introduction of endoscopic submucosal dissection (ESD) in Japan around 2000, a high en bloc resection rate allowing pathological assessment of margin and depth has become possible. While ER is a diagnostic method of treatment and may not always be curative, it is widely accepted as standard treatment because it is less invasive than surgery and can provide an accurate diagnosis for deciding whether additional surgery is necessary. The curability of ER is currently assessed by the completeness of primary tumor removal and the possibility of lymph node metastasis. This review introduces methods, indications, and curability criteria for ER of EGC. Despite recent advances, several problems remain unsolved. This review will also outline the latest evidence concerning future issues.

Diagnostic accuracy of convolutional neural network-based endoscopic image analysis in diagnosing gastric cancer and predicting its invasion depth: a systematic review and meta-analysis

BACKGROUND AND AIMS

This study aimed to evaluate the accuracy and effectiveness of the convolutional neural network (CNN) in diagnosing gastric cancer and predicting the invasion depth of gastric cancer and to compare the performance of the CNN with that of endoscopists.

METHODS

PubMed, Embase, Web of Science, and gray literature were searched until July 23, 2021 for studies that assessed the diagnostic accuracy of CNN-assisted examinations for gastric cancer or the invasion depth of gastric cancer. Studies meeting inclusion criteria were included in the systematic review and meta-analysis.

RESULTS

Seventeen studies comprising 51,446 images and 174 videos of 5539 patients were included. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), and area under the curve (AUC) of the CNN for diagnosing gastric cancer were 89% (95% confidence interval [CI], 85-93), 93% (95% CI, 88-97), 13.4 (95% CI, 7.3-25.5), .11 (95% CI, .07-.17), and .94 (95% CI, .91-.98), respectively. The performance of the CNN in diagnosing gastric cancer was not significantly different from that of expert endoscopists (.95 vs .90, P > .05) and was better than that of overall endoscopists (experts and nonexperts) (.95 vs .87, P < .05). The pooled sensitivity, specificity, LR+, LR-, and AUC of the CNN for predicting the invasion depth of gastric cancer were 82% (95% CI, 78-85), 90% (95% CI, 82-95), 8.4 (95% CI, 4.2-16.8), .20 (95% CI, .16-.26), and .90 (95% CI, .87-.93), respectively.

CONCLUSIONS

The CNN is highly accurate in diagnosing gastric cancer and predicting the invasion depth of gastric cancer. The performance of the CNN in diagnosing gastric cancer is not significantly different from that of expert endoscopists. Studies of the real-time performance of the CNN for gastric cancer diagnosis are needed to confirm these findings.

Photoacoustic imaging of fresh human surgically and endoscopically resected gastrointestinal specimens

Objective

Photoacoustic (PA) imaging is a novel noninvasive technique that offers high‐contrast tomographic imaging with ultrasound‐like resolution at depths of centimeters, enabling visualization of deep small vessels. The aim of this pilot study was to survey the characteristics of deep vessel networks in the mucosa of neoplastic gastrointestinal (GI) lesions using PA imaging.

Methods

Specimens of patients who had undergone surgical and endoscopic resection for GI lesions were included in this study. The PA/ultrasound imaging system for clinical research is characterized by a technology that can superimpose a PA image over an ultrasound image. Three‐dimensional PA images were acquired for the resected specimen before fixation. The stomach and colon of live pigs were incised, and the walls were scanned from the mucosa.

Results

A total of 32 specimens (nine esophageal, 12 gastric, 11 colorectal) were scanned. The pathological diagnoses were adenomas (n = 2), intramucosal cancers (n = 14), and invasive cancers (n = 16). The deep vessel networks of all lesions could be visualized. In the intramucosal lesions, the deep vessel network was similar to that of a normal tissue. In invasive cancers, the thick and prominent vessel network was visible in the surface layer of esophageal cancers, infiltrated area of gastric cancers, and surface layer and infiltrated area of colorectal cancers. In the images of living pigs, visualizing the vascular network deeper than the submucosa in both the stomach and large intestine was possible.

Conclusion

Our study confirmed that the deep vessel networks of neoplastic GI lesions were visible by PA imaging.

Predicting the depth of superficial adenocarcinoma of the esophagogastric junction

BACKGROUND AND AIM

Preoperative determination of the invasion depth of superficial adenocarcinoma of the esophagogastric junction is important for appropriate endoscopic or surgical resection. There are no objective criteria regarding this; therefore, we investigated the factors associated with the invasion depth of superficial adenocarcinoma of the esophagogastric junction.

METHODS

This retrospective study evaluated patients with superficial adenocarcinoma of the esophagogastric junction who had undergone endoscopic or surgical resection at a Japanese tertiary cancer center between April 2004 and December 2017. We analyzed endoscopic features of intramucosal to slight submucosal (M-SM1; < 500 μm) and deep submucosal (SM2; ≥ 500 μm) adenocarcinoma of the esophagogastric junction and extracted significant factors associated with and assessed the diagnostic performance of endoscopic features for SM2 lesion.

RESULTS

A total of 106 cases were included in this study. Multivariate analysis indicated that depressed or protruded type (odds ratio [OR], 11.1), lesion size ≥ 15 mm (OR, 3.11), uneven surface (OR, 6.31), and subsquamous extension (OR, 5.41) were significantly associated with SM2 adenocarcinomas of the esophagogastric junction. When the macroscopic type was depressed or protruded, high sensitivity (97%) but fair specificity (46%) were observed for SM2 adenocarcinoma of the esophagogastric junction, whereas uneven surface and subsquamous extension showed high specificity (96% and 87%) but fair sensitivity (36% and 46%).

CONCLUSIONS

Depressed or protruded type, lesion size ≥ 15 mm, uneven surface, and subsquamous extension were significantly associated with the invasion depth of superficial adenocarcinoma of the esophagogastric junction. These endoscopic features are useful in determining the treatment method preoperatively.

Deep learning system compared with expert endoscopists in predicting early gastric cancer and its invasion depth and differentiation status (with videos)

BACKGROUND AND AIMS

We aimed to develop and validate a deep learning-based system that covers various aspects of early gastric cancer (EGC) diagnosis, including detecting gastric neoplasm, identifying EGC, and predicting EGC invasion depth and differentiation status. Herein, we provide a state-of-the-art comparison of the system with endoscopists using real-time videos in a nationwide human-machine competition.

METHODS

This multicenter, prospective, real-time, competitive comparative, diagnostic study enrolled consecutive patients who received magnifying narrow-band imaging endoscopy at the Peking University Cancer Hospital from June 9, 2020 to November 17, 2020. The offline competition was conducted in Wuhan, China, and the endoscopists and the system simultaneously read patients' videos and made diagnoses. The primary outcomes were sensitivity in detecting neoplasms and diagnosing EGCs.

RESULTS

One hundred videos, including 37 EGCs and 63 noncancerous lesions, were enrolled; 46 endoscopists from 44 hospitals in 19 provinces in China participated in the competition. The sensitivity rates of the system for detecting neoplasms and diagnosing EGCs were 87.81% and 100%, respectively, significantly higher than those of endoscopists (83.51% [95% confidence interval [CI], 81.23-85.79] and 87.13% [95% CI, 83.75-90.51], respectively). Accuracy rates of the system for predicting EGC invasion depth and differentiation status were 78.57% and 71.43%, respectively, slightly higher than those of endoscopists (63.75% [95% CI, 61.12-66.39] and 64.41% [95% CI, 60.65-68.16], respectively).

CONCLUSIONS

The system outperformed endoscopists in identifying EGCs and was comparable with endoscopists in predicting EGC invasion depth and differentiation status in videos. This deep learning-based system could be a powerful tool to assist endoscopists in EGC diagnosis in clinical practice.

Diagnostic Efficacy and Decision-Making Role of Preoperative Endoscopic Ultrasonography in Early Gastric Cancer

Objectives: Endoscopic ultrasonography (EUS) is the most commonly used method for T staging of early gastric cancer (EGC). However, the studies pertaining to EUS for staging EGC reported widely varied sensitivities and specificities. This study aimed to estimate the overall diagnostic accuracy of EUS for staging the depth of EGCs and to explore the influential factors. Methods: We retrospectively reviewed data from 208 consecutive patients with EGC, and all patients underwent EUS for estimating tumor invasion depth, followed by either curative surgery or endoscopic submucosal dissection (ESD). The diagnostic accuracy of EUS was evaluated by comparing the final histologic results of resected specimens. The correlation between accuracy of EUS and characteristics of EGC lesion was analyzed. Results: A total of 211 EGC lesions in 208 patients were included. The overall diagnostic accuracy of EUS in assessing the tumor invasion depth of EGC was 55.9%. Multivariate analysis showed that submucosal invasion (OR 2.615; 95% CI 1.203-5.684, P = 0.015) was independently associated with misdiagnosis of the depth of EGC and 0-III type lesions (OR 31.495; 95% CI 2.083-476.256, P = 0.013) were an independent risk factor for over-diagnosis of invasion depth by EUS. However, EUS was only suitable for lesions within absolute indications for endoscopic resection. Conclusions: The overall accuracy of EUS in diagnosing invasion depth of EGC was relatively low. Thus, EUS is not necessary routinely for determining the therapeutic strategy for EGC.

Application of convolutional neural network in detecting and classifying gastric cancer

Gastric cancer (GC) is the fifth most common cancer in the world, and at present, esophagogastroduodenoscopy is recognized as an acceptable method for the screening and monitoring of GC. Convolutional neural networks (CNNs) are a type of deep learning model and have been widely used for image analysis. This paper reviews the application and prospects of CNNs in detecting and classifying GC, aiming to introduce a computer-aided diagnosis system and to provide evidence for subsequent studies.

[Clinical Practice Guideline for Endoscopic Resection of Early Gastrointestinal Cancer]

Although surgery was the standard treatment for early gastrointestinal cancers, endoscopic resection is now a standard treatment for early gastrointestinal cancers without regional lymph node metastasis. High-definition white light endoscopy, chromoendoscopy, and image-enhanced endoscopy such as narrow band imaging are performed to assess the edge and depth of early gastrointestinal cancers for delineation of resection boundaries and prediction of the possibility of lymph node metastasis before the decision of endoscopic resection. Endoscopic mucosal resection and/or endoscopic submucosal dissection can be performed to remove early gastrointestinal cancers completely by en bloc fashion. Histopathological evaluation should be carefully made to investigate the presence of risk factors for lymph node metastasis such as depth of cancer invasion and lymphovascular invasion. Additional treatment such as radical surgery with regional lymphadenectomy should be considered if the endoscopically resected specimen shows risk factors for lymph node metastasis. This is the first Korean clinical practice guideline for endoscopic resection of early gastrointestinal cancer. This guideline was developed by using mainly de novo methods and encompasses endoscopic management of superficial esophageal squamous cell carcinoma, early gastric cancer, and early colorectal cancer. This guideline will be revised as new data on early gastrointestinal cancer are collected.

Discrepancy between endoscopic and pathological ulcerative findings in clinical intramucosal early gastric cancer

BACKGROUND

Ulcerative finding (UL) is one of the factors that define the indication and curability of endoscopic resection (ER) in early gastric cancer (EGC). Discrepancies between endoscopic UL (cUL) and pathological UL (pUL) sometimes occur in clinical practice. The aim of this study was to investigate the discrepancy rate in UL diagnosis and the risk factors associated with such discrepancies.

METHODS

Patients with clinical intramucosal (cT1a) EGC who underwent ER or surgery between September 2002 and December 2017 were analyzed. The proportion of cUL-negative (cUL0) lesions that were identified as pUL-positive (pUL1) and that of cUL-positive (cUL1) lesions that were identified as pUL-negative (pUL0) were calculated. Logistic regression analysis was performed to estimate the associations between discrepancy in UL diagnosis and clinical variables of the lesion, such as the size, histology, location, and macroscopic type.

RESULTS

In total, 5382 lesions were evaluated; 5.5% of cUL0 lesions (256/4619) were identified as pUL1, while 38.7% of cUL1 lesions (295/763) were pUL0. Multivariate analysis indicated that in cUL1 lesions, tumor location in the lower third of the stomach (odds ratio 3.11, 95% confidence interval 1.90-5.08) was identified as an independent risk factor for overestimation.

CONCLUSIONS

Endoscopic diagnosis of UL in cT1a EGC was overestimated in 38.7% of lesions, especially for lesions located in the lower third of the stomach. This discrepancy should be considered in the management of cT1a EGC with UL.

Examination of Endoscopic Ultrasonographic Diagnosis for the Depth of Early Gastric Cancer

Background

Endoscopic ultrasonography (EUS) is one of the helpful tools to diagnose depth of early gastric cancer (EGC). In this study, we examined efficiencies of EUS for EGC such as overall accuracy, risk factors of over/under-staging, and accuracies of each invasive distance.

Methods

A total of 403 EGC lesions that could be investigated by EUS during pre-operation and histological diagnosis after endoscopic submucosal dissection (ESD) or surgery were enrolled in this study. For the 403 cases, we analyzed the accuracies of depth by conventional endoscopy (CE) and EUS retrospectively. We evaluated the clinical survey items of CE and EUS which will be described later to compare the differences between “accuracy group” and “over-staging group”, and between “accuracy group” and “under-staging group”, retrospectively. Additionally, 78 EGC lesions which were confined to the submucosa and for which it was possible to measure accurate invasive distance from the muscularis mucosae were examined for the relationship between preoperative diagnosis of depth by CE and EUS and invasive distance retrospectively.

Results

The overall accuracies of both CE and EUS in predicting EGC invasion depth were 87.3%. For CE staging, histological classification was the factor which influenced over-staging. Gastric regions and tumor area were the factors which influenced under-staging of CE. For EUS staging, tumor area was the factor which influenced over-staging, and gastric regions were the factors which influenced under-staging. Both CE and EUS were not sufficient for predicting the lesions confined to < 500 µm from the muscularis mucosae because the accuracies of both in predicting depth were less than 50%. However, EUS has a higher accuracy than CE for the lesions confined to 500 - 2,000 µm.

Conclusions

The overall accuracies of both CE and EUS in predicting EGC invasion depth were equal, but the contributing factors for over/under-staging were different. Both CE and EUS are not sufficient at present to predict the lesions confined to < 500 µm from the muscularis mucosae. However, the accuracy of EUS in predicting them may increase if high-performance EUS systems are developed in the future.

A Novel Model Based on Deep Convolutional Neural Network Improves Diagnostic Accuracy of Intramucosal Gastric Cancer (With Video)

Background and Aims

Prediction of intramucosal gastric cancer (GC) is a big challenge. It is not clear whether artificial intelligence could assist endoscopists in the diagnosis.

Methods

A deep convolutional neural networks (DCNN) model was developed via retrospectively collected 3407 endoscopic images from 666 gastric cancer patients from two Endoscopy Centers (training dataset). The DCNN model’s performance was tested with 228 images from 62 independent patients (testing dataset). The endoscopists evaluated the image and video testing dataset with or without the DCNN model’s assistance, respectively. Endoscopists’ diagnostic performance was compared with or without the DCNN model’s assistance and investigated the effects of assistance using correlations and linear regression analyses.

Results

The DCNN model discriminated intramucosal GC from advanced GC with an AUC of 0.942 (95% CI, 0.915–0.970), a sensitivity of 90.5% (95% CI, 84.1%–95.4%), and a specificity of 85.3% (95% CI, 77.1%–90.9%) in the testing dataset. The diagnostic performance of novice endoscopists was comparable to those of expert endoscopists with the DCNN model’s assistance (accuracy: 84.6% vs. 85.5%, sensitivity: 85.7% vs. 87.4%, specificity: 83.3% vs. 83.0%). The mean pairwise kappa value of endoscopists was increased significantly with the DCNN model’s assistance (0.430–0.629 vs. 0.660–0.861). The diagnostic duration reduced considerably with the assistance of the DCNN model from 4.35s to 3.01s. The correlation between the perseverance of effort and diagnostic accuracy of endoscopists was diminished using the DCNN model (r: 0.470 vs. 0.076).

Conclusions

An AI-assisted system was established and found useful for novice endoscopists to achieve comparable diagnostic performance with experts.

Mixed histology poses a greater risk for noncurative endoscopic resection in early gastric cancers regardless of the predominant histologic types

OBJECTIVES

Clinicopathologic characteristics and treatment outcomes of mixed-histological-type (MT) early gastric cancers (EGCs) treated with endoscopic submucosal dissection (ESD) have not been sufficiently elucidated. We aimed to clarify them in comparison with pure-histological-type EGCs.

METHODS

We used 3022 consecutive EGCs in 2281 patients treated with ESD from our prospectively maintained database. Cases were stratified into four groups according to the final diagnosis of the resected specimen are as follows: 2780 pure differentiated-type (DT), 127 DT-predominant MT (D-MT), 87 pure undifferentiated-type (UDT), and 28 UDT-predominant MT (U-MT). Clinicopathologic characteristics and treatment outcome were compared between pure DT and D-MT, and between pure UDT and U-MT separately. Risk factors for deep submucosal invasion, lymphovascular invasion, and a final diagnosis of MT were identified using multivariate analysis.

RESULTS

Both D-MT (41.7 vs. 92.0%; P < 0.0001) and U-MT (35.7 vs. 75.9%; P = 0.0002) showed a significantly lower curative resection rate than their pure histologic counterparts. Multivariate analysis revealed that MT was an independent risk factor for deep submucosal (OR 6.55; 95% CI, 4.18-10.14) and lymphovascular (OR 4.74; 95% CI, 2.72-8.29) invasion. Preoperative biopsy results that did not show well-differentiated tubular adenocarcinoma (OR 28.2; 95% CI, 18.9-42.9) were an independent risk factor for a final diagnosis of MT.

CONCLUSIONS

MT poses a greater risk for noncurative resection regardless of the predominant histologic types, reflecting more aggressive malignant potential. Although a biopsy examination rarely shows MT, clinicians should consider the possibility of MT when a biopsy examination does not show well-differentiated tubular adenocarcinoma.

Hierarchical Analysis of Factors Associated with T Staging of Gastric Cancer by Endoscopic Ultrasound

BACKGROUND

Size, ulcer, differentiation, and location are known to be factors affecting the T stage accuracy of EUS in gastric cancer. However, whether an interaction exists among recognized variables is poorly understood. The aim of this study was to identify the combinatorial characteristics of group with high overestimation rate to determine which group should be considered carefully for EUS-based treatment plans.

METHODS

We retrospectively analyzed early gastric cancer patients who underwent EUS from 2005 to 2016. The accuracy of EUS T stage and factors affecting over-/underestimation were examined by using decision tree analysis, the CHAID method.

RESULTS

The most significant factor affecting the accuracy of the EUS T stage was the size. The rate of overestimation was higher in lesions > 3 cm (37.2% vs. 28.8% vs. 17.1%, p < 0.001). In lesions > 3 cm, the rate of overestimation was higher in lesions with an ulcer (62.1% vs. 35.0%, p < 0.001). Moreover, for lesions ≤ 3 cm, the accuracy of the EUS T stage was more affected by differentiation and location. The rate of overestimation was higher in undifferentiated-type lesions ≤ 2 cm (24.5% vs. 13.9%, p < 0.001) and 2-3 cm (33.3% vs. 25.7%, p = 0.011). In the differentiated type, the location affected the accuracy of the EUS T stage.

CONCLUSION

In this hierarchical analysis, the rate of overestimation was higher in lesions > 3 cm with ulcer, lesions > 3 cm irrespective of ulcer, and undifferentiated-type lesions measuring 2-3 cm.

Clinico-pathologic determinants of non-e-curative outcome following en-bloc endoscopic submucosal dissection in patients with early gastric neoplasia

Background

Endoscopic submucosal dissection (ESD) is gaining enormous popularity in the treatment of early gastric cancers (EGCs) in many institutions across the world. However, appropriate selection of candidates for endoscopic resection is crucial to sufficiently mitigate non-e-curative (NEC) resection. This study aims at identifying the various clinico-pathologic factors that independently predict the NEC outcome and depth of submucosal invasion following ESD procedure in patients with EGC.

Methods

Multiple logistic regression analysis was applied to investigate factors that independently predict both non-curability phenomenon and the level of submucosal invasion in patients with early gastric neoplasia. Statistical Packages for the Social Sciences version 23 was used for analysis.

Results

A total of 153 patients (162 EGC lesions) underwent en-bloc ESD after which the rate of complete resection and non-e-curative outcome were 95% and 22.2%, correspondingly. Multivariate analysis depicted that tumor location in the upper two third of stomach (odds ratio [OR], 5.46; 95% confidence interval [95% CI], 1.65–18.12; p = 0.006), tumor size > 2 cm (OR, 7.63; 95% CI, 2.29–25.42; p = 0.001), histologically undifferentiated tumor (OR, 15.54; 95% CI, 1.65–146.22; p = 0.001), and tumors with 0-IIa/0-IIc or their mixed variants with predominant 0-IIa/0-IIc (OR, 9.77; 95% CI, 1.23–77.65; p = 0.031) were all independent predictors of NEC resection for early gastric tumors. Additionally, location in the upper two third of the stomach (OR, 8.88; 95% CI, 2.90–27.17; p < 0.001), ulcerated lesions (OR, 3.70; 95% CI, 1.15–11.90; p = 0.028), lesions with > 2 cm (OR, 2.94; 95% CI, 1.08–8.02; p = 0.036) and those with poor differentiation (OR, 6.51; 95% CI, 2.23–18.98; p = 0.001) were found to have significant association with submucosal invasion.

Conclusions

Tumors located in the upper two third of the stomach having a larger size (> 2 cm), poor histo-differentiation and a gross type of 0-IIa/0-IIc or their mixed variants with predominant 0-IIa/0-IIc were significantly associated with a risk of NEC after ESD procedure. Thus, early gastric tumors displaying these features need to be handled carefully during endoscopic resection. Our findings may shed light on the pre-procedural detection of clinicopathologic factors that determine non-e-curability in patients with EGC.

Preceding endoscopic submucosal dissection in submucosal invasive gastric cancer patients does not impact clinical outcomes

Submucosal deep invasion of gastric cancer (T1b2; depth of submucosal invasion ≥ 500 μm) is a risk factor for lymph node metastasis and, thus, is one of the criteria for curative treatment. Our aim was to evaluate the specific influence of endoscopic submucosal dissection (ESD) on the prognosis of patients with T1b2 gastric cancer. This was a retrospective analysis of 248 consecutive patients, with 252 pT1b2 gastric cancer lesions, who underwent ESD prior to additional surgery (Group A, n = 101) or surgery only (Group B, n = 147). After propensity score-matching (for sex, age, tumor diameter and gross type), we compared pathological characteristics between the 2 groups and the prognosis over a follow-up period ≥ 60 months. Compared to Group B, patients in Group A were older, with a higher proportion of men. The proportion of depressed and undifferentiated type tumors was greater in Group B than A, with larger tumor size and depth of submucosal invasion as well. There was no incidence of local recurrence, but distant metastasis was identified in 5% of cases in Group A and 3% in Group B. After propensity score-matching, there were no difference in the 5-year overall survival rate between Group A and B (87.5% vs. 91.2%, respectively), nor in the 5-year disease-specific survival rate (96.3% vs. 96.4%, respectively). ESD prior to surgery for T1b2 gastric cancer did not adversely affect clinical outcomes after additional surgery.

Clinical significance of endoscopic ultrasonography in diagnosing invasion depth of early gastric cancer prior to endoscopic submucosal dissection

BACKGROUND AND AIM

Endoscopic ultrasonography is a reliable diagnostic modality for determining indications of endoscopic submucosal dissection for early gastric cancer. We aimed to clarify the clinical significance of endoscopic ultrasonography in the invasion depth diagnosis of early gastric cancer.

METHODS

We retrospectively assessed 1598 consecutive patients with 2001 early gastric cancers who underwent EUS before ESD or surgery between October 2010 and April 2019 at our institution. Lesions were classified according to endoscopic ultrasonography-determined invasion depth as EUS-M/SM1 (lesions confined to sonographic layers 1 and 2 or lesions with changes in sonographic layer 3; depth, < 1 mm) and EUS-SM2 (lesions with changes in sonographic layer 3; depth, ≥ 1 mm). We evaluated the invasion depth determination accuracy of endoscopic ultrasonography and analyzed the clinicopathological features of misdiagnosed early gastric cancer cases.

RESULTS

The invasion depth determination accuracy was as follows: EUS-M/SM1: pathological T1a/T1b1 early gastric cancer, 97%; EUS-SM2: pathological T1b2 early gastric cancer, 79%. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 95%, 98%, 69%, 97%, and 79%, respectively. In EUS-M/SM1 early gastric cancer, tumor size of ≥ 15 mm, presence of ulceration, and undifferentiated histological type were significantly associated with endoscopic ultrasonography accuracy. In EUS-SM2 early gastric cancer, tumor size of ≥ 30 mm was significantly associated with endoscopic ultrasonography accuracy.

CONCLUSIONS

Endoscopic ultrasonography is a useful modality in accurately determining the invasion depth of early gastric cancer before endoscopic submucosal dissection.

Clinical practice guideline for endoscopic resection of early gastrointestinal cancer

Although surgery was the standard treatment for early gastrointestinal cancers, endoscopic resection is now a standard treatment for early gastrointestinal cancers without regional lymph node metastasis. High-definition white light endoscopy, chromoendoscopy, and image-enhanced endoscopy such as narrow band imaging are performed to assess the edge and depth of early gastrointestinal cancers for delineation of resection boundaries and prediction of the possibility of lymph node metastasis before the decision of endoscopic resection. Endoscopic mucosal resection and/or endoscopic submucosal dissection can be performed to remove early gastrointestinal cancers completely by en bloc fashion. Histopathological evaluation should be carefully made to investigate the presence of risk factors for lymph node metastasis such as depth of cancer invasion and lymphovascular invasion. Additional treatment such as radical surgery with regional lymphadenectomy should be considered if the endoscopically resected specimen shows risk factors for lymph node metastasis. This is the first Korean clinical practice guideline for endoscopic resection of early gastrointestinal cancer. This guideline was developed by using mainly de novo methods and encompasses endoscopic management of superficial esophageal squamous cell carcinoma, early gastric cancer, and early colorectal cancer. This guideline will be revised as new data on early gastrointestinal cancer are collected.

Highly accurate artificial intelligence systems to predict the invasion depth of gastric cancer: efficacy of conventional white-light imaging, nonmagnifying narrow-band imaging, and indigo-carmine dye contrast imaging

BACKGROUND AND AIMS

Diagnosing the invasion depth of gastric cancer (GC) is necessary to determine the optimal method of treatment. Although the efficacy of evaluating macroscopic features and EUS has been reported, there is a need for more accurate and objective methods. The primary aim of this study was to test the efficacy of novel artificial intelligence (AI) systems in predicting the invasion depth of GC.

METHODS

A total of 16,557 images from 1084 cases of GC for which endoscopic resection or surgery was performed between January 2013 and June 2019 were extracted. Cases were randomly assigned to training and test datasets at a ratio of 4:1. Through transfer learning leveraging a convolutional neural network architecture, ResNet50, 3 independent AI systems were developed. Each system was trained to predict the invasion depth of GC using conventional white-light imaging (WLI), nonmagnifying narrow-band imaging (NBI), and indigo-carmine dye contrast imaging (Indigo).

RESULTS

The area under the curve of the WLI AI system was .9590. The lesion-based sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the WLI AI system were 84.4%, 99.4%, 94.5%, 98.5%, and 92.9%, respectively. The lesion-based accuracies of the WLI, NBI, and Indigo AI systems were 94.5%, 94.3%, and 95.5%, respectively, with no significant difference.

CONCLUSIONS

These new AI systems trained with multiple images from different angles and distances could predict the invasion depth of GC with high accuracy. The lesion-based accuracy of the WLI, NBI, and Indigo AI systems was not significantly different.

Guidelines for endoscopic diagnosis of early gastric cancer

The Japan Gastroenterological Endoscopy Society developed the Guideline for Endoscopic Diagnosis of Early Gastric Cancer based on scientific methods. Endoscopy for the diagnosis of early gastric cancer has been acknowledged as a useful and highly precise examination, and its use has become increasingly more common in recent years. However, the level of evidence in this field is low, and it is often necessary to determine recommendations based on expert consensus only. This clinical practice guideline consists of the following sections to provide the current guideline: [I] Risk stratification of gastric cancer before endoscopic examination, [II] Detection of early gastric cancer, [III] Qualitative diagnosis of early gastric cancer, [IV] Diagnosis to choose the therapeutic strategy for gastric cancer, [V] Risk stratification after endoscopic examination, and [VI] Surveillance of early gastric cancer.

Clinical Practice Guideline for Endoscopic Resection of Early Gastrointestinal Cancer

Although surgery was the standard treatment for early gastrointestinal cancers, endoscopic resection is now a standard treatment for early gastrointestinal cancers without regional lymph node metastasis. High-definition white light endoscopy, chromoendoscopy, and image-enhanced endoscopy such as narrow band imaging are performed to assess the edge and depth of early gastrointestinal cancers for delineation of resection boundaries and prediction of the possibility of lymph node metastasis before the decision of endoscopic resection. Endoscopic mucosal resection and/or endoscopic submucosal dissection can be performed to remove early gastrointestinal cancers completely by en bloc fashion. Histopathological evaluation should be carefully made to investigate the presence of risk factors for lymph node metastasis such as depth of cancer invasion and lymphovascular invasion. Additional treatment such as radical surgery with regional lymphadenectomy should be considered if the endoscopically resected specimen shows risk factors for lymph node metastasis. This is the first Korean clinical practice guideline for endoscopic resection of early gastrointestinal cancer. This guideline was developed by using mainly de novo methods and encompasses endoscopic management of superficial esophageal squamous cell carcinoma, early gastric cancer, and early colorectal cancer. This guideline will be revised as new data on early gastrointestinal cancer are collected.

Clinical Practice Guideline for Endoscopic Resection of Early Gastrointestinal Cancer

Although surgery was the standard treatment for early gastrointestinal cancers, endoscopic resection is now a standard treatment for early gastrointestinal cancers without regional lymph node metastasis. High-definition white light endoscopy, chromoendoscopy, and image-enhanced endoscopy such as narrow band imaging are performed to assess the edge and depth of early gastrointestinal cancers for delineation of resection boundaries and prediction of the possibility of lymph node metastasis before the decision of endoscopic resection. Endoscopic mucosal resection and/or endoscopic submucosal dissection can be performed to remove early gastrointestinal cancers completely by <i>en bloc</i> fashion. Histopathological evaluation should be carefully made to investigate the presence of risk factors for lymph node metastasis such as depth of cancer invasion and lymphovascular invasion. Additional treatment such as radical surgery with regional lymphadenectomy should be considered if the endoscopically resected specimen shows risk factors for lymph node metastasis. This is the first Korean clinical practice guideline for endoscopic resection of early gastrointestinal cancer. This guideline was developed by using mainly de novo methods and encompasses endoscopic management of superficial esophageal squamous cell carcinoma, early gastric cancer, and early colorectal cancer. This guideline will be revised as new data on early gastrointestinal cancer are collected.

Diagnosis of invasion depth in resectable advanced gastric cancer for neoadjuvant chemotherapy: An exploratory analysis of Japan clinical oncology group study: JCOG1302A

BACKGROUND

Neoadjuvant chemotherapy (NAC) is increasingly used for resectable locally advanced gastric cancer (LAGC). JCOG1302A investigated the diagnostic criteria of LAGC patients with cT3-4/N1-3 to minimize contamination of pathological stage I as a candidate for NAC. In JCOG1302A, 77.2% of cT3-4 tumors diagnosed via a combination of endoscopy and computed tomography (CT) were pT3-4. However, the role of endoscopic ultrasonography (EUS) and additional diagnostic procedures/modalities remains unclear. Here, we investigated whether EUS, thin-slice CT, and foaming agent (FA) in CT contribute to accurate diagnosis of AGC invasion depth.

METHODS

Using JCOG1302A study data, we compared positive predictive value (PPV), negative predictive value (NPV), and kappa index (KI) between conventional and additional diagnostic procedures to identify pT3-4: conventional endoscopy (CE) with versus without EUS, 1-mm versus 5-mm CT slice, and CT with versus without FA.

RESULTS

We analyzed 1232 patients' data. PPV, NPV, and KI were 79.2%/73.7%, 59.2%/58.8%, and 0.38/0.39 (CE alone/CE with EUS), 77.8%/75.5%, 62.9%/71.2%, and 0.38/0.39 (5-mm CT/1-mm CT), and 78.6%/75.1%, 60.9%/69.7%, and 0.38/0.40 (CT without FA/CT with FA), respectively. Overall, there were no remarkable differences in any comparison. More specifically, PPV and KI were slightly higher with CE alone rather than CE with EUS. Although NPV was higher for 1-mm CT and CT with FA, PPV was rather higher for 5-mm CT and CT without FA.

CONCLUSION

Additional diagnostic procedures/modalities, like EUS, 1-mm slice CT, or FA in CT may not improve the diagnostic accuracy of invasion depth in resectable LAGC.

Clinicopathological Features of Advanced Gastric Cancers which Were Misjudged and Subjected to Endoscopic Submucosal Dissection

Background and Aims

Endoscopic submucosal dissection (ESD) is widely performed for early gastric cancer (EGC). We have sometimes encountered gastric cancer lesions for which ESD was performed and at which pathologically advanced cancer was found. In this study, we performed clinicopathological examination of lesions whose endoscopic diagnosis and pathology differed substantially.

Methods

ESD was performed for 2,194 gastric cancer lesions (1,753 cases) in our institute from April 2005 through March 2015. The vertical margin was positive or status unknown in 51 lesions (2.3%); among these, muscularis propria (MP) or deeper infiltration was identified in 6 lesions from specimens obtained during subsequent surgery. In 1 lesion with MP invasion, the vertical margin was negative. We evaluated the clinicopathological features of these 7 lesions and retrospectively reviewed endoscopic indicators of submucosal invasion for EGC on white light imaging (WLI), narrow-band imaging magnifying endoscopy (NBI-ME), and endoscopic ultrasonography (EUS) performed previously.

Results

Average age was 73.2 ± 7.2 years, and all cases were men. The 7 lesions diagnosed as advanced cancer were 0.32% of 2,194 lesions and were all located in the U region (fundus). On retrospective review of endoscopic findings, 2 of 7 lesions on WBI, 3 of 6 lesions on NBI-ME, and 2 of 5 lesions on EUS met the criteria for indicating submucosal invasion of EGC. No lesions had findings on all 3 modalities.

Conclusion

In rare cases, advanced gastric cancer could not be accurately diagnosed by endoscopy using various modalities. Each case had special characteristics making identification of deep infiltration difficult.

Factors Affecting the Accuracy of Endoscopic Ultrasonography in the Diagnosis of Early Gastric Cancer Invasion Depth: A Meta-analysis

Background

Endoscopic ultrasonography (EUS) is the first imaging modality for investigating the depth of invasion in early gastric cancer (EGC). However, there is presently no consensus on the accuracy of EUS in diagnosing the invasion depth of EGC.

Aim

This study is aimed at systematically evaluating the accuracy of EUS in diagnosing the invasion depth of EGC and its affecting factors.

Methods

The literatures were identified by searching PubMed, SpringerLink, Cochrane Library, Web of Science, Nature, and Karger knowledge databases. Two researchers extracted the data from the literature and reconstructed these in 2 × 2 tables. The Meta-DiSc software was used to evaluate the overall sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic advantage ratio, and 95% confidence interval (CI). The SROC was drawn, and the area under the curve (AUC) was calculated to evaluate the diagnostic value.

Results

A total of 17 articles were selected, which included 4525 cases of lesions. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic dominance ratio, and 95% CI of EUS for diagnosing EGC was 0.87 (95% CI: 0.86-0.88), 0.67 (95% CI: 0.65-0.70), 2.90 (95% CI: 2.25-3.75), 0.17 (95% CI: 0.13-0.23), and 18.25 (95% CI: 12.61-26.39), respectively. The overall overstaging rate of mucosa/submucosa 1 (M/SM1) and SM by EUS was 13.31% and 32.8%, respectively, while the overall understaging rate of SM was 29.7%. The total misdiagnosis rates for EUS were as follows: 30.4% for lesions ≥ 2 cm and 20.9% for lesions < 2 cm, 27.7% for ulcerative lesions and 21.4% for nonulcerative lesions, and 22% for differentiated lesions and 26.9% for undifferentiated lesions.

Conclusion

EUS has a moderate diagnostic value for the depth of invasion of EGC. The shape, size, and differentiation of lesions might be the main factors that affect the accuracy of EUS in diagnosing EGC.

Possible indication of endoscopic resection in undifferentiated early gastric cancer

Endoscopic resection for early gastric cancer (EGC) without lymph node metastasis may be a valuable treatment option. To date, endoscopic resection for undifferentiated EGC is being investigated. We evaluated the risk of lymph node metastasis in undifferentiated EGC by examining the preoperative endoscopic findings and operated pathologic specimen. The medical records of patients who underwent surgical resection because of undifferentiated EGC between November 2008 and December 2015 were reviewed retrospectively. The risk factors associated with lymph node metastasis and the lymph node metastasis rate in the expanded indication of undifferentiated EGC were evaluated. A total of 376 patients with undifferentiated EGC (233 signet ring cell type and 143 poorly differentiated type) were analyzed. Lymph node metastasis was found in 9.8% of the patients. Among the patients who met the expanded criteria (59 patients), only one patient had lymph node metastasis (signet ring cell type without ulceration and 15 mm in size). The risk factors associated with lymph node metastasis were lesion size >20 mm (OR 3.013), scar deformity (OR 2.248), surface depression (OR 2.360), submucosal invasion (OR 3.427), and lymphovascular invasion (OR 6.296). Before endoscopic resection of undifferentiated EGC, careful selection of patients should be considered. The undifferentiated EGC with size ≥15 mm, scar deformity, surface depression, submucosal invasion, and lymphovascular invasion should be considered surgical resection instead of endoscopic resection.

Characteristics of proximal early gastric cancer differentiating distal early gastric cancer

Previous studies reported substantial differences between proximal and distal gastric cancer, however, most of the cases included in these studies were advanced gastric cancers (AGCs). The aim of this study was to investigate the unique characteristics of proximal early gastric cancer (EGC) by comparing with distal EGC. From March 2007 to March 2016, proximal and distal EGC patients who underwent endoscopic or surgical resection at our institution were matched 1:3 according to age and sex. We retrospectively analyzed the clinical and histopathological information. A total of 368 patients were enrolled including 92 (25%) in the proximal and 276 (75%) in the distal group. The proportion of patients who underwent surgery (56.5 vs. 20.3%, p<0.001), undifferentiated type (38.0 vs. 19.6%, p<0.001), tumor size (29.5 ±19.4 vs. 20.3 ±16.8 mm, p<0.001) and submucosal (SM) invasion (60.9 vs. 25.7%, p<0.001) were significantly higher in the proximal group than in the distal group. In multivariate analysis, the proximal location of EGC was a significant risk factor for SM invasion in the total population (odds ratio [OR], 3.541; 95% confidence interval [CI], 2.053–6.110; p<0.001), and in subgroup with EGC < 30mm (n = 279) (OR, 5.940; 95% CI, 2.974–11.862; p<0.001). In conclusion, careful therapeutic decision of proximal EGC is essential due to the different histopathological characteristics such as large tumor size and higher potential for SM invasion.

A Lesion-Based Convolutional Neural Network Improves Endoscopic Detection and Depth Prediction of Early Gastric Cancer

In early gastric cancer (EGC), tumor invasion depth is an important factor for determining the treatment method. However, as endoscopic ultrasonography has limitations when measuring the exact depth in a clinical setting as endoscopists often depend on gross findings and personal experience. The present study aimed to develop a model optimized for EGC detection and depth prediction, and we investigated factors affecting artificial intelligence (AI) diagnosis. We employed a visual geometry group(VGG)-16 model for the classification of endoscopic images as EGC (T1a or T1b) or non-EGC. To induce the model to activate EGC regions during training, we proposed a novel loss function that simultaneously measured classification and localization errors. We experimented with 11,539 endoscopic images (896 T1a-EGC, 809 T1b-EGC, and 9834 non-EGC). The areas under the curves of receiver operating characteristic curves for EGC detection and depth prediction were 0.981 and 0.851, respectively. Among the factors affecting AI prediction of tumor depth, only histologic differentiation was significantly associated, where undifferentiated-type histology exhibited a lower AI accuracy. Thus, the lesion-based model is an appropriate training method for AI in EGC. However, further improvements and validation are required, especially for undifferentiated-type histology.

Self-study of the non-extension sign in an e-learning program improves diagnostic accuracy of invasion depth of early gastric cancer

Background and study aims  We developed an e-learning program for endoscopic diagnosis of invasion depth of early gastric cancer (EGC) using a simple diagnostic criterion called non-extension sign, and the contribution of self-study quizzes to improvement of diagnostic accuracy was evaluated. Methods  We conducted a prospective randomized controlled study that recruited endoscopists throughout Japan. After completing a pretest, the participants watched video lectures and undertook post-test 1. The participants were then randomly allocated to either the self-study or non-self-study group, and participants in the first group completed the self-study program that comprised 100-case quizzes. Finally, participants in both groups undertook post-test 2. The primary endpoint was the difference in post-test 2 scores between the groups. The perfect score for the tests was set as 100 points. Results  A total of 423 endoscopists completed the pretest and were enrolled. Post-test 1 was completed by 415 endoscopists and 208 were allocated to the self-study group and 207 to the non-self-study group. Two hundred and four in the self-study group and 205 in the non-self-study group were included in the analysis. Video lectures improved the mean score of post-test 1 from 72 to 77 points. Participants who completed the self-study quizzes showed significantly better post-test 2 scores compared with the non-self-study group (80 vs. 76 points, respectively, P  < 0.0001). Conclusions  Our e-learning program showed that self-study quizzes consolidated knowledge of the non-extension sign and improved diagnostic ability of endoscopists for invasion depth of EGC.

Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy

BACKGROUND AND AIMS

According to guidelines, endoscopic resection should only be performed for patients whose early gastric cancer invasion depth is within the mucosa or submucosa of the stomach regardless of lymph node involvement. The accurate prediction of invasion depth based on endoscopic images is crucial for screening patients for endoscopic resection. We constructed a convolutional neural network computer-aided detection (CNN-CAD) system based on endoscopic images to determine invasion depth and screen patients for endoscopic resection.

METHODS

Endoscopic images of gastric cancer tumors were obtained from the Endoscopy Center of Zhongshan Hospital. An artificial intelligence-based CNN-CAD system was developed through transfer learning leveraging a state-of-the-art pretrained CNN architecture, ResNet50. A total of 790 images served as a development dataset and another 203 images as a test dataset. We used the CNN-CAD system to determine the invasion depth of gastric cancer and evaluated the system's classification accuracy by calculating its sensitivity, specificity, and area under the receiver operating characteristic curve.

RESULTS

The area under the receiver operating characteristic curve for the CNN-CAD system was .94 (95% confidence interval [CI], .90-.97). At a threshold value of .5, sensitivity was 76.47%, and specificity 95.56%. Overall accuracy was 89.16%. Positive and negative predictive values were 89.66% and 88.97%, respectively. The CNN-CAD system achieved significantly higher accuracy (by 17.25%; 95% CI, 11.63-22.59) and specificity (by 32.21%; 95% CI, 26.78-37.44) than human endoscopists.

CONCLUSIONS

We constructed a CNN-CAD system to determine the invasion depth of gastric cancer with high accuracy and specificity. This system distinguished early gastric cancer from deeper submucosal invasion and minimized overestimation of invasion depth, which could reduce unnecessary gastrectomy.

Natural history of early gastric cancer: series of 21 cases

Background and study aims  While knowledge of the natural history of early gastric cancer (EGC) may be useful in relevant clinical situations, few relevant reports are available. Therefore, we investigated the progression of EGC. We gathered data regarding 114 cases of EGC from 2005 to 2015 from a hospital cancer registry and analyzed 21 lesions that fulfilled five inclusion criteria. Deep progression was defined as submucosal invasion by a mucosal tumor and proper muscle invasion by a submucosal tumor. Lateral progression was defined as ≥ 20 % increase in size. During median follow-up of 23 months, one of 18 mucosal tumors showed deep progression and six showed lateral progression. Of three submucosal tumors, two showed deep progression and three showed lateral progression. Our study suggests that a certain proportion of mucosal cancers can lie dormant for several years. Further large-scale studies in a multicenter setting should overcome the limitations of this study.

Endoscopic diagnosis and treatment of esophageal adenocarcinoma: introduction of Japan Esophageal Society classification of Barrett's esophagus

Endoscopic surveillance of Barrett's esophagus has become a foundation of the management of esophageal adenocarcinoma (EAC). Surveillance for Barrett's esophagus commonly involves periodic upper endoscopy with biopsies of suspicious areas and random four-quadrant biopsies. However, targeted biopsies using narrow-band imaging can detect more dysplastic areas and thus reduce the number of biopsies required. Several specific mucosal and vascular patterns characteristic of Barrett's esophagus have been described, but the proposed criteria are complex and diverse. Simpler classifications have recently been developed focusing on the differentiation between dysplasia and non-dysplasia. These include the Japan Esophageal Society classification, which defines regular and irregular patterns in terms of mucosal and vascular shapes. Cancer invasion depth is diagnosed by endoscopic ultrasonography (EUS); however, a meta-analysis of EUS staging of superficial EAC showed favorable pooled values for mucosal cancer staging, but unsatisfactory diagnostic results for EAC at the esophagogastric junction. Endoscopic resection has recently been suggested as a more accurate staging modality for superficial gastrointestinal cancers than EUS. Following endoscopic resection for gastrointestinal cancers, the risk of metastasis can be evaluated based on the histology of the resected specimen. European guidelines describe endoscopic resection as curative for well- or moderately differentiated mucosal cancers without lymphovascular invasion, and these criteria might be extended to lesions invading the submucosa (≤ 500 μm), i.e., to low-risk, well- or moderately differentiated tumors without lymphovascular involvement, and < 3 cm. These criteria were confirmed by a recent study in Japan.

A Comparison by Meta-Analysis of Papillary Early Gastric Carcinoma to Its Tubular Counterpart for the Risk of Lymph Node Metastasis and Submucosal Invasion

BACKGROUND AND AIM

At present, the decision to perform endoscopic resection for treating either papillary early gastric cancer (EGC) or tubular EGC is made according to identical criteria. However, there is controversy in the literature whether the risk of lymph node metastasis (LNM) and submucosal invasion for both disease modalities is equal, and this prompts investigation to clarify this issue.

METHODS

The PubMed and Web of Science databases were searched for relevant studies published up to January 2017. Data were extracted, and the pooled odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated using a random-effects or a fixed-effects model, according to heterogeneity.

RESULTS

A total of 13 studies were included in this analysis. Papillary EGC had a significantly higher LNM risk (OR, 1.97; 95% CI, 1.38-2.82) and submucosal invasion risk (OR, 1.44; 95% CI, 1.08-1.93), compared with tubular EGC. Stratified by geographic location, a significantly increased risk of LNM (OR, 2.28; 95% CI, 1.57-3.30) and submucosal invasion (OR, 1.52; 95% CI, 1.13-2.04) associated with papillary EGC was found in Asian studies. In addition, papillary EGC exhibited significantly more frequent elevated/flat growth patterns (OR, 7.54, 95% CI, 4.76-11.96).

CONCLUSIONS

Our study identifies an increased risk for submucosal invasion and LNM in papillary EGC compared with tubular EGC, indicating that papillary EGC requires more careful clinical management compared with tubular EGC.