Endoscopic ultrasonography for staging depth of invasion in early gastric cancer: A meta-analysis

Artificial intelligence-assisted diagnosis of early gastric cancer: present practice and future prospects

Gastric cancer (GC) occupies the first few places in the world among tumors in terms of incidence and mortality, causing serious harm to human health, and at the same time, its treatment greatly consumes the health care resources of all countries in the world. The diagnosis of GC is usually based on histopathologic examination, and it is very important to be able to detect and identify cancerous lesions at an early stage, but some endoscopists' lack of diagnostic experience and fatigue at work lead to a certain rate of under diagnosis. The rapid and striking development of Artificial intelligence (AI) has helped to enhance the ability to extract abnormal information from endoscopic images to some extent, and more and more researchers are applying AI technology to the diagnosis of GC. This initiative has not only improved the detection rate of early gastric cancer (EGC), but also significantly improved the survival rate of patients after treatment. This article reviews the results of various AI-assisted diagnoses of EGC in recent years, including the identification of EGC, the determination of differentiation type and invasion depth, and the identification of borders. Although AI has a better application prospect in the early diagnosis of ECG, there are still major challenges, and the prospects and limitations of AI application need to be further discussed.

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.

Development of a deep learning model for early gastric cancer diagnosis using preoperative computed tomography images

Background

Gastric cancer is a highly prevalent and fatal disease. Accurate differentiation between early gastric cancer (EGC) and advanced gastric cancer (AGC) is essential for personalized treatment. Currently, the diagnostic accuracy of computerized tomography (CT) for gastric cancer staging is insufficient to meet clinical requirements. Many studies rely on manual marking of lesion areas, which is not suitable for clinical diagnosis.

Methods

In this study, we retrospectively collected data from 341 patients with gastric cancer at the First Affiliated Hospital of Wenzhou Medical University. The dataset was randomly divided into a training set (n=273) and a validation set (n=68) using an 8:2 ratio. We developed a two-stage deep learning model that enables fully automated EGC screening based on CT images. In the first stage, an unsupervised domain adaptive segmentation model was employed to automatically segment the stomach on unlabeled portal phase CT images. Subsequently, based on the results of the stomach segmentation model, the image was cropped out of the stomach area and scaled to a uniform size, and then the EGC and AGC classification models were built based on these images. The segmentation accuracy of the model was evaluated using the dice index, while the classification performance was assessed using metrics such as the area under the curve (AUC) of the receiver operating characteristic (ROC), accuracy, sensitivity, specificity, and F1 score.

Results

The segmentation model achieved an average dice accuracy of 0.94 on the hand-segmented validation set. On the training set, the EGC screening model demonstrated an AUC, accuracy, sensitivity, specificity, and F1 score of 0.98, 0.93, 0.92, 0.92, and 0.93, respectively. On the validation set, these metrics were 0.96, 0.92, 0.90, 0.89, and 0.93, respectively. After three rounds of data regrouping, the model consistently achieved an AUC above 0.9 on both the validation set and the validation set.

Conclusion

The results of this study demonstrate that the proposed method can effectively screen for EGC in portal venous CT images. Furthermore, the model exhibits stability and holds promise for future clinical applications.

Update on diagnosis and treatment of early signet-ring cell gastric carcinoma: A literature review

Gastric signet-ring cell gastric carcinoma (GSRC) is an unfavorable subtype of gastric cancer (GC) that presents with greater invasiveness and poorer prognosis in advanced stage than other types of GC. However, GSRC in early stage is often considered an indicator of less lymph node metastasis and more satisfying clinical outcome compared to poorly differentiated GC. Therefore, the detection and diagnosis of GSRC at early stage undoubtedly play a crucial role in the management of GSRC patients. In recent years, technological advancement in endoscopy including narrow-band imaging and magnifying endoscopy has significantly improved the accuracy and sensitivity of the diagnosis under endoscopy for GSRC patients. Researches have confirmed that early stage GSRC that meets the expanded criteria of endoscopic resection showed comparable outcomes to surgery after receiving endoscopic submucosal dissection (ESD), indicating that ESD could be considered standard treatment for GSRC after thorough selection and evaluation. This article summarizes the current knowledge and updates pertaining to the endoscopic diagnosis and treatment of early stage signet-ring cell gastric carcinoma.

Gastric cancer in 2022: Is there still a role for endoscopic ultrasound?

Gastric cancer (GC) represents the fourth leading cause of cancer death worldwide and many factors can influence its development (diet, geographic area, genetic, Helicobacter pylori or Epstein-Barr virus infections). High quality endoscopy represents the modality of choice for GC diagnosis. The correct morphologic classification during a high-resolution endoscopy is fundamental for oncologic diagnosis, staging and therapeutic decisions. Since its initial introduction in clinical practice the endoscopic ultrasound (EUS) has been considered a valuable tool for tumor (T-) and lymph nodes (N-) staging also in GC, in order to establish the best therapeutic strategy for the patient (e.g., upfront surgery vs neoadjuvant treatments). EUS tools as elastography, Doppler and contrast administration can improve diagnosis mainly in case of malignant lymph node evaluation. EUS has a marginal role in disease staging but has a fundamental role in case of a pre-endoscopic resection management and in the new era of endoscopic mucosal resection or submucosal dissection as minimally invasive surgery. Diagnosis and locoregional staging of GC with EUS are a method of inarguable value for the assessment of gastric wall involvement and presence of infiltrated paragastric lymph nodes. EUS can also have a role in disease restaging in those patients who have undergone neoadjuvant treatment. EUS can also have a role in the advanced phases of the disease, in facilitating palliative, minimally-invasive treatments, such as gastroenterostomy or biliary drainages. This review intends to discuss the modern role of EUS in GC topic.

Application of artificial intelligence in the endoscopic diagnosis of early gastric cancer, atrophic gastritis, and Helicobacter pylori infection

Gastric cancer (GC) is one of the most serious health problems worldwide. Chronic atrophic gastritis (CAG) is most commonly caused by Helicobacter pylori (H. pylori) infection. Currently, endoscopic detection of early gastric cancer (EGC) and CAG remains challenging for endoscopists, and the diagnostic accuracy of H. pylori infection by endoscopy is approximately 70%. Artificial intelligence (AI) can assist endoscopic diagnosis including detection, prediction of depth of invasion, boundary delineation, and anatomical location of EGC, and has achievable diagnostic ability even comparable to experienced endoscopists. In this review we summarized various AI-assisted systems in the diagnosis of EGC, CAG, and H. pylori infection.

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.

Advances in screening and detection of gastric cancer

With an estimated one million new cases and 769 000 deaths in 2020, gastric cancer is the fifth most frequent cancer and fourth leading cause of cancer death globally. Incidence rates are highest in Asia and Eastern Europe. This manuscript will review the current modalities of diagnosis, staging, and screening of gastric cancer. We will also highlight development of novel diagnostics and advancements in endoscopic detection of early gastric cancer.

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.

Comparison of diagnostic accuracy between linear EUS and miniprobe EUS for submucosal invasion in suspected early gastric cancer

OBJECTIVE

This study assessed the accuracy of linear endoscopic ultrasound (EUS) in diagnosing submucosal (SM) invasion and compared linear EUS with mini-probe EUS in suspected early gastric cancer (EGC) patients.

METHODS

Patients diagnosed with biopsy-verified suspected EGC were analysed retrospectively. They were all examined by linear EUS or miniprobe EUS for preoperative diagnosis of invasion depth and underwent endoscopic or surgical treatment for radical resection. The invasion depth evaluated by EUS and pathology were categorized into no invasion of SM and invasion of SM or deeper. We compared the diagnosis of EUS with postoperative pathology results.

RESULTS

A total of 105 patients were included in the final analysis. We found that the overall prediction accuracy of linear EUS (n = 57) for SM invasion in suspected EGC was higher than that of mini-probe EUS (n = 48), but no statistically significant differences were noted (82.5% vs 72.9%, p = 0.344). The negative predictive value (NPV) of linear EUS was significantly higher than that of mini-probe EUS (100% vs 82.8%, p = 0.037). The binary logistic regression analysis identified that tumor size (p = 0.036), the presence of ulceration (p < 0.001) and the EUS type (p = 0.027) were independent risk factors for the diagnosis of SM invasion by EUS. The area under the receiver operating curve (ROC) was 0.889 and 0.719 for linear and mini-probe EUS, respectively.

CONCLUSION

Linear EUS diagnosed suspected EGC for SM invasion with higher accuracy than mini-probe EUS. Additionally, large and ulcerative lesions may lead to overestimation.

Application of Artificial Intelligence in Early Gastric Cancer Diagnosis

BACKGROUND

With the development of new technologies such as magnifying endoscopy with narrow band imaging, endoscopists achieved better accuracy for diagnosis of gastric cancer (GC) in various aspects. However, to master such skill takes substantial effort and could be difficult for inexperienced doctors. Therefore, a novel diagnostic method based on artificial intelligence (AI) was developed and its effectiveness was confirmed in many studies. AI system using convolutional neural network has showed marvelous results in the ongoing trials of computer-aided detection of colorectal polyps.

SUMMARY

With AI's efficient computational power and learning capacities, endoscopists could improve their diagnostic accuracy and avoid the overlooking or over-diagnosis of gastric neoplasm. Several systems have been reported to achieved decent accuracy. Thus, AI-assisted endoscopy showed great potential on more accurate and sensitive ways for early detection, differentiation, and invasion depth prediction of gastric lesions. However, the feasibility, effectiveness, and safety in daily practice remain to be tested. Key messages: This review summarizes the current status of different AI applications in early GC diagnosis. More randomized controlled trails will be needed before AI could be widely put into clinical practice.

Controversies in EUS: Do we need miniprobes?

This is the fifth in a series of papers entitled "Controversies in EUS." In the current paper, we deal with high-resolution catheter probes, otherwise known as EUS miniprobes (EUS-MPs). The application of miniprobes for early carcinomas in the entire intestinal tract, for subepithelial lesions, and for findings in the bile duct and pancreatic duct as well as endobronchial use is critically discussed. Submucous lesions, especially in the colon, but also early carcinomas in special cases are considered the most important indications. The argument is illustrated by numerous examples.

Usefulness of artificial intelligence in gastric neoplasms

Recently, studies in many medical fields have reported that image analysis based on artificial intelligence (AI) can be used to analyze structures or features that are difficult to identify with human eyes. To diagnose early gastric cancer, related efforts such as narrow-band imaging technology are on-going. However, diagnosis is often difficult. Therefore, a diagnostic method based on AI for endoscopic imaging was developed and its effectiveness was confirmed in many studies. The gastric cancer diagnostic program based on AI showed relatively high diagnostic accuracy and could differentially diagnose non-neoplastic lesions including benign gastric ulcers and dysplasia. An AI system has also been developed that helps to predict the invasion depth of gastric cancer through endoscopic images and observe the stomach during endoscopy without blind spots. Therefore, if AI is used in the field of endoscopy, it is expected to aid in the diagnosis of gastric neoplasms and determine the application of endoscopic therapy by predicting the invasion depth.

Application of artificial intelligence-driven endoscopic screening and diagnosis of gastric cancer

The landscape of gastrointestinal endoscopy continues to evolve as new technologies and techniques become available. The advent of image-enhanced and magnifying endoscopies has highlighted the step toward perfecting endoscopic screening and diagnosis of gastric lesions. Simultaneously, with the development of convolutional neural network, artificial intelligence (AI) has made unprecedented breakthroughs in medical imaging, including the ongoing trials of computer-aided detection of colorectal polyps and gastrointestinal bleeding. In the past demi-decade, applications of AI systems in gastric cancer have also emerged. With AI's efficient computational power and learning capacities, endoscopists can improve their diagnostic accuracies and avoid the missing or mischaracterization of gastric neoplastic changes. So far, several AI systems that incorporated both traditional and novel endoscopy technologies have been developed for various purposes, with most systems achieving an accuracy of more than 80%. However, their feasibility, effectiveness, and safety in clinical practice remain to be seen as there have been no clinical trials yet. Nonetheless, AI-assisted endoscopies shed light on more accurate and sensitive ways for early detection, treatment guidance and prognosis prediction of gastric lesions. This review summarizes the current status of various AI applications in gastric cancer and pinpoints directions for future research and clinical practice implementation from a clinical perspective.

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.

Endoscopic Ultrasonography Miniature Probe Performance for Depth Diagnosis of Early Gastric Cancer with Suspected Submucosal Invasion

Background/Aims

The accurate assessment of the depth of invasion of early gastric cancer (EGC) is critical to determine the most appropriate treatment option. However, it is difficult to distinguish shallow submucosal (SM1) invasion from deeper submucosal (SM2) invasion. We investigated the diagnostic performance of endoscopic ultrasonography (EUS) using a miniature probe for EGC with suspected SM invasion.

Methods

From April 2008 to June 2018, EGCs with suspected SM invasion were analyzed retrospectively. The EGCs examined by a 20 MHz high-frequency miniature probe was included in our study. Esophago-gastric junction cancers and patients treated by chemotherapy before resection were excluded. The sensitivity and specificity for the detection of SM2 invasion by EUS were compared with those of white light imaging (WLI). Additionally, factors related to depth underestimation or overestimation were investigated using multivariate analysis.

Results

A total of 278 EGCs in 259 patients were included in the final analysis. The sensitivity and specificity for SM2 or deeper by EUS were 73.7% (87/118) and 74.4% (119/160), respectively. The sensitivity and specificity by WLI were 47.5% (56/118) and 68.1% (109/160), respectively. The sensitivity of EUS was significantly superior to that of conventional endoscopy (p<0.01). Multivariate analysis revealed that an anterior location of the EGC was an independent risk factor for underestimation by EUS (odds ratio, 3.3; 95% confidence interval, 1.1 to 9.8; p=0.03).

Conclusions

The depth diagnostic performance for EGCs with suspected SM invasion using EUS was satisfactory and superior to that of conventional endoscopy. Additionally, it is important to recognize factors that may lead to misdiagnosis in those lesions.

Computed tomography texture analysis in patients with gastric cancer: a quantitative imaging biomarker for preoperative evaluation before neoadjuvant chemotherapy treatment

PURPOSE

The aim of the study is to explore the role of computed tomography texture analysis (CT-TA) for predicting clinical T and N stages and tumor grade before neoadjuvant chemotherapy treatment in gastric cancer (GC) patients during the preoperative period.

MATERIALS AND METHODS

CT images of 114 patients with GC were included in this retrospective study. Following pre-processing steps, textural features were extracted using MaZda software in the portal venous phase. We evaluated and analyzed texture features of six principal categories for differentiating between T stages (T1,2 vs T3,4), N stages (N+ vs N-) and grades (low-intermediate vs. high). Classification was performed based on texture parameters with high model coefficients in linear discriminant analysis (LDA).

RESULTS

Dimension-reduction steps yielded five textural features for T stage, three for N stage and two for tumor grade. The discriminatory capacities of T stage, N stage and tumor grade were 90.4%, 81.6% and 64.5%, respectively, when LDA algorithm was employed.

CONCLUSION

CT-TA yields potentially useful imaging biomarkers for predicting the T and N stages of patients with GC and can be used for preoperative evaluation before neoadjuvant treatment planning.

Linear-array endoscopic ultrasound improves the accuracy of preoperative submucosal invasion prediction in suspected early gastric cancer compared with radial endoscopic ultrasound: A prospective cohort study

BACKGROUND AND AIM

There is a lack of literature comparing linear endoscopic ultrasound (EUS) and radial EUS for the prediction of the depth of invasion in early gastric cancer (EGC). The aim of this study is to evaluate the accuracy of linear EUS for the diagnosis of submucosal (SM) invasion and compare linear EUS with radial EUS in suspected EGC patients.

METHODS

Seventy-two consecutive patients with suspected EGC who underwent a preoperative assessment using linear EUS or radial EUS were prospectively enrolled. The depth of invasion was categorized into mucosal to SM (< T1b) and SM or deeper (≥ T1b), and the EUS-determined diagnosis was compared with postoperative histopathological findings.

RESULTS

Thirty-nine patients underwent radial EUS, and 33 patients underwent linear EUS examination. The baseline characteristics between the groups were well balanced. The diagnostic accuracy was much higher for patients who underwent linear EUS compared with radial EUS (90.9% vs 69.2%, P = 0.024). The sensitivity was 92.3% (95% confidence interval [CI] 66.7-98.6%) for linear EUS and 90.9% (95% CI 62.3-98.4%) for radial EUS. The specificity was 90.0% (95% CI 69.9-97.2%) in the linear EUS group, while the specificity was 60.7% (95% CI 42.4-76.4%) in the radial EUS group. Univariate analysis showed that EUS type (odds ratio 0.225, 95% CI 0.057-0.884, P = 0.033) was an associated risk factor of incorrect T1b staging in EGC patients. The area under the receiver operating curve was 0.912 and 0.758 for linear and radial EUS, respectively.

CONCLUSION

Linear EUS was more accurate for determining SM invasion and therapeutic strategy in suspected EGC patients compared with radial EUS.

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.

A Meta-Analysis And Systematic Review Of Accuracy Of Endoscopic Ultrasound For N Staging Of Gastric Cancers

BACKGROUND

Endoscopic ultrasonography (EUS) is widely used as a staging modality for gastric cancer. However, the results of studies on the use of EUS for N staging in gastric cancer vary. This study aimed at studying the overall diagnostic accuracy of EUS for N staging of gastric cancer.

METHODS

Published studies were identified through searching the MEDLINE, Web of Science, EMBASE, SpringerLink and ScienceDirect databases. A bivariate random effect model was used to estimate the sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). A hierarchical summary receiver operating characteristic curves (HSROC) based on the pooled data was also computed.

RESULTS

Fifty studies (5223 patients) were included in this analysis. The pooled sensitivity, specificity, PLR, NLR and DOR of EUS for N staging were 0.82 (95% CI 0.78 to 0.85), 0.68 (0.63 to 0.73), 2.6 (2.2 to 3.0), 0.27 (0.22 to 0.32), and 10 (8 to 12), respectively. The area under the HSROC was 0.83.

CONCLUSION

The EUS may provide a clinically useful tool to guide physicians in the N staging of gastric cancer. However, physicians must note that the EUS has a relatively low specificity.

Nomograms for predicting survival in patients with metastatic gastric adenocarcinoma who undergo palliative gastrectomy

BACKGROUND

Recently, evidence has emerged that palliative gastrectomy in patients with stage IV gastric cancer may offer some survival benefits. However, the decision whether to perform primary tumor surgery remains challenging for surgeons, and investigations into models that are predictive of prognosis are scarce. Current study aimed to develop and validate prognostic nomograms for patients with metastatic gastric adenocarcinoma treated with palliative gastrectomy.

METHODS

The development dataset comprised 1186 patients from the Surveillance, Epidemiology, and End Results Program who were diagnosed with metastatic gastric adenocarcinoma in 2004-2011, while the validation dataset included 407 patients diagnosed in 2012-2015. Variables were incorporated into a Cox proportional hazards model to identify independent risk factors for survival. Both pre- and postoperative nomograms for predicting 1- or 2-year survival probabilities were constructed using the development dataset. The concordance index (c-index) and calibration curves were plotted to determine the accuracy of the nomogram models. Finally, the cut-off value of the calculated total scores based on preoperative nomograms was set and validated by comparing survival with contemporary cases without primary tumor surgery.

RESULTS

Age, tumor size, location, grade, T stage, N stage, metastatic site, scope of gastrectomy, number of examined lymph node(s), chemotherapy and radiotherapy were risk factors of survival and were included as variables in the postoperative nomogram; the c-indices of the development and validation datasets were 0.701 (95% confidence interval [CI]: 0.693-0.710) and 0.699 (95% CI: 0.682-0.716), respectively. The preoperative nomogram incorporated age, tumor size, location, grade, depth of invasion, regional lymph node(s) status, and metastatic site. The c-indices for the internal (bootstrap) and external validation sets were 0.629 (95% CI: 0.620-0.639) and 0.607 (95% CI: 0.588-0.626), respectively. Based on the preoperative nomogram, patients with preoperative total score > 28 showed no survival benefit with gastrectomy compared to no primary tumor surgery.

CONCLUSIONS

Our survival nomograms for patients with metastatic gastric adenocarcinoma undergoing palliative gastrectomy can assist surgeons in treatment decision-making and prognostication.

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.

Is Radical Surgery Necessary for All Patients Diagnosed as Having Non-Curative Endoscopic Submucosal Dissection?

If a lesion does not meet the expanded indication criteria for treatment with endoscopic therapy for early gastric cancer or has a positive resection margin, it is regarded as suitable for non-curative resection. Non-curative resection is closely related to the risk of local recurrence, lymph node metastasis, and poor prognosis. If the result is confirmed as non-curative resection, additional treatment should be considered depending on the risks of residual tumor, local recurrence, and lymph node metastasis. As lymphatic invasion is the most important risk factor of recurrence and poor prognosis, surgical treatment should be considered if lymphatic invasion is present. If patients are not suitable for additional surgery owing to old age or coexisting severe disease, close surveillance can be an alternative treatment option.

Validation of the prognostic impact of the new tumor-node-metastasis clinical staging in patients with gastric cancer

BACKGROUND

In the 8th edition of the tumor-node-metastasis (TNM) classification, the gastric cancer staging system includes two classifications: the clinical stage (cStage) and the postoperative pathologic stage. However, the correlation between the new cStage and overall survival has not been studied. Moreover, clinical N (cN) grade analysis is not included in the new clinical staging system. This study validated the prognostic value of cStage in the 8th edition of the TNM classification and the significance of N classification for pretreatment staging in gastric cancer.

METHODS

A total of 4374 patients with primary gastric cancer who underwent surgery at the Shizuoka Cancer Center were included. Survival analysis was conducted based on the newly proposed cStage criteria of the TNM 8th edition. Prognostic accuracy was evaluated using Harrell's concordance index (C-index).

RESULTS

The five-year survival rates according to cStage were as follows: cStageI, 91.2%, cStageIIA: 75.1%, cStageIIB: 57.7%, cStageIII: 43.2%, cStageIVA: 31.6%, and cStageIVB: 7.7%. Significant differences were observed among all stages (P < 0.001). The cStage C-index was 0.802. Meanwhile, the five-year survival rates based on cN were as follows: cN0: 83.7%, cN1: 57.2%, cN2: 42.4%, cN3a: 22.1%, and cN3b: 0.0%. Significant differences were also observed among all cN grades (P < 0.001).

CONCLUSIONS

The cStage of the 8th edition of the TNM classification has a good capability to predict prognosis; thus, it may be a useful indicator for selecting appropriate gastric cancer treatment modalities. In addition, cN can be included in developing a more precise clinical staging of gastric cancer.

Accuracy of endoscopic ultrasound in gastric adenocarcinoma patient selection for neoadjuvant therapy

Background

Recent studies demonstrated the positive impact of neoadjuvant treatment in locally advanced gastric cancer.

Objective

To assess the accuracy of endoscopic ultrasound (EUS) in the selection of patients with gastric adenocarcinoma for neoadjuvant therapy (T ≥ 2 and/or N+).

Methods

Retrospective analysis of patients with an anatomopathological diagnosis of gastric adenocarcinoma between January 2011 and June 2017, who had EUS for staging and underwent surgery as a first therapeutic attempt. The concordance (k) and accuracy (area under the curve (AUC)) of EUS for T ≥ 2 and/or N+ were assessed using the anatomopathological staging of the resected surgical specimen as the gold standard.

Results

The final sample included 152 patients (66.4% male, 67.1 ± 12.2 years). The concordance, accuracy, sensitivity and specificity of the EUS for T ≥ 2 and/or N+ were 0.72, 0.86 ± 0.03, 88.5% and 83.1%, respectively. The results were higher in proximal (k = 0.93, AUC = 0.96 ± 0.05, sensitivity (S) = 99.0% and specificity (E) = 90.9%) compared with distal lesions (k = 0.67, AUC = 0.84 ± 0.04, S = 85.7% and E = 81.5%), and in intestinal subtype (k = 0.77, AUC = 0.88 ± 0.04, S = 92.6% and E = 84.1%) compared with diffuse (k = 0.58, AUC = 0.79 ± 0.10, S = 85.0% and E = 72.7%) or mixed-subtype tumours (k = 0.65, AUC = 0.84 ± 0.10, S = 76.9% and E = 90.0%).

Conclusion

In one of the largest series of patients, we showed that EUS has overall high agreement and accuracy in the selection of patients with gastric adenocarcinoma for neoadjuvant therapy, although the agreement and accuracy are greater for proximal and intestinal lesions.

Strategy for esophageal non-epithelial tumors based on a retrospective analysis of a single facility

BACKGROUND

Although most esophageal non-epithelial tumors are benign tumors, such as leiomyomas, they also include gastrointestinal tumors (GISTs); thus, a histopathological diagnosis is indispensable to determine the optimal treatment strategy. However, no consensus has been reached as to the diagnostic methods and treatments for esophageal non-epithelial tumors. The purpose of this study was to evaluate the reliability of the diagnostic methods and treatments for esophageal non-epithelial tumors in our hospital.

METHODS

All 28 cases of esophageal non-epithelial tumors at Kobe University Hospital from 2008 to 2016 were analyzed retrospectively with respect to the diagnostic methods, histopathological diagnosis, and treatments.

RESULTS

Three diagnostic methods, endoscopic ultrasonography-guided fine needle aspiration (EUS-FNA), endoscopic incisional biopsy, and endoscopic submucosal dissection (ESD)/endoscopic mucosal resection (EMR), were performed in our hospital. All GIST cases could be correctly diagnosed by EUS-FNA. Tumors less than approximately 20 mm in diameter and located in the superficial layer are good indications for ESD/EMR, which both play roles in diagnosis and treatment. The final diagnoses by these methods consisted of the following: 13 leiomyomas, 5 GISTs, 3 schwannomas, 2 liposarcomas, 3 cysts, 1 reactive lymphoid hyperplasia, and 1 granulosa cell tumor. Fifteen cases underwent surgery. Enucleation or partial resection was performed for leiomyomas, schwannomas and liposarcomas, while esophagectomy was performed for GISTs. Thus, sufficient management of non-epithelial tumors is achieved.

CONCLUSIONS

Improved endoscopic procedures, including EUS-FNA and ESD/EMR, enabled the appropriate diagnosis and treatment of esophageal non-epithelial tumors.

Construction and external validation of a nomogram that predicts lymph node metastasis in early gastric cancer patients using preoperative parameters

Objective

To create a nomogram to predict the incidence of lymph node metastasis (LNM) in early gastric cancer (EGC) patients and to externally validate the nomogram.

Methods

To construct the nomogram, we retrospectively analyzed a primary cohort of 272 EGC patients. Univariate analysis and a binary logistic regression were performed. A nomogram predicting the incidence of LNM in EGC patients was created. The discrimination ability of the nomogram was measured using the concordance index (c-index), and the nomogram was also calibrated. Then, another prospective cohort of 81 patients was analyzed to validate the nomogram.

Results

In the primary cohort, LNM was pathologically confirmed in 37 (13.6%) patients. In multivariate analysis, the presence of an ulcer, the maximum lesion diameter observed via gastroscopy, the thickness of the lesion observed via endoscopic ultrasonography, and the presence of enlarged lymph nodes on computed tomography (CT) were independent risk factors for LNM. A nomogram was then created based on the regression model with the c-index of 0.905, and the calibration curve of the nomogram fell approximately on the ideal 45-degree line. The cut-off score of the nomogram was 110, and the sensitivity, specificity, positive predictive and negative predictive values of the nomogram in the primary cohort were 81.1%, 86.0%, 47.6% and 96.7%, respectively, and in the prospective validation cohort were 75.0%, 91.0%, 60.0% and 95.5%, respectively. The calibration curve of the external validation cohort was almost on the 45-degree line.

Conclusions

We developed an effective nomogram predicting the incidence of LNM for EGC patients.

Endoscopic ultrasound in gastric cancer staging before and after neoadjuvant chemotherapy. A comparison with PET-CT in a clinical series

BACKGROUND

Treatment of gastric cancer is based on accurate staging. Emerging methods, such as PET-CT, are increasingly being used for this purpose. Our aim was to analyze the results of EUS and PET-CT in staging and restaging our patients with gastric cancer, comparing both of them with the histological results.

METHODS

Patients with confirmed gastric cancer were prospectively enrolled. Inclusion criteria for the final analysis included only patients who finally received a surgical resection. All patients underwent preoperative TNM staging by means of EUS and PET-CT within 21 days prior to the surgical treatment.

RESULTS

A total of 256 patients were included. The overall EUS accuracy for T staging was 78% and 80.2% in restaging. EUS showed its best accuracy when distinguishing T1-T2 tumors vs. T3-T4, with an increased accuracy in restaging. Regarding N staging, the overall accuracy of EUS was 76.2%, and 72.5% for PET-CT (p = 0.02). With regards to restaging, accuracy of EUS and PET-CT for N staging was 88.5% and 69%, respectively, with significant differences (p < 0.0001).

CONCLUSIONS

EUS performed better than PET-CT in gastric cancer N staging and restaging. EUS accuracy in this setting is still suboptimal and probably more than one single diagnostic procedure should be used.

Endoscopic submucosal dissection of gastric superficial neoplastic lesions: a single Western center experience

Background

Endoscopic submucosal dissection (ESD) allows "en bloc" resection of superficial gastric lesions.

Objective

The aim of this study is to report "en bloc" resection rate, procedure results, post resection features, analysis of complications, and relapses at mid-long term follow-up on an Italian center case series of patients with superficial gastric neoplastic lesions treated with ESD.

Methods

This is a retrospective evaluation of a prospectively collected series of patients diagnosed with gastric superficial lesions who underwent ESD between November 2007 and May 2015. The analysis included demographic, clinical, endoscopic, and histological data. In detail, the following features were analyzed: (1) "en bloc" resection rate; (2) R0, R1, and RX rate; (3) complication rate; and (4) neoplastic recurrence during follow-up. A comparison was performed between lesions treated according to standard and to extended criteria.

Results

In the analysis, therefore, 70 superficial lesions and 70 endoscopic procedures were considered.Overall, 53 (76%) procedures were performed according to the extended-ESD criteria. The remaining 17 (24%) cases were performed according to standard criteria. In 68/70 (97%) procedures "en bloc" resection was successful. Forty-four (65.6%) patients had a R0 resection, while 15 (22%) patients had a R1 and the remaining 8 (12.4%) patients were RX. Two patients (2.8%) experienced a major complication. Overall, 7 patients (10.4%) required surgery: in two cases because of local recurrence, in one case for an intraprocedural complication not amenable to endoscopic treatment, in three patients with R1 vertical margins, and in the last patient because of an aggressive pathology (i.e. signet ring cell adenocarcinoma). The mean follow-up of patients with neoplastic lesions was 36.6 ± 13.1 months.

Conclusion

ESD for early gastric cancer is a safe and effective technique when performed by experienced Endoscopists also in Western setting. When comparing the outcomes of the ESD in patients with standard and extended indication there is no significant difference in terms of complete resection, complications and local recurrence rates. Larger Western studies are needed to clearly define the role and the outcomes of ESD in regions at low incidence for gastric neoplasia.

Endoscopic ultrasonography compared with multidetector computed tomography for the preoperative staging of gastric cancer: a meta-analysis

Background

The current study sought to perform a meta-analysis to compare the preoperative staging of endoscopic ultrasonography (EUS) and multidetector computed tomography (MDCT) in gastric carcinoma.

Methods

Articles published between January 1, 2000, and April 1, 2016, that compared EUS with MDCT were included, and data were presented as 2 × 2 tables. The sensitivities, specificities and summary receiver operating characteristic (ROC) curves for T and N staging were calculated using a bivariate mixed effects model. Data were weighted by generic variance and then pooled by random-effects modeling.

Results

Eight studies comprising 1736 patients were included in this meta-analysis. For T1 staging, the sensitivity value for EUS (82%) was significantly higher than that for MDCT (41%) (relative risk (RR): 2.06, 95% confidence interval (CI) 1.07–3.94; P = 0.030). For lymph node involvement, the sensitivity value for EUS (91%) was also significantly higher than that for MDCT (77%) (RR 1.14, 95% CI 1.05–1.23; P = 0.001). However, the specificity values of both EUS and MDCT were quite low, at 49 and 63%, respectively. No significant differences in T2–4 staging between EUS and MDCT were noted.

Conclusion

This meta-analysis indicates that EUS may be superior to MDCT in preoperative T1 and N staging. Additionally, the low specificity values of EUS and MDCT for N staging merits attention.

Diagnostic accuracies of endoscopic ultrasound-guided fine-needle aspiration with distinct negative pressure suction techniques in solid lesions: A retrospective study

Endoscopic ultrasound (EUS)-guided fine-needle aspiration (EUS-FNA) is highly accurate in obtaining specific diagnoses for various diseases. The present study aimed to evaluate the diagnostic yields, accuracies and sampling adequacies, of slow-pull, 5 ml suction and 10 ml suction techniques in EUS-FNA of solid lesions. The present study was a retrospective comparative study, which was performed in tertiary academic centers, recognized for their expertise in EUS and EUS-guided FNA. The present study involved 149 patients who underwent EUS-FNA of solid masses. A total of 34 (22.8%), 37 (24.8%) and 78 (52.4%) patients underwent EUS-FNA with slow-pull, 5 ml suction and 10 ml suction techniques, respectively. The EUS-FNA cytology and histology results were compared with those from the gold standard of surgical histopathology [hematoxylin-eosin staining; immunohistochemical test of cluster of differentiation (CD) 79a, CD20 and flow cytometry test] or long-term clinical follow-up. The present retrospective comparative study demonstrated that the diagnostic yields and accuracies of EUS-FNA with slow-pull (86.1%) were significantly superior to those achieved with 5 ml suction (83.3%) or 10 ml suction (69.9%; P<0.0001; χ2 test). Consistently, 86.5% (32/37) of the samples obtained from the 5 ml suction group were adequate for histological diagnosis. By contrast, 70.6 (24/34) and 85.9% (67/78) of samples from the slow-pull and 10 ml suction groups were adequate for histological diagnosis, respectively. The samples obtained using 10 ml suction contained more blood compared with those obtained via slow-pull and 5 ml suction (P=0.0056; χ2 test). No complications were noted in any of the three groups. The samples that were obtained for histopathological diagnosis using 5 ml suction were superior to those obtained using slow-pull or 10 ml suction. Additional multi-central prospective studies in which EUS-FNA is performed with variable negative pressures are required to improve the defining of the diagnostic roles of those techniques.

Role of endoscopic ultrasonography in TNM staging of gastric cancer

Gastric cancer is a common malignancy that has a poor prognosis and high mortality. Cancer staging is the optimal method for evaluating prognosis. Endoscopic ultrasonography (EUS) has been considered the first-choice imaging modality for regional staging of gastric cancer because different structural layers of the gastric wall show remarkable differences in their echogenic appearance. However, the results of recent studies about the accuracy of EUS for staging of gastric cancer are contradictory. The aim of this article is to review the role of EUS in preoperative TNM staging of gastric cancer.

Effect of World Health Organization (WHO) Histological Classification on Predicting Lymph Node Metastasis and Recurrence in Early Gastric Cancer

Background

The World Health Organization (WHO) histological classification for gastric cancer is widely accepted and used. However, its impact on predicting lymph node metastasis and recurrence in early gastric cancer (EGC) is not well studied.

Material/Methods

From 1987 to 2005, 2873 EGC patients with known WHO histological type who had undergone curative resection were enrolled in this study. In all, 637 well-differentiated adenocarcinomas (WD), 802 moderately-differentiated adenocarcinomas (MD), 689 poorly-differentiated adenocarcinomas (PD), and 745 signet-ring cell adenocarcinomas (SRC) were identified.

Results

The distribution of demographic and clinical features in early gastric cancer among WD, MD, PD, and SRC were significantly different. Lymph node metastasis was observed in 317 patients (11.0%), with the lymph node metastasis rate being 5.3%, 14.8%, 17.0%, and 6.3% in WD, MD, PD, and SRC, respectively. Univariate and multivariate analyses indicated that gender, tumor size, gross appearance, depth of invasion, and WHO classification were significantly associated with lymph node metastasis. Recurrence was observed in 83 patients (2.9%), with the recurrence rate being 2.2%, 4.5%, 3.0%, and 1.6% in WD, MD, PD, and SRC, respectively. Multivariate analysis confirmed that MD, elevated gross type, and lymph node metastasis were independent risk factors for recurrence in EGC. MD patients showed worse disease-free survival than non-MD patients (P=0.001).

Conclusions

WHO classification is useful and necessary to evaluate during the perioperative management of EGC. Treatment strategies for EGC should be made prudently according to WHO classification, especially for MD patients.

The role of endoscopic ultrasound on the preoperative T staging of gastric cancer: A retrospective study

Endoscopic ultrasonography (EUS) is used for preoperative assessment of gastric cancer. However, recent studies suggested that EUS staging accuracy is lower than previously thought. We aimed to assess EUS efficacy and image characteristics in preoperative gastric cancer T staging.A retrospective review of clinical and imaging features of 232 gastric carcinoma patients who underwent preoperative EUS assessment of T stage was performed. Only cases with tumor-free resection margin status and no metastases were enrolled. Comparisons of preoperative EUS and postoperative histopathological stagings were also performed to identify vital EUS image features for evaluating gastric carcinoma.EUS accuracy for T staging was 64.2% (149/232) with the highest accuracy for T3 (75.0%). Enlarged lymph nodes, well differentiated histological type and Borrmann IV type were associated with diagnostic accuracy in predicting tumor invasion. Although no factors were associated with overstaging, circumferential lesions ≥1/2, signet ring cell adenocarcinoma, and Borrmann IV type had significantly higher risks of understaging. Gastric wall outer edge irregularity was also an indicator of serosal involvement with a sensitivity of 82.0%. The pancreas and colon were more frequent disease extension sites than previously predicted.Although EUS is likely the best and most accurate option that we have used to stage gastric cancer, the finding that factors including circumferential lesions, signet ring cell adenocarcinoma, and Borrmann IV type carcinoma were more frequently related to incorrect staging warrants attention.

What types of early gastric cancer are indicated for endoscopic ultrasonography staging of invasion depth?

AIM

To clarify the diagnostic efficacy and limitations of endoscopic ultrasonography (EUS) and the characteristics of early gastric cancers (EGCs) that are indications for EUS-based assessment of cancer invasion depth.

METHODS

We retrospectively investigated the cases of 153 EGC patients who underwent conventional endoscopy (CE) and EUS (20 MHz) before treatment.

RESULTS

We found that 13.7% were “inconclusive” cases with low-quality EUS images, including all nine of the cases with protruded (0-I)-type EGCs. There was no significant difference in the diagnostic accuracy between CE and EUS. Two significant independent risk factors for misdiagnosis by EUS were identified-ulcer scarring [UL(+); odds ratio (OR) = 4.49, P = 0.003] and non-indication criteria for endoscopic resection (ER) (OR = 3.02, P = 0.03). In the subgroup analysis, 23.1% of the differentiated-type cancers exhibiting SM massive invasion (SM2) invasion (submucosal invasion ≥ 500 μm) by CE were correctly diagnosed by EUS, and 23.1% of the undifferentiated-type EGCs meeting the expanded-indication criteria for ER were correctly diagnosed by EUS.

CONCLUSION

There is no need to perform EUS for UL(+) EGCs or 0-I-type EGCs, but EUS may enhance the pretreatment staging of differentiated-type EGCs with SM2 invasion without UL or undifferentiated-type EGCs revealed by CE as meeting the expanded-indication criteria for ER.

How to Interpret the Pathological Report before and after Endoscopic Submucosal Dissection of Early Gastric Cancer

Possible lymph node metastasis (LNM) and residual cancer are major concerns in endoscopic submucosal dissection (ESD) for early gastric cancer. To reduce the risk of LNM and cancer recurrence, the proper indications for ESD should be considered. Histology, size, depth of invasion, and presence of ulceration should be thoroughly evaluated before proceeding with ESD. However, with incomplete information, discrepancies often arise between the pathological diagnosis based on the forceps biopsy and that based on the totally resected specimen. In addition, the presence of lymphovascular involvement and histological homogeneity can be clarified only after ESD. If the pathological diagnosis changes after ESD, we should reevaluate the curativeness and reformulate the goal of treatment. Additional surgery is a reasonable strategy for non-curative ESD, but a patient's other health conditions should also be considered. It is simple to read pathological reports before and after ESD, but it can be a complicated art to interpret the report and formulate an optimal approach. In this review, various considerations regarding the pathological diagnosis will be discussed.

Diagnostic group classifications of gastric neoplasms by endoscopic resection criteria before and after treatment: real-world experience

BACKGROUND AND STUDY AIMS

There are often discrepancies between the pretreatment evaluation of gastric neoplasms by endoscopy with biopsy and the final diagnosis of resected specimen in terms of pathology and depth of invasion. We evaluated the spectrum of discrepancies between pretreatment and posttreatment diagnosis which may deliver significant differences on clinical practice.

PATIENTS AND METHODS

A total of 2041 patients with gastric dysplasia or cancer who underwent curative endoscopic resections or surgeries in 2012 were enrolled. Patients were classified into five different diagnostic groups: low-grade dysplasia (LGD), high-grade dysplasia (HGD), absolute indication early gastric cancer (AI-EGC), beyond absolute indication early gastric cancer (BAI-EGC), and advanced gastric cancer (AGC). The choice of initial treatment and final pathologic diagnosis was analyzed.

RESULTS

The study patients belonged to the following pretreatment diagnostic groups: LGDs in 162, HGDs in 164, AI-EGCs in 396, BAI-EGCs in 824, and AGCs in 495 cases. Posttreatment diagnostic groups were LGDs in 140, HGDs in 121, AI-EGCs in 322, BAI-EGCs in 947, AGCs in 505, and no residual tumor in 6 cases. In general, 6.9 % (141/2041) of cases were downgraded and 15.9 % (324/2041) were upgraded. Thirty-four percent of pretreatment HGDs (56/164) were changed to cancers after endoscopic resection. Thirty-three percent of pretreatment AI-EGCs (131/396) were regrouped as posttreatment BAI-EGCs. The additional surgery rate in each pretreatment group was 0.6 % in LGD, 4.3 % in HGD, 15.7 % in AI-EGC, 23.6 % in BAI-EGC among the patients with initial endoscopic resection (p < 0.01).

CONCLUSIONS

Twenty-three percent of gastric neoplasms changed in their final diagnostic group after endoscopic resection or surgery. This discrepancy should be considered when the initial treatment strategy is being selected.