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

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.

Double contrast-enhanced ultrasonography improves diagnostic accuracy of T staging compared with multi-detector computed tomography in gastric cancer patients

BACKGROUND

Gastric cancer (GC) is the most common malignant tumor and ranks third for cancer-related deaths among the worldwide. The disease poses a serious public health problem in China, ranking fifth for incidence and third for mortality. Knowledge of the invasive depth of the tumor is vital to treatment decisions.

AIM

To evaluate the diagnostic performance of double contrast-enhanced ultrasonography (DCEUS) for preoperative T staging in patients with GC by comparing with multi-detector computed tomography (MDCT).

METHODS

This single prospective study enrolled patients with GC confirmed by preoperative gastroscopy from July 2021 to March 2023. Patients underwent DCEUS, including ultrasonography (US) and intravenous contrast-enhanced ultrasonography (CEUS), and MDCT examinations for the assessment of preoperative T staging. Features of GC were identified on DCEUS and criteria developed to evaluate T staging according to the 8^th edition of AJCC cancer staging manual. The diagnostic performance of DCEUS was evaluated by comparing it with that of MDCT and surgical-pathological findings were considered as the gold standard.

RESULTS

A total of 229 patients with GC (80 T1, 33 T2, 59 T3 and 57 T4) were included. Overall accuracies were 86.9% for DCEUS and 61.1% for MDCT (P < 0.001). DCEUS was superior to MDCT for T1 (92.5% vs 70.0%, P < 0.001), T2 (72.7% vs 51.5%, P = 0.041), T3 (86.4% vs 45.8%, P < 0.001) and T4 (87.7% vs 70.2%, P = 0.022) staging of GC.

CONCLUSION

DCEUS improved the diagnostic accuracy of preoperative T staging in patients with GC compared with MDCT, and constitutes a promising imaging modality for preoperative evaluation of GC to aid individualized treatment decision-making.

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.

The investigation of volatile organic compounds in diagnosing (early) esophageal squamous cell carcinoma and gastric adenocarcinoma

PURPOSE

The diagnosis of upper gastrointestinal cancer (UGIC) and early UGIC is currently based on endoscopy and histopathology. In this study, we aimed to explore whether intraluminal and exhaled volatile organic compounds (VOCs) could be used to diagnose (early) esophageal squamous cell carcinoma (ESCC) and gastric adenocarcinoma (GC).

METHODS

We prospectively recruited 259 patients and first collected intraluminal gas simples directly from upper GI tract via our designed device after passing endoscopic biopsy channel and collected exhaled gas samples in pairs.

RESULTS

509 gas samples were totally collected and VOCs composed by peak compounds detected by gas chromatography-mass spectrometry (GC-MS) were used to train and test Multilayer Perceptron Network (MPN) for discrimination. Intraluminal and exhaled gas had more than 0.95 area under the curve (AUC) to discriminate UGIC (ESCC and GC) and early UGIC from benign control with different VOCs compositions.

CONCLUSION

Both intraluminal and exhaled VOCs had cancer-specific compositions to accurately discriminate early UGIC and UGIC, and the ability of intraluminal VOCs was better than that of exhaled VOCs. These suggested the potential role of VOCs in diagnosing and screening early UGIC and UGIC in the future.

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.

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.

Diagnostic evaluation of endoscopic ultrasonography with submucosal saline injection for differentiating between T1a and T1b early gastric cancer

BACKGROUND

Endoscopic ultrasonography (EUS) has become a reliable method for predicting the invasion depth of early gastric cancer (EGC). However, diagnostic accuracy of EUS is affected by several factors. In particular, it is difficult to differentiate between T1a and T1b EGC through EUS.

AIM

To confirm whether submucosal saline injection (SSI) could improve the accuracy of EUS in distinguishing T1a and T1b lesions in EGC.

METHODS

Twenty-four patients with EGC were examined by EUS and subsequently by SSI combined EUS to compare the degree of tumor invasion. Then, they underwent endoscopic or surgical resection within 7 d. The diagnostic accuracy of EUS and SSI combined EUS was evaluated based on the final pathological findings postoperatively. Saline injected into the submucosa acted as an echoic contrast enhancing agent and had the effect of distinguishing the mucosal and submucosal layers clearly.

RESULTS

Of total 24 patients, 23 were diagnosed with EGC (T1 cancer: 13 as T1a, and 10 as T1b). Standard EUS identified 6 of 13 T1a cancer patients and 3 of 10 T1b cancer patients. Whereas, EUS-SSI identified 12 of 13 T1a cancer patients and 6 of 10 T1b cancer patients. In this study, SSI combined EUS was more accurate than EUS alone in diagnosing T1a and T1b lesions of EGC (75.0% and 37.5%, respectively).

CONCLUSION

SSI improved the diagnostic accuracy of EUS in distinguishing between the T1a and T1b stages in EGC.

Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images

BACKGROUND

Early gastric cancer (EGC) is defined as a lesion restricted to the mucosa or submucosa, independent of size or evidence of regional lymph node metastases. Although computed tomography (CT) is the main technique for determining the stage of gastric cancer (GC), the accuracy of CT for determining tumor invasion of EGC was still unsatisfactory by radiologists. In this research, we attempted to construct an AI model to discriminate EGC in portal venous phase CT images.

METHODS

We retrospectively collected 658 GC patients from the first affiliated hospital of Nanchang university, and divided them into training and internal validation cohorts with a ratio of 8:2. As the external validation cohort, 93 GC patients were recruited from the second affiliated hospital of Soochow university. We developed several prediction models based on various convolutional neural networks, and compared their predictive performance.

RESULTS

The deep learning model based on the ResNet101 neural network represented sufficient discrimination of EGC. In two validation cohorts, the areas under the curves (AUCs) for the receiver operating characteristic (ROC) curves were 0.993 (95% CI: 0.984-1.000) and 0.968 (95% CI: 0.935-1.000), respectively, and the accuracy was 0.946 and 0.914. Additionally, the deep learning model can also differentiate between mucosa and submucosa tumors of EGC.

CONCLUSIONS

These results suggested that deep learning classifiers have the potential to be used as a screening tool for EGC, which is crucial in the individualized treatment of EGC patients.

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.

Management of Superficial Esophageal Squamous Cell Carcinoma and Early Gastric Cancer following Non-Curative Endoscopic Resection

Simple Summary

Guidelines recommend additional treatment following non-curative endoscopic resection in cases of superficial esophageal squamous cell carcinoma and early gastric cancer because of the potential risk of lymph node metastasis. This review discusses recent advances in this field, including the development of pathological risk stratification for metastatic recurrence and identification of different recurrence patterns after non-curative endoscopic resection for superficial esophageal squamous cell carcinoma or early gastric cancer, and the establishment of a novel treatment strategy for clinical T1b-SM esophageal squamous cell carcinoma. For optimal therapeutic decision-making in such patients, it is also important to predict prognoses other than superficial esophageal squamous cell carcinoma or early gastric cancer and impaired quality of life. Thus, a novel algorithm that considers these factors and metastatic recurrence is required.

Abstract

According to the European and Japanese guidelines, additional treatment is recommended for cases of superficial esophageal squamous cell carcinoma (ESCC) and early gastric cancer (EGC) that do not meet the curability criteria for endoscopic resection (ER), i.e., non-curative ER, owing to the risk of lymph node metastasis (LNM). However, the rates of LNM in such cases were relatively low (e.g., 8% for EGC). Several recent advances have been made in this field. First, pathological risk stratification for metastatic recurrence following non-curative ER without additional treatment was developed for both superficial ESCC and EGC. Second, the pattern of metastatic recurrence and prognosis after recurrence following non-curative ER without additional treatment was found to be considerably different between superficial ESCC and EGC. Third, a combination of ER and selective chemoradiotherapy was developed as a minimally invasive treatment method for clinical T1b-SM ESCC. These findings may help clinicians decide the treatment strategy for patients following non-curative ER; however, for optimal therapeutic decision-making in such patients, it is also important to predict the prognosis other than SESCC or EGC and impaired quality of life. Thus, a novel algorithm that considers these factors, as well as metastatic recurrence, should be developed.

A potential decision-making algorithm based on endoscopic ultrasound for staging early gastric cancer: a retrospective study

BACKGROUND

Clinical staging of gastric cancer (GC) before treatment is essential. Endoscopic ultrasound (EUS) is a recommended staging tool, but its efficacy remains controversial. Our previous prospective study evaluated the potential value of EUS for T staging and presented discrepancies. In this study, we aimed to evaluate the efficacy of EUS in T staging by comparing it with pathological staging. We analyze the factors that can potentially affect accuracy to identify suitable subgroups for EUS staging.

METHODS

Data from a total of 1763 consecutive patients with GC from January 2015 to December 2017 were analyzed. Results from EUS and pathological T staging were compared. The factors that might affect EUS's accuracy were analyzed.

RESULTS

The sensitivity, specificity, positive predictive value, and negative predictive value of EUS in patients with early GC were 62.08%, 96.13%, 90.94%, and 80.21%, respectively. The accuracy rates of uT1, uT2-uT4, and uT3-uT4 were 90.94%, 79.02%, and 78.39%, respectively. In multivariate analysis, underestimation was more likely to be observed in patients with tumors located in the middle or upper third of the stomach. Overestimation was more likely to be observed in patients with tumors located in the lower third or those without ulcer. Other factors affecting accuracy included ulcer, differentiation, larger size and undergoing surgery.

CONCLUSION

Our findings highlight the role of EUS in determining the T staging of GC. Overestimation and underestimation in T-staging were significantly associated with the tumor location in early GC, and a decision-making algorithm was proposed for clinical practice in early cancers based on these findings.

The future of endoscopic resection for early gastric cancer

Endoscopic resection for early gastric cancer is recommended when the risk of lymph node metastasis is negligible and should be performed through submucosal dissection due to well-established short- and long-term results. To overcome technical difficulties and decrease adverse events some techniques have been studied. This review outlines current strategies for improving patient selection and highlights innovative techniques that help minimize adverse events. Moreover, we discuss how to improve management after curative and noncurative resections.

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.

Role of endoscopic ultrasonography for differential diagnosis of upper gastrointestinal submucosal lesions

OBJECTIVE

To determine the accuracy of endoscopic ultrasonography (EUS) in the diagnosis of upper gastrointestinal submucosal lesions (SMLs).

METHODS

This was a retrospective study involving patients diagnosed with SMLs using EUS and confirmed by histopathology from November 2014 to December 2020 at The Third Xiangya Hospital of Central South University.

RESULTS

A total of 231 patients with SMLs were examined by EUS. Histologically, 107 lesions were stromal tumors, and 75 lesions were leiomyomas. Stromal tumors were mainly located in the stomach (89.7%), and leiomyomas were predominantly seen in the esophagus (69.3%). The diagnostic accuracy of EUS for stromal tumors and leiomyomas was 80.4% and 68.0%, respectively. The diagnostic accuracy was highest for lesions located in the muscularis mucosa. The mean diameter of stromal tumors measured using EUS was significantly larger than that of leiomyomas (21.89 mm vs. 12.35 mm, p < 0.001). Stromal tumors and leiomyomas originated mainly from the muscularis propria (94.4%) and the muscularis mucosa (56.0%), respectively. Compared with the very low-risk and low-risk groups of stromal tumors according to the National Institute of Health guidelines, the intermediate-risk and high-risk groups were more likely to have a lesion > 3 cm (p < 0.001) and a surface ulcer (p < 0.01) identified by EUS.

CONCLUSIONS

EUS has good diagnostic value for the diagnosis of upper gastrointestinal SMLs based on the lesion size and the muscle layer of origin. The diagnostic accuracy of EUS lesions is related to the origin, and the diagnostic accuracy is greatest in the mucosal muscularis layer. Stromal tumors > 3 cm and a surface ulcer on EUS are likely to be intermediate or high risk for invasion.

Enlarged folds on endoscopic gastritis as a predictor for submucosal invasion of gastric cancers

BACKGROUND

Accurate diagnosis of the depth of gastric cancer invasion is crucial in clinical practice. The diagnosis of gastric cancer depth is often made using endoscopic characteristics of the tumor and its margins; however, evaluating invasion depth based on endoscopic background gastritis remains unclear.

AIM

To investigate predicting submucosal invasion using the endoscopy-based Kyoto classification of gastritis.

METHODS

Patients with gastric cancer detected on esophagogastroduodenoscopy at Toyoshima Endoscopy Clinic were enrolled. We analyzed the effects of patient and tumor characteristics, including age, sex, body mass index, surveillance endoscopy within 2 years, current Helicobacter pylori infection, the Kyoto classification, and Lauren’s tumor type, on submucosal tumor invasion and curative endoscopic resection. The Kyoto classification included atrophy, intestinal metaplasia, enlarged folds, nodularity, and diffuse redness. Atrophy was characterized by non-reddish and low mucosa. Intestinal metaplasia was detected as patchy whitish or grayish-white flat elevations, forming an irregular uneven surface. An enlarged fold referred to a fold width ≥ 5 mm in the greater curvature of the corpus. Nodularity was characterized by goosebump-like multiple nodules in the antrum. Diffuse redness was characterized by uniform reddish non-atrophic mucosa in the greater curvature of the corpus.

RESULTS

A total of 266 gastric cancer patients (mean age, 66.7 years; male sex, 58.6%; mean body mass index, 22.8 kg/m²) were enrolled. Ninety-three patients underwent esophagogastroduodenoscopy for surveillance within 2 years, and 140 had current Helicobacter pylori infection. The mean Kyoto score was 4.54. Fifty-eight cancers were diffuse-type, and 87 cancers had invaded the submucosa. Multivariate analysis revealed that low body mass index (odds ratio 0.88, P = 0.02), no surveillance esophagogastroduodenoscopy within 2 years (odds ratio 0.15, P < 0.001), endoscopic enlarged folds of gastritis (odds ratio 3.39, P = 0.001), and Lauren’s diffuse-type (odds ratio 5.09, P < 0.001) were independently associated with submucosal invasion. Similar results were obtained with curative endoscopic resection. Among cancer patients with enlarged folds, severely enlarged folds (width ≥ 10 mm) were more related to submucosal invasion than mildly enlarged folds (width 5-9 mm, P < 0.001).

CONCLUSION

Enlarged folds of gastritis were associated with submucosal invasion. Endoscopic observation of background gastritis as well as the lesion itself may help diagnose the depth of cancer invasion.

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.

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.

Utility of the gastric window in computed tomography for differentiation of early gastric cancer (T1 stage) from muscularis involvement (T2 stage)

OBJECTIVE

To analyze the diagnostic value of using the gastric window in computed tomography for differentiation of early gastric cancer (T1 stage) from muscularis involvement (T2 stage).

MATERIALS AND METHODS

All patients with pathologically confirmed T1 stage and T2 stage gastric cancer and who underwent endoscopic resection or gastrectomy at our institution from January 2011 to November 2018 were examined. Each patient received an enhanced CT scan of the abdomen before the operation. T staging of tumors based on the CT scans was performed independently by two radiologists using the gastric window (width 150-200 HU, level 80-100 HU) and the abdominal window (width 350-400 HU, level 50 HU).

RESULTS

Use of the gastric window to diagnose stage T1 EGC led to an accuracy of 88.9% for observer1 and 91.5% for observer2; use of the abdominal window led to an accuracy of 53.6% for observer1 and 51.6% (38/106) for observer2. Use of the gastric window to diagnose stage T2 led to an accuracy of 85.6% for observer1 and 82.4% for observer2; use of the abdominal window led to an accuracy of 52.3% for both observer1 and observer2. For observer1, use of the gastric window had a diagnostic accuracy of 69.2% for stage T1a and 62.5% for stage T1b; for observer2, the diagnostic accuracy was 65.1% for stage T1a and 67.0% for stage T1b. A Kappa test indicated moderate and substantial inter-observer agreement for T staging with gastric window (κ = 0.598, P < 0.001) and abdominal window (κ = 0.745, P < 0.001).

CONCLUSION

Use of the gastric window in computed tomography provided more accurate staging for T1 and T2 stages of gastric cancer than the conventional abdominal window.

Prediction model for curative endoscopic submucosal dissection of undifferentiated-type early gastric cancer

BACKGROUND

Endoscopic submucosal dissection (ESD) is an effective treatment for early gastric cancer (EGC); however, its curative resection rate is low for undifferentiated-type EGC. We developed and externally validated a prediction model for curative ESD of undifferentiated-type EGC.

METHODS

In this cross-sectional study, we included 448 patients who underwent ESD for undifferentiated-type EGC at 18 hospitals in Korea between 2005 and 2015 in the development cohort and 1342 patients who underwent surgery at two hospitals in the validation cohort. A prediction model was developed using the logistic regression model.

RESULTS

Endoscopic tumor size 1-2 cm (odds ratio [OR], 2.40; 95% confidence interval [CI] 1.54-3.73), tumor size > 2 cm (OR, 14.00; 95% CI 6.81-28.77), and proximal tumor location from the lower to upper third of the stomach (OR, 1.45; 95% CI 1.03-2.04) were independent predictors of non-curative ESD. A six-score prediction model was developed by assigning points to endoscopic tumor size > 2 cm (five points), tumor size 1-2 cm (two points), upper third location (two points), and middle third location (one point). The rate of curative ESD ranged from 70.6% (score 0) to 11.6% (score 5) with an area under the receiver operating characteristic curve (AUC) of 0.720 (95% CI 0.673-0.766). The model also showed good performance in the validation cohort (AUC, 0.775; 95% CI 0.748-0.803).

CONCLUSIONS

This six-score prediction model may help in predicting curative ESD and making informed decisions about the treatment selection between ESD and surgery for undifferentiated-type EGC.