The simplified depth-predicting score outperforms the depth-predicting score for predicting the depth of invasion in differentiated early gastric cancer patients among nonexpert endoscopists.

AIM: Endoscopists utilize depth-predicting score (DPS) and simplified depth-predicting score (S-DPS) to predict the invasion depth of early gastric cancer based on conventional white-light endoscopic features. The effectiveness of these scores has not been fully elucidated among nonexpert endoscopists. This study aimed to compare the ability of DPS and S-DPS to predict invasion depth of differentiated early gastric cancers by nonexpert endoscopists. PARTICIPANTS AND METHODS: We collected subitem scores of DPS and S-DPS from 19 nonexpert endoscopists for early gastric cancer conventional white-light endoscopy images in the test dataset to predict the invasion depth of the early gastric cancer conventional white-light endoscopy images. Accuracy, specificity, overdiagnosis rate, and underdiagnosis rate were subsequently calculated using the histological invasion depth as the gold standard. RESULTS: Using 3 as the cutoff line, the overall S-DPS diagnostic accuracy for invasion depth was significantly greater than that of DPS [73.86% (69.32%, 75.00%) vs. 67.05% (62.50%, 71.60%), p= 0.005]. The overall S-DPS overdiagnosis rate was significantly lower than that of DPS [7.58% (3.03%, 13.64%) vs. 28.79% (18.18%, 37.88%), p= 0.000]. The overall S-DPS under-diagnosed rate was significantly higher than that of DPS [86.36% (68.18%, 90.91%) vs. 45.45% (31.82%, 59.09%), p=0.000]. The specificity of the S-DPS was significantly greater than that of DPS [92.42% (86.36%, 96.97%) vs. 71.21% (62.12%, 81.82%), p= 0.000]. CONCLUSION: The diagnostic accuracy of the S-DPS was greater than that of the DPS among nonexpert endoscopists. Furthermore, S-DPS is simpler than other methods, making it more conducive to clinical application for nonexpert endoscopists.

Histological Risk Factors for Lymph Node Metastasis in pT1 Colorectal Cancer: Does Submucosal Invasion Depth Really Matter?

OBJECTIVE: After endoscopic resection of colorectal cancer with submucosal invasion (pT1 CRC), additional surgical treatment is recommended if deep submucosal invasion (DSI) is present. This study aimed to further elucidate the risk factors for lymph node metastasis (LNM) in patients with pT1 CRC, especially the effect of DSI on LNM. METHODS: Patients with pT1 CRC who underwent lymph node dissection were selected. The Chi-square test and multivariate logistic regression were used to analyze the relationship between clinicopathological characteristics and LNM. The submucosal invasion depth (SID) was measured via 4 methods and analyzed with 3 cut-off values. RESULTS: Twenty-eight of the 239 patients presented with LNM (11.7%), and the independent risk factors for LNM included high histological grade (P=0.003), lymphovascular invasion (LVI) (P=0.004), intermediate to high budding (Bd 2/3) (P=0.008), and cancer gland rupture (CGR) (P=0.008). Moreover, the SID, width of submucosal invasion (WSI), and area of submucosal invasion (ASI) were not significantly different. When one, two, three or more risk factors were identified, the LNM rates were 1.1% (1/95), 12.5% (7/56), and 48.8% (20/41), respectively. CONCLUSION: Indicators such as the SID, WSI, and ASI are not risk factors for LNM and are subjective in their measurement, which renders them relatively inconvenient to apply in clinical practice. In contrast, histological grade, LVI, tumor budding and CGR are relatively straightforward to identify and have been demonstrated to be statistically significant. It would be prudent to focus on these histological factors rather than subjective measurements.

Association between tumor necrosis factor receptor 2 and progression and poor prognosis of tumor stage 2­3 esophageal squamous cell carcinoma and stratified analysis.

Although tumor necrosis factor receptor 2 (TNFR2) may serve a protumor role in several types of tumors, the clinical significance of TNFR2, including the diagnostic and prognostic value in tumor (T) stage 2-3 esophageal squamous cell carcinoma (ESCC), remains unclear. Therefore, the present study aimed to explore the clinical significance of TNFR2 in stage T2-3 ESCC. The present study collected the mRNA expression data of TNFR2 from two databases and confirmed the high expression of TNFR2 in ESCC tissue. TNFR2 expression in stage T2-3 ESCC tissue (n=404) was detected using immunohistochemistry and a stratified analysis was performed. For all patients with stage T2-3 ESCC, TNFR2 expression was associated with clinical stage, invasion depth and metastatic lymph nodes. Stage T3 and low differentiation was associated with an increase in the risk of lymph node metastasis, but older age was associated with a decrease. TNFR2 expression was associated with poor overall survival (OS) of all patients with stage T2-3 ESCC and stratified patients with stage T3 ESCC. Moreover, TNFR2 expression and metastatic lymph nodes were independent prognostic factors for these patients. For stratified patients aged ≤60 years, TNFR2 expression was associated with clinical stage and metastatic lymph nodes. In addition, TNFR2 expression was associated with poor OS in stratified patients with stage T2 ESCC. The presence of metastatic lymph nodes was also an independent prognostic factor for these patients. For stratified patients aged >60 years, TNFR2 expression was associated with invasion depth. TNFR2 expression was also associated with poor OS in all patients with stage T2-3 ESCC and stratified patients with stage T3 ESCC. TNFR2 expression and metastatic lymph nodes were identified as independent prognostic factors for these patients. In conclusion, TNFR2 expression is associated with progression and poor prognosis in patients with stage T2-3 ESCC as an independent prognostic factor, except in the subgroup of patients with stage T2-3 ESCC aged ≤60 years.

A deep unenhanced area on contrast-enhanced ultrasound suggests T2 gallbladder carcinoma.

CE-US (Advanced Dynamic Flow, mechanical index: 1.6) in the perfusion phase (48 s after injection of Sonazoid®, marked with a red underline): A heterogeneously enhanced, sessile elevated lesion with a clearly visible deep unenhanced area (arrowhead), 3 mm in diameter, at the fundus.

Refining Risk Criteria May Substantially Reduce Unnecessary Additional Surgeries after Local Resection of T1 Colorectal Cancer.

BACKGROUND: The low positive predictive value for lymph node metastases (LNM) of common practice risk criteria (CPRC) in T1 colorectal carcinoma (CRC) leads to manyunnecessary additional surgeries following local resection. This study aimed to identify criteria that may improve on the CPRC. METHODS: Logistic regression analysis was performed to determine the association of diverse variables with LNM or 'poor outcome' (LNM and/or distant metastases and/or recurrence) in a single center T1 CRC cohort. The diagnostic capacity of the set of variables obtained was compared with that of the CPRC. RESULTS: The study comprised 161 cases. Poorly differentiated clusters (PDC) and tumor budding grade > 1 (TB > 1) were the only independent variables associated with LNM. The area under the curve (AUC) for these criteria was 0.808 (CI 95% 0.717-0.880) compared to 0.582 (CI 95% 0.479-0.680) for CPRC. TB > 1 and lymphovascular invasion (LVI) were independently associated with 'poor outcome', with an AUC of 0.801 (CI 95% 0.731-0.859), while the AUC for CPRC was 0.691 (CI 95% 0.603-0.752). TB > 1, combined either with PDC or LVI, would reduce false positives between 41.5% and 45% without significantly increasing false negatives. CONCLUSIONS: Indicating additional surgery in T1 CRC only when either TB > 1, PDC, or LVI are present could reduce unnecessary surgeries significantly.

Artificial Intelligence Model for a Distinction between Early-Stage Gastric Cancer Invasive Depth T1a and T1b.

Background: Endoscopic submucosal dissection (ESD) is a widely accepted treatment for patients with mucosa (T1a) disease without lymph node metastasis. However, the inconsistency of inspection quality of tumor staging under the standard tool combining endoscopic ultrasound (EUS) with computed tomography (CT) scanning makes it restrictive. Methods: We conducted a study using data augmentation and artificial intelligence (AI) to address the early gastric cancer (EGC) staging problem. The proposed AI model simplifies early cancer treatment by eliminating the need for ultrasound or other staging methods. We developed an AI model utilizing data augmentation and the You-Only-Look-Once (YOLO) approach. We collected a white-light image dataset of 351 stage T1a and 542 T1b images to build, test, and validate the model. An external white-light images dataset that consists of 47 T1a and 9 T1b images was then collected to validate our AI model. The result of the external dataset validation indicated that our model also applies to other peer health institutes. Results: The results of k-fold cross-validation using the original dataset demonstrated that the proposed model had a sensitivity of 85.08% and an average specificity of 87.17%. Additionally, the k-fold cross-validation model had an average accuracy rate of 86.18%; the external data set demonstrated similar validation results with a sensitivity of 82.98%, a specificity of 77.78%, and an overall accuracy of 82.14%. Conclusions: Our findings suggest that the AI model can effectively replace EUS and CT in early GC staging, with an average validation accuracy rate of 86.18% for the original dataset from Linkou Cheng Gun Memorial Hospital and 82.14% for the external validation dataset from Kaohsiung Cheng Gun Memorial Hospital. Moreover, our AI model's accuracy rate outperformed the average EUS and CT rates in previous literature (around 70%).

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

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

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

Background and Aim: The microsurface structure reflects the degree of damage to the glands, which is related to the invasion depth of early gastric cancer. To evaluate the diagnostic value of quantitative microsurface structure analysis for estimating the invasion depth of early gastric cancer. Methods: White-light imaging and narrow-band imaging (NBI) endoscopy were used to visualize the lesions of the included patients. The area ratio and depth-predicting score (DPS) of each patient were calculated; meanwhile, each lesion was examined by endoscopic ultrasonography (EUS). Results: Ninety-three patients were included between 2016 and 2019. Microsurface structure is related to the histological differentiation and progression of early gastric cancer. The receiver operating characteristic curve showed that when an area ratio of 80.3% was used as a cut-off value for distinguishing mucosal (M) and submucosal (SM) type 0-II gastric cancers, the sensitivity, specificity, and accuracy were 82.9%, 80.2%, and 91.6%, respectively. The accuracies for distinguishing M/SM differentiated and undifferentiated early gastric cancers were 87.4% and 84.8%, respectively. The accuracy of EUS for distinguishing M/SM early gastric cancer was 74.9%. DPS can only distinguish M-SM1 (SM infiltration <500 µm)/SM (SM infiltration ≥500 µm) with an accuracy of 83.8%. The accuracy of using area ratio for distinguishing 0-II early gastric cancers was better than those of using DPS and EUS (P < 0.05). Conclusion: Quantitative analysis of microsurface structure can be performed to assess M/SM type 0-II gastric cancer and is expected to be effective for judging the invasion depth of gastric cancer.

Desmin immunostaining is effective for improving interobserver variability in the depth assessment of the submucosal invasion of colorectal cancers.

Tumor depth evaluation is essential for pathological tumor staging because it affects clinical management as an independent risk factor for lymph node metastasis in colorectal cancers. However, poor interobserver variability of invasion depth has been reported. This study aimed to clarify the effectiveness of desmin immunostaining in the histological diagnosis of colorectal cancer. Overall, 63 sets of slides of colorectal cancer stained with hematoxylin and eosin (H&E) and desmin were prepared and independently reviewed by four examiners. After reviewing the desmin-stained slides, the interobserver variability of H&E slides alone was significantly improved for all examiners. For the assessment of Tis vs. T1, the sensitivity and accuracy were significantly improved for all examiners by combining H&E and desmin immunostaining. For the diagnosis of T1b vs. Tis or T1a, specificity and accuracy were significantly improved by adding desmin immunostaining. Ancillary desmin staining to assess submucosal invasion in colorectal cancers significantly improved interobserver agreement, led to efficient screening of T1 cancers, and reduced excessive T1b diagnoses. The combination of desmin immunostaining and H&E staining is highly recommended for diagnosing invasive colorectal cancer.

Computer-aided diagnosis in predicting the invasion depth of early colorectal cancer: a systematic review and meta-analysis of diagnostic test accuracy.

BACKGROUND: Endoscopic resection (ER) is widely applied to treat early colorectal cancer (CRC). Predicting the invasion depth of early CRC is critical in determining treatment strategies. The use of computer-aided diagnosis (CAD) algorithms could theoretically make accurate and objective predictions regarding the suitability of lesions for ER indication based on invasion depth. This study aimed to assess diagnostic test accuracy of CAD algorithms in predicting the invasion depth of early CRC and to compare the performance between the CAD algorithms and endoscopists. METHODS: Multiple databases were searched until June 30, 2022 for studies that evaluated the diagnostic performance of CAD algorithms for invasion depth of CRC. Meta-analysis of diagnostic test accuracy using a bivariate mixed-effects model was performed. RESULTS: Ten studies consisting of 13 arms (13,918 images from 1472 lesions) were included. Due to significant heterogeneity, studies were stratified into Japan/Korea-based or China-based studies. For the former, the area under the curve (AUC), sensitivity, and specificity of the CAD algorithms were 0.89 (95% CI 0.86-0.91), 62% (95% CI 50-72%), and 96% (95% CI 93-98%), respectively. For the latter, AUC, sensitivity, and specificity were 0.94 (95% CI 0.92-0.96), 88% (95% CI 78-94%), and 88% (95% CI 80-93%), respectively. The performance of the CAD algorithms in Japan/Korea-based studies was not significantly different from that of all endoscopists (0.88 vs. 0.91, P = 0.10) but was inferior to that of expert endoscopists (0.88 vs. 0.92, P = 0.03). The performance of the CAD algorithms in China-based studies was better than that of all endoscopists (0.94 vs. 0.90, P = 0.01). CONCLUSION: The CAD algorithms showed comparable accuracy for prediction of invasion depth of early CRC compared to all endoscopists, which was still lower than expert endoscopists in diagnostic accuracy; more improvements should be achieved before it can be extensively applied to clinical practice.

Analysis of white-light imaging-based features predictive for determination of lesion depths of superficial flat esophageal squamous cell carcinoma: a retrospective multicenter study from China.

OBJECTIVES: Endoscopic diagnosis of invasion depth of superficial esophageal squamous cell carcinoma (SESCC) by white-light imaging (WLI) modality remains difficult. This study aims to clarify WLI-based features which are predictive for invasion depth of SESCC. METHODS: A two-phase study was performed by enrolling 1288 patients with 1396 SESCC lesions. Endoscopic appearances, clinical characteristics and post-operative pathological outcomes were collected and reviewed. The association between lesion features and invasion depth were analyzed. A predictive nomogram was constructed for prediction of invasion depth. RESULTS: Among 1396 lesions in derivation and validation cohort, 1139 (81.6%), 194 (13.9%) and 63 (4.5%) lesions were diagnosed as lesions confined into the intraepithelium or the lamina propria mucosa (T1a-EP/LPM), lesions invading the muscularis mucosa (T1a-MM) or superficial submucosa (T1b-SM1) and tumor with moderate invasion into the submucosa or deeper submucosal invasion (≥ T1b-SM2), respectively. Lesion length > 2 cm (p < 0.001), wider circumferential extension (p < 0.001, 0.002 and 0.048 for > 3/4, 1/2-3/4 and 1/4-1/2 circumferential extension, respectively), surface unevenness (p < 0.001 for both type 0-IIa/0-IIc lesions and mixed type lesions), spontaneous bleeding (p < 0.001), granularity (p < 0.001) and nodules (p < 0.001) were identified as significant factors predictive for lesion depth. A nomogram based on these factors was constructed and the values of area under the Receiver Operating Characteristics curve were 0.89 and 0.90 in the internal and external patient cohort. CONCLUSIONS: Our study provides six WLI-based morphological features predicting for lesion depth of SESCC. Our findings will make endoscopic evaluation of invasion depth for SESCC more convenient by assessing these profiles.

The effect of short-term training about depth predicting score on the diagnostic ability of invasion depth for differentiated early gastric Cancer among non-expert endoscopists.

BACKGROUND: The depth-predicting score (DPS) was proposed based on conventional white-light imaging (C-WLI) endoscopic features of early gastric cancer (EGC) to determine the invasion depth of the neoplasm. However, the effect of DPS on training endoscopists remains unclear. Therefore, we aimed to investigate the effect of short-term DPS training on improving the diagnostic ability of EGC invasion depth and compare the training effect among non-expert endoscopists at different levels. METHODS: In the training session, the definitions and scoring rules of DPS were instructed, and classic C-WLI endoscopic example graphics were exhibited to the participants. Another C-WLI endoscopic images of 88 cases of histologically proven differentiated EGC were selected as an independent test dataset for evaluating the training effect. Each participant was tested, and the diagnostic accuracy rate of invasion depth was calculated differently one week before the training and after the completion of training. RESULTS: A total of 16 participants were enrolled and completed the training. Participants were divided into a trainee group and a junior endoscopist group according to the total number of C-WLI endoscopies performed. The total number of C-WLI endoscopies performed showed a significant difference between the trainee group and junior endoscopist group (350 vs. 2500, P = 0.001). No significant difference between the trainee group and junior endoscopist group was observed for pre-training accuracy. The overall diagnostic accuracy of invasion depth was improved significantly after completing DPS training compared with before (68.75 ± 5.71% vs. 61.58 ± 9.61%, P = 0.009). In the subgroup analysis, the post-training accuracy was higher than the pre-training accuracy, but significant improvement was observed only in the trainee group (61.65 ± 7.33% vs. 68.32 ± 5.71%, P = 0.034). In addition, no significant difference in post-training accuracy between the two groups was observed. CONCLUSION: Short-term DPS training can improve the diagnostic ability of the invasion depth of EGC and homogenize the diagnostic ability of non-expert endoscopists at different levels. The depth-predicting score was convenient and effective for endoscopist training.

Risk factors for conversion from endoscopic resection to laparoscopic resection for gastric gastrointestinal stromal tumors.

OBJECTIVE: Endoscopic resection (ER) of gastric gastrointestinal stromal tumors (gGISTs) is a commonly used treatment; however, it is associated with a risk of conversion to laparoscopic resection (LR). This study was performed to identify factors influencing conversion from ER to LR and the effects of conversion on outcomes. METHODS: The clinicopathological features of patients treated for gGISTs from March 2010 to May 2021 were retrospectively collected. Endpoints included the determination of risk factors associated with LR conversion, with comparisons of surgical outcomes with and without conversion. Propensity score matching was performed to compare the two groups. RESULTS: In total, 371 gGISTs were analyzed. Sixteen patients required conversion from ER to LR. Propensity score matching demonstrated that invasion depth (muscularis propria with exophytic growth) and gGIST size (≥3 cm) were independent risk factors for conversion to LR. The procedure duration (median, 160.5 vs. 60.0 minutes), postoperative hospitalization duration (median, 8 vs. 6 days), and postoperative fasting duration (median, 5 vs. 3 days) were significantly longer in patients who underwent conversion to LR. CONCLUSIONS: Accurate preoperative measurements of tumor size and invasion depth may help determine more appropriate surgical approaches for patients with gGISTs.

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.

Development and validation of an artificial intelligence-based system for predicting colorectal cancer invasion depth using multi-modal data.

OBJECTIVES: Accurate endoscopic optical prediction of the depth of cancer invasion is critical for guiding an optimal treatment approach of large sessile colorectal polyps but was hindered by insufficient endoscopists expertise and inter-observer variability. We aimed to construct a clinically applicable artificial intelligence (AI) system for the identification of presence of cancer invasion in large sessile colorectal polyps. METHODS: A deep learning-based colorectal cancer invasion calculation (CCIC) system was constructed. Multi-modal data including clinical information, white light (WL) and image-enhanced endoscopy (IEE) were included for training. The system was trained using 339 lesions and tested on 198 lesions across three hospitals. Man-machine contest, reader study and video validation were further conducted to evaluate the performance of CCIC. RESULTS: The overall accuracy of CCIC system using image and video validation was 90.4% and 89.7%, respectively. In comparison with 14 endoscopists, the accuracy of CCIC was comparable with expert endoscopists but superior to all the participating senior and junior endoscopists in both image and video validation set. With CCIC augmentation, the average accuracy of junior endoscopists improved significantly from 75.4% to 85.3% (P = 0.002). CONCLUSIONS: This deep learning-based CCIC system may play an important role in predicting the depth of cancer invasion in colorectal polyps, thus determining treatment strategies for these large sessile colorectal polyps.

Cooperation between artificial intelligence and endoscopists for diagnosing invasion depth of early gastric cancer.

BACKGROUND AND STUDY AIMS: The diagnostic ability of endoscopists to determine invasion depth of early gastric cancer is not favorable. We designed an artificial intelligence (AI) classifier for differentiating intramucosal and submucosal gastric cancers and examined it to establish a diagnostic method based on cooperation between AI and endoscopists. PATIENTS AND METHODS: We prepared 500 training images using cases of mainly depressed-type early gastric cancer from 250 intramucosal cancers and 250 submucosal cancers. We also prepared 200 test images each of 100 cancers from another institution. We designed an AI classifier to differentiate between intramucosal and submucosal cancers by deep learning. We examined the performance of the AI classifier and the majority vote of the endoscopists as high confidence and low confidence diagnostic probability, respectively, and cooperatively combined them to establish a diagnostic method providing high accuracy. RESULTS: Internal evaluation of the training images showed that accuracy, sensitivity, specificity, and F1 measure by the AI classifier were 77%, 76%, 78%, and 0.768, and those of the majority vote of the endoscopists were 72.6%, 53.6%, 91.6%, and 0.662, respectively. A diagnostic method based on cooperation between AI and the endoscopists showed that the respective values were 78.0%, 76.0%, 80.0%, and 0.776 for the test images. The value of F1 measure was especially higher than those by AI or the endoscopists alone. CONCLUSIONS: Cooperation between AI and endoscopists improved the diagnostic ability to determine invasion depth of early gastric cancer.

Regulatory T Cells but Not Tumour-Infiltrating Lymphocytes Correlate with Tumour Invasion Depth in Basal Cell Carcinoma.

Basal cell carcinoma (BCC) is the most common skin malignancy worldwide. Current evidence suggests tumour-infiltrating lymphocytes (TILs) may influence the clinical outcomes of patients with BCC. The present study aimed to profile the infiltrative characteristics of stromal TILs and regulatory T cells (Treg cells) in the tumour centre (TC), tumour periphery (TP), and normal adjacent tissue (NAT) of BCC. A total of 111 samples from 43 cutaneous BCC cases were examined for TIL (CD3+) and Treg cell (FOXP3+/CD3+) expression using immunohistochemical techniques. The correlations of Treg cells with TILs, invasion depth, and tumour morphological risk were analysed. We identified a high mean proportion of Treg cells within the tumour (TC = 46.9%, TP = 56.1%, NAT = 51.8%) despite a relatively low median of TILs (TC = 12.7%, TP = 10.3%, NAT = 3.6%), supporting the classification of BCC as a cold tumour. A significant positive correlation was observed between the proportion of Treg cells and sTILs (ρ = 0.325, p < 0.001), suggesting a predominant role of TILs in the infiltration of Treg cells. An inverse correlation discovered between Treg cells and tumour invasion depth (r = −0.36, p = 0.017) might indicate Treg cells' anti-tumour capacity in BCC.

Endoscopic imaging modalities for diagnosing the invasion depth of superficial esophageal squamous cell carcinoma: a systematic review.

Endoscopic diagnosis of the invasion depth of superficial esophageal squamous cell carcinoma (ESCC) is an important determinant of the treatment strategy. The three endoscopic imaging modalities commonly used to predict the invasion depth of superficial ESCC in Japan are non-magnifying endoscopy (non-ME), magnifying endoscopy (ME), and endoscopic ultrasonography (EUS). However, which of these three modalities is most effective remains unclear. We performed a systematic review of the literature to compare the diagnostic accuracy of the three modalities for prediction of the invasion depth of superficial ESCC. We used Medical Subject Heading terms and free keywords to search the PubMed, Cochrane Central, and Ichushi databases to identify direct comparison studies published from January 2000 to August 2020. The results of direct comparison studies were used to compare the diagnostic accuracy of each modality. The primary outcome was defined as the proportion of overdiagnosis of pT1b-SM2/3 cancers, and the main secondary outcome was the proportion of underdiagnosis of pT1b-SM2/3 cancers. Other secondary outcomes were the sensitivity and specificity values of the modalities. Four articles were finally selected for qualitative evaluation. Although ME showed no significant advantages over non-ME in terms of sensitivity and specificity, it had a slightly lower proportion of overdiagnosis. EUS had sensitivity and specificity similar to those of non-ME and ME, but EUS had a higher proportion of overdiagnosis. Non-ME and ME are useful for the diagnosis of cancer invasion depth. EUS may increase overdiagnosis, and caution is required in determining its indications.