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Background and Aim Methods Results Conclusions Accurate stratification of the risk of lymph node metastasis (LNM) following endoscopic resection of submucosal invasive (T1) colorectal cancer (CRC) is imperative for determining the necessity for additional surgery. In this systematic review, we evaluated the efficacy of prediction of LNM by artificial intelligence (AI) models utilizing whole slide image (WSI) in patients with T1 CRC.In accordance with the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines, a systematic review was conducted through searches in PubMed (MEDLINE), Embase, and the Cochrane Library for relevant studies published up to December 2023. The inclusion criteria were studies assessing the accuracy of hematoxylin and eosin‐stained WSI‐based AI models for predicting LNM in patients with T1 CRC.Four studies met the criteria for inclusion in this systematic review. The area under the receiver operating characteristic curve for these AI models ranged from 0.57 to 0.76. In the three studies in which AI performance was compared directly with current treatment guidelines, AI consistently exhibited a higher area under the receiver operating characteristic curve. At a fixed sensitivity of 100%, specificities ranged from 18.4% to 45.0%.Artificial intelligence models based on WSI can potentially address the issue of diagnostic variability between pathologists and exceed the predictive accuracy of current guidelines. However, these findings require confirmation by larger studies that incorporate external validation. [ABSTRACT FROM AUTHOR]

Objectives: Lymphovascular invasion (LVI) is a critical risk factor for lymph node metastasis (LNM), which requires additional surgery after endoscopic resection of T1 colorectal cancer (CRC). However, the impact of additional staining on estimating LNM is unclear. This systematic review aimed to evaluate the impact of additional staining on determining LNM in T1 CRC. Methods: We searched five electronic databases. Outcomes were diagnostic odds ratio (DOR), assessed using hierarchical summary receiver operating characteristic curves, and interobserver agreement among pathologists for positive LVI, assessed using Kappa coefficients (κ). We performed a subgroup analysis of studies that simultaneously included a multivariable analysis for other risk factors (deep submucosal invasion, poor differentiation, and tumor budding). Results: Among the 64 studies (18,097 patients) identified, hematoxylin–eosin (HE) and additional staining for LVI had pooled sensitivities of 0.45 (95% confidence interval [CI] 0.32–0.58) and 0.68 (95% CI 0.44–0.86), specificities of 0.88 (95% CI 0.78–0.94) and 0.76 (95% CI 0.62–0.86), and DORs of 6.26 (95% CI 3.73–10.53) and 6.47 (95% CI 3.40–12.32) for determining LNM, respectively. In multivariable analysis, the DOR of additional staining for LNM (DOR 5.95; 95% CI 2.87–12.33) was higher than that of HE staining (DOR 1.89; 95% CI 1.13–3.16) (P = 0.01). Pooled κ values were 0.37 (95% CI 0.22–0.52) and 0.62 (95% CI 0.04–0.99) for HE and additional staining for LVI, respectively. Conclusion: Additional staining for LVI may increase the DOR for LNM and interobserver agreement for positive LVI among pathologists. [ABSTRACT FROM AUTHOR]

Objectives: Computer‐aided characterization (CADx) may be used to implement optical biopsy strategies into colonoscopy practice; however, its impact on endoscopic diagnosis remains unknown. We aimed to evaluate the additional diagnostic value of CADx when used by endoscopists for assessing colorectal polyps. Methods: This was a single‐center, multicase, multireader, image‐reading study using randomly extracted images of pathologically confirmed polyps resected between July 2021 and January 2022. Approved CADx that could predict two‐tier classification (neoplastic or nonneoplastic) by analyzing narrow‐band images of the polyps was used to obtain a CADx diagnosis. Participating endoscopists determined if the polyps were neoplastic or not and noted their confidence level using a computer‐based, image‐reading test. The test was conducted twice with a 4‐week interval: the first test was conducted without CADx prediction and the second test with CADx prediction. Diagnostic performances for neoplasms were calculated using the pathological diagnosis as reference and performances with and without CADx prediction were compared. Results: Five hundred polyps were randomly extracted from 385 patients and diagnosed by 14 endoscopists (including seven experts). The sensitivity for neoplasia was significantly improved by referring to CADx (89.4% vs. 95.6%). CADx also had incremental effects on the negative predictive value (69.3% vs. 84.3%), overall accuracy (87.2% vs. 91.8%), and high‐confidence diagnosis rate (77.4% vs. 85.8%). However, there was no significant difference in specificity (80.1% vs. 78.9%). Conclusions: Computer‐aided characterization has added diagnostic value for differentiating colorectal neoplasms and may improve the high‐confidence diagnosis rate. [ABSTRACT FROM AUTHOR]

Objectives: A computer‐aided detection (CAD) system was developed to support the detection of colorectal lesions by deep learning using video images of lesions and normal mucosa recorded during colonoscopy. The study's purpose was to evaluate the stand‐alone performance of this device under blinded conditions. Methods: This multicenter prospective observational study was conducted at four Japanese institutions. We used 326 videos of colonoscopies recorded with patient consent at institutions in which the Ethics Committees approved the study. The sensitivity of successful detection of the CAD system was calculated using the target lesions, which were detected by adjudicators from two facilities for each lesion appearance frame; inconsistencies were settled by consensus. Successful detection was defined as display of the detection flag on the lesion for more than 0.5 s within 3 s of appearance. Results: Of the 556 target lesions from 185 cases, detection success sensitivity was 97.5% (95% confidence interval [CI] 95.8–98.5%). The "successful detection sensitivity per colonoscopy" was 93% (95% CI 88.3–95.8%). For the frame‐based sensitivity, specificity, positive predictive value, and negative predictive value were 86.6% (95% CI 84.8–88.4%), 84.7% (95% CI 83.8–85.6%), 34.9% (95% CI 32.3–37.4%), and 98.2% (95% CI 97.8–98.5%), respectively. Trial registration: University Hospital Medical Information Network (UMIN000044622). [ABSTRACT FROM AUTHOR]

Objectives: Lymph node metastasis (LNM) prediction for T1 colorectal cancer (CRC) is critical for determining the need for surgery after endoscopic resection because LNM occurs in 10%. We aimed to develop a novel artificial intelligence (AI) system using whole slide images (WSIs) to predict LNM. Methods: We conducted a retrospective single center study. To train and test the AI model, we included LNM status‐confirmed T1 and T2 CRC between April 2001 and October 2021. These lesions were divided into two cohorts: training (T1 and T2) and testing (T1). WSIs were cropped into small patches and clustered by unsupervised K‐means. The percentage of patches belonging to each cluster was calculated from each WSI. Each cluster's percentage, sex, and tumor location were extracted and learned using the random forest algorithm. We calculated the areas under the receiver operating characteristic curves (AUCs) to identify the LNM and the rate of over‐surgery of the AI model and the guidelines. Results: The training cohort contained 217 T1 and 268 T2 CRCs, while 100 T1 cases (LNM‐positivity 15%) were the test cohort. The AUC of the AI system for the test cohort was 0.74 (95% confidence interval [CI] 0.58–0.86), and 0.52 (95% CI 0.50–0.55) using the guidelines criteria (P = 0.0028). This AI model could reduce the 21% of over‐surgery compared to the guidelines. Conclusion: We developed a pathologist‐independent predictive model for LNM in T1 CRC using WSI for determination of the need for surgery after endoscopic resection. Trial registration: UMIN Clinical Trials Registry (UMIN000046992, https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000053590). [ABSTRACT FROM AUTHOR]

Objectives: Advances in endoscopic technology, including magnifying and image‐enhanced techniques, have been attracting increasing attention for the optical characterization of colorectal lesions. These techniques are being implemented into clinical practice as cost‐effective and real‐time approaches. Additionally, with the recent progress in endoscopic interventions, endoscopic resection is gaining acceptance as a treatment option in patients with ulcerative colitis (UC). Therefore, accurate preoperative characterization of lesions is now required. However, lesion characterization in patients with UC may be difficult because UC is often affected by inflammation, and it may be characterized by a distinct "bottom‐up" growth pattern, and even expert endoscopists have relatively little experience with such cases. In this systematic review, we assessed the current status and limitations of the use of optical characterization of lesions in patients with UC. Methods: A literature search of online databases (MEDLINE via PubMed and CENTRAL via the Cochrane Library) was performed from 1 January 2000 to 30 November 2021. Results: The database search initially identified 748 unique articles. Finally, 25 studies were included in the systematic review: 23 focused on differentiation of neoplasia from non‐neoplasia, one focused on differentiation of UC‐associated neoplasia from sporadic neoplasia, and one focused on differentiation of low‐grade dysplasia from high‐grade dysplasia and cancer. Conclusions: Optical characterization of neoplasia in patients with UC, even using advanced endoscopic technology, is still challenging and several issues remain to be addressed. We believe that the information revealed in this review will encourage researchers to commit to the improvement of optical diagnostics for UC‐associated lesions. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) can be classified into four consensus molecular subtypes (CMS) according to genomic aberrations and gene expression profiles. CMS is expected to be useful in predicting prognosis and selecting chemotherapy regimens. However, there are still no reports on the relationship between the morphology and CMS. Methods: This retrospective study included 55 subjects with T2 CRC undergoing surgical resection, of whom 30 had the depressed type and 25 the protruded type. In the classification of the CMS, we first defined cases with deficient mismatch repair as CMS1. And then, CMS2/3 and CMS4 were classified using an online classifier developed by Trinh et al. The staining intensity of CDX2, HTR2B, FRMD6, ZEB1, and KER and the percentage contents of CDX2, FRMD6, and KER are input into the classifier to obtain automatic output classifying the specimen as CMS2/3 or CMS4. Results: According to the results yielded by the online classifier, of the 30 depressed-type cases, 15 (50%) were classified as CMS2/3 and 15 (50%) as CMS4. Of the 25 protruded-type cases, 3 (12%) were classified as CMS1 and 22 (88%) as CMS2/3. All of the T2 CRCs classified as CMS4 were depressed CRCs. More malignant pathological findings such as lymphatic invasion were associated with the depressed rather than protruded T2 CRC cases. Conclusions: Depressed-type T2 CRC had a significant association with CMS4, showing more malignant pathological findings such as lymphatic invasion than the protruded-type, which could explain the reported association between CMS4 CRC and poor prognosis. [ABSTRACT FROM AUTHOR]

With the prevalence of endoscopic submucosal dissection and endoscopic full thickness resection, which enable complete resection of T1 colorectal cancer with a negative margin, the treatment strategy following endoscopic resection has become more important. The necessity of secondary surgical resection is determined on the basis of the risk of lymph node metastasis according to the histopathological findings of resected specimens because ~10% of T1 colorectal cancer cases have lymph node metastasis. The current Japanese treatment guidelines state four risk factors for lymph node metastasis: lymphovascular invasion, histological differentiation, depth of submucosal invasion, and tumor budding. These guidelines have succeeded in stratifying the low‐risk group for lymph node metastasis, in which endoscopic resection alone is acceptable for cure. On the other hand, there are some problems: there is variation in diagnosis methods and low interobserver agreement for each pathological factor and 90% of surgical resections are unnecessary, with lymph node metastasis negativity. To ensure patients with T1 colorectal cancer receive more appropriate treatment, these problems should be addressed. In this systematic review, we gave some suggestions to these practical issues of four pathological factors as predictors. [ABSTRACT FROM AUTHOR]

Objectives: Complete endoscopic healing, defined as Mayo endoscopic score (MES) = 0, is an optimal target in the treatment of ulcerative colitis (UC). However, some patients with MES = 0 show clinical relapse within 12 months. Histologic goblet mucin depletion has emerged as a predictor of clinical relapse in patients with MES = 0. We observed goblet depletion in vivo using an endocytoscope, and analyzed the association between goblet appearance and future prognosis in UC patients. Methods: In this retrospective cohort study, all enrolled UC patients had MES = 0 and confirmed clinical remission between October 2016 and March 2020. We classified the patients into two groups according to the goblet appearance status: preserved‐goblet and depleted‐goblet groups. We followed the patients until March 2021 and evaluated the difference in cumulative clinical relapse rates between the two groups. Results: We identified 125 patients with MES = 0 as the study subjects. Five patients were subsequently excluded. Thus, we analyzed the data for 120 patients, of whom 39 were classified as the preserved‐goblet group and 81 as the depleted‐goblet group. The patients were followed‐up for a median of 549 days. During follow‐up, the depleted‐goblet group had a significantly higher cumulative clinical relapse rate than the preserved‐goblet group (19% [15/81] vs. 5% [2/39], respectively; P = 0.02). Conclusions: Observing goblet appearance in vivo allowed us to better predict the future prognosis of UC patients with MES = 0. This approach may assist clinicians with onsite decision‐making regarding treatment interventions without a biopsy. [ABSTRACT FROM AUTHOR]

Background and Aim: Although patients report either improved or worsened halitosis after Helicobacter pylori eradication therapy, such complaints are subjective. Only a few studies have objectively evaluated reports of changes in halitosis after H. pylori eradication; thus, this study aimed to investigate these changes after a successful H. pylori eradication. Methods: Between February 2015 and October 2018, 56 347 patients visited the clinic. Informed consent for participation in this study was obtained from 164 patients scheduled to undergo upper gastrointestinal endoscopy due to halitosis. Of the 91 patients with H. pylori infection, the halitosis values were evaluated as Refres breath (RB) values using a Total Gas Detector™ System and compared before and after successful H. pylori eradication, as confirmed with urea breath testing. Results: Among the 91 patients treated, 77 patients were successfully eradicated of H. pylori and had their Refres values measured (21 men and 56 women; mean age, 64.2 ± 11.5 years, including 10 smokers); among these 77 patients, 27 showed RB values of > 60. Their RB values significantly improved from 73.5 Â (95% confidence interval [CI], 64.1–82.9) to 59.4 Â (95% CI, 50.0–68.8) (P = 0.038). Of the 30 patients who could be followed up for > 2 years after successful H. pylori eradication, 8 with an RB value ≥ 60 showed significant RB value improvements from 77.9 Â (95% CI, 59.4–96.4) to 30.1 Â (95% CI, 11.6–48.6) (P = 0.0016). Conclusions: Helicobacter pylori eradication therapy could improve halitosis, and such improvement could be maintained even 2 years after successful eradication. [ABSTRACT FROM AUTHOR]