A novel artificial intelligence-assisted "vascular healing" diagnosis for prediction of future clinical relapse in patients with ulcerative colitis: a prospective cohort study (with video).

BACKGROUND AND AIMS: Image-enhanced endoscopy has attracted attention as a method for detecting inflammation and predicting outcomes in patients with ulcerative colitis (UC); however, the procedure requires specialist endoscopists. Artificial intelligence (AI)-assisted image-enhanced endoscopy may help nonexperts provide objective accurate predictions with the use of optical imaging. We aimed to develop a novel AI-based system using 8853 images from 167 patients with UC to diagnose "vascular-healing" and establish the role of AI-based vascular-healing for predicting the outcomes of patients with UC. METHODS: This open-label prospective cohort study analyzed data for 104 patients with UC in clinical remission. Endoscopists performed colonoscopy using the AI system, which identified the target mucosa as AI-based vascular-active or vascular-healing. Mayo endoscopic subscore (MES), AI outputs, and histologic assessment were recorded for 6 colorectal segments from each patient. Patients were followed up for 12 months. Clinical relapse was defined as a partial Mayo score >2 RESULTS: The clinical relapse rate was significantly higher in the AI-based vascular-active group (23.9% [16/67]) compared with the AI-based vascular-healing group (3.0% [1/33)]; P = .01). In a subanalysis predicting clinical relapse in patients with MES ≤1, the area under the receiver operating characteristic curve for the combination of complete endoscopic remission and vascular healing (0.70) was increased compared with that for complete endoscopic remission alone (0.65). CONCLUSIONS: AI-based vascular-healing diagnosis system may potentially be used to provide more confidence to physicians to accurately identify patients in remission of UC who would likely relapse rather than remain stable.

Serum Leucine-Rich α2 Glycoprotein: A Novel Biomarker for Transmural Inflammation in Crohn's Disease.

INTRODUCTION: Leucine-rich alpha-2 glycoprotein (LRG) is a newly studied biomarker for inflammatory diseases. This study aimed to investigate whether LRG can be used for evaluating transmural activity in patients with Crohn's disease (CD). METHODS: We performed magnetic resonance enterography (MRE) in 227 consecutive patients with CD from June 2020 to August 2021. We prospectively compared MRE findings with clinical and laboratory data including LRG. MRE was evaluated using 2 validated scoring systems, and transmural inflammation was defined as having a maximum simplified magnetic resonance index of activity (sMaRIA) score of ≥4 and a 5-point classification score of ≥9, respectively. RESULTS: The correlation between LRG and the total MRE score showed a positive correlation ( r = 0.576 for the sMaRIA score, P < 0.01, and r = 0.633 for the 5-point score, P < 0.01). Serum concentrations of LRG significantly increased as MRE scores increased ( P < 0.01). The area under the curve of LRG for a sMaRIA score of ≥4 and a 5-point score of ≥9 was 0.845 and 0.869, respectively, which was significantly higher than that of CDAI ( P < 0.01) or C-reactive protein ( P < 0.01). LRG levels of ≥14 µg/mL had a 67% sensitivity and 90% specificity for a sMaRIA score of ≥4 and a 73% sensitivity and 89% specificity for a 5-point score of ≥9. Patients with high LRG levels were also strongly associated with CD-related hospitalization, surgery, and clinical relapse compared with those with low LRG levels ( P < 0.01 for all). DISCUSSION: LRG is a highly accurate serum biomarker for detecting transmural activity in patients with CD. Results need to be validated in further multicenter studies.

Serum Leucine-Rich α2 Glycoprotein: A Novel Biomarker For Small Bowel Mucosal Activity in Crohn's Disease.

Active lesions in the small bowel (SB) have been independently associated with poorer prognoses in patients with Crohn's disease (CD)1; however, there has been a lack of accurate and convenient screening methods. Past studies have found that serum levels of the glycoprotein leucine-rich α2 glycoprotein (LRG) correlates with endoscopic activity in ulcerative colitis,2,3 and this is now available for routine clinical use as a biomarker in patients with inflammatory bowel disease in Japan. LRG has not yet been thoroughly verified in CD, and we investigated whether it can be used as a serum biomarker for detecting SB mucosal activity in patients with CD.

Artificial Intelligence System to Determine Risk of T1 Colorectal Cancer Metastasis to Lymph Node.

BACKGROUND & AIMS: In accordance with guidelines, most patients with T1 colorectal cancers (CRC) undergo surgical resection with lymph node dissection, despite the low incidence (∼10%) of metastasis to lymph nodes. To reduce unnecessary surgical resections, we used artificial intelligence to build a model to identify T1 colorectal tumors at risk for metastasis to lymph node and validated the model in a separate set of patients. METHODS: We collected data from 3134 patients with T1 CRC treated at 6 hospitals in Japan from April 1997 through September 2017 (training cohort). We developed a machine-learning artificial neural network (ANN) using data on patients' age and sex, as well as tumor size, location, morphology, lymphatic and vascular invasion, and histologic grade. We then conducted the external validation on the ANN model using independent 939 patients at another hospital during the same period (validation cohort). We calculated areas under the receiver operator characteristics curves (AUCs) for the ability of the model and US guidelines to identify patients with lymph node metastases. RESULTS: Lymph node metastases were found in 319 (10.2%) of 3134 patients in the training cohort and 79 (8.4%) of /939 patients in the validation cohort. In the validation cohort, the ANN model identified patients with lymph node metastases with an AUC of 0.83, whereas the guidelines identified patients with lymph node metastases with an AUC of 0.73 (P < .001). When the analysis was limited to patients with initial endoscopic resection (n = 517), the ANN model identified patients with lymph node metastases with an AUC of 0.84 and the guidelines identified these patients with an AUC of 0.77 (P = .005). CONCLUSIONS: The ANN model outperformed guidelines in identifying patients with T1 CRCs who had lymph node metastases. This model might be used to determine which patients require additional surgery after endoscopic resection of T1 CRCs. UMIN Clinical Trials Registry no: UMIN000038609.

Evaluation in real-time use of artificial intelligence during colonoscopy to predict relapse of ulcerative colitis: a prospective study.

BACKGROUND AND AIMS: The use of artificial intelligence (AI) during colonoscopy is attracting attention as an endoscopist-independent tool to predict histologic disease activity of ulcerative colitis (UC). However, no study has evaluated the real-time use of AI to directly predict clinical relapse of UC. Hence, it is unclear whether the real-time use of AI during colonoscopy helps clinicians make real-time decisions regarding treatment interventions for patients with UC. This study aimed to establish the role of real-time AI in stratifying the relapse risk of patients with UC in clinical remission. METHODS: This open-label, prospective, cohort study was conducted in a referral center. The cohort comprised 145 consecutive patients with UC in clinical remission who underwent AI-assisted colonoscopy with a contact-microscopy function. We classified patients into either the Healing group or Active group based on the AI outputs during colonoscopy. The primary outcome measure was clinical relapse of UC (defined as a partial Mayo score >2) during 12 months of follow-up after colonoscopy. RESULTS: Overall, 135 patients completed the 12-month follow-up after AI-assisted colonoscopy. AI-assisted colonoscopy classified 61 patients as the Healing group and 74 as the Active group. The relapse rate was significantly higher in the AI-Active group (28.4% [21/74]; 95% confidence interval, 18.5%-40.1%) than in the AI-Healing group (4.9% [3/61]; 95% confidence interval, 1.0%-13.7%; P < .001). CONCLUSIONS: Real-time use of AI predicts the risk of clinical relapse in patients with UC in clinical remission, which helps clinicians make real-time decisions regarding treatment interventions. (Clinical trial registration number: UMIN000036650.).

Impact of artificial intelligence on colorectal polyp detection for early-career endoscopists: an international comparative study.

BACKGROUND: Artificial intelligence (AI) for polyp detection is being introduced to colonoscopy, but there is uncertainty how this affects endoscopists' ability to detect polyps and neoplasms. We performed a video-based study to address whether AI improved the endoscopists' performance to detect polyps. METHODS: We established a dataset of 200 colonoscopy videos (length 5 s; 100 without polyps and 100 with one polyp). About 33 early-career endoscopists (50-400 colonoscopies performed) from 10 European countries classified each video as either 'polyp present' or 'polyp not present'. The video assessment was performed twice with a four-week interval. The first assessment was performed without any AI tool, whereas the second was performed with an AI tool for polyp detection. The primary endpoint was early-career endoscopists' sensitivity to detect polyps. Gold standard for presence and histology of polyps were confirmed by two expert endoscopists and pathologists, respectively. McNemar's test was used for statistical significance. RESULTS: There were 86 neoplastic and 14 non-neoplastic polyps (mean size 5.6 mm) in the 100 videos with polyps. Early-career endoscopists' sensitivity to detect polyps increased from 86.3% (95% confidence interval [CI]: 85.1-87.5%) to 91.7% (95%CI: 90.7-92.6%) with the AI aid (p < .0001). Their sensitivity to detect neoplastic polyps increased from 85.4% (95% CI: 84.0-86.7%) to 92.1% (95%CI: 91.1-93.1%) with the AI aid (p < .0001). CONCLUSION: The polyp detection AI tool helped early-career endoscopists to increase their sensitivity to identify all polyps and neoplastic polyps during colonoscopy.

Mucosal healing of small intestinal stricture is associated with improved prognosis post-dilation in Crohn's disease.

BACKGROUND: Small intestinal stricture is a major cause for surgery in Crohn's disease (CD). Endoscopic balloon dilation (EBD) is performed for small intestinal strictures to avoid surgery, often repeatedly. However, factors that are associated with prognosis after EBD of small intestinal strictures remain poorly investigated. Mucosal healing is the therapeutic target in CD. We aimed to investigate the impact of mucosal healing defined by the presence of ulcers at the small intestinal stricture site on the prognosis of EBD in CD patients. METHODS: We retrospectively included patients with CD who underwent initial EBD for endoscopically impassable small intestinal strictures from January 2012 to March 2020 at a single center. The association between presence of ulcer at the stricture site and surgery after EBD was examined by Cox proportional hazards model. RESULTS: Of the 98 patients included, 63 (64.3%) had ulcer at the stricture site. 20 (31.7%) of these patients underwent surgery for the stricture in due course, whereas 4 (11.4%) of the patients without ulcer of the stricture underwent surgery. In multivariate analysis, patients with ulcer of the stricture had a significantly higher risk for surgery than those without ulcer (hazard ratio 4.84; 95% confidence interval 1.58-14.79). CONCLUSION: Mucosal healing at the stricture site indicated a favorable prognosis after EBD for small intestinal strictures in CD.

Systematic review of artificial intelligence-based image diagnosis for inflammatory bowel disease.

OBJECTIVES: Diagnosis of inflammatory bowel diseases (IBD) involves combining clinical, laboratory, endoscopic, histologic, and radiographic data. Artificial intelligence (AI) is rapidly being developed in various fields of medicine, including IBD. Because a key part in the diagnosis of IBD involves evaluating imaging data, AI is expected to play an important role in this aspect in the coming decades. We conducted a systematic literature review to highlight the current advancement of AI in diagnosing IBD from imaging data. METHODS: We performed an electronic PubMed search of the MEDLINE database for studies up to January 2022 involving IBD and AI. Studies using imaging data as input were included, and nonimaging data were excluded. RESULTS: A total of 27 studies are reviewed, including 18 studies involving endoscopic images and nine studies involving other imaging data. CONCLUSION: We highlight in this review the recent advancement of AI in diagnosing IBD from imaging data by summarizing the relevant studies, and discuss the future role of AI in clinical practice.

Use of advanced endoscopic technology for optical characterization of neoplasia in patients with ulcerative colitis: Systematic review.

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.

Combined endocytoscopy with pit pattern diagnosis in ulcerative colitis-associated neoplasia: Pilot study.

OBJECTIVES: Ulcerative colitis-associated neoplasias (UCAN) are often flat with an indistinct boundary from surrounding tissues, which makes differentiating UCAN from non-neoplasias difficult. Pit pattern (PIT) has been reported as one of the most effective indicators to identify UCAN. However, regenerated mucosa is also often diagnosed as a neoplastic PIT. Endocytoscopy (EC) allows visualization of cell nuclei. The aim of this retrospective study was to demonstrate the diagnostic ability of combined EC irregularly-formed nuclei with PIT (EC-IN-PIT) diagnosis to identify UCAN. METHODS: This study involved patients with ulcerative colitis whose lesions were observed by EC. Each lesion was diagnosed by two independent expert endoscopists, using two types of diagnostic strategies: PIT alone and EC-IN-PIT. We evaluated and compared the diagnostic abilities of PIT alone and EC-IN-PIT. We also examined the difference in the diagnostic abilities of an EC-IN-PIT diagnosis according to endoscopic inflammation severity. RESULTS: We analyzed 103 lesions from 62 patients; 23 lesions were UCAN and 80 were non-neoplastic. EC-IN-PIT diagnosis had a significantly higher specificity and accuracy compared with PIT alone: 84% versus 58% (P < 0.001), and 88% versus 67% (P < 0.01), respectively. The specificity and accuracy were significantly higher for Mayo endoscopic score (MES) 0-1 than MES 2-3: 93% versus 68% (P < 0.001) and 95% versus 74% (P < 0.001), respectively. CONCLUSIONS: Our novel EC-IN-PIT strategy had a better diagnostic ability than PIT alone to predict UCAN from suspected and initially detected lesions using conventional colonoscopy. UMIN clinical trial (UMIN000040698).

Beyond complete endoscopic healing: Goblet appearance using an endocytoscope to predict future sustained clinical remission in ulcerative colitis.

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.

Guidelines for endoscopic balloon dilation in treating Crohn's disease-associated small intestinal strictures (supplement to the Clinical Practice Guidelines for Enteroscopy).

Balloon-assisted enteroscopy allows endoscopic treatments in the deeper segments of the small bowel. Endoscopic balloon dilation has become a popular minimally invasive alternative for the treatment of Crohn's disease-associated small intestinal strictures. As a supplement to the Clinical Practice Guidelines for Enteroscopy, the Japan Gastroenterological Endoscopy Society's Working Committee has developed the present "Guidelines for endoscopic balloon dilation in treating Crohn's disease-associated small intestinal strictures," based on new scientific techniques and evidence. The guidelines cover standard procedures for the insertion route of the balloon endoscope, bowel preparation, indications, procedure-related complications, efficacy, target diameter and duration, management of multiple strictures, and the current state of combined and alternative treatments. Unresolved future research questions are also listed in this guideline.

Current status of diagnostic and therapeutic colonoscopy in Japan: The Japan Endoscopic Database Project.

OBJECTIVES: The Japan Endoscopy Database Project was initiated to develop the world's largest endoscopy data repository. This study describes the first phase of the colonoscopy project in Japan. METHODS: Data were aggregated offline by integrating information from the endoscopy database software from January 2015 through March 2017. The study population included all patients who underwent colonoscopy at eight centers. RESULTS: A total of 31,395 patients who underwent 38,497 colonoscopy procedures were registered. The majority of procedures were performed for screening (n = 14,156), followed by fecal immunochemical test positivity (n = 3960), abdominal symptoms (n = 3864), post-colorectal surgery surveillance (n = 3431), post-endoscopic treatment surveillance (n = 3757), thorough pre-treatment examination (n = 2822), and therapeutic purposes (n = 6507). In the screening group, advanced cancers, early cancers, and adenomas were diagnosed endoscopically in 2.1%, 1.3%, and 28.7% of cases, respectively, while in the fecal immunochemical test-positive group, they were diagnosed in 2.5%, 1.9%, and 41.6% of cases, respectively. The incidence of complications was 0.177% and 0.152% in the screening and fecal immunochemical test-positive groups, respectively. The therapeutic procedures included 1446 cold forceps polypectomy procedures, 4770 cold snare polypectomy procedures, 368 hot biopsies, 2998 hot snare polypectomy procedures, 9775 endoscopic or piecemeal endoscopic mucosal resections, and 1660 endoscopic submucosal dissections. A total of 173 procedure-related complications (0.82%) occurred in 21,017 therapeutic procedures performed in 15,744 patients. CONCLUSIONS: The first phase of the Japan Endoscopy Database Project established the proportions of the diagnostic and therapeutic colonoscopy procedures, and complication rates in real-world settings.

[A curatively resectable case of cholangiocarcinoma 52 years after cholecystoduodenostomy for biliary atresia].

A 52-year-old female had cholecystoduodenostomy for biliary atresia of type I cyst at 120 days of age. The patient's surgery recovery was uneventful;however, the patient had recurring cholangitis at the age of 27. The patient had high hepatobiliary enzymes in the outpatient clinic and was diagnosed with cholangitis. In general, the Kasai method is the mainstream for biliary atresia, since it has a much-reduced incidence of both early and late postoperative problems. However, this patient had biliary atresia of type I cyst and had undergone cholecystoduodenostomy. We suspected that the obstructive cholangitis was caused by the relatively wide anastomosis opening into the duodenal bulb, where the stomach contents pass through the most, and the poor clearance owing to the convoluted cystic duct;therefore, we chose to place a stent endoscopically. However, to our surprise, Class V was detected in the bile cytology performed as a precaution. Although no tumor was seen on imaging such as contrast-enhanced CT, EUS, and PET/CT, mapping biopsy results showed the presence of cancer at the bifurcation of the cystic duct. The patient had cholangiocarcinoma confined to the extrahepatic bile ducts only;thus, extrahepatic bile duct resection was conducted. The patient was discovered to have biliary intraepithelial neoplasia-3, and the tumor was entirely respectable. The patient had a good postoperative course, with normalization of liver function and no recurrence of cholangitis. In this case, cholangiocarcinoma was detected at an early stage by cytological examination performed as a precaution during endoscopic therapy for recurrent cholangitis. In addition to the fact that the long-term pathogenesis of biliary atresia is still unknown, it is important to note the presence of malignancy, which has the greatest effect on the patient prognosis, considering that the course of the disease varies depending on the operation carried out. Because cholecystoduodenostomy for biliary atresia is a rare approach, and there has been no previous report of related cholangiocarcinoma, we report this case for the benefit of gastroenterologists who may encounter similar cases in the future.

Development and Validation of a Deep Neural Network for Accurate Evaluation of Endoscopic Images From Patients With Ulcerative Colitis.

BACKGROUND & AIMS: There are intra- and interobserver variations in endoscopic assessment of ulcerative colitis (UC) and biopsies are often collected for histologic evaluation. We sought to develop a deep neural network system for consistent, objective, and real-time analysis of endoscopic images from patients with UC. METHODS: We constructed the deep neural network for evaluation of UC (DNUC) algorithm using 40,758 images of colonoscopies and 6885 biopsy results from 2012 patients with UC who underwent colonoscopy from January 2014 through March 2018 at a single center in Japan (the training set). We validated the accuracy of the DNUC algorithm in a prospective study of 875 patients with UC who underwent colonoscopy from April 2018 through April 2019, with 4187 endoscopic images and 4104 biopsy specimens. Endoscopic remission was defined as a UC endoscopic index of severity score of 0; histologic remission was defined as a Geboes score of 3 points or less. RESULTS: In the prospective study, the DNUC identified patients with endoscopic remission with 90.1% accuracy (95% confidence interval [CI] 89.2%-90.9%) and a kappa coefficient of 0.798 (95% CI 0.780-0.814), using findings reported by endoscopists as the reference standard. The intraclass correlation coefficient between the DNUC and the endoscopists for UC endoscopic index of severity scoring was 0.917 (95% CI 0.911-0.921). The DNUC identified patients in histologic remission with 92.9% accuracy (95% CI 92.1%-93.7%); the kappa coefficient between the DNUC and the biopsy result was 0.859 (95% CI 0.841-0.875). CONCLUSIONS: We developed a deep neural network for evaluation of endoscopic images from patients with UC that identified those in endoscopic remission with 90.1% accuracy and histologic remission with 92.9% accuracy. The DNUC can therefore identify patients in remission without the need for mucosal biopsy collection and analysis. Trial number: UMIN000031430.

Development of a computer-aided detection system for colonoscopy and a publicly accessible large colonoscopy video database (with video).

BACKGROUND AND AIMS: Artificial intelligence (AI)-assisted polyp detection systems for colonoscopic use are currently attracting attention because they may reduce the possibility of missed adenomas. However, few systems have the necessary regulatory approval for use in clinical practice. We aimed to develop an AI-assisted polyp detection system and to validate its performance using a large colonoscopy video database designed to be publicly accessible. METHODS: To develop the deep learning-based AI system, 56,668 independent colonoscopy images were obtained from 5 centers for use as training images. To validate the trained AI system, consecutive colonoscopy videos taken at a university hospital between October 2018 and January 2019 were searched to construct a database containing polyps with unbiased variance. All images were annotated by endoscopists according to the presence or absence of polyps and the polyps' locations with bounding boxes. RESULTS: A total of 1405 videos acquired during the study period were identified for the validation database, 797 of which contained at least 1 polyp. Of these, 100 videos containing 100 independent polyps and 13 videos negative for polyps were randomly extracted, resulting in 152,560 frames (49,799 positive frames and 102,761 negative frames) for the database. The AI showed 90.5% sensitivity and 93.7% specificity for frame-based analysis. The per-polyp sensitivities for all, diminutive, protruded, and flat polyps were 98.0%, 98.3%, 98.5%, and 97.0%, respectively. CONCLUSIONS: Our trained AI system was validated with a new large publicly accessible colonoscopy database and could identify colorectal lesions with high sensitivity and specificity. (Clinical trial registration number: UMIN 000037064.).

Intravenous tacrolimus is a superior induction therapy for acute severe ulcerative colitis compared to oral tacrolimus.

BACKGROUND: Intravenous corticosteroid is the mainstay for managing acute severe ulcerative colitis, but one-third of patients do not respond to intravenous corticosteroid. Tacrolimus, a salvage therapy before colectomy, is usually orally administered, though its bioavailability is low compared intravenous administration. The efficacy of intravenous tacrolimus has not been widely studied. AIM: To determine the efficacy and safety of intravenous tacrolimus for the treatment of acute severe ulcerative colitis. METHODS: Eighty-seven hospitalized acute severe ulcerative colitis patients were enrolled for a prospective cohort study between 2009 and 2017. Sixty-five patients received intravenous tacrolimus and 22 received oral tacrolimus. The primary outcome was the achievement of clinical remission within 2 weeks. Relapse and colectomy incidence and adverse events were assessed at 24 weeks. RESULTS: Response rates of both treatments exceeded 50% but were not significantly different. The remission rate was higher in intravenous tacrolimus compared with oral tacrolimus. At 24 weeks, oral and intravenous tacrolimus showed similar relapse-free survival rates; however, colectomy-free survival rates were higher in intravenous tacrolimus compared with oral tacrolimus. CONCLUSIONS: Patients receiving intravenous tacrolimus achieved superior remission and colectomy-free survival rates compared with patients receiving oral tacrolimus. Safety was similar between the two treatments.

Adenocarcinoma arising in the multiple heterotopic submucosal glands of the intestine in a Satoyoshi syndrome patient: A case report.

Satoyoshi syndrome is a rare multisystemic disorder of unknown etiology characterized by progressive muscle spasms, alopecia and diarrhea. Multiple protruding lesions with cystic glands, namely gastroenterocolitis cystica polyposa, manifest in the gastrointestinal tract. Since the first report of these lesions in 1977, which was unique to Satoyoshi syndrome, few studies have focused on their role, and the associated clinicopathological features are not well understood. Here, we report a 64-year-old Japanese woman with Satoyoshi syndrome who presented with multiple polypoid lesions in the stomach, duodenum, jejunum, ileum and colon. Histologically, the polypoid lesions in the intestine comprised multiple heterotopic submucosal glands containing cystically dilated glands and smooth muscle fibers in the lamina propria mucosa and/or submucosa. Additionally, we observed stromal changes, such as fibrosis, discontinuous and thinning muscularis mucosae, and diffuse neural fiber proliferation in the entire intestinal tract. Furthermore, multiple foci of adenocarcinomas were identified within several heterotopic submucosal glands. We hypothesized that multiple heterotopic submucosal glands in the present case corresponded to previously reported gastroenterocolitis cystica polyposa, suggesting that these lesions are essential in the histopathology and are a unique manifestation of Satoyoshi syndrome.

Small Bowel Healing Detected by Endoscopy in Patients With Crohn's Disease After Treatment With Antibodies Against Tumor Necrosis Factor.

BACKGROUND & AIMS: It is important to objectively assess Crohn's disease (CD) activity in patients treated with antibodies against tumor necrosis factor (anti-TNF). Detection of healing by endoscopy (endoscopic healing) associates with patient outcome, based on evidence from studies of ileocolonoscopy. We assessed endoscopic healing after treatment, based on findings from balloon-assisted enteroscopy (BAE), in patients with CD. METHODS: We performed a post-hoc analysis of data from a clinical trial from 116 patients with CD (46 with ileal and 70 with ileocolonic type) who received induction and then maintenance therapy with anti-TNF agents from January 2013 through March 2018 at a single center in Japan. We compared findings from BAE before induction therapy and then again during maintenance therapy (median 13 months later). Endoscopic healing was defined as the modified simple endoscopic score for CD below 5. We also collected data on previous treatments, makers of inflammation, and disease type. RESULTS: Before treatment, small bowel ulcerations were present in 114 patients (98%); 42 patients (60%) with ileocolonic disease had colon ulcerations. During maintenance therapy, 41/114 patients (36%) had small bowel endoscopic healing; all the patients with small bowel endoscopic healing also had colonic endoscopic healing. Colonic endoscopic healing was observed in 33/42 patients (79%). The proportion of patients with small bowel endoscopic healing was significantly lower than that of colonic endoscopic healing (P < .001). Among all patients, failure to achieve small bowel endoscopic healing was significantly associated with structuring or penetrating disease (P = .014), lack of concomitant treatment with immunomodulators (P = .015), and having received previous treatment with an anti-TNF agents (P = .018). CONCLUSIONS: In a post-hoc analysis of patients with CD treated with anti-TNF agents, we found small bowel ulcerations, detected by BAE, to be more difficult to heal than colon ulcerations.

Artificial Intelligence-assisted System Improves Endoscopic Identification of Colorectal Neoplasms.

BACKGROUND & AIMS: Precise optical diagnosis of colorectal polyps could improve the cost-effectiveness of colonoscopy and reduce polypectomy-related complications. However, it is difficult for community-based non-experts to obtain sufficient diagnostic performance. Artificial intelligence-based systems have been developed to analyze endoscopic images; they identify neoplasms with high accuracy and low interobserver variation. We performed a multi-center study to determine the diagnostic accuracy of EndoBRAIN, an artificial intelligence-based system that analyzes cell nuclei, crypt structure, and microvessels in endoscopic images, in identification of colon neoplasms. METHODS: The EndoBRAIN system was initially trained using 69,142 endocytoscopic images, taken at 520-fold magnification, from patients with colorectal polyps who underwent endoscopy at 5 academic centers in Japan from October 2017 through March 2018. We performed a retrospective comparative analysis of the diagnostic performance of EndoBRAIN vs that of 30 endoscopists (20 trainees and 10 experts); the endoscopists assessed images from 100 cases produced via white-light microscopy, endocytoscopy with methylene blue staining, and endocytoscopy with narrow-band imaging. EndoBRAIN was used to assess endocytoscopic, but not white-light, images. The primary outcome was the accuracy of EndoBrain in distinguishing neoplasms from non-neoplasms, compared with that of endoscopists, using findings from pathology analysis as the reference standard. RESULTS: In analysis of stained endocytoscopic images, EndoBRAIN identified colon lesions with 96.9% sensitivity (95% CI, 95.8%-97.8%), 100% specificity (95% CI, 99.6%-100%), 98% accuracy (95% CI, 97.3%-98.6%), a 100% positive-predictive value (95% CI, 99.8%-100%), and a 94.6% negative-predictive (95% CI, 92.7%-96.1%); these values were all significantly greater than those of the endoscopy trainees and experts. In analysis of narrow-band images, EndoBRAIN distinguished neoplastic from non-neoplastic lesions with 96.9% sensitivity (95% CI, 95.8-97.8), 94.3% specificity (95% CI, 92.3-95.9), 96.0% accuracy (95% CI, 95.1-96.8), a 96.9% positive-predictive value, (95% CI, 95.8-97.8), and a 94.3% negative-predictive value (95% CI, 92.3-95.9); these values were all significantly higher than those of the endoscopy trainees, sensitivity and negative-predictive value were significantly higher but the other values are comparable to those of the experts. CONCLUSIONS: EndoBRAIN accurately differentiated neoplastic from non-neoplastic lesions in stained endocytoscopic images and endocytoscopic narrow-band images, when pathology findings were used as the standard. This technology has been authorized for clinical use by the Japanese regulatory agency and should be used in endoscopic evaluation of small polyps more widespread clinical settings. UMIN clinical trial no: UMIN000028843.