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.

Impact of computer-aided characterization for diagnosis of colorectal lesions, including sessile serrated lesions: Multireader, multicase study.

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.

Whole slide image-based prediction of lymph node metastasis in T1 colorectal cancer using unsupervised artificial intelligence.

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).

Changes in halitosis value before and after Helicobacter pylori eradication: A single-institutional prospective study.

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.

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).

Current status and future perspective on artificial intelligence for lower endoscopy.

The global incidence and mortality rate of colorectal cancer remains high. Colonoscopy is regarded as the gold standard examination for detecting and eradicating neoplastic lesions. However, there are some uncertainties in colonoscopy practice that are related to limitations in human performance. First, approximately one-fourth of colorectal neoplasms are missed on a single colonoscopy. Second, it is still difficult for non-experts to perform adequately regarding optical biopsy. Third, recording of some quality indicators (e.g. cecal intubation, bowel preparation, and withdrawal speed) which are related to adenoma detection rate, is sometimes incomplete. With recent improvements in machine learning techniques and advances in computer performance, artificial intelligence-assisted computer-aided diagnosis is being increasingly utilized by endoscopists. In particular, the emergence of deep-learning, data-driven machine learning techniques have made the development of computer-aided systems easier than that of conventional machine learning techniques, the former currently being considered the standard artificial intelligence engine of computer-aided diagnosis by colonoscopy. To date, computer-aided detection systems seem to have improved the rate of detection of neoplasms. Additionally, computer-aided characterization systems may have the potential to improve diagnostic accuracy in real-time clinical practice. Furthermore, some artificial intelligence-assisted systems that aim to improve the quality of colonoscopy have been reported. The implementation of computer-aided system clinical practice may provide additional benefits such as helping in educational poorly performing endoscopists and supporting real-time clinical decision-making. In this review, we have focused on computer-aided diagnosis during colonoscopy reported by gastroenterologists and discussed its status, limitations, and future prospects.

GTF2IRD1 on chromosome 7 is a novel oncogene regulating the tumor-suppressor gene TGFßR2 in colorectal cancer.

Chromosome 7q (Ch.7q) is clonally amplified in colorectal cancer (CRC). We aimed to identify oncogenes on Ch.7q that are overexpressed through DNA copy number amplification and determine the biological and clinical significance of these oncogenes in CRC. We identified general transcription factor 2I repeat domain-containing protein 1 (GTF2IRD1) as a potential oncogene using a CRC dataset from The Cancer Genome Atlas with a bioinformatics approach. We measured the expression of GTF2IRD1 in 98 patients with CRC using immunohistochemistry and RT-quantitative PCR (RT-qPCR). The biological effects of GTF2IRD1 expression were explored by gene set enrichment analysis (GSEA). Next, we undertook in vitro cell proliferation and cell cycle assays using siGTF2IRD1-transfected CRC cells. We further investigated the oncogenic mechanisms through which GTF2IRD1 promoted CRC progression. Finally, we assessed the clinical significance of GTF2IRD1 expression by RT-qPCR. GTF2IRD1 was overexpressed in tumor cells and liver metastatic lesions. The GSEA revealed a positive correlation between GTF2IRD1 expression and cell cycle progression-related genes. GTF2IRD1 knockdown inhibited cell proliferation and induced cell cycle arrest in Smad4-mutated CRC. GTF2IRD1 downregulated the expression of the gene encoding transforming growth factor ß receptor 2 (TGFßR2), a tumor-suppressor gene in Smad4-mutated CRC. On multivariate analysis, high GTF2IRD1 expression was an independent poor prognostic factor. Clinicopathological analysis showed that GTF2IRD1 expression was positively correlated with liver metastasis. In conclusion, GTF2IRD1 promoted CRC progression by downregulating TGFßR2 and could be a prognostic biomarker on Ch.7q in CRC. GTF2IRD1 could also be a novel oncogene in CRC.

Endocytoscopy for the differential diagnosis of colorectal low-grade adenoma: a novel possibility for the "resect and discard" strategy.

BACKGROUND AND AIMS: Endocytoscopy, a next-generation endoscopic system, facilitates observation at a maximum magnification of ×520. To our knowledge, no study has reported high-precision diagnosis of colorectal low-grade adenoma, endoscopically. We aimed to reveal which endocytoscopic findings may be used as indicators of low-grade adenoma and to assess whether a "resect and discard" strategy using endocytoscopy is feasible. METHODS: Lesions diagnosable with endocytoscopy were examined retrospectively between May 2005 and July 2017. A normal pit-like structure in endocytoscopic images was considered a normal pit (NP) sign and used as an indicator of low-grade adenoma. The primary outcome was the diagnostic accuracy of the NP sign for low-grade adenoma. We evaluated agreement rates between endocytoscopic and pathologic diagnosis for surveillance colonoscopy interval recommendation (SCIR) and performed a validation study to verify the agreement rates. RESULTS: For 748 lesions in 573 cases diagnosed as colorectal adenoma using endocytoscopy, the results were as follows: sensitivity of the NP sign for low-grade adenoma, 85.0%; specificity, 90.7%; positive predictive value, 96.6%; negative predictive value, 66.1%; accuracy, 86.4%; and positive likelihood ratio, 9.2 (P < .001). The agreement rate between endocytoscopic and pathologic diagnosis for SCIR was 94.4% (95% confidence interval [CI], 92.2%-96.1%; P < .001) under United States guidelines and 96.3% (95% CI, 94.5%-97.7%; P < .001) under European Union guidelines. All inter- and intraobserver agreement rates for expert and nonexpert endoscopists had κ values ≥0.8 except one nonexpert pair. CONCLUSIONS: Endocytoscopy is an effective modality in determining the differential diagnosis of colorectal low-grade adenoma. (University Hospital Medical Information Network Clinical Trials database registration number: UMIN000018623.).

Endocytoscopic intramucosal capillary network changes and crypt architecture abnormalities can predict relapse in patients with an ulcerative colitis Mayo endoscopic score of 1.

OBJECTIVES: Recent studies have suggested the necessity of therapeutic intervention for patients with ulcerative colitis at high risk of clinical relapse with a Mayo endoscopic score (MES) of 1. The aim of this retrospective cohort study was to demonstrate the impact of intramucosal capillary network changes and crypt architecture abnormalities to stratify the risk of relapse in patients with an MES of 1. METHODS: All included patients had an MES of ≤1 and confirmed sustained clinical remission between October 2016 and April 2019. We classified patients with an MES of 1 as "intramucosal capillary/crypt (ICC)-active" or "ICC-inactive" using endocytoscopic evaluation. We followed patients until October 2019 or until relapse; the main outcome measure was the difference in clinical relapse-free rates between ICC-active and ICC-inactive patients with an MES of 1. RESULTS: We included 224 patients and analyzed data for 218 (82 ICC-active and 54 ICC-active with an MES of 1 and 82 with an MES of 0). During follow-up, among the patients with an MES of 1, 30.5% (95% confidence interval 20.8-41.6; 25/82) of the patients relapsed in the ICC-active group and 5.6% (95% confidence interval 1.2-15.4; 3/54) of the patients relapsed in the ICC-inactive group. The ICC-inactive group had a significantly higher clinical relapse-free rate compared with the ICC-active group (P < 0.01). CONCLUSIONS: In vivo intramucosal capillary network and crypt architecture patterns stratified the risk of clinical relapse in patients with an MES of 1 (UMIN 000032580; UMIN 000036359).

Oncogenic splicing abnormalities induced by DEAD-Box Helicase 56 amplification in colorectal cancer.

Alternative splicing, regulated by DEAD-Box Helicase (DDX) families, plays an important role in cancer. However, the relationship between the DDX family and cancer has not been fully elucidated. In the present study, we identified a candidate oncogene DDX56 on Ch.7p by a bioinformatics approach using The Cancer Genome Atlas (TCGA) dataset of colorectal cancer (CRC). DDX56 expression was measured by RT-qPCR and immunochemical staining in 108 CRC patients. Clinicopathological and survival analyses were carried out using three CRC datasets. Biological roles of DDX56 were explored by gene set enrichment analysis (GSEA), and cell proliferation in vitro and in vivo, cell cycle assays, and using DDX56-knockdown or overexpressed CRC cells. RNA sequencing was carried out to elucidate the effect of DDX56 on mRNA splicing. We found that DDX56 expression was positively correlated with the amplification of DDX56 and was upregulated in CRC cells. High DDX56 expression was associated with lymphatic invasion and distant metastasis and was an independent poor prognostic factor. In vitro analysis, in vivo analysis and GSEA showed that DDX56 promoted proliferation ability through regulating the cell cycle. DDX56 knockdown reduced intron retention and tumor suppressor WEE1 expression, which functions as a G2-M DNA damage checkpoint. We have identified DDX56 as a novel oncogene and prognostic biomarker of CRC that promotes alternative splicing of WEE1.

Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy: A Prospective Study.

Background: Computer-aided diagnosis (CAD) for colonoscopy may help endoscopists distinguish neoplastic polyps (adenomas) requiring resection from nonneoplastic polyps not requiring resection, potentially reducing cost. Objective: To evaluate the performance of real-time CAD with endocytoscopes (×520 ultramagnifying colonoscopes providing microvascular and cellular visualization of colorectal polyps after application of the narrow-band imaging [NBI] and methylene blue staining modes, respectively). Design: Single-group, open-label, prospective study. (UMIN [University hospital Medical Information Network] Clinical Trial Registry: UMIN000027360). Setting: University hospital. Participants: 791 consecutive patients undergoing colonoscopy and 23 endoscopists. Intervention: Real-time use of CAD during colonoscopy. Measurements: CAD-predicted pathology (neoplastic or nonneoplastic) of detected diminutive polyps (≤5 mm) on the basis of real-time outputs compared with pathologic diagnosis of the resected specimen (gold standard). The primary end point was whether CAD with the stained mode produced a negative predictive value (NPV) of 90% or greater for identifying diminutive rectosigmoid adenomas, the threshold required to "diagnose-and-leave" nonneoplastic polyps. Best- and worst-case scenarios assumed that polyps lacking either CAD diagnosis or pathology were true- or false-positive or true- or false-negative, respectively. Results: Overall, 466 diminutive (including 250 rectosigmoid) polyps from 325 patients were assessed by CAD, with a pathologic prediction rate of 98.1% (457 of 466). The NPVs of CAD for diminutive rectosigmoid adenomas were 96.4% (95% CI, 91.8% to 98.8%) (best-case scenario) and 93.7% (CI, 88.3% to 97.1%) (worst-case scenario) with stained mode and 96.5% (CI, 92.1% to 98.9%) (best-case scenario) and 95.2% (CI, 90.3% to 98.0%) (worst-case scenario) with NBI. Limitation: Two thirds of the colonoscopies were conducted by experts who had each experienced more than 200 endocytoscopies; 186 polyps not assessed by CAD were excluded. Conclusion: Real-time CAD can achieve the performance level required for a diagnose-and-leave strategy for diminutive, nonneoplastic rectosigmoid polyps. Primary Funding Source: Japan Society for the Promotion of Science.

Identification of UHRF2 as a Negative Regulator of Epithelial-Mesenchymal Transition and Its Clinical Significance in Esophageal Squamous Cell Carcinoma.

OBJECTIVE: The involvement of epithelial-mesenchymal transition (EMT) in esophageal squamous cell carcinoma (ESCC) has not been fully elucidated. Here, we aimed to identify EMT-related genes associated with TGF-ß in ESCC and to clarify the role of these genes in the progression of ESCC. METHODS: EMT-related genes associated with TGF-ß expression were identified in patients with ESCC using microarray analysis and public datasets. The effects of ubiquitin-like with PHD and ring finger domains 2 (UHRF2) expression were analyzed in ESCC cell lines. Cell proliferation and invasion were measured using MTT and invasion assays, respectively. UHRF2 mRNA expression was also analyzed in 75 ESCC specimens to determine the clinical significance of UHRF2 in ESCC. RESULTS: Treatment of ESCC cell lines with TGF-ß increased UHRF2 expression. UHRF2 overexpression increased CDH1 (E-cadherin) expression and decreased invasive capacity. The 75 ESCC specimens were divided into the UHRF2 high-expression group (n = 61) and the UHRF2 low-expression group (n = 14). Low UHRF2 expression was significantly correlated with vascular invasion (p = 0.034) and was an independent prognostic factor for poor prognosis (p = 0.005). CONCLUSION: UHRF2 may be a negative regulator of EMT and a novel prognostic biomarker for ESCC.

Attenuated RND1 Expression Confers Malignant Phenotype and Predicts Poor Prognosis in Hepatocellular Carcinoma.

BACKGROUND: The RND1 gene encodes a protein that belongs to the Rho GTPase family, which regulates various cellular functions. Depletion of RND1 expression activates the oncogenic Ras signaling pathway. In this study, we aimed to clarify the clinical significance of RND1 expression in predicting prognosis and to investigate its biological role in human hepatocellular carcinoma (HCC). METHODS: The association between RND1 expression and clinical outcomes in patients with HCC was analyzed in three independent cohorts: 120 cases resected in our hospital; 370 cases in The Cancer Genome Atlas (TCGA); and 242 cases in GSE14520. Gene set enrichment analysis (GSEA) was also conducted. Finally, knockdown experiments were performed using small interfering RNA (siRNA) in vitro. RESULTS: In all cohorts, RND1 expression was decreased as cancer progressed, and was affected by promoter methylation. In our HCC cases, the 5-year overall survival (OS) and recurrence-free survival of patients with low RND1 expression was significantly poorer than those of patients with high RND1 expression. TCGA and GSE14520 analyses provided similar results for OS. Multivariate analysis indicated that RND1 expression was an independent prognostic factor for OS in all three cohorts. Additionally, GSEA showed an inverse correlation between RND1 expression and the Ras signaling activity. In vitro, knockdown of RND1 expression resulted in significant increases in proliferation, invasion, and chemoresistance to cisplatin in HCC cells. CONCLUSIONS: Reduced RND1 expression in HCC was associated with cancer progression, likely through regulation of the Ras signaling pathway, and may serve as a novel clinical biomarker for predicting prognosis in patients with HCC.

Diagnosis of sessile serrated adenomas/polyps using endocytoscopy (with videos).

Sessile serrated adenomas/polyps (SSA/P) are considered to be precursors of colorectal cancers. They therefore need to be distinguished from hyperplastic polyps, and should be treated similarly to adenomas. Various endoscopic classifications for discriminating SSA/P have recently been proposed and validated, including the 'Type II-O' pit pattern in magnifying chromoendoscopy and the 'varicose microvascular vessel' in narrow-band imaging. However, there is currently no diagnostic consensus on the endoscopic appearance of SSA/P. Endocytoscopy (EC) is an emerging modality with diagnostic potential for SSA/P. EC is a type of a contact light microscopy, which allows in vivo visualization of cells and nuclei facilitating precise, real-time pathological prediction. SSA/P show oval gland lumens with small round nuclei in EC, indirectly reflecting the pathological features. EC has shown a sensitivity of 83.3% and a specificity of 97.8% for the diagnosis of SSA/P. EC is also a promising tool for the diagnosis of SSA/P with cytological dysplasia because of its ability to detect morphological changes in nuclei, which is the most important factor determining the presence of dysplasia in the lesion. However, clinical data validating the usefulness of EC are lacking, and further studies are required.

Mechanisms and clinical uses of miRNAs in colorectal cancer.

MicroRNAs(miRNAs) are key regulators involved in various tumors. Recently, it has become clear that aberrant miRNA expression has a functional role in the initiation and progression of colorectal cancer (CRC). Distinct miRNA expression profiles have cancer- specific patterns and are associated with diagnosis, prognosis and therapeutic outcome in CRC. Many studies have shown that the miRNA play an important role in carcinogenesis, invasion and metastasis by regulating multiple target gene expressions and should be early detection biomarkers and therapeutic targets for CRC. Here we review the mechanisms and clinical uses of representative miRNAs in colorectal cancer.

Novel computer-aided diagnostic system for colorectal lesions by using endocytoscopy (with videos).

BACKGROUND: Endocytoscopy enables in vivo observation of nuclei at 450× magnification during GI endoscopy, thus allowing precise prediction of lesion pathology. However, because it requires training and experience, it may be beneficial only when performed by expert endoscopists. OBJECTIVE: To develop and evaluate a novel computer-aided diagnosis system for endocytoscopic imaging (EC-CAD) of colorectal lesions. DESIGN: Pilot study. SETTING: University hospital. PATIENTS: One hundred fifty-two patients with small colorectal polyps (≤10 mm) who had undergone endocytoscopy. INTERVENTION: Test sets of white-light endoscopic images and endocytoscopic images from 176 small colorectal polyps (137 neoplastic and 39 non-neoplastic polyps) were assessed by EC-CAD, 2 expert endoscopists, and 2 trainee endoscopists. MAIN OUTCOME MEASUREMENT: Sensitivity, specificity, and accuracy in predicting neoplastic change by EC-CAD comparing expert and trainee endoscopists. RESULTS: EC-CAD had a sensitivity of 92.0% and an accuracy of 89.2%; these were comparable to those achieved by expert endoscopists (92.7% and 92.3%; P = .868 and .256, respectively) and significantly higher than those achieved by trainee endoscopists (81.8% and 80.4%; P < .001 and .002, respectively). EC-CAD achieved a specificity of 79.5%; this did not differ significantly from that achieved by the experts and trainees. EC-CAD also enabled instant diagnosis, taking only 0.3 seconds for each lesion with perfect reproducibility. LIMITATIONS: No sample size calculation. CONCLUSIONS: EC-CAD provides fully automated instant classification of colorectal polyps with excellent sensitivity, accuracy, and objectivity. Thus, it can be a powerful tool for facilitating decision making during routine colonoscopy.

Efficiency of endocytoscopy in differentiating types of serrated polyps.

BACKGROUND: Accurate endoscopic criteria are needed to differentiate serrated polyps, including hyperplastic polyp (HP), sessile serrated adenoma/polyp (SSA/P), and traditional serrated adenoma (TSA), because some are precursors of colorectal cancers. OBJECTIVE: To determine the endocytoscopic features of each type of serrated polyp, especially the shapes of lumens and nuclei. DESIGN: Retrospective study. SETTING: Single, tertiary-care referral center. PATIENTS: Patients who underwent removal of serrated polyps from May 2005 to December 2012. INTERVENTION: Endocytoscopy was performed. Endocytoscopic images were evaluated by assessing the shapes of the lumens and nuclei of the target lesions. MAIN OUTCOME MEASUREMENTS: The significant endocytoscopic features in differentiating among types of serrated polyps. RESULTS: Of the 58 eligible lesions, 27 were classified as HP, 12 as SSA/P, and 19 as TSA. Most HPs (77.8%) had star-like lumens, and most SSA/Ps (83.3%) had oval lumens. The lumens of TSAs were serrated (31.6%) or villous (68.4%), with both shapes seen only in TSAs. Most HPs (92.6%) and SSA/Ps (75.0%) had small, round nuclei, and all TSAs had fusiform nuclei. Features significantly differentiating TSAs from HPs and SSA/Ps were the presence of fusiform nuclei (P < .001) and villous (P < .001) and serrated (P = .002) lumens. The presence of oval lumens was significantly characteristic of SSA/Ps (P < .001), and the presence of star-like lumens was significantly characteristic of HPs (P < .001). LIMITATIONS: Retrospective design. Single-center study. CONCLUSION: The shape of lumens and nuclei on endocytoscopy can efficiently differentiate among the different types of serrated polyps. ( CLINICAL TRIAL REGISTRATION NUMBER: UMIN Clinical Trials Registry UMIN000007850.).

Artificial intelligence-assisted video colonoscopy for disease monitoring of ulcerative colitis: A prospective study.

BACKGROUNDS AND AIMS: The Mayo endoscopic subscore (MES) is the most popular endoscopic disease activity measure of ulcerative colitis (UC). Artificial intelligence (AI)-assisted colonoscopy is expected to reduce diagnostic variability among endoscopists. However, no study has been conducted to ascertain whether AI-based MES assignments can help predict clinical relapse, nor has AI been verified to improve the diagnostic performance of non-specialists. METHODS: This open-label, prospective cohort study enrolled 110 patients with UC in clinical remission. The AI algorithm was developed using 74713 images from 898 patients who underwent colonoscopy at three centers. Patients were followed up after colonoscopy for 12 months, and clinical relapse was defined as a partial Mayo score >2. A multi-video, multi-reader analysis involving 124 videos was conducted to determine whether the AI system reduced the diagnostic variability among six non-specialists. RESULTS: The clinical relapse rate for patients with AI-based MES = 1 (24.5% [12/49]) was significantly higher (log-rank test, P = 0.01) than that for patients with AI-based MES = 0 (3.2% [1/31]). Relapse occurred during the 12-month follow-up period in 16.2% (13/80) of patients with AI-based MES = 0 or 1 and 50.0% (10/20) of those with AI-based MES = 2 or 3 (log-rank test, P = 0.03). Using AI resulted in better inter- and intra-observer reproducibility than endoscopists alone. CONCLUSIONS: Colonoscopy using the AI-based MES system can stratify the risk of clinical relapse in patients with UC and improve the diagnostic performance of non-specialists.