[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100640413":3},{"organization":4,"armGroups":7,"interventions":19,"overallOfficials":28,"centralContacts":32,"locations":38,"responsibleParty":51,"collaborators":11,"id":53,"slug":54,"hasResults":55,"nctId":56,"briefTitle":57,"officialTitle":57,"acronym":58,"eligibilityCriteria":59,"healthyVolunteers":55,"sex":60,"minAge":61,"maxAge":11,"enrollmentInfo":62,"targetDuration":11,"studyType":65,"phases":66,"briefSummary":68,"conditions":69,"keywords":71,"overallStatus":76,"whyStopped":11,"lastUpdateSubmitDate":77,"lastUpdatePostDateStruct":78,"startDateStruct":81,"completionDateStruct":83,"leadSponsor":85,"locationsCount":86},{"fullName":5,"class":6},"University Hospital, Ghent","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"JNET Classification Training (Active Comparator)","ACTIVE_COMPARATOR",null,[13],"Other: Learning tool (JNET\u002FCRIS)",{"label":15,"type":16,"description":11,"interventionNames":17},"CRIS Classification Training (Experimental)","EXPERIMENTAL",[18],"Other: Learning tool (CRIS\u002FJNET)",[20,24],{"type":6,"name":21,"description":22,"armGroupLabels":23,"otherNames":11},"Learning tool (CRIS\u002FJNET)","Participants will follow a 5-minute learning video (intervention) on CRIS and later for JNET.",[15],{"type":6,"name":25,"description":26,"armGroupLabels":27,"otherNames":11},"Learning tool (JNET\u002FCRIS)","Participants will follow a 5-minute learning video (intervention) on JNET and later for CRIS.",[9],[29],{"name":30,"affiliation":5,"role":31},"David Tate","PRINCIPAL_INVESTIGATOR",[33],{"name":34,"role":35,"phone":36,"phoneExt":11,"email":37},"David J Tate","CONTACT","+3293321063","StudiesTissue.Resectie@uzgent.be",[39],{"facility":40,"status":11,"city":41,"state":11,"zip":42,"country":43,"countryCode":44,"cosmosGeoPoint":45,"geoPoint":50,"contacts":11},"UZ Gent","Ghent","9000","Belgium","BE",{"type":46,"coordinates":47},"Point",[48,49],3.71667,51.05,{"lat":49,"lon":48},{"type":52,"investigatorFullName":11,"investigatorTitle":11,"investigatorAffiliation":11,"oldNameTitle":11,"oldOrganization":11},"SPONSOR","100640413","development-of-a-new-simplified-tool-to-predict-lnpcps-histology-and-assess-the-risk-of-submucosal-invasive-cancer-the-colorectal-regular-irregular-score-100640413",false,"NCT07584486","Development of a New Simplified Tool to Predict LNPCPs Histology and Assess the Risk of Submucosal Invasive Cancer. The Colorectal Regular-Irregular Score","CRIS","Inclusion Criteria:\n\n* Consenting Endoscopists of varying abilities and grades (endoscopist)\n* Endoscopists who did not previously encounter the score (endoscopist)\n* LNPCPs detected or referred for resection (patient)\n\nExclusion Criteria:\n\n* Endoscopist does not consent to inclusion (endoscopist)\n* Video of inadequate quality as per opinion of the principal investigator\n* Endoscopist does not undergo learning intervention (endoscopist)\n* Patient does not consent to data collection for the study (patient)","ALL","18 Years",{"count":63,"type":64},764,"ESTIMATED","INTERVENTIONAL",[67],"NA","Colorectal cancer (CRC) is the third most common malignancy worldwide and the second leading cause of cancer related death. It can be prevented by endoscopic detection and complete resection of colorectal polyps. The JNET (Japanese NBI Expert Team) classification is clinically useful to predict the histology of large non-pedunculated colorectal polyps (LNPCPs) using narrow-band imaging at endoscopy. Japanese experts can reliably predict histology including the presence and depth of submucosal invasive cancer (SMI) using JNET with accuracy \\>87%. On the other hand, the International Evaluation of Endoscopy classification-JNET (IEE-JNET) group demonstrated that ESGE and JGES endoscopists had sufficient accuracy for JNET 1 (93.0%) but insufficient accuracy for JNET 2A\u002FB and 3 (respectively 62.1%, 55.1% and 85.1%). Reliably distinguishing between JNET 2A, 2B and 3 has a profound clinical relevance, since JNET 2A lesions can safely be resected using pEMR whereas JNET 2B lesions should be resected en-bloc (EMR or ESD) due to the increased risk of cancer and JNET 3 lesions are preferably treated with surgery due to the high risk of deeply invasive carcinoma and the necessity of lymph node resection.\n\nThis study aims to validate a new simplified score, the Colorectal Regular-Irregular Score (CRIS) to fulfill the urgent need for a more effective and easier to use tool to predict LNPCPs histology. CRIS is a simplification of the JNET score which is mainly used by Japanese endoscopists or experts, recent evidence suggests its accuracy when used in everyday endoscopy in the Western world is insufficient. The investigators aim to compare JNET with CRIS for LNPCPs histology prediction amongst Western endoscopists using both original JNET interpretation and a clinically relevant approach.\n\nThe study consists of three work packages (WPs):\n\nWork package one involves an expert online study where twelve expert endoscopists will evaluate 32 high-quality images of colorectal polyps using both JNET and CRIS classifications. Work package two involves an image\u002Fvideo-based online study where non-expert participants will be randomly assigned to rate images and videos using either JNET or CRIS, with performance re-evaluated after three months. Work package three involves a clinical study in a live endoscopy environment where non-expert endoscopists will participate in a randomized controlled trial assessing 10 colorectal polyps using either JNET or CRIS.",[70],"Colorectal Cancer",[72,73,74,75],"Colorectal cancer","Endoscopic imaging","colorectal polyps","endoscopist","NOT_YET_RECRUITING","2026-05-07",{"date":79,"type":80},"2026-05-13","ACTUAL",{"date":82,"type":64},"2026-07-01",{"date":84,"type":64},"2028-12-31",{"name":5,"class":6},1]