Pathological changes of JNET classification of colorectal tumors / 中华消化内镜杂志

ABSTRACT

Objective:To investigate the pathological changes of JNET classification of colorectal tumors.Methods:Data of patients with colorectal neoplasms who underwent narrow-band imaging combined with magnifying endoscopy, and whose postoperative pathological specimens were obtained at the endoscopy center of the Affiliated Hospital of Guizhou Medical University from January 2015 to June 2018 were analyzed retrospectively. The endoscopic JNET diagnosis and pathological features (surface pit epithelial exfoliation, surface mucosal necrosis structure, surface mucosal ethmoid reticular structure, fibrous tissue reactive hyperplasia, mucosa muscle without residual and carcinomatous interstitial reaction) were analyzed.Results:A total of 81 patients with colorectal neoplasms (diameter >1.5 cm) were enrolled with 74 cases receiving endoscopic treatment and seven cases receiving surgical treatment. The frequency of surface mucosal sieve reticular structure in type 3 of JNET (20.00%) was lower than that in type 2B (42.86%), which was in turn higher than that in type 2A (2.78%), with significant differences ( P<0.05). The frequencies of fibrous tissue reactive proliferation, no residual mucosal muscles, and carcinomatous interstitial reaction in type 2A and 2B were lower than that of type 3, with significant differences (all P<0.05). There were significant differences in the consistency group and inconsistency group of JNET classification and pathological diagnosis in surface pit epithelial exfoliation and surface mucosal necrosis (all P<0.05). Conclusion:Type 2B of JNET classification indicates that the pathological features may be the surface mucosal reticular structure; type 3 indicates reactive hyperplasia of fibrous tissue, no residual mucosal muscles and cancerous interstitial reaction. The exfoliation of the surface pit epithelium and the necrosis of the surface mucosa may be the pathological interference factors affecting the accuracy of JNET classification diagnosis.