Feature-Based Patterns Associated With Japan NBI Expert Team Type 1 Classification of Serrated Colorectal Lesions: A Post Hoc Decision Tree Analysis.

Japan NBI Expert Team (JNET) Type 1 classification is used for optical characterization of colorectal lesions with magnifying NBI, but reported Type 1-related findings may differ between readers when assessing serrated lesions. This small-scale post hoc reader-based study explored reported feature-classification patterns associated with JNET Type 1 selection among serrated colorectal lesions and compared them between expert and non-expert endoscopists. We analyzed de-identified data from two web-based image-interpretation studies using the same colorectal lesion image dataset. Thirteen histologically confirmed serrated lesions (11 sessile serrated lesions [SSLs] and two hyperplastic polyps [HPs]) assessed by 27 expert and 49 non-expert Japanese endoscopists were included. Simplified JNET responses were dichotomized as Type 1 versus non-Type 1. Classification and regression tree analysis was used to visualize associations between this outcome and predefined features, including Type 1-supportive findings and an anti-Type 1 composite variable. Variability was assessed using entropy. In the expert and non-expert core models, the root splits were anti-Type 1 findings and regular dark or white spots, respectively. These root splits were stable in leave-one-reader-out analyses, but the non-expert root split changed to anti-Type 1 findings after excluding unassessable responses. Median entropy was lower among experts than non-experts (0.229 vs. 0.954). This exploratory post hoc decision tree analysis suggested relatively stable expert-associated feature-classification patterns, whereas non-expert patterns were influenced by the treatment of unassessable responses. These findings reflect exploratory associations in Type 1 assignment, not cognitive decision sequences, SSL-HP differentiation, or a validated diagnostic algorithm.