CRIS: A Novel Tool Described Using Only Three Self-Explanatory Words Performs Similarly To JNET Amongst Western Experts In The Detection Of Cancer And Determination Of Correct Treatment In Large Non-Pedunculated Colorectal Polyps

Aims The JNET classification can be used to predict large non-pedunculated colorectal polyp (LNPCP) histology and presence/depth of submucosal invasion (SMI). However, Japanese endoscopist accuracies>84% have not been replicated amongst Western experts; for example, in a recent study of European experts JNET accuracy was 55%. Furthermore, European guidelines suggest both low-grade dysplasia (LGD) and high-grade dysplasia (HGD) can be treated using endoscopic mucosal resection, thus there is no clinical consequence to interpreting HGD as JNET 2A. We aimed to compare JNET with CRIS (Colorectal Regular-Irregular Score) for LNPCP histology prediction amongst Western experts using both the original JNET interpretation and a clinically relevant approach.

Methods 32 images of flat LNPCPs under NBI were obtained with matched histopathology data. Each image was rated (anonymous online survey) using JNET and then again (order randomised) using CRIS, a novel score which asks whether the most disordered vascular pattern visible is ‘regular(=CRIS-R), regularly-irregular(=CRIS-RI) or completely-irregular(=CRIS-I)’. No further explanation of JNET or CRIS was offered. Accuracy of JNET and CRIS vs. histopathology was analysed using the original JNET interpretation (JNET 1/2A and CRIS-R=LGD) and a clinically relevant approach (JNET1/2A and CRIS-R can also=HGD). JNET2B & CRIS-RI were matched to HGD & superficial SMI and JNET3 & CRIS-I to deep SMI. Correct treatment was defined as pEMR for LGD or HGD, en-bloc EMR/ESD for HGD or superficial SMI and surgery for deep SMI.

Results 12 experts rated 32 images. 8 low-quality images were excluded from the analysis. The overall accuracy of JNET vs. histopathology using the original interpretation was 69.7% (95% confidence interval [95% CI] 46.0-89.3%) and CRIS 69.6% (95% CI 45.3-88.7%). Expert agreement was similar between JNET (91.3% [95% CI 82.8-98.3%] and CRIS (88.5% [95% CI 78.2-97.3%]) with Κ=0.64 (substantial) for JNET and Κ=0.57 (moderate) for CRIS. Correct treatment was determined in 144/288 (50.0%) observations using JNET and 144 (50.0%) using CRIS. The overall accuracy of JNET vs. histopathology using the clinically relevant approach was 73.9% (95% CI 46.0-89.3%) vs. CRIS 72.3% (95% CI 45.3-88.7%). Similarly the accuracy for JNET 2b and CRIS-RI was 51.7% (95% CI 45.9-57.4%) and 51.0% (45.2-56.7%). The accuracy, sensitivity and specificity of JNET 1/2A changed by 13.2%, -8.0% and 38.0% and CRIS-R by 8.3%, -8.2% and 35.5%. Correct treatment determination increased by 29.5% to 79.5% for JNET and by 27.1% to 77.1% for CRIS. [1] [2] [3] [4]

Conclusions High rates of accuracy can be obtained by Western experts using JNET to predict histology of colorectal LNPCPs. Similar accuracy is obtained using CRIS, a novel score named and described by 3 self-explanatory words suggesting JNET may be able to be simplified for non-experts. Importantly the specificity of both systems can be increased by framing their interpretation versus. histology in a clinically relevant fashion.

Publication History

Article published online:
15 April 2024

• References

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