Accuracy and inter-observer agreement of the nice and kudo classifications of superficial colonic lesions: a comparative study

A Semi-Supervised Learning Framework for Classifying Colorectal Neoplasia Based on the NICE Classification

Labelling medical images is an arduous and costly task that necessitates clinical expertise and large numbers of qualified images. Insufficient samples can lead to underfitting during training and poor performance of supervised learning models. In this study, we aim to develop a SimCLR-based semi-supervised learning framework to classify colorectal neoplasia based on the NICE classification. First, the proposed framework was trained under self-supervised learning using a large unlabelled dataset; subsequently, it was fine-tuned on a limited labelled dataset based on the NICE classification. The model was evaluated on an independent dataset and compared with models based on supervised transfer learning and endoscopists using accuracy, Matthew's correlation coefficient (MCC), and Cohen's kappa. Finally, Grad-CAM and t-SNE were applied to visualize the models' interpretations. A ResNet-backboned SimCLR model (accuracy of 0.908, MCC of 0.862, and Cohen's kappa of 0.896) outperformed supervised transfer learning-based models (means: 0.803, 0.698, and 0.742) and junior endoscopists (0.816, 0.724, and 0.863), while performing only slightly worse than senior endoscopists (0.916, 0.875, and 0.944). Moreover, t-SNE showed a better clustering of ternary samples through self-supervised learning in SimCLR than through supervised transfer learning. Compared with traditional supervised learning, semi-supervised learning enables deep learning models to achieve improved performance with limited labelled endoscopic images.

Colon polyps: updates in classification and management

PURPOSE OF REVIEW

Colon polyps are potential precursors to colorectal cancer (CRC), which remains one of the most common causes of cancer-associated death. The proper identification and management of these colorectal polyps is an important quality measure for colonoscopy outcomes. Here, we review colon polyp epidemiology, their natural history, and updates in endoscopic classification and management.

RECENT FINDINGS

Colon polyps that form from not only the adenoma, but also the serrated polyp pathway have significant risk for future progression to CRC. Therefore, correct identification and management of sessile serrated lesions can improve the quality of screening colonoscopy. Malignant polyp recognition continues to be heavily reliant on well established endoscopic classification systems and plays an important role in intraprocedural management decisions. Hot snare remains the gold standard for pedunculated polyp resection. Nonpedunculated noninvasive lesions can be effectively removed by large forceps if diminutive, but cold snare is preferred for colon polyps 3-20 mm in diameter. Larger lesions at least 20 mm require endoscopic mucosal resection. Polyps with the endoscopic appearance of submucosal invasion require surgical referral or advanced endoscopic resection in select cases. Advances in artificial intelligence may revolutionize endoscopic polyp classification and improve both patient and cost-related outcomes of colonoscopy.

SUMMARY

Clinicians should be aware of the most recent updates in colon polyp classification and management to provide the best care to their patients initiating screening colonoscopy.

Real-time, computer-aided, detection-assisted colonoscopy eliminates differences in adenoma detection rate between trainee and experienced endoscopists

Background and study aims  Adenoma detection rate (ADR) is a well-accepted quality indicator of screening colonoscopy. In recent years, the added value of artificial intelligence (AI) has been demonstrated in terms of ADR and adenoma miss rate (AMR). To date, there are no studies evaluating the impact of AI on the performance of trainee endoscopists (TEs). This study aimed to assess whether AI might eliminate any difference in ADR or AMR between TEs and experienced endoscopists (EEs). Patients and methods  We performed a prospective observational study in 45 subjects referred for screening colonoscopy. A same-day tandem examination was carried out for each patient by a TE with the AI assistance and subsequently by an EE unaware of the lesions detected by the TE. Besides ADR and AMR, we also calculated for each subgroup of endoscopists the adenoma per colonoscopy (APC), polyp detection rate (PDR), polyp per colonoscopy (PPC) and polyp miss rate (PMR). Subgroup analyses according to size, morphology, and site were also performed. Results  ADR, APC, PDR, and PPC of AI-supported TEs were 38 %, 0.93, 62 %, 1.93, respectively. The corresponding parameters for EEs were 40 %, 1.07, 58 %, 2.22. No significant difference was found for each analysis between the two groups ( P  > 0.05). AMR and PMR for AI-assisted TEs were 12.5 % and 13 %, respectively. Sub-analyses did not show any significant difference ( P  > 0.05) between the two categories of operators. Conclusions  In this single-center prospective study, the possible impact of AI on endoscopist quality training was demonstrated. In the future, this could result in better efficacy of screening colonoscopy by reducing the incidence of interval or missed cancers.

Utility of the narrow-band imaging international colorectal endoscopic classification for optical diagnosis of colorectal polyp histology in clinical practice: a retrospective study

BACKGROUND

Narrow-band imaging (NBI) highlights the surface structures and vessels of colorectal polyps and is useful for determining the polyp histology. The narrow-band imaging international colorectal endoscopic (NICE) classification is a diagnostic tool for determining colorectal polyp histology based on NBI without optical magnification. In this study, we aimed to investigate the value of each type of the NICE classification for determining colorectal polyp histology using endoscopy data accumulated in a clinical setting.

METHODS

Endoscopy data for 534 colorectal polyps (316 patients) treated at our facility were retrospectively analyzed. First, we investigated the diagnostic performance of each type of the NICE classification for the optical diagnosis of colorectal polyp histology. The procedures were performed by experienced endoscopists using high-definition colonoscopy without optical magnification. Second, inter-observer and intra-observer agreements were assessed after providing experts and non-experts with a short lecture on the NICE classification. Using 50 fine NBI images of colorectal polyps without optical magnification, the inter-observer and intra-observer agreements between five experts and five non-experts were assessed.

RESULTS

The sensitivity, specificity, and accuracy values were 86.0%, 99.6%, and 98.5% for NICE type 1 lesions; 99.2%, 85.2%, and 97.8% for NICE type 2 lesions; and 81.8%, 99.6%, and 99.3% for NICE type 3 lesions, respectively. The inter-observer and intra-observer agreements ranged from substantial to excellent for both experts and non-experts.

CONCLUSIONS

The NICE classification had good diagnostic ability in terms of determining the polyp histology and demonstrated a high level of reproducibility among experts and non-experts. Thus, the NICE classification is a useful clinical tool that can be used without optical magnification.