Updates in narrow-band imaging for colorectal polyps: Narrow-band imaging generations, detection, diagnosis, and artificial intelligence

The Interpretation of Magnifying Endoscopy for the Diagnosis of Colorectal Lesions

BACKGROUND

Accurate endoscopic diagnosis is crucial for determining the appropriate treatment strategy for colorectal lesions, which may include cold snare polypectomy, endoscopic mucosal resection, or endoscopic submucosal dissection.

SUMMARY

While white light imaging (WLI) serves as the basic and initial method for endoscopic diagnosis, additional techniques such as narrow band imaging (NBI), blue laser/light imaging (BLI), and magnified observation of pit patterns are necessary when WLI results are inconclusive. These advanced diagnostic methods enable precise differentiation of lesions such as adenoma, T1 cancer, and sessile serrated lesion. Furthermore, recent advancements in endoscopic systems have enhanced image clarity and detail, thereby improving diagnostic accuracy.

KEY MESSAGES

This review provides an in-depth discussion on how magnified endoscopy, utilizing the Japan NBI Expert Team (JNET) classification with NBI/BLI and pit pattern classification with chromoendoscopy, aids in the accurate diagnosis of colorectal lesions.

Practical utility of linked color imaging in colonoscopy: Updated literature review

The remarkable recent developments in image-enhanced endoscopy (IEE) have significantly contributed to the advancement of diagnostic techniques. Linked color imaging (LCI) is an IEE technique in which color differences are expanded by processing image data to enhance short-wavelength narrow-band light. This feature of LCI causes reddish areas to appear redder and whitish areas to appear whiter. Because most colorectal lesions, such as neoplastic and inflammatory lesions, have a reddish tone, LCI is an effective tool for identifying colorectal lesions by clarifying the redder areas and distinguishing them from the surrounding normal mucosa. To date, eight randomized controlled trials have been conducted to evaluate the effectiveness of LCI in identifying colorectal adenomatous lesions. The results of a meta-analysis integrating these studies demonstrated that LCI was superior to white-light endoscopy for detecting colorectal adenomatous lesions. LCI also improves the detection of serrated lesions by enhancing their whiteness. Furthermore, accumulating evidence suggests that LCI is superior to white-light endoscopy for the diagnosis of the colonic mucosa in patients with ulcerative colitis. In this review, based on a comprehensive search of the current literature since the implementation of LCI, the utility of LCI in the detection and diagnosis of colorectal lesions is discussed. Additionally, the latest data, including attempts to combine artificial intelligence and LCI, are presented.

Diagnostic Accuracy of Referral Biopsy Compared to Optical Biopsy in Large Non-pedunculated Colorectal Polyps

BACKGROUND

Endoscopic mucosal resection (EMR) of large non-pedunculated colorectal polyps (LNPCPs) offers excellent efficacy and safety. Referral biopsies are commonly obtained prior to EMR despite their potential adverse effects. We aimed to assess the diagnostic accuracy of referral biopsy as compared with optical evaluations and the polyp's final pathology.

METHODS

We analyzed a cohort from two centers that included adult patients referred for EMR of LNPCPs between 2017 and 2022. The NICE classification system was used for optical classification. We compared procedural outcomes of lesions with or without a referral biopsy and diagnostic accuracy of referral biopsy and optical evaluation to the final histopathology of the resected polyp.

RESULTS

Of 605 EMR procedures, 398 (65.8%) had referral biopsies. Polyp size was larger in the biopsy group. No significant differences were observed in en-bloc resection rate, adequate lifting, procedural bleeding, or surveillance recurrence rates. Optical biopsies had higher diagnostic concordance with final histology (Cohen's Kappa 0.62 vs. 0.55) and enhanced sensitivity for sessile serrated polyps/hyperplastic polyps (SSP/HP) and cancer as compared to referral biopsies (0.75 vs. 0.72, p < 0.01, and 0.19 vs. 0.0, p < 0.01, respectively). Optical evaluation was more accurate in non-biopsied lesions.

CONCLUSION

Referral biopsies do not offer additional diagnostic accuracy and may reduce the accuracy of optical evaluation for LNPCPs.

Additional 30-second observation of the right-sided colon for missed polyp detection with linked color imaging compared with narrow band imaging

Background and study aims We previously demonstrated the efficacy of an additional-30-seconds (Add-30s) observation with linked color imaging (LCI) or narrow band imaging (NBI) of the cecum and ascending colon (right-sided colon) after white light imaging (WLI) observation for improving adenoma detection rate (ADR) by 3% to 10%. We herein compared Add-30s LCI with Add-30s NBI in a large number of cases. Patients and methods We retrospectively collected 1023 and 1011 cases with Add-30s LCI and NBI observation for right-sided colon in 11 affiliated institutions from 2018 to 2022 and propensity score matching was performed. Add-30s observation was as follows. First observation: WLI observation of the right-sided colon as first observation. Second observation: Reobservation of right-sided colon by Add-30s LCI or NBI. The comparison of the mean numbers of adenoma+sessile serrated lesions (SSLs) and adenomas per patient (MASP and MUTYH-associated polyposis) were analyzed in the Add-30s LCI/NBI groups. The increase in right-sided ADR was also analyzed in the groups. Results Among 748 matched cases in the Add-30s LCI/NBI groups, the MASP and MAP were 0.18/0.19 ( P = 0.54) and 0.14/0.15 ( P = 0.70). Among experts, they were 0.17/0.22 ( P = 0.16) and 0.15/0.21 ( P = 0.08). Among non-experts, they were 0.13/0.12 ( P = 0.71) and 0.12/0.07 ( P = 0.04). The right-sided ADRs of the first+second observations in the LCI and NBI groups were 32.2% and 28.9% ( P = 0.16) and the increase of ADRs were 7.5% and 7.2% ( P = 0.84). Conclusions In right-sided colon, the detection of adenoma/SSL did not differ between Add-30s LCI and NBI. Both of them significantly increased ADR.

Hyperspectral imaging facilitating resect-and-discard strategy through artificial intelligence-assisted diagnosis of colorectal polyps: A pilot study

BACKGROUND AND AIMS

The resect-and-discard strategy for colorectal polyps based on accurate optical diagnosis remains challenges. Our aim was to investigate the feasibility of hyperspectral imaging (HSI) for identifying colorectal polyp properties and diagnosis of colorectal cancer in fresh tissues during colonoscopy.

METHODS

144,900 two dimensional images generated from 161 hyperspectral images of colorectal polyp tissues were prospectively obtained from patients undergoing colonoscopy. A residual neural network model was trained with transfer learning to automatically differentiate colorectal polyps, validated by histopathologic diagnosis. The diagnostic performances of the HSI-AI model and endoscopists were calculated respectively, and the auxiliary efficiency of the model was evaluated after a 2-week interval.

RESULTS

Quantitative HSI revealed histological differences in colorectal polyps. The HSI-AI model showed considerable efficacy in differentiating nonneoplastic polyps, non-advanced adenomas, and advanced neoplasia in vitro, with sensitivities of 96.0%, 94.0%, and 99.0% and specificities of 99.0%, 99.0%, and 96.5%, respectively. With the assistance of the model, the median negative predictive value of neoplastic polyps increased from 50.0% to 88.2% (p = 0.013) in novices.

CONCLUSION

This study demonstrated the feasibility of using HSI as a diagnostic tool to differentiate neoplastic colorectal polyps in vitro and the potential of AI-assisted diagnosis synchronized with colonoscopy. The tool may improve the diagnostic performance of novices and facilitate the application of resect-and-discard strategy to decrease the cost.

Application of narrow band imaging in the diagnosis of pharyngeal tumors

BACKGROUND

Narrow-band imaging (NBI) endoscopy is used in various tumor detection and is important in detecting early tumors.

OBJECTIVE

To explore the application value of NBI endoscopy in diagnosing pharyngeal tumors.

MATERIAL AND METHODS

Ninety-one patients with pharyngeal masses who attended the Department of Otorhinolaryngology, Head and Neck Surgery in Gansu Provincial Hospital from January 2023 to February 2024 were selected, and NBI and white light (WL) endoscopy were applied to examine the pharynx and the relationship between the two was observed. SPSS 25.0 software was used for statistical analysis.

RESULTS

The sensitivity of NBI endoscopy for diagnosing laryngeal malignant lesions was 92.0 %, the specificity was 93.0 %, the positive predictive value was 88.5 %, and the negative predictive value was 95.2 %, with a high degree of concordance between the results of NBI endoscopy and the pathology; WL endoscopy had a sensitivity of 64.0 %, a specificity of 76. 7 %, a positive predictive value of 61.5 %, and a negative predictive value of 78.6 %, with WL endoscopic findings had moderate concordance with pathology. The diagnostic accuracy of NBI endoscopy was higher than that of WL endoscopy for both benign and malignant lesions and precancerous lesions.

CONCLUSION

NBI endoscopy can detect laryngeal cancer lesions more accurately.

A colonial serrated polyp classification model using white-light ordinary endoscopy images with an artificial intelligence model and TensorFlow chart

In this study, we implemented a combination of data augmentation and artificial intelligence (AI) model-Convolutional Neural Network (CNN)-to help physicians classify colonic polyps into traditional adenoma (TA), sessile serrated adenoma (SSA), and hyperplastic polyp (HP). We collected ordinary endoscopy images under both white and NBI lights. Under white light, we collected 257 images of HP, 423 images of SSA, and 60 images of TA. Under NBI light, were collected 238 images of HP, 284 images of SSA, and 71 images of TA. We implemented the CNN-based artificial intelligence model, Inception V4, to build a classification model for the types of colon polyps. Our final AI classification model with data augmentation process is constructed only with white light images. Our classification prediction accuracy of colon polyp type is 94%, and the discriminability of the model (area under the curve) was 98%. Thus, we can conclude that our model can help physicians distinguish between TA, SSA, and HPs and correctly identify precancerous lesions such as TA and SSA.

The Utility of Narrow-Band Imaging International Colorectal Endoscopic Classification in Predicting the Histologies of Diminutive Colorectal Polyps Using I-Scan Optical Enhancement: A Prospective Study

(1) Background: This study aimed to evaluate the accuracy of predicting the histology of diminutive colonic polyps (DCPs) (≤5 mm) using i-scan optical enhancement (OE) based on the narrow-band imaging international colorectal endoscopic (NICE) classification. The study compared the diagnostic accuracy between experts who were already familiar with the NICE classification and trainees who were not, both before and after receiving brief training on the NICE classification. (2) Method: This prospective, single-center clinical trial was conducted at the Dong-A University Hospital from March 2020 to August 2020 and involved two groups of participants. The first group comprised two experienced endoscopists who were proficient in using i-scan OE and had received formal training in optical diagnosis and dye-less chromoendoscopy (DLC) techniques. The second group consisted of three endoscopists in the process of training in internal medicine at the Dong-A University Hospital. Each endoscopist examined the polyps and evaluated them using the NICE classification through i-scan OE. The results were not among the participants. Trained endoscopists were divided into pre- and post-training groups. (3) Results: During the study, a total of 259 DCPs were assessed using i-scan OE by the two expert endoscopists. They made real-time histological predictions according to the NICE classification criteria. For the trainee group, before training, the area under the receiver operating characteristic curves (AUROCs) for predicting histopathological results using i-scan OE were 0.791, 0.775, and 0.818. However, after receiving training, the AUROCs improved to 0.935, 0.949, and 0.963, which were not significantly different from the results achieved by the expert endoscopists. (4) Conclusions: This study highlights the potential of i-scan OE, along with the NICE classification, in predicting the histopathological results of DCPs during colonoscopy. In addition, this study suggests that even an endoscopist without experience in DLC can effectively use i-scan OE to improve diagnostic performance with only brief training.