Comparison of the diagnostic performance of NBI, Laser-BLI and LED-BLI: a randomized controlled noninferiority trial

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.

Impact of narrow band imaging in prediction of histology of advanced colorectal neoplasia

We assessed the diagnostic performance of the Narrow-Band Imaging (NBI) International Colorectal Endoscopic Classification (NICE) and the Japan NBI Expert Team classification (JNET) in predicting histological outcomes of advanced colorectal lesions. Additionally, we evaluated the sensitivity and positive predictive value (PPV) of the JNET and NICE classifications individually for high-grade lesions (including HGD adenomas, intramucosal carcinomas, and T1 carcinomas). This was a retrospective analysis of prospectively collected data, involving 211 patients (130 men, mean age 60 years) who underwent colonoscopy with endoscopic resection of advanced colorectal neoplasia (lesions ≥ 10 mm). Lesions were classified using both NICE and JNET criteria, and final histopathological results were used for comparison. Of the 257 lesions analyzed, the NICE classification accurately classifies a large proportion of lesions (93.8%). In JNET classification we observed 77.4% correctly classified lesions. Specifically, the sensitivity and positive predictive value (PPV) of the NICE classification for high-grade lesions were 100% and 24.4%, respectively. For the JNET classification, the sensitivity and PPV for high-grade lesions were 56.6% and 57.7%, respectively. The JNET classification, with a positive predictive value of 57.7% for high-grade colorectal lesions (including HGD adenomas, intramucosal carcinomas, and T1 carcinomas), should be used for decision-making regarding appropriate subsequent endoscopic therapy.

Fluorescence excitation-scanning hyperspectral imaging with scalable 2D-3D deep learning framework for colorectal cancer detection

Colorectal cancer is one of the top contributors to cancer-related deaths in the United States, with over 100,000 estimated cases in 2020 and over 50,000 deaths. The most common screening technique is minimally invasive colonoscopy using either reflected white light endoscopy or narrow-band imaging. However, current imaging modalities have only moderate sensitivity and specificity for lesion detection. We have developed a novel fluorescence excitation-scanning hyperspectral imaging (HSI) approach to sample image and spectroscopic data simultaneously on microscope and endoscope platforms for enhanced diagnostic potential. Unfortunately, fluorescence excitation-scanning HSI datasets pose major challenges for data processing, interpretability, and classification due to their high dimensionality. Here, we present an end-to-end scalable Artificial Intelligence (AI) framework built for classification of excitation-scanning HSI microscopy data that provides accurate image classification and interpretability of the AI decision-making process. The developed AI framework is able to perform real-time HSI classification with different speed/classification performance trade-offs by tailoring the dimensionality of the dataset, supporting different dimensions of deep learning models, and varying the architecture of deep learning models. We have also incorporated tools to visualize the exact location of the lesion detected by the AI decision-making process and to provide heatmap-based pixel-by-pixel interpretability. In addition, our deep learning framework provides wavelength-dependent impact as a heatmap, which allows visualization of the contributions of HSI wavelength bands during the AI decision-making process. This framework is well-suited for HSI microscope and endoscope platforms, where real-time analysis and visualization of classification results are required by clinicians.

The Comparison of Diagnostic Ability between Blue Laser/Light Imaging and Narrowband Imaging for Sessile Serrated Lesions with or without Dysplasia

Objectives

Diagnostic ability of sessile serrated lesions (SSL) and SSL with dysplasia (SSLD) using blue laser/light imaging (BLI) has not been well examined. We analyzed the diagnostic accuracy of BLI for SSL and SSLD using several endoscopic findings compared to those of narrow band imaging (NBI).

Materials and Methods

This was a subgroup analysis of prospective studies. 476 suspiciously serrated lesions of ≥2 mm on the proximal colon showing serrated change with magnified NBI or BLI in our institution between 2014 and 2021 were examined histopathologically. After propensity score matching, we evaluated the diagnostic ability of SSL and SSLD of the NBI and BLI groups regarding various endoscopic findings. For WLI findings, granule, depression, and reddish were examined for diagnosing SSLD. For NBI/BLI findings, expanded crypt opening (ECO) or thick and branched vessels (TBV) were examined for diagnosing SSL. Network vessels (NV) and white dendritic change (WDC) defined originally were examined for diagnosing SSLD.

Results

Among matched 176 lesions, the sensitivity of lesions with either ECO or TBV for SSL in the NBI/BLI group was 97.5%/98.5% (p = 0.668). Those with either WDC or NV for diagnosing SSLD in the groups were 81.0%/88.9% (p = 0.667). Regarding the rates of endoscopic findings among 30 SSLD and 290 SSL, there were significant differences in WDC (66.4% vs. 8.6%, p < 0.001), NV (55.3% vs. 1.4%, p < 0.001), and either WDC or NV (86.8% vs. 9.0%, p < 0.001).

Conclusions

The diagnostic ability of BLI for SSL and SSLD was not different from NBI. NV and WDC were useful for diagnosing SSLD.

Linked-color imaging versus high definition white-light endoscopy for evaluation of post-polypectomy scars of nonpedunculated lesions: LCI-Scar study

BACKGROUND

Detection and treatment of recurrence after piecemeal endoscopic mucosal resection of nonpedunculated colorectal polyps are crucial for avoidance of post-colonoscopy cancer. Linked-color imaging (LCI) has demonstrated improved polyp detection but has never been assessed for evaluation of post-polypectomy scars. Our aim was to compare sensitivity and negative predictive value (NPV) between LCI and white-light endoscopy (WLE) for detection of post-polypectomy recurrence.

METHODS

Patients undergoing surveillance colonoscopy after resection of lesions ≥15 mm were included in this prospective, single-center, randomized, crossover study. Each post-polypectomy scar underwent two examinations, one with LCI and the other with WLE, performed by two blinded endoscopists. Blue-light imaging (BLI) was then applied. A diagnosis of recurrence with a level of confidence was made for each modality and histopathology was the gold standard.

RESULTS

129 patients with 173 scars were included. Baseline patient, lesion, and procedural characteristics were similar in both arms. Recurrence was detected in 56/173 (32.4%), with 27/56 (48.2%) adenomas and 29/56 (51.8%) serrated lesions. LCI had greater sensitivity (96.4% [95%CI 87.8%-99.5%]) versus WLE (89.3% [95%CI 78.1%-95.9%]) and greater NPV (98.1% [95%CI 93.4%-99.8%] versus 94.6% [95%CI 88.7%-98.0%]). Paired concordance between modalities was 96.0%. In discordant cases, LCI identified four true-positive cases not detected by WLE and reclassified one false-positive of WLE. WLE reclassified two false positives of LCI without any increase in recurrence detection.

CONCLUSIONS

LCI was highly accurate and had greater ability than WLE to rule out recurrence on post-polypectomy scars after resection of large polyps.

Blue-light imaging or narrow-band imaging for proximal colonic lesions: a prospective randomized tandem colonoscopy study

BACKGROUND AND AIMS

Blue-light imaging (BLI) is a new image-enhanced endoscopy with a wavelength filter similar to narrow-band imaging (NBI). We compared the 2 with white-light imaging (WLI) on proximal colonic lesion detection and miss rates.

METHODS

In this 3-arm prospective randomized study with tandem examination of the proximal colon, we enrolled patients aged ≥40 years. Eligible patients were randomized in 1:1:1 ratio to receive BLI, NBI, or WLI during the first withdrawal from the proximal colon. The second withdrawal was performed using WLI in all patients. Primary outcomes were proximal polyp (pPDRs) and adenoma (pADRs) detection rates. Secondary outcomes were miss rates of proximal lesions found on tandem examination.

RESULTS

Of 901 patients included (mean age, 64.7 years; 52.9% men), 48.1% underwent colonoscopy for screening or surveillance. The corresponding pPDRs of the BLI, NBI, and WLI groups were 45.8%, 41.6, and 36.6%, whereas the corresponding pADRs were 36.6%, 33.8%, and 28.3%. There was a significant difference in pPDR and pADR between BLI and WLI groups (difference, 9.2% [95% confidence interval {CI}, 3.3-16.9] and 8.3% [95% CI, 2.7-15.9]) and between NBI and WLI groups (difference, 5.0% [95% CI, 1.4-12.9] and 5.6% [95% CI, 2.1-13.3]). Proximal adenoma miss rates were significantly lower with BLI (19.4%) than with WLI (27.4%; difference, -8.0%; 95% CI, -15.8 to -.1) but not between NBI (27.2%) and WLI.

CONCLUSIONS

Both BLI and NBI were superior to WLI on detecting proximal colonic lesions, but only BLI had lower proximal adenoma miss rates than WLI. (Clinical trial registration number: NCT03696992.).

Comparison of LED and LASER Colonoscopy About Linked Color Imaging and Blue Laser/Light Imaging of Colorectal Tumors in a Multinational Study

INTRODUCTION

In light-emitting diode (LED) and LASER colonoscopy, linked color imaging (LCI) and blue light/laser imaging (BLI) are used for lesion detection and characterization worldwide. We analyzed the difference of LCI and BLI images of colorectal lesions between LED and LASER in a multinational study.

METHODS

We prospectively observed lesions with white light imaging (WLI), LCI, and BLI using both LED and LASER colonoscopies from January 2020 to August 2021. Images were graded by 27 endoscopists from nine countries using the polyp visibility score: 4 (excellent), 3 (good), 2 (fair), and 1 (poor) and the comparison score (LED better/similar/LASER better) for WLI/LCI/BLI images of each lesion.

RESULTS

Finally, 32 lesions (polyp size: 20.0 ± 15.2 mm) including 9 serrated lesions, 13 adenomas, and 10 T1 cancers were evaluated. The polyp visibility scores of LCI/WLI for international and Japan-expert endoscopists were 3.17 ± 0.73/3.17 ± 0.79 (p = 0.92) and 3.34 ± 0.78/2.84 ± 1.22 (p < 0.01) for LED and 3.30 ± 0.71/3.12 ± 0.77 (p < 0.01) and 3.31 ± 0.82/2.78 ± 1.23 (p < 0.01) for LASER. Regarding the comparison of lesion visibility about between LED and LASER colonoscopy in international endoscopists, a significant difference was achieved not for WLI, but for LCI. The rates of LED better/similar/LASER better for brightness under WLI were 54.5%/31.6%/13.9% (International) and 75.0%/21.9%/3.1% (Japan expert). Those under LCI were 39.2%/35.4%/25.3% (International) and 31.3%/53.1%/15.6% (Japan expert). There were no significant differences in the diagnostic accuracy and the comparison score of BLI images between LED and LASER.

CONCLUSIONS

The differences of lesion visibility for WLI/LCI/BLI between LED and LASER in international endoscopists could be compared to those in Japanese endoscopists.

Detecting colorectal lesions with image-enhanced endoscopy: an updated review from clinical trials

Colonoscopy plays an important role in reducing the incidence and mortality of colorectal cancer by detecting adenomas and other precancerous lesions. Image-enhanced endoscopy (IEE) increases lesion visibility by enhancing the microstructure, blood vessels, and mucosal surface color, resulting in the detection of colorectal lesions. In recent years, various IEE techniques have been used in clinical practice, each with its unique characteristics. Numerous studies have reported the effectiveness of IEE in the detection of colorectal lesions. IEEs can be divided into two broad categories according to the nature of the image: images constructed using narrowband wavelength light, such as narrowband imaging and blue laser imaging/blue light imaging, or color images based on white light, such as linked color imaging, texture and color enhancement imaging, and i-scan. Conversely, artificial intelligence (AI) systems, such as computer-aided diagnosis systems, have recently been developed to assist endoscopists in detecting colorectal lesions during colonoscopy. To better understand the features of each IEE, this review presents the effectiveness of each type of IEE and their combination with AI for colorectal lesion detection by referencing the latest research data.

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

Narrow-band imaging (NBI) is an optical digital enhancement method that allows the observation of vascular and surface structures of colorectal lesions. Its usefulness in the detection and diagnosis of colorectal polyps has been demonstrated in several clinical trials and the diagnostic algorithms have been simplified after the establishment of endoscopic classifications such as the Japan NBI Expert Team classification. However, there were issues including lack of brightness in the earlier models, poor visibility under insufficient bowel preparation, and the incompatibility of magnifying endoscopes in certain endoscopic platforms, which had impeded NBI from becoming standardized globally. Nonetheless, NBI continued its evolution and the newest endoscopic platform launched in 2020 offers significantly brighter and detailed images. Enhanced visualization is expected to improve the detection of polyps while universal compatibility across all scopes including magnifying endoscopy will promote the global standardization of magnifying diagnosis. Therefore, knowledge related to magnifying colonoscopy will become essential as magnification becomes standardly equipped in future models, although the advent of computer-aided diagnosis and detection may greatly assist endoscopists to ensure quality of practice. Given that most endoscopic departments will be using both old and new models, it is important to understand how each generation of endoscopic platforms differ from each other. We reviewed the advances in the endoscopic platforms, artificial intelligence, and evidence related to NBI essential for the next generation of endoscopic practice.