Yellow enhance mode is useful for distinguishing tissues in endoscopic transnasal surgery: case series with preliminary results

Precise tissue differentiation is vital in neurosurgery, especially during endoscopic endonasal surgery (ETS), where visual information is critical. The Yellow Enhance (YE) mode, a novel image-enhanced endoscopy technology, emphasizes yellow pigments to potentially improve tissue differentiation. This study retrospectively evaluated the efficacy of YE mode in five cases (two primary pituitary neuroendocrine tumors, one recurrent skull base-invasive pituitary neuroendocrine tumor, one pituitary apoplexy, and one recurrent craniopharyngioma) using the Olympus VISERA ELITE III endoscope. Eight experienced neurosurgeons reviewed surgical videos and provided 40 structured evaluations. Statistical analyses (Kruskal-Wallis and Mann-Whitney U tests) compared scores among cases. Gross or near-total resection was achieved in all cases without neurological complications. YE mode improved differentiation between normal pituitary tissue and tumors in 80% of cases, but was less effective in cases like pituitary apoplexy with degenerative changes. Across 40 evaluations, 68% rated YE mode as "useful" or "somewhat useful," while 20% noted limited utility in complex cases, such as recurrent craniopharyngiomas. YE mode shows promise in enhancing visual differentiation during ETS, particularly for normal pituitary tissue, but its utility depends on tissue characteristics. Larger prospective studies are needed to validate these findings and explore broader applications in neurosurgery.

Flexible endoscopy in the visualization of 3D-printed maxillary sinus and clinical application

Background

During postoperative follow-up, the visible range of maxillary sinus (MS) is limited, even combining 0° and 70° rigid endoscopes together. Flexible endoscope has been used in larynx examinations for a long time, but rarely in nasal cavity and sinus. We aimed to evaluate the application values of rigid and flexible endoscopes for visualization of MS.

Methods

We followed up 70 patients with lesions in MS via both rigid and flexible endoscopes. In addition, we used thin-slice CT image of the sinus to create a MS model and divided it into two parts for 3D printing. The inner surface of the 3D-printed sinus was marked with grid papers of the same size (5 mm × 5 mm), then the visual range under rigid endoscopes with different angle and flexible endoscopes was calculated and analyzed.

Results

In clinical follow-up, we found that flexible endoscopy can reach where rigid endoscopy cannot, which is more sensitive than medical imaging. Endoscopes showed the largest observation range of the posterolateral wall, more than half of which can be visualized by 0° endoscope. Almost all of the posterolateral wall can be revealed under 45° endoscope, 70° endoscope and flexible endoscope. The visual range of each wall under flexible endoscope is generally greater than that under rigid endoscopes, especially of the anterior wall, medial wall and inferior wall.

Conclusion

There was obviously overall advantage of using flexible endoscope in postoperative follow-up of MS lesions. Flexible endoscopy can expand the range of observation, and improve the early detection of the recurrent lesion. We recommend flexible endoscope as a routine application.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s00464-022-09410-8.

Videomics of the Upper Aero-Digestive Tract Cancer: Deep Learning Applied to White Light and Narrow Band Imaging for Automatic Segmentation of Endoscopic Images

Introduction

Narrow Band Imaging (NBI) is an endoscopic visualization technique useful for upper aero-digestive tract (UADT) cancer detection and margins evaluation. However, NBI analysis is strongly operator-dependent and requires high expertise, thus limiting its wider implementation. Recently, artificial intelligence (AI) has demonstrated potential for applications in UADT videoendoscopy. Among AI methods, deep learning algorithms, and especially convolutional neural networks (CNNs), are particularly suitable for delineating cancers on videoendoscopy. This study is aimed to develop a CNN for automatic semantic segmentation of UADT cancer on endoscopic images.

Materials and Methods

A dataset of white light and NBI videoframes of laryngeal squamous cell carcinoma (LSCC) was collected and manually annotated. A novel DL segmentation model (SegMENT) was designed. SegMENT relies on DeepLabV3+ CNN architecture, modified using Xception as a backbone and incorporating ensemble features from other CNNs. The performance of SegMENT was compared to state-of-the-art CNNs (UNet, ResUNet, and DeepLabv3). SegMENT was then validated on two external datasets of NBI images of oropharyngeal (OPSCC) and oral cavity SCC (OSCC) obtained from a previously published study. The impact of in-domain transfer learning through an ensemble technique was evaluated on the external datasets.

Results

219 LSCC patients were retrospectively included in the study. A total of 683 videoframes composed the LSCC dataset, while the external validation cohorts of OPSCC and OCSCC contained 116 and 102 images. On the LSCC dataset, SegMENT outperformed the other DL models, obtaining the following median values: 0.68 intersection over union (IoU), 0.81 dice similarity coefficient (DSC), 0.95 recall, 0.78 precision, 0.97 accuracy. For the OCSCC and OPSCC datasets, results were superior compared to previously published data: the median performance metrics were, respectively, improved as follows: DSC=10.3% and 11.9%, recall=15.0% and 5.1%, precision=17.0% and 14.7%, accuracy=4.1% and 10.3%.

Conclusion

SegMENT achieved promising performances, showing that automatic tumor segmentation in endoscopic images is feasible even within the highly heterogeneous and complex UADT environment. SegMENT outperformed the previously published results on the external validation cohorts. The model demonstrated potential for improved detection of early tumors, more precise biopsies, and better selection of resection margins.

Deep Learning for nasopharyngeal Carcinoma Identification Using Both White Light and Narrow-Band Imaging Endoscopy

OBJECTIVES/HYPOTHESIS

To develop a deep-learning-based automatic diagnosis system for identifying nasopharyngeal carcinoma (NPC) from noncancer (inflammation and hyperplasia), using both white light imaging (WLI) and narrow-band imaging (NBI) nasopharyngoscopy images.

STUDY DESIGN

Retrospective study.

METHODS

A total of 4,783 nasopharyngoscopy images (2,898 WLI and 1,885 NBI) of 671 patients were collected and a novel deep convolutional neural network (DCNN) framework was developed named Siamese deep convolutional neural network (S-DCNN), which can simultaneously utilize WLI and NBI images to improve the classification performance. To verify the effectiveness of combining the above-mentioned two modal images for prediction, we compared the proposed S-DCNN with two baseline models, namely DCNN-1 (only considering WLI images) and DCNN-2 (only considering NBI images).

RESULTS

In the threefold cross-validation, an overall accuracy and area under the curve of the three DCNNs achieved 94.9% (95% confidence interval [CI] 93.3%-96.5%) and 0.986 (95% CI 0.982-0.992), 87.0% (95% CI 84.2%-89.7%) and 0.930 (95% CI 0.906-0.961), and 92.8% (95% CI 90.4%-95.3%) and 0.971 (95% CI 0.953-0.992), respectively. The accuracy of S-DCNN is significantly improved compared with DCNN-1 (P-value <.001) and DCNN-2 (P-value = .008).

CONCLUSION

Using the deep-learning technology to automatically diagnose NPC under nasopharyngoscopy can provide valuable reference for NPC screening. Superior performance can be obtained by simultaneously utilizing the multimodal features of NBI image and WLI image of the same patient.

LEVEL OF EVIDENCE

3 Laryngoscope, 2021.

Comparison of Narrowband Imaging and White-Light Endoscopy for Diagnosis and Screening of Nasopharyngeal Cancer

OBJECTIVES

We compared the diagnostic accuracies of narrowband imaging and white-light endoscopy in the detection of nasopharyngeal cancer.

DATA SOURCES

Six databases (PubMed, Cochrane Database, Embase, Web of Science, SCOPUS, and Google Scholar).

REVIEW METHODS

The 6 databases were thoroughly reviewed by 2 authors (working independently) from their dates of inception to December 2019. Nasopharyngeal mucosal or vascular changes detected by narrowband imaging were compared to those detected by white-light endoscopy. The authors extracted true-positive, true-negative, false-positive, and false-negative parameters for each study. Methodological quality was evaluated using the Quality Assessment of Diagnostic Accuracy Studies version 2 tool. The extent of interrater agreement was assessed.

RESULTS

Eighteen prospective or retrospective studies were included. The diagnostic odds ratio of narrowband imaging was 77.560 (95% confidence interval [CI], 37.424-160.739). The area under the summary receiver operating characteristic curve was 0.926. The sensitivity, specificity, and negative predictive value were 0.871 (95% CI, 0.808-0.915), 0.905 (95% CI, 0.816-0.953), and 0.955 (95% CI, 0.906-0.979), respectively. The correlation between sensitivity and the false-positive rate was 0.284, indicating that heterogeneity was absent. Narrowband imaging exhibited moderate interrater reliability (0.7037; 95% CI, 0.6558-0.746). Subgroup analysis showed that vascular patterns revealed by endoscopy in a screened subgroup were significantly more diagnostically accurate than mucosal patterns used for surveillance of a recurrent cancer subgroup.

CONCLUSIONS

Narrowband imaging exhibits high diagnostic accuracy and should be used in the diagnostic workup of nasopharyngeal cancer. However, further studies are necessary to confirm our results.

The value of narrowband imaging using the Ni classification in the diagnosis of laryngeal cancer

INTRODUCTION

Narrowband imaging (NBI) is a special endoscopic optical enhancement setting allowing better visualization of mucosal microvasculature compared to white light endoscopy. This study evaluates the validity of NBI using the Ni classification in the detection and differentiation of severe dysplasia (SD) and glottic squamous cell carcinoma (SCC).

METHODS

Patients with suspicious vocal cord lesions underwent conventional white light endoscopy followed by clinically indicated biopsy. At the same time, NBI images were obtained and graded independently. Lesions were graded from I to V according to the Ni classification and compared to histopathological findings.

RESULTS

Fifty-two patients were included in this study (40 SCC and 12 SD). The sensitivity and specificity of NBI in diagnosing laryngeal cancer was 95.0% (CI, 83.9%-99.4%) and 83.3% (CI, 51.6%-97.9%), respectively. The negative likelihood ratio was 0.06. Higher Ni grades correlated very strongly with more advanced disease.

CONCLUSIONS

NBI using the Ni classification is a sensitive diagnostic tool for the detection and differentiation of early neoplastic and preneoplastic glottic lesions. As higher Ni classification correlates strongly with advanced disease, it serves as a useful adjunct to white light endoscopy in the diagnosis of laryngeal cancer.Level of Evidence: Level IV.