Expert-level diagnosis of nasal polyps using deep learning on whole-slide imaging

Advances in Image-Based Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery: A Systematic Review

OBJECTIVE

To update the literature and provide a systematic review of image-based artificial intelligence (AI) applications in otolaryngology, highlight its advances, and propose future challenges.

DATA SOURCES

Web of Science, Embase, PubMed, and Cochrane Library.

REVIEW METHODS

Studies written in English, published between January 2020 and December 2022. Two independent authors screened the search results, extracted data, and assessed studies.

RESULTS

Overall, 686 studies were identified. After screening titles and abstracts, 325 full-text studies were assessed for eligibility, and 78 studies were included in this systematic review. The studies originated from 16 countries. Among these countries, the top 3 were China (n = 29), Korea (n = 8), the United States, and Japan (n = 7 each). The most common area was otology (n = 35), followed by rhinology (n = 20), pharyngology (n = 18), and head and neck surgery (n = 5). Most applications of AI in otology, rhinology, pharyngology, and head and neck surgery mainly included chronic otitis media (n = 9), nasal polyps (n = 4), laryngeal cancer (n = 12), and head and neck squamous cell carcinoma (n = 3), respectively. The overall performance of AI in accuracy, the area under the curve, sensitivity, and specificity were 88.39 ± 9.78%, 91.91 ± 6.70%, 86.93 ± 11.59%, and 88.62 ± 14.03%, respectively.

CONCLUSION

This state-of-the-art review aimed to highlight the increasing applications of image-based AI in otorhinolaryngology head and neck surgery. The following steps will entail multicentre collaboration to ensure data reliability, ongoing optimization of AI algorithms, and integration into real-world clinical practice. Future studies should consider 3-dimensional (3D)-based AI, such as 3D surgical AI.

Machine learning method for the cellular phenotyping of nasal polyps from multicentre tissue scans

BACKGROUND

This study aimed to establish a convenient and accurate chronic rhinosinusitis evaluation platform CRSAI 1.0 according to four phenotypes of nasal polyps.

RESEARCH DESIGN AND METHODS

Tissue sections of a training (n = 54) and test cohort (n = 13) were sourced from the Tongren Hospital, and those for a validation cohort (n = 55) from external hospitals. Redundant tissues were automatically removed by the semantic segmentation algorithm of Unet++ with Efficientnet-B4 as backbone. After independent analysis by two pathologists, four types of inflammatory cells were detected and used to train the CRSAI 1.0. Dataset from Tongren Hospital were used for training and testing, and validation tests used the multicentre dataset.

RESULTS

The mean average precision (mAP) in the training and test cohorts for tissue eosinophil%, neutrophil%, lymphocyte%, and plasma cell% was 0.924, 0.743, 0.854, 0.911 and 0.94, 0.74, 0.839, and 0.881, respectively. The mAP in the validation dataset was consistent with that of the test cohort. The four phenotypes of nasal polyps varied significantly according to the occurrence of asthma or recurrence.

CONCLUSIONS

CRSAI 1.0 can accurately identify various types of inflammatory cells in CRSwNP from multicentre data, which could enable rapid diagnosis and personalized treatment.

A review of current biomarkers in chronic rhinosinusitis with or without nasal polyps

INTRODUCTION

Chronic rhinosinusitis (CRS) is a heterogeneous disease with a variety of cellular and molecular pathophysiologic mechanisms. Biomarkers have been explored in CRS using various phenotypes, such as polyp recurrence after surgery. Recently, the presence of regiotype in CRS with nasal polyps (CRSwNP) and the introduction of biologics for the treatment of CRSwNP has indicated the importance of endotypes, and there is a need to elucidate endotype-based biomarkers.

AREAS COVERED

Biomarkers for eosinophilic CRS, nasal polyps, disease severity, and polyp recurrence have been identified. Additionally, endotypes are being identified for CRSwNP and CRS without nasal polyps using cluster analysis, an unsupervised learning technique.

EXPERT OPINION

Endotypes in CRS have still being established, and biomarkers capable of identifying endotypes of CRS are not yet clear. When identifying endotype-based biomarkers, it is necessary to first identify endotypes clarified by cluster analysis for outcomes. With the application of machine learning, the idea of predicting outcomes using a combination of multiple integrated biomarkers, rather than a single biomarker, will become mainstream.

Endotyping Difficult-to-Treat Chronic Rhinosinusitis with Nasal Polyps by Structured Histopathology

INTRODUCTION

This study aimed to identify the histopathologic characteristics associated with difficult-to-treat chronic rhinosinusitis with nasal polyps (CRSwNPs), enabling physicians to predict the risk of poor outcome after endoscopic sinus surgery (ESS).

METHODS

A prospective cohort study performed at the First Affiliated Hospital of Sun Yat-sen University between January 2015 and December 2018 with CRSwNP patients who underwent ESS. Polyp specimens were collected during surgery and were subjected to structured histopathological evaluation. Difficult-to-treat CRSwNPs were determined at 12-15 months post-operation according to the European Position Paper. Multiple logistic regression model was used to assess the association between histopathological parameters and the difficult-to-treat CRSwNP.

RESULTS

Among 174 subjects included in the analysis, 49 (28.2%) were classified with difficult-to-treat CRSwNP, which had higher numbers of total inflammatory cells, tissue eosinophils, and percentages of eosinophil aggregates and Charcot-Leyden crystals (CLC) formation but a lower number of interstitial glands than the nondifficult-to-treat CRSwNP. Inflammatory cell infiltration (adjusted OR: 1.017), tissue eosinophilia (adjusted OR: 1.005), eosinophil aggregation (adjusted OR: 3.536), and CLC formation (adjusted OR: 6.972) were independently associated with the difficult-to-treat outcome. Furthermore, patients with tissue eosinophil aggregation and CLC formation had an increasingly higher likelihood of uncontrolled disease versus those with tissue eosinophilia.

CONCLUSION

The difficult-to-treat CRSwNP appears to be characterized by increased total inflammatory infiltrates, tissue eosinophilia, eosinophil aggregation, and CLC formation in structured histopathology.

Artificial intelligence, machine learning, and deep learning in rhinology: a systematic review

PURPOSE

This PRISMA-compliant systematic review aims to analyze the existing applications of artificial intelligence (AI), machine learning, and deep learning for rhinological purposes and compare works in terms of data pool size, AI systems, input and outputs, and model reliability.

METHODS

MEDLINE, Embase, Web of Science, Cochrane Library, and ClinicalTrials.gov databases. Search criteria were designed to include all studies published until December 2021 presenting or employing AI for rhinological applications. We selected all original studies specifying AI models reliability. After duplicate removal, abstract and full-text selection, and quality assessment, we reviewed eligible articles for data pool size, AI tools used, input and outputs, and model reliability.

RESULTS

Among 1378 unique citations, 39 studies were deemed eligible. Most studies (n = 29) were technical papers. Input included compiled data, verbal data, and 2D images, while outputs were in most cases dichotomous or selected among nominal classes. The most frequently employed AI tools were support vector machine for compiled data and convolutional neural network for 2D images. Model reliability was variable, but in most cases was reported to be between 80% and 100%.

CONCLUSIONS

AI has vast potential in rhinology, but an inherent lack of accessible code sources does not allow for sharing results and advancing research without reconstructing models from scratch. While data pools do not necessarily represent a problem for model construction, presently available tools appear limited in allowing employment of raw clinical data, thus demanding immense interpretive work prior to the analytic process.

Neutrophils Play an Important Role in the Recurrence of Chronic Rhinosinusitis with Nasal Polyps

Despite the heterogeneity of chronic rhinosinusitis (CRS), a clear link exists between type 2 immunity and the severity of CRS with nasal polyps (CRSwNP). However, recent studies have demonstrated that patients with severe type 2 CRSwNP also display abundant neutrophilic inflammation. Therefore, we investigated the factors associated with the recurrence of CRSwNP following sinus surgery using a machine-learning algorithm. We collected the demographics, clinical variables, and inflammatory profiles of 210 patients with CRSwNP who underwent sinus surgery. After one year, we evaluated whether each patient showed recurrence. Machine-learning methods, such as decision trees, random forests, and support vector machine models, have been used to predict the recurrence of CRSwNP. The results indicated that neutrophil inflammation, such as tissue and serum neutrophils, is an important factor affecting the recurrence of surgical CRSwNP. Specifically, the random forest model showed the highest accuracy in detecting recurrence among the three machine-learning methods, which revealed tissue neutrophilia to be the most important variable in determining surgical outcomes. Therefore, our machine-learning approach suggests that neutrophilic inflammation is increased in patients with difficult-to-treat CRSwNP, and the increased presence of neutrophils in subepithelial regions is closely related to poor surgical outcomes in patients with CRSwNP.

Deep Learning-Based Prediction of Treatment Prognosis from Nasal Polyp Histology Slides

BACKGROUND

Histopathology of nasal polyps contains rich prognostic information, which is difficult to objectively extract. In the present study, we aimed to develop a prognostic indicator of patient outcomes by analyzing scanned conventional haematoxylin and eosin (H&E) -stained slides alone using deep learning.

METHODS

An interpretable supervised deep learning model was developed using 185 H&E-stained whole-slide images (WSIs) of nasal polyps, each from a patient randomly selected from the pool of 232 patients who underwent endoscopic sinus surgery at the First Affiliated Hospital of Sun Yat-sen University (internal cohort). We internally validated the model on a holdout dataset from the internal cohort (47 H&E-stained WSIs) and externally validated the model on 122 H&E-stained WSIs from the Seventh Affiliated Hospital of Sun Yat-sen University and the University of Hong Kong-Shenzhen Hospital (external cohort). A poor prognosis score (PPS) was established to evaluate patient outcomes, and then risk activation mapping was applied to visualize the histopathological features underlying PPS.

RESULTS

The model yielded a patient-level sensitivity of 79.5%, and specificity of 92.3%, with areas under the receiver operating characteristic curve of 0.943, on the multi-center external cohort. The predictive ability of PPS was superior to that of conventional tissue eosinophil number. Notably, eosinophil infiltration, goblet cell hyperplasia, glandular hyperplasia, squamous metaplasia, and fibrin deposition were identified as the main underlying features of PPS.

CONCLUSIONS

Our deep learning model is an effective method for decoding pathological images of nasal polyps, providing a valuable solution for disease prognosis prediction and precise patient treatment. This article is protected by copyright. All rights reserved.

Automated classification of nasal polyps in endoscopy video-frames using handcrafted and CNN features

Nasal polyps are edematous polypoid masses covered by smooth, gray, shiny, soft and gelatinous mucosa. They often pose a threat for the patients to result in allergic rhinitis, sinus infections and asthma. The aim of this paper is to design a reliable rhinology assistance system for recognizing the nasal polyps in endoscopic videos. We introduce NP-80, a novel dataset that contains high-quality endoscopy video-frames of 80 participants with and without nasal polyps (NP). We benchmark vanilla machine learning and deep learning-based classifiers on the proposed dataset with respect to robustness and accuracy. We conduct a series of classification experiments and an exhaustive empirical comparison on handcrafted features (texture features -Local Binary Patterns (LBP) and shape features- Histogram of Oriented Gradients (HOG) and Convolutional Neural Network (CNN) features for recognizing nasal polyps automatically. The classification experiments are carried out by K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT) and CNN classifiers. The best obtained precision, recall, and accuracy rates are 99%, 98%, and 98.3%, respectively. The classifier methods built with handcrafted features have shown poor recognition performance (best accuracy of %96.3) from the proposed CNN classifier (best accuracy of %98.3). The empirical results of the proposed learning techniques on NP-80 dataset are promising to support clinical decision systems. We make our dataset publicly available to encourage further research on rhinology experiments. The major research objective accomplished in this study is the creation of a high-accuracy deep learning based nasal polyps classification model using easily obtainable portable rhino fiberoscope images to be integrated into an otolaryngologist decision support system. We conclude from the research that using appropriate image processing techniques along with suitable deep learning networks allow researchers to obtain high accuracy recommendations in identifying nasal polyps. Furthermore, the results from the study encourages us to develop deep learning models for various other medical conditions.

Low-rank fusion convolutional neural network for prediction of remission after stereotactic radiosurgery in patients with acromegaly: a proof-of-concept study

Artificial intelligence approaches to analyze pathological images (pathomic) for outcome prediction have not been sufficiently considered in the field of pituitary research. A total of 5,504 Hematoxylin & Eosin-stained pathology image tiles from 58 acromegalic patients with a good or poor outcome were integrated with other clinical and genetic information to train a low-rank fusion convolutional neural network (LFCNN). The model was externally validated in 1,536 patches from an external cohort. The primary outcome was the time to the first endocrine remission after SRS. The median time of initial endocrine remission was 43 months [IQR: 13-60 months] after SRS, and the 24-month initial cumulative remission rate was 57.9% [IQR: 46.4-72.3%]. The patient-wise accuracy of the LFCNN model in predicting the primary outcome was 92.9% in the internal test dataset, and the sensitivity and specificity were 87.5% and 100.0%, respectively. The LFCNN model was a strong predictor of initial cumulative remission in the training cohort (HR 9.58, 95% CI 3.89-23.59; p < 0.001) and was higher than that of established prognostic markers. The predictive value of LFCNN model was further validated in an external cohort (HR 9.06, 95% CI 1.14-72.25; p = 0.012). In this proof-of-concept study, clinically and genetically useful prognostic markers were integrated with digital images to predict endocrine outcomes after SRS in patients with active acromegaly. The model considerably outperforms established prognostic markers and can potentially be used by clinicians to improve decision-making regarding adjuvant treatment choices. This article is protected by copyright. All rights reserved.

Chinese Central Compartment Atopic Disease: The Clinical Characteristics and Cellular Endotypes Based on Whole-Slide Imaging

Purpose

Histopathologic characterizations of central compartment atopic disease (CCAD) by whole-slide imaging remains lacking. We aim to study clinical presentations and cellular endotyping diagnosis of Chinese CCAD using artificial intelligence (AI).

Methods

A total of 72 patients diagnosed with chronic rhinosinusitis with nasal polyps (CRSwNP) were enrolled. CCAD was defined by positive result of serology specific IgE, endoscopic and radiological findings. The aeroallergen sensitization status, endoscopic results, radiological findings, and symptoms were evaluated and compared between patients with CCAD (n=14), eosinophilic CRSwNP (ENP, n=32) and non-eosinophilic CRSwNP (NENP, n=26). The cellular endotypes including eosinophils, neutrophils, lymphocytes, and plasma cells were analyzed by the AI chronic rhinosinusitis evaluation platform 2.0.

Results

CCAD was most common in male (71.43%). The positive rate of aeroallergen in patients with CCAD is 100%, which is much higher than those in patients with ENP (40.63%) and NENP (23.08%). Allergic rhinitis incidence was found to be 57.14% in Chinese CCAD subjects, which is obviously higher when compared with those in patients with ENP (21.88%) or NENP (0.00%). The presence of asthma was not significantly different between groups. Chinese CCAD population demonstrated mild symptoms and lower endoscopic and radiological scores than those in patients with ENP and NENP. For cellular endotypes in CCAD subjects, the median of eosinophils, neutrophils, lymphocytes, and plasma cells was 26.55%, 0.49%, 60.85%, and 7.33%, respectively. The proportion of eosinophils in nasal tissue and peripheral blood mononuclear cells from the CCAD group is between the proportions in those patients with ENP and NENP.

Conclusion

Chinese CCAD was associated with aeroallergen sensitivity, and displayed an eosinophil-dominant inflammatory pattern. Thus, proper management with allergy control and topical steroids could be recommended for CCAD treatment.

2021 year in review: Spotlight on eosinophils

This review highlights recent advances in the understanding of eosinophils and eosinophilic diseases, particularly eosinophilic gastrointestinal diseases during the last year. The increasing incidence of diseases marked by eosinophilia has been documented and highlighted the need to understand eosinophil biology and eosinophilic contributions to disease. Significant insight into the nature of eosinophilic diseases has been achieved using next-generation sequencing technologies, proteomic analysis, and machine learning to analyze tissue biopsies. These technologies have elucidated mechanistic underpinnings of eosinophilic inflammation, delineated patient endotypes, and identified patient responses to therapeutic intervention. Importantly, recent clinical studies using mAbs that interfere with type 2 cytokine signaling or deplete eosinophils point to multiple and complex roles of eosinophils in tissues. Several studies identified distinct activation features of eosinophils in different tissues and disease states. The confluence of these studies supports a new paradigm of tissue-resident eosinophils that have pro- and anti-inflammatory immunomodulatory roles in allergic disease. Improved understanding of unique eosinophil activation states is now poised to identify novel therapeutic targets for eosinophilic diseases.

Feasibility of a deep learning-based algorithm for automated detection and classification of nasal polyps and inverted papillomas on nasal endoscopic images

BACKGROUND

Discrimination of nasal cavity mass lesions is a challenging work requiring extensive experience. A deep learning-based automated diagnostic system may help clinicians to classify nasal cavity mass lesions. We demonstrated the feasibility of a convolutional neural network (CNN)-based diagnosis system for automatic detection and classification of nasal polyps (NP) and inverted papillomas (IP).

METHODS

We developed a CNN-based algorithm using a transfer learning strategy and trained it on nasal endoscopic images. A total of 99 nasal endoscopic images with normal findings, 98 images with NP, and 100 images with IP were analyzed using the developed CNN. Six otolaryngologists participated in clinical visual assessment. Image-based classification performance was measured by calculating the accuracy and area under the receiver operating characteristic curve (AUC). The diagnostic performance was compared between the CNN and clinical visual assessment by human experts.

RESULTS

The algorithm achieved an overall accuracy of 0.742 ± 0.058 with the following class accuracies: normal, 0.81± 0.14; IP, 0.57 ± 0.07; and NP, 0.83 ± 0.21. The AUC values for normal, IP, and NP were 0.91 ± 0.06, 0.82 ± 0.09, and 0.84 ± 0.06, respectively. The overall accuracy of the CNN model was comparable with the average performance of human experts (0.742 vs. 0.749; p = 0.11).

CONCLUSIONS

The trained CNN model appears to reliably classify NP and IP of the nasal cavity from nasal endoscopic images; it also yields a reliable reference for diagnosing nasal cavity mass lesions during nasal endoscopy. However, further studies with more test data are warranted to improve the diagnostic accuracy of our CNN model.

Artificial intelligence for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging

Background

artificial intelligence (AI) for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging (WSI) is lacking. We aim to establish an AI chronic rhinosinusitis evaluation platform 2.0 (AICEP 2.0) to obtain the proportion of inflammatory cells for cellular phenotyping diagnosis of nasal polyps and to explore the clinical significance of different phenotypes of nasal polyps on the WSI.

Methods

a total of 453 patients were enrolled in our study. For the development of AICEP 2.0, 179 patients (WSIs) were obtained from the Third Affiliated Hospital of Sun Yat-Sen University (3HSYSU) from January 2008 to December 2018. A total of 24,625 patches were automatically extracted from the regions of interest under a 400× HPF by Openslide and the number of inflammatory cells in these patches was counted by two pathologists. For the application of AICEP 2.0 in a prospective cohort, 158 patients aged 14–70 years old with chronic rhinosinusitis with nasal polyps (CRSwNP) who had undergone endoscopic sinus surgery at 3HSYSU from June 2020 to December 2020 were included for preoperative demographic characteristics. For the application of AICEP 2.0 in a retrospective cohort, 116 patients with CRSwNP who had undergone endoscopic sinus surgery from May 2016 to June 2017 were enrolled for the recurrence rate. The proportion of inflammatory cells of these patients on WSI was calculated by our AICEP 2.0.

Findings

for AICEP 2.0, the mean absolute errors of the ratios of eosinophils, lymphocytes, neutrophils, and plasma cells were 1.64%, 2.13%, 1.06%, and 1.22%, respectively. The four phenotypes of nasal polyps were significantly different in clinical characteristics (including asthma, itching, sneezing, total IgE, peripheral eosinophils%, tissue eosinophils%, tissue neutrophils%, tissue lymphocytes%, tissue plasma cells%, and recurrence rate; P <0.05), but there were no significant differences in age distribution, onset time, total VAS score, Lund-Kennedy score, or Lund-Mackay score. The percentage of peripheral eosinophils was positively correlated with the percentage of tissue eosinophils (r = 0.560, P <0.001) and negatively correlated with tissue lymphocytes% (r = -0.489, P <0.001), tissue neutrophils% (r = -0.225, P = 0.005), and tissue plasma cells% (r = -0.266, P = 0.001) in WSIs.

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

This study presents an up-to-date survey of the use of artificial intelligence (AI) in the field of otorhinolaryngology, considering opportunities, research challenges, and research directions. We searched PubMed, the Cochrane Central Register of Controlled Trials, Embase, and the Web of Science. We initially retrieved 458 articles. The exclusion of non-English publications and duplicates yielded a total of 90 remaining studies. These 90 studies were divided into those analyzing medical images, voice, medical devices, and clinical diagnoses and treatments. Most studies (42.2%, 38/90) used AI for image-based analysis, followed by clinical diagnoses and treatments (24 studies). Each of the remaining two subcategories included 14 studies. Machine learning and deep learning have been extensively applied in the field of otorhinolaryngology. However, the performance of AI models varies and research challenges remain.

Medical data science in rhinology: Background and implications for clinicians

BACKGROUND

An important challenge of big data is using complex information networks to provide useful clinical information. Recently, machine learning, and particularly deep learning, has enabled rapid advances in clinical practice. The application of artificial intelligence (AI) and machine learning (ML) in rhinology is an increasingly relevant topic.

PURPOSE

We review the literature and provide a detailed overview of the recent advances in AI and ML as applied to rhinology. Also, we discuss both the significant benefits of this work as well as the challenges in the implementation and acceptance of these methods for clinical purposes.

METHODS

We aimed to identify and explain published studies on the use of AI and ML in rhinology based on PubMed, Scopus, and Google searches. The search string "nasal OR respiratory AND artificial intelligence OR machine learning" was used. Most of the studies covered areas of paranasal sinuses radiology, including allergic rhinitis, chronic rhinitis, computed tomography scans, and nasal cytology.

RESULTS

Cluster analysis and convolutional neural networks (CNNs) were mainly used in studies related to rhinology. AI is increasingly affecting healthcare research, and ML technology has been used in studies of chronic rhinitis and allergic rhinitis, providing some exciting new research modalities.

CONCLUSION

AI is especially useful when there is no conclusive evidence to aid decision making. ML can help doctors make clinical decisions, but it does not entirely replace doctors. However, when critically evaluating studies using this technique, rhinologists must take into account the limitations of its applications and use.

Objective evaluation of allergic conjunctival disease (with a focus on the application of artificial intelligence technology)

We have summarized the past efforts and results of objective measurement methods for conjunctival hyperemia classification. Severity classification using conjunctival blood vessel occupancy rate, ocular surface temperature analysis, and artificial intelligence have been reported to be clinically useful, as they have been found to correlate with the severity of conjunctival hyperemia by doctors. The AI method using slit lamp microscope images, whose main purpose is to be widely used in daily clinical practice, can be spread all over the world. As a result, it may lay the foundation for clinical research using large amounts of clinical data collected on the same basis without human bias.

Latest advances in chronic rhinosinusitis with nasal polyps endotyping and biomarkers, and their significance for daily practice

The term chronic rhinosinusitis (CRS) comprises of an assortment of diseases that share a common feature: inflammation of the sinonasal mucosa. The phenotype classification of CRS, based on the presence of polyps, has failed to offer a curative treatment for the disease, particularly in refractory cases. Chronic rhinosinusitis with nasal polyps (CRSwNP) remains a challenging entity. Researchers have made efforts trying to characterize subtypes of the disease according to the endotypes, which are delineated by different immunological pathways, using biomarkers. Even if the inflammatory processes controlling CRSwNP are not fully understood, data suggested that the disease associated with a type 2 inflammatory mechanisms can be also linked to the type 1 or type 3 pathomechanism, being highly heterogeneous. Biomarkers for CRSwNP are proposed, such as: eosinophil count, cytokines, metalloproteinases, bitter and sweet taste receptors, and the nasal microbiome. For endotyping to be clinically applicable and simply determined, biomarkers referring to the intrinsic biomolecular mechanism still need to be found. Precision medicine is becoming the new standard of care, but innovative therapies such as biologics may be rather challenging for the clinicians in their daily practice. This new approach to CRSwNP implies patient selection and a simple algorithm for deciding the right treatment, easy to implement and adjust. Our review points out the ongoing new research on the pathophysiology of CRSwNP, biomarkers and treatment opportunities. It allows clinicians to keep abreast of current evidence-based knowledge and to individualize the management of CRSwNP, especially in refractory cases.