[The value of CT radiomics in predicting treatment outcomes in patients with nasal polyps].

Objective: To evaluate the predictive efficacy of sinus CT radiomics for treatment outcomes in nasal polyp patients undergoing endoscopic sinus surgery. Methods: A retrospective cohort study was conducted at the First Affiliated Hospital of Sun Yat-sen University, including 194 patients with nasal polyps treated between January 2015 and December 2019. The cohort comprised 132 males and 62 females, aged 16 to 75 years. Patients were divided into a training set (n=135) and an internal validation set (n=59). An external validation set (n=34), consisting of 22 males and 12 females aged 16 to 59 years, was included from January 2020 to December 2021. Disease control was evaluated using the criteria from the European Position Paper on Rhinosinusitis and Nasal Polyps 2020 (EPOS 2020). Radiomic features were extracted from sinus CT images and analyzed using the least absolute shrinkage and selection operator (LASSO) regression. Models combining radiomic and clinical features were developed to predict treatment efficacy. Results: The radiomics and combined models, based on four selected features, outperformed the clinical feature model in the training set, with AUC values of 0.901 and 0.915, versus 0.874, respectively. In the internal validation set, AUCs were 0.839, 0.832, and 0.716. Despite reduced AUCs in the external set, the radiomics model maintained good generalizability (0.748, 0.764, 0.620). Decision curve analysis showed significant clinical benefits in both radiomics and combined models. Conclusion: The CT-based radiomics model demonstrates significant predictive power in identifying refractory nasal polyps, suggesting its potential for clinical application in treatment outcome prediction.

Deep learning model for differentiating nasal cavity masses based on nasal endoscopy images.

BACKGROUND: Nasal polyps and inverted papillomas often look similar. Clinically, it is difficult to distinguish the masses by endoscopic examination. Therefore, in this study, we aimed to develop a deep learning algorithm for computer-aided diagnosis of nasal endoscopic images, which may provide a more accurate clinical diagnosis before pathologic confirmation of the nasal masses. METHODS: By performing deep learning of nasal endoscope images, we evaluated our computer-aided diagnosis system's assessment ability for nasal polyps and inverted papilloma and the feasibility of their clinical application. We used curriculum learning pre-trained with patches of nasal endoscopic images and full-sized images. The proposed model's performance for classifying nasal polyps, inverted papilloma, and normal tissue was analyzed using five-fold cross-validation. RESULTS: The normal scores for our best-performing network were 0.9520 for recall, 0.7900 for precision, 0.8648 for F1-score, 0.97 for the area under the curve, and 0.8273 for accuracy. For nasal polyps, the best performance was 0.8162, 0.8496, 0.8409, 0.89, and 0.8273, respectively, for recall, precision, F1-score, area under the curve, and accuracy. Finally, for inverted papilloma, the best performance was obtained for recall, precision, F1-score, area under the curve, and accuracy values of 0.5172, 0.8125, 0.6122, 0.83, and 0.8273, respectively. CONCLUSION: Although there were some misclassifications, the results of gradient-weighted class activation mapping were generally consistent with the areas under the curve determined by otolaryngologists. These results suggest that the convolutional neural network is highly reliable in resolving lesion locations in nasal endoscopic images.

A multi-view fusion lightweight network for CRSwNPs prediction on CT images.

Accurate preoperative differentiation of the chronic rhinosinusitis (CRS) endotype between eosinophilic CRS (eCRS) and non-eosinophilic CRS (non-eCRS) is an important topic in predicting postoperative outcomes and administering personalized treatment. To this end, we have constructed a sinus CT dataset, which comprises CT scan data and pathological biopsy results from 192 patients of chronic rhinosinusitis with nasal polyps (CRSwNP), treated at the Second Affiliated Hospital of Shantou University Medical College between 2020 and 2022. To differentiate CRSwNP endotype on preoperative CT and improve efficiency at the same time, we developed a multi-view fusion model that contains a mini-architecture with each network of 10 layers by modifying the deep residual neural network. The proposed model is trained on a training set and evaluated on a test set. The multi-view deep learning fusion model achieved the area under the receiver-operating characteristics curve (AUC) of 0.991, accuracy of 0.965 and F1-Score of 0.970 in test set. We compared the performance of the mini-architecture with other lightweight networks on the same Sinus CT dataset. The experimental results demonstrate that the developed ResMini architecture contribute to competitive CRSwNP endotype identification modeling in terms of accuracy and parameter number.

Diagnostic accuracy of clinical visual assessment using endoscopic images for nasal cavity mass lesions.

Objectives: Discrimination of nasal cavity lesions using nasal endoscopy is challenging because of the differences in clinical manifestations and treatment strategies. We aimed to investigate the diagnostic accuracy of clinical visual assessment (CVA) of nasal cavity masses using endoscopic images and determine whether there is a difference according to pathologic class and the examiners' experience. Methods: We collected pathologically confirmed endoscopic images of normal findings, nasal polyp (NP), benign tumor, and malignant tumor (each class contained 100 images) randomly selected. Eighteen otolaryngologists, including six junior residents, six senior residents, and six board-certified rhinologists classified the test set images into four classes of lesions by CVA. Diagnostic performance according to the pathologic class and the examiner's experience level was evaluated based on overall accuracy, F1-score, confusion matrix, and area under the receiver operating characteristic curve (AUC). Results: Diagnostic performance was significantly different according to the pathological class of nasal cavity mass lesions with the overall accuracy reported high in the order of normal, NP, benign tumor, and malignant tumor (0.926 ± 0.100; 0.819 ± 0.135; 0.580 ± 0.112; 0.478 ± 0.187, respectively), F1 score (0.937 ± 0.076; 0.730 ± 0.093; 0.549 ± 0.080; 0.554 ± 0.146, respectively) and AUC value (0.96 ± 0.06; 0.84 ± 0.07; 0.70 ± 0.05; 0.71 ± 0.08, respectively). The expert rhinologist group achieved higher overall accuracy than the resident group (0.756 ± 0.157 vs. 0.680 ± 0.239, p < .05). Conclusion: CVA for nasal cavity mass was highly dependent on the pathologic class and examiner's experience. The overall accuracy was reliably high for normal findings, but low in classifying benign and malignant tumors. Differential diagnosis of lesions solely based on nasal endoscopic evaluation is challenging. Therefore, clinicians should consider further clinical evaluation for suspicious cases.

Imaging of chronic rhinosinusitis with nasal polyps in the era of biological therapies.

PURPOSE OF REVIEW: Chronic rhinosinusitis (CRS) is a chronic inflammatory disorder of the sinonasal cavities classified into two major phenotypes: CRS with nasal polyps (CRSwNP) and without nasal polyps (CRSsNP). The diagnosis of CRS is based on clinical symptoms associated with imaging and/or nasal endoscopy findings of mucosal inflammation. RECENT FINDINGS: Recently, novel biological therapies have emerged as therapeutic options for CRSwNP. Imaging is helpful in deciding whether surgery is likely to be beneficial and in guiding surgery. It can also help demonstrate a clinical response to medical therapy. However, specific guidelines concerning the role of imaging in CRwNP are lacking. SUMMARY: This article provides a comprehensive and critical multidisciplinary review of the role of conventional radiology, computed tomography (CT), and magnetic resonance imaging (MRI) in the diagnosis and characterization of CRSwNP. Since the complete characterization of nasal polyps on CT or MR images is very challenging, we provide a critical review of the best imaging methods and essential reporting elements used to assess nasal polyps.

Deep learning in computed tomography to predict endotype in chronic rhinosinusitis with nasal polyps.

BACKGROUND: As treatment strategies differ according to endotype, rhinologists must accurately determine the endotype in patients affected by chronic rhinosinusitis with nasal polyps (CRSwNP) for the appropriate management. In this study, we aim to construct a novel deep learning model using paranasal sinus computed tomography (CT) to predict the endotype in patients with CRSwNP. METHODS: We included patients diagnosed with CRSwNP between January 1, 2020, and April 31, 2023. The endotype of patients with CRSwNP in this study was classified as eosinophilic or non-eosinophilic. Sinus CT images (29,993 images) were retrospectively collected, including the axial, coronal, and sagittal planes, and randomly divided into training, validation, and testing sets. A residual network-18 was used to construct the deep learning model based on these images. Loss functions, accuracy functions, confusion matrices, and receiver operating characteristic curves were used to assess the predictive performance of the model. Gradient-weighted class activation mapping was performed to visualize and interpret the operating principles of the model. RESULTS: Among 251 included patients, 86 and 165 had eosinophilic or non-eosinophilic CRSwNP, respectively. The median (interquartile range) patient age was 49 years (37-58 years), and 153 (61.0%) were male. The deep learning model showed good discriminative performance in the training and validation sets, with areas under the curves of 0.993 and 0.966, respectively. To confirm the model generalizability, the receiver operating characteristic curve in the testing set showed good discriminative performance, with an area under the curve of 0.963. The Kappa scores of the confusion matrices in the training, validation, and testing sets were 0.985, 0.928, and 0.922, respectively. Finally, the constructed deep learning model was used to predict the endotype of all patients, resulting in an area under the curve of 0.962. CONCLUSIONS: The deep learning model developed in this study may provide a novel noninvasive method for rhinologists to evaluate endotypes in patients with CRSwNP and help develop precise treatment strategies.

Anatomical findings in patients with chronic rhinosinusitis without nasal polyps requiring revision surgery.

OBJECTIVES: For the treatment of chronic rhinosinusitis functional endoscopic sinus surgery is a well-established therapy with high initial success rates. However, a significant proportion of patients have persistent disease requiring revision surgery. To date, studies including data of large patient collectives are missing. In this study, we aimed to identify anatomic factors increasing the need for revision surgery in a large patient collective with chronic rhinosinusitis without nasal polyps. METHODS: Data were collected retrospectively on patients with recurrent or persistent chronic rhinosinusitis without nasal polyps requiring revision surgery. The patients' symptomatology, endoscopic and radiographic findings were analyzed. Preoperatively, patients were evaluated with endoscopic examination of the nose and paranasal sinuses. In all individuals computed tomography of the sinuses was performed. Images were evaluated according to the Lund-Mackay system. Information was also collected intraoperatively. RESULTS: 253 patients were included. The most common anatomic factor was incomplete anterior ethmoidectomy (51%), followed by residual uncinated process (37%), middle turbinate lateralization (25%), incomplete posterior ethmoidectomy (20%), frontal recess scarring (19%), and middle meatal stenosis (9%). Other factors such as persistent sphenoid pathology was less frequent. CONCLUSION: Iatrogenic causes with inadequate resection of obstructing structures seem to be a principal risk factor for recurrent chronic rhinosinusitis and the need for revision sinus surgery. Meticulous attention in the area of the ostiomeatal complex during surgery with ventilation of obstructed anatomy as well as avoidance of scarring and turbinate destabilization may reduce the failure rate after primary endoscopic sinus surgery. LEVEL OF EVIDENCE: 2b.

Investigation of the Value of T 2 Mapping in the Prediction of Eosinophilic Chronic Rhinosinusitis With Nasal Polyps.

OBJECTIVES: Patients with eosinophilic chronic rhinosinusitis with nasal polyps (eosCRSwNP) usually have more extensive sinus disease, severe symptoms, and poorer disease control compared with patients with non-eosCRSwNP. Separating these entities will be crucial for patient management. The purpose of this study is to investigate T 1, T 2 , and apparent diffusion coefficient (ADC) values of the nasal polyps in patients with CRSwNP and evaluate the usefulness of these parameters for differentiating these diseases. METHODS: Sinonasal magnetic resonance imaging was performed in 36 patients with eosCRSwNP and 20 patients with non-eosCRSwNP (including T 1 mapping, T 2 mapping, and diffusion-weighted imaging) before surgery. The T 1 , T 2 , and ADC values were calculated and correlated with pathologically assessed inflammatory cells of nasal polyps. RESULTS: Significant higher T 2 value, higher eosinophil count, and lower lymphocyte count of the nasal polyps were observed in eosCRSwNP than those in non-eosCRSwNP. There was no significant difference in T 1 or ADC values between the 2 groups. T 2 value was correlated with eosinophil count and lymphocyte count in CRSwNP. The area under the curve of T 2 value for predicting eosCRSwNP was 0.78 with 89.9% sensitivity and 60.0% specificity. CONCLUSION: T 2 value is a promising imaging biomarker for predicting eosCRSwNP. It can help to distinguish eosCRSwNP from non-eosCRSwNP.

Development and multicenter validation of a novel radiomics-based model for identifying eosinophilic chronic rhinosinusitis with nasal polyps.

BACKGROUND: Reliable noninvasive methods are needed to identify endotypes of chronic rhinosinusitis with nasal polyps (CRSwNP) to facilitate personalized therapy. Previous computed tomography (CT) scoring system has limited and inconsistent performance in identifying eosinophilic CRSwNP. We aimed to develop and validate a radiomics-based model to identify eosinophilic CRSwNP. METHODS: Surgical patients with CRSwNP were recruited from Tongji Hospital and randomly divided into training (n = 232) and internal validation cohort (n = 61). Patients from two additional hospitals served as external validation cohort-1 (n = 84) and cohort-2 (n = 54), respectively. Data were collected from October 2013 to May 2021. Eosinophilic CRSwNP was determined by histological criterion. The least absolute shrinkage and selection operator and the logistic regression (LR) algorithm were used to develop a radiomics model. Univariate and multivariate LR were employed to build models based on CT scores, clinical characteristics, and the combination of radiological and clinical characteristics. Model performance was evaluated by assessing discrimination, calibration, and clinical utility. RESULTS: The radiomics model based on 10 radiomic features achieved an area under the curve (AUC) of 0.815 in the training cohort, significantly better than the CT score model based on ethmoid-to-maxillary sinus score ratio with an AUC of 0.655. The combination of radiomic features and blood eosinophil count had a further improved performance, achieving an AUC of 0.903. The performance of these models was confirmed in all validation cohorts with satisfying predictive calibration and clinical application value. CONCLUSIONS: A CT radiomics-based model is promising to identify eosinophilic CRSwNP. This radiomics-based method may provide novel insights in solving other clinical concerns, such as guiding personalized treatment and predicting prognosis in patients with CRSwNP.

Predictive significance of computed tomography in bilateral sinonasal respiratory epithelial adenomatoid hamartoma.

KEY POINTS: Respiratory epithelial adenomatoid hamartoma (REAH) is easily confused with nasal polyps (NP). The typical manifestation of REAH on CT is the enlargement of bilateral olfactory clefts (OCs). The widening of the OCs in the CT scan is a biomarker for diagnosing REAH associated with NP.

Pólipo solitario con inserción en techo de fosa nasal / Solitary polyp with insertion into the roof of the nostril

Pólipo coanal es el término utilizado para una masa de tejido blando, solitaria y benigna que se extiende hacia la unión de la cavidad nasal y la nasofaringe; es decir, la coana. Los pólipos coanales nasales se presentan típicamente en tres formas diferentes: pólipos antrocoanales, esfenocoanales y etmoidocoanales. Sitios atípicos de origen han sido reportados en la literatura; por ejemplo, el tabique nasal y el cornete inferior. El conocimiento de los médicos sobre la existencia de pólipos coanales de sitios inusuales puede ayudar en el diagnóstico de los mismos, considerando crucial descartar previamente otros potenciales diagnósticos para estos casos de presentación atípica. A continuación reportamos el caso de un pólipo inflamatorio de inserción en techo de fosa nasal en un paciente de 65 años diagnosticado y tratado en nuestro servicio, cuyo caso es el primero reportado en la literatura.

Coanal polyp is the term used for a solitary, benign soft tissue mass extending into the junction of the nasal cavity and nasopharynx, i.e., the choana. Nasal coanal polyps typically present in three different forms: antrochoanal, sphenocoanal, and ethmoidocoanal polyps. Atypical sites of origin have been reported in the literature; these include the nasal septum and inferior turbinate. Physicians' awareness of the existence of coanal polyps from unusual sites may help in the diagnosis of coanal polyps considering it crucial to previously rule out other potential diagnoses for these cases of atypical presentation. Here we report the case of an inflammatory polyp of insertion in the roof of the nostril in a 65-year-old patient diagnosed and treated in our department, which is the first case reported in the literature.

Inverted papilloma and nasal polyp classification using a deep convolutional network integrated with an attention mechanism.

BACKGROUND: Inverted papilloma (IP) is a common sinus neoplasm with a probability of malignant transformation. Nasal polyps (NP) are the most frequent masses in the sinus. The classification of IP and NP using computed tomography (CT) is highly significant for preoperative recognition, treatment, and clinical examination. Few visible differences exist between IP and NP in CT, making it a challenge for otolaryngologists to distinguish between them. This study intended to classify IP and NP using a neural network and analyze its ability to discriminate the differences. METHODS: IP and NP in CT were classified using a deep convolutional neural network (CNN) with an attention mechanism, which combines a densely connected convolutional network (DenseNet) and squeeze-and-excitation network (SENet). Using SENet's channel attention, the specific channel weights in the feature maps are improved, which can enhance feature discriminativeness. To discuss the interpretability of SE-DenseNet, we analyzed the heatmap of the final convolutional layer. RESULTS: We evaluated the classification performance of SE-DenseNet on a clinical dataset containing 3382 slices for 136 patients. The experimental results and a heatmap show that SE-DenseNet can effectively locate sinonasal lesions in patients and distinguish IP from NP with an average Acc of 88.4% and AUC of 0.87. CONCLUSION: Otolaryngologists can use the proposed model to diagnose IP and NP in CT because of its accuracy and efficiency. Moreover, the visualized heatmaps produced by the convolutional layers show that the method is reliable.

Clinical symptoms of chronic rhinosinusitis with nasal polyps (eosinophilic and non-eosinophilic) are related to sinus computed tomography but not to endoscopic findings.

BACKGROUND: In the clinical assessment of chronic rhinosinusitis with nasal polyps (CRSwNP), evaluation of symptoms, nasoendoscopy findings and sinus computed tomography (CT) scan staging is employed in the management plan. Although prior studies have been done to assess the correlation of the three modalities in CRSwNP, such evaluation in patients with eosinophilic and non-eosinophilic types of CRSwNP requires further investigation. The aim of this study was to correlate the sinus CT scan staging to symptom and endoscopic scores in both types of CRSwNP. MATERIAL AND METHOD: Patients with CRSwNP were classified into 2 types, eosinophilic and non-eosinophilic. Both types were assessed by Sino-Nasal Outcome Test 22 (SNOT-22) symptom score, nasal endoscopy Lund Kennedy grading (LK) score and sinus CT Lund Mackay (LM) staging score. All three modalities were correlated. RESULTS: Forty-four patients were recruited for this study. There was significant correlation between sinus CT and symptom scores in the eosinophilic (r=.51, p=.031) and the non-eosinophilic (r=.76, p<.001) types. There was no significant correlation between symptom and nasoendoscopic scores in the eosinophilic (r=.12, p=.641) and the non-eosinophilic (r=.22, p=.276) types. There was also no significant correlation between sinus CT and nasoendoscopic scores in the eosinophilic (r=.20, p=.418) and the non-eosinophilic (r=.14, p=.508) types. CONCLUSIONS: Sinus CT staging correlates well with the symptom score for both the eosinophilic and the non-eosinophilic types of CRSwNP. The good correlation suggests both modalities are a reliable guide for their evaluation and management planning.

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.

Fibroepithelial Polyps Originating from the Posterior Nasal Septum: A Case Report.

Fibroepithelial polyps are benign tumors of mesodermal origin. They rarely occur in the nasal cavity, with only four such cases reported in the relevant English literature. The location was the inferior turbinate in three cases and anterior nasal septum in one case. The etiology has been suggested in other organs; however, it is entirely unknown in the nasal cavity. Pathological findings play an essential role in the diagnosis of fibroepithelial polyps. We report the case of a 76-year-old woman with fibroepithelial polyps originating from the posterior part of the nasal septum. The fibroepithelial polyps were white-yellow lobular masses that differed from a common inflammatory polyp. We made the diagnosis by excluding the other possible tumors based on a pathological examination. This is the first report about fibroepithelial polyps arising from the posterior nasal septum. She had no potential risk factors that might trigger fibroepithelial polyps in the nasal cavity. This case is a valuable example when considering the potential causes (e.g., female hormones and mechanical pressure) of nasal fibroepithelial polyps.

Chronic respiratory rhinitis: Toward endoscopic diagnosis of nasal allergy? An observational study.

OBJECTIVE: The study objective was to assess the functional, endoscopic and tomodensitometric semiology of a cohort of patients with chronic respiratory rhinitis (CRR). The concept of CRR is based on the anatomical, pathophysiological and semiological individualization of the respiratory nose within the sinonasal organ, in which three noses are distinguished by the parallel study of evolution and development in the "evo-devo" theory of the origins of the nose, anterior base of the skull and middle third of the face. MATERIAL AND METHOD: A single-center retrospective study included a cohort of 28 patients (16 men and 12 women, aged 19 to 69years) with CRR. The main objective was to describe symptoms clinically, endoscopically and on CT. The secondary objective was to compare clinical symptomatology and CT data between CRR and a control group of 31 patients with nasal polyposis (NP). The endoscopic semiology of CRR was analyzed consensually on video recordings using a pre-established grid. The DyNaChron self-administered questionnaire was used to compare symptom intensity and deterioration in quality of life. Olfaction was compared using the Sniffin' Sticks test. CT opacities were compared between CRR and NP on Lund-Mackay sinus score and a specific ethmoid opacities score. RESULTS: In CRR, endoscopy found a constant association of inflammatory or edematous signs in the inferior or middle turbinates with signs of hypersecretion. Ethmoid opacities in CRR were discrete and significantly smaller than in NP (P<0.0001), and were mainly located in the medial compartment in contact with the olfactory cleft (P<0.0001). Allergological assessment was positive in 17 of the 28 cases of CRR. Chronic nasal dysfunction was similar in CRR and NP, but olfactory impairment was significantly lower in CRR (P<0.0001). CONCLUSION: The CRR entity clinically resembles atopic central compartment disease. In both entities, endoscopy reveals inflammatory lesions restricted to the nasal cavities without significant ethmoid opacity on CT, an observation which seems to contradict the pathophysiological united airway concept.

Preoperative Sinonasal Computed Tomography Score in Chronic Rhinosinusitis with Nasal Polyps.

This study investigated the relationship between sinonasal inflammatory involvement according to the computed tomography (CT) staging system (Lund-Mackay score) with clinical, laboratory, histopathological and prognostic features of chronic rhinosinusitis with nasal polyps (CRSwNP). Seventy-eight patients with CRSwNP who had undergone surgery were enrolled. Total (p = 0.0062), ethmoid (p = 0.0496), sphenoid (p = 0.0335), ostiomeatal complex (OMC) (p = 0.0235) and frontal (p = 0.0164) CT scores were predictive of non-steroidal anti-inflammatory drugs-exacerbated respiratory disease (NERD) in the univariate analysis. Total (p = 0.0022), ethmoid (p = 0.0290), sphenoid (p = 0.0370), frontal (p = 0.0116), maxillary (p = 0.0357) and OMC (p = 0.0058) CT scores were predictve of asthma at the univariate analysis. No significant differences were found between patients with vs. without allergy in terms of total and partial CT scores. High blood eosinophil counts (>0.24 vs. ≤0.24 cells × 109/L) resulted in being associated with total (p = 0.0213), maxillary (p = 0.0227) and ethmoid (p = 0.0491) CT scores in the univariate analysis. Higher ethmoid (p = 0.0006) and total sinonasal (p = 0.0027) CT scores were found to predict histopathologically eosinophil CRSwNPs in the univariate analysis. CT scores did not result as predictive of NSAID-exacerbated respiratory disease, asthma, or blood eosinophil count at the multivariate analysis. Risk of relapse was related to the presence of NERD (p = 0.0207, HR [95% CI] 3.914 [1.232-12.435]), higher preoperative total (HR = 1.098 95%CI: 1.001-1.204, p = 0.0486) and frontal sinus CT scores (HR = 1.555 95%CI: 1.006-1.886, p = 0.0218), but these results were not confirmed by the multivariable analysis. Sinonasal CT scores showed significant differences in this heterogeneous inflammatory condition. Identifying CRSwNP characteristics is necessary to avoid generic treatments with poor outcomes.

Prevalence of respiratory epithelial adenomatoid hamartomas (REAH) associated with nasal polyposis: an epidemiological study - how to diagnose.

OBJECTIVES: To highlight the prevalence of respiratory epithelial adenomatoid hamartomas in the olfactory cleft of patients with nasal polyposis. To demonstrate characteristics indicative of hamartoma on the CT scans of paranasal sinuses during surgery and in histopathological exams. METHODS: Cross-sectional study carried out in Hospital das Clínicas da UFMG and Núcleo de Otorrino BH. We performed 114 nasal endoscopic surgeries for polyposis, between February 2015 and November 2019. We assessed the olfactory cleft width in all preoperative CT scans. Upon seeing an indication of hamartoma on the CT scan, we took a tissue sample from the olfactory cleft during the surgery and sent for histopathological exam. We referred the samples to a pathologist experienced in the anatomopathological diagnosis of respiratory epithelial adenomatoid hamartomas. RESULTS: Of the 114 patients with polyposis, 54 (47.4%) had olfactory cleft enlargement and, 100% of them had tissue with a dense and hardened polypoid aspect, with a slight cerebriform appearance in this region during the surgery. Histology confirmed a respiratory epithelial adenomatoid hamartoma. CONCLUSION: This observation suggests that the presence of hamartomas in polyposis is common, but underdiagnosed. LEVEL OF EVIDENCE: Step 3 (Level 3).