DIAGNOSTIC ACCURACY OF X-RAY PARA NASAL SINUS IN CLINICALLY SUSPECTED SINUSITIS TAKING COMPUTED TOMOGRAPHY SCAN AS GOLD STANDARD: A CROSS-SECTIONAL STUDY
DOI:
https://doi.org/10.71000/jmz8ga16Keywords:
Computed Tomography, Diagnostic Accuracy, Paranasal Sinuses, Radiography, Sensitivity and Specificity, Sinusitis, X-RayAbstract
Background: Sinusitis is a prevalent inflammatory disorder affecting the paranasal sinuses, leading to significant morbidity and reduced quality of life. Imaging plays a pivotal role in its diagnosis, with computed tomography (CT) regarded as the gold standard. However, X-ray paranasal sinus (PNS) imaging remains widely utilized due to its accessibility, lower cost, and reduced radiation exposure, particularly in primary care settings. Limited local evidence exists regarding the diagnostic accuracy of X-ray PNS compared to CT.
Objective: To determine the diagnostic accuracy of X-ray PNS in clinically suspected cases of sinusitis, using CT as the reference standard.
Methods: A cross-sectional validation study was conducted over six months in the Department of Radiology, Khyber Teaching Hospital, Peshawar, including 197 patients aged 18–65 years with clinically suspected sinusitis. Non-probability consecutive sampling was used. Each participant underwent both X-ray PNS (Water’s, Caldwell’s, and Lateral views) and CT scan of the paranasal sinuses. Diagnostic performance was assessed using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy. Data were analyzed using SPSS version 27.
Results: Among 197 participants, sinusitis was detected in 132 (67.0%) cases by X-ray PNS and 145 (73.6%) cases by CT. X-ray PNS demonstrated a sensitivity of 86.2%, specificity of 86.5%, PPV of 94.7%, NPV of 69.2%, and overall diagnostic accuracy of 86.3%. Higher diagnostic accuracy was observed in patients aged 30–50 years and those with normal BMI.
Conclusion: X-ray PNS is a reliable and practical diagnostic tool for evaluating clinically suspected sinusitis, especially where CT is not readily available. While CT remains superior for detailed assessment, X-ray PNS offers a valuable alternative for timely diagnosis and management in resource-limited settings.
References
Yim MT, Tan BK, Nathan CO, Orlandi RR. What is the Optimal Timing of Computed Tomography Imaging to Objectively Confirm Chronic Rhinosinusitis? Laryngoscope. 2021;131(2):248-9.
de Carvalho MF, Vieira JNM, Figueiredo R, Reher P, Chrcanovic BR, Chaves M. Validity of computed tomography in diagnosing midfacial fractures. Int J Oral Maxillofac Surg. 2021;50(4):471-6.
Lin N, Qi M, Wang Z, Luo S, Pan Y, Zhang F, et al. Small Cell Neuroendocrine Carcinoma of Paranasal Sinuses: Radiologic Features in 14 Cases. J Comput Assist Tomogr. 2021;45(1):135-41.
Pasupat D, Aeumjaturapat S, Snidvongs K, Chusakul S, Seresirikachorn K, Kanjanaumporn J. A Predictive Model for Diagnosis of Acute Invasive Fungal Rhinosinusitis Among High-Risk Patients. Am J Rhinol Allergy. 2025;39(3):245-52.
Zou J, Lyu Y, Lin Y, Chen Y, Lai S, Wang S, et al. A multi-view fusion lightweight network for CRSwNPs prediction on CT images. BMC Med Imaging. 2024;24(1):112.
Gregurić T, Prokopakis E, Vlastos I, Doulaptsi M, Cingi C, Košec A, et al. Imaging in chronic rhinosinusitis: A systematic review of MRI and CT diagnostic accuracy and reliability in severity staging. J Neuroradiol. 2021;48(4):277-81.
Shujaat S, Alfadley A, Morgan N, Jamleh A, Riaz M, Aboalela AA, et al. Emergence of artificial intelligence for automating cone-beam computed tomography-derived maxillary sinus imaging tasks. A systematic review. Clin Implant Dent Relat Res. 2024;26(5):899-912.
Petsiou DP, Spinos D, Martinos A, Muzaffar J, Garas G, Georgalas C. Effectiveness of Artificial Intelligence in detecting sinonasal pathology using clinical imaging modalities: a systematic review. Rhinology. 2025;63(4):448-62.
de Mendonça DS, Ribeiro EC, de Barros Silva PG, Rodrigues AA, Kurita LM, de Aguiar ASW, et al. Diagnostic accuracy of paranasal sinus measurements on multislice computed tomography for sex estimation: A systematic review, meta-analysis, and meta-regression. J Forensic Sci. 2022;67(6):2151-64.
Wali H, Ansari SS, Umair M, Dar AF. Diagnostic accuracy of double density sign on CT scan of para-nasal sinuses for fungal sinusitis taking microbiology culture as gold standard. J Pak Med Assoc. 2024;74(7):1261-4.
Zhang J, Liu L, Yang L, Wang J, Tan X, Huang D. Diagnosis of Odontogenic Maxillary Sinusitis by Cone-beam Computed Tomography: A Critical Review. J Endod. 2023;49(11):1445-56.
Li J, Mao N, Aodeng S, Zhang H, Zhu Z, Wang L, et al. Development and Validation an Integrated Deep Learning Model to Assist Eosinophilic Chronic Rhinosinusitis Diagnosis: A Multicenter Study. Int Forum Allergy Rhinol. 2025;15(10):1070-81.
Zhang J, Wang D, Li W, Wang J. Deep learning-based AI model for sinusitis diagnosis. Technol Health Care. 2025;33(4):1800-17.
Quan TN, Nghiem DT, Do TA. Correlation between diagnostic nasal endoscopy and computed tomography scans of patients with chronic rhinosinusitis: A cross-sectional study. Medicine (Baltimore). 2025;104(45):e45748.
Abouzari M, Sarna B, You J, Risbud A, Tsutsumi K, Goshtasbi K, et al. A Comparison of Near-Infrared Imaging and Computerized Tomography Scan for Detecting Maxillary Sinusitis. Ann Otol Rhinol Laryngol. 2022;131(10):1144-50.
Ryoo I, Poésy S, Kaliaev A, Buch K, Sakai O. Comparison of Imaging Findings between Granulomatosis with Polyangiitis and Eosinophilic Granulomatosis with Polyangiitis on Sinus CT: Importance of High-Density Opacification of the Paranasal Sinuses. AJNR Am J Neuroradiol. 2025;46(2):355-61.
Kotaki S, Gamoh S, Tsuji K, Akiyama H, Ikeda C, Yoshida A. The Combination of Panoramic Imaging and Waters' Projection Contributes to the Diagnosis of Odontogenic Maxillary Sinusitis. Kobe J Med Sci. 2021;66(5):E180-e6.
Ohira S, Matsuura K, Matsui H, Nakamura M, Kamiyama K, Kajiwara R, et al. Anatomical Factors that Can Predict the Structure of Lamina Papyracea for Endoscopic Sinus Surgery. Laryngoscope. 2021;131(1):E19-e25.
Sukswai P, Hnoohom N, Hoang MP, Aeumjaturapat S, Chusakul S, Kanjanaumporn J, et al. The accuracy of deep learning models for diagnosing maxillary fungal ball rhinosinusitis. Eur Arch Otorhinolaryngol. 2024;281(12):6485-92.
Slavin RG. Nasal polyps and sinusitis. Allergic Dis. 2023;15(27):295-309.
Julka BS, Patil SB, Chandrakiran C. Incidence and Prevalence of Fungal Sinusitis in Cases of Chronic Rhinosinusitis. Indian J Otolaryngol Head Neck Surg. 2023;75(1):1041-6.
Akhter SM, Abbas RF, Jamal Y, Rafique S, Saleemi YM, Ali S. Diagnostic Accuracy of X-ray in Clinically Suspected Acute Sinusitis taking Computed Tomography Scan as Gold Standard. Pak J Med Health Sci.2020;30(18):499-501.
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