Prevalence of and factors associated with maxillary sinus cyst in a Chinese population

Ren, Lingfei; Chao-zhen, Chen; Li, Na; Hu, Jinxing; Jiang, Zhiwei; Yang, Guoli

doi:10.2334/josnusd.21-0217

Cited by 3 publications

(5 citation statements)

References 28 publications

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“…MRCs are commonly found in the maxillary sinus ( Figure 5 ). MRCs were found in 10.1– 28.6% of the maxillary sinus [ 28 , 75 , 76 , 77 ]. Up to four MRCs were reported within a single sinus [ 75 , 77 ].…”

Section: Pathologymentioning

confidence: 99%

“…MRCs were found in 10.1– 28.6% of the maxillary sinus [ 28 , 75 , 76 , 77 ]. Up to four MRCs were reported within a single sinus [ 75 , 77 ]. MRCs were more commonly located at the sinus walls (19.9–53.6%) and the floor (28.6–76.7%) instead of the roof (3.4–17.9%) [ 75 , 77 ].…”

Section: Pathologymentioning

confidence: 99%

“…Up to four MRCs were reported within a single sinus [ 75 , 77 ]. MRCs were more commonly located at the sinus walls (19.9–53.6%) and the floor (28.6–76.7%) instead of the roof (3.4–17.9%) [ 75 , 77 ]. The average size of an MRC was reported to be 551 ± 1368 mm 3 with a diameter of 9.6 ± 5.4 mm [ 76 ].…”

Section: Pathologymentioning

confidence: 99%

“…The average size of an MRC was reported to be 551 ± 1368 mm 3 with a diameter of 9.6 ± 5.4 mm [ 76 ]. The presence of MRCs on the sinus floor was more likely if there were teeth with periapical lesions, were endodontically treated, or with severe bone loss [ 76 , 77 ].…”

Section: Pathologymentioning

confidence: 99%

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The Use of CBCT in Evaluating the Health and Pathology of the Maxillary Sinus

Yeung

Hung

et al. 2022

Diagnostics

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The use of cone-beam computed tomography (CBCT) has been increasing in dental practice. This narrative review summarized the relevance and utilizations of CBCT to visualize anatomical structures of the maxillary sinus and common pathologies found in the maxillary sinus. The detection/visualization rate, the location and the morphometric characteristics were described. For sinus anatomy, the reviewed features included the posterior superior alveolar artery, sinus pneumatization, sinus hypoplasia, sinus septa, and primary and accessory sinus ostia. For pathology, the following items were reviewed: membrane thickening associated with periapical lesions/periodontal lesions, mucous retention cyst, and antrolith. The visualization and assessment of the maxillary sinus is very important prior to procedures that take place in close proximity with the sinus floor, such as tooth extraction, implant insertion, and sinus floor elevation. Some sinus pathologies may be associated with odontogenic lesions, such as periapical diseases and periodontal bone loss.

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Section: Pathologymentioning

confidence: 99%

Section: Pathologymentioning

confidence: 99%

Section: Pathologymentioning

confidence: 99%

Section: Pathologymentioning

confidence: 99%

See 2 more Smart Citations

The Use of CBCT in Evaluating the Health and Pathology of the Maxillary Sinus

Yeung

Hung

et al. 2022

Diagnostics

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“…With a prevalence of 15.46% at the sinus level and 23.44% at the patient level (Ren et al, 2022), maxillary sinus cysts are commonly observed in Chinese dental implant patients. Maxillary sinus pseudocysts, also known as antral pseudocysts (APs), are asymptomatic, solitary or multiple maxillary sinus “cysts” (Carter et al, 1998; Gardner, 1984; Nosaka et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Maxillary sinus floor augmentation comparing removing versus retaining antral pseudocyst: A retrospective cohort study

Jiang

Ren

et al. 2022

Clinical Oral Implants Res

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Objectives: To retrospectively compare multilevel volumetric changes in both hard and soft tissues between antral pseudocyst (AP) removal and retainment before maxillary sinus floor augmentation (MSFA) and immediate implant placement. Material and methods:Twenty-six patients with 38 implants placed from 2016 to 2021 were included and divided according to a cohort design as follows: 13 removing the cyst (RC group) and 13 "leaving alone" the cyst (LC group). 3D radiographic parameters (primary outcome), 2D parameters and clinical records (secondary outcome) involving both hard and soft tissues were evaluated for four periods (T1: immediate postoperative, T2: 6-month, T3: 12-month, and T4: 2-to 5-year follow-up). Possible confounding factors, including sinus anatomical features and implant distribution, were also analyzed to eliminate their disturbance. Results:The 3D volumetric change rate of bone grafts in the RC group (−9.32% ± 10.01%) from T2 to T3 was significantly lower than that in the LC group (−19.8% ± 10.59%) (p < .05). The change rate of apical bone height (ABH), endo-sinus bone gain (ESBG) and other 2D parameters were not significantly different between the two groups.5.3% implants in RC group and 9.1% implants in LC group failed during follow-ups. 0% postoperative complications were observed in RC group. The Schneiderian membrane of RC group was significantly thinner than that of LC group at two measuring points in sinus. Conclusion:The present study demonstrated that compared to AP retainment, AP removal before MSFA and immediate implant placement can obtain higher bone graft volumetric stability and favorable prognosis.

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Deep learning in the diagnosis of maxillary sinus diseases: a systematic review

Wu,

Yu,

Chen

et al. 2024

Dentomaxillofacial Radiology

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Objectives To assess the performance of deep learning (DL) in the detection, classification, and segmentation of maxillary sinus diseases. Methods An electronic search was conducted by two reviewers on databases including PubMed, Scopus, Cochrane, and IEEE. All English papers published no later than February 7, 2024, were evaluated. Studies related to DL for diagnosing maxillary sinus diseases were also searched in journals manually. Results Fourteen of 1167 studies were eligible according to the inclusion criteria. All studies trained DL models based on radiographic images. Six studies applied to detection tasks, one focused on classification, two segmented lesions, and five studies made a combination of two types of DL models. The accuracy of the DL algorithms ranged from 75.7% to 99.7%, and the area under curves (AUC) varied between 0.7 and 0.997. Conclusion DL can accurately deal with the tasks of diagnosing maxillary sinus diseases. Students, residents, and dentists could be assisted by DL algorithms to diagnose and make rational decisions on implant treatment related to maxillary sinuses.

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Prevalence of and factors associated with maxillary sinus cyst in a Chinese population

Cited by 3 publications

References 28 publications

The Use of CBCT in Evaluating the Health and Pathology of the Maxillary Sinus

The Use of CBCT in Evaluating the Health and Pathology of the Maxillary Sinus

Maxillary sinus floor augmentation comparing removing versus retaining antral pseudocyst: A retrospective cohort study

Deep learning in the diagnosis of maxillary sinus diseases: a systematic review

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