International Forum of Allergy & Rhinology最新文献

Background

Residual disease after endoscopic sinus surgery (ESS) contributes to poor outcomes and revision surgery. Image-guided surgery systems cannot dynamically reflect intraoperative changes. We propose a sensorless, video-based method for intraoperative CT updating using neural radiance fields (NeRF), a deep learning algorithm used to create 3D surgical field reconstructions.

Methods

Bilateral ESS was performed on three 3D-printed models (n = 6 sides). Postoperative endoscopic videos were processed through a custom NeRF pipeline to generate 3D reconstructions, which were co-registered to preoperative CT scans. Digitally updated CT models were created through algorithmic subtraction of resected regions, then volumetrically segmented, and compared to ground-truth postoperative CT. Accuracy was assessed using Hausdorff distance (surface alignment), Dice similarity coefficient (DSC) (volumetric overlap), and Bland‒Altman analysis (BAA) (statistical agreement).

Results

Comparison of the updated CT and the ground-truth postoperative CT indicated an average Hausdorff distance of 0.27 ± 0.076 mm and a 95th percentile Hausdorff distance of 0.82 ± 0.165 mm, indicating sub-millimeter surface alignment. The DSC was 0.93 ± 0.012 with values >0.9 suggestive of excellent spatial overlap. BAA indicated modest underestimation of volume on the updated CT versus ground-truth CT with a mean difference in volumes of 0.40 cm³ with 95% limits of agreement of 0.04‒0.76 cm³ indicating that all samples fell within acceptable bounds of variability.

Conclusions

Computer vision can enable dynamic intraoperative imaging by generating highly accurate CT updates from monocular endoscopic video without external tracking. By directly visualizing resection progress, this software-driven tool has the potential to enhance surgical completeness in ESS for next-generation navigation platforms.

Background

Prompt detection and intervention are crucial for improving outcomes in acute invasive fungal rhinosinusitis (AIFS). Diagnostic prediction models assist in risk-stratification, but their accuracy requires testing through external validation. This study aims to validate a previously published diagnostic prediction model for AIFS in an independent cohort.

Methods

A retrospective chart review was conducted at a tertiary care center (2008–2023) to identify patients with an otolaryngology consult for suspected AIFS. Of 65 patients identified, 11 (16.9%) were diagnosed with AIFS based on histopathology. Risk was calculated using Yin et al.’s predictive model. Predictive performance was assessed by calibration and discrimination.

Results

Patients had significantly higher rates of diabetes (46.2% vs. 26.1%, p = 0.002), long-term steroid use (60% vs. 28.2%, p < 0.0001), and solid organ transplantation (38.5% vs. 8.5%, p < 0.001), compared with the development cohort, with conversely lower rates of hematologic malignancy (29.2% vs. 58.7, p < 0.001) and neutropenia (19.4% vs. 41%, p = 0.001). Despite these differences, both the three-variable (C-index: 0.844; 95% CI, 0.736–0.952) and four-variable models (C-index: 0.963; 95% CI, 0.919–1) showed adequate discrimination. Both models exhibited slight overprediction of risk, with a calibration-in-the-large predicted risk of 24.1% (95% CI, 13.68–34.46) for the three-variable model and 24.2% (95% CI, 13.76–34.57) for the four-variable model. Calibration plots confirmed overprediction.

Conclusion

The AIFS diagnostic model demonstrates acceptable discrimination and calibration on external validation, with generalizability to patients with different comorbidities. Larger studies are recommended to further test the model's predictive performance and clinical applicability.

Background

Central compartment atopic disease (CCAD) is a nasal inflammatory condition associated with allergy and categorized as type 2 dominant chronic rhinosinusitis with nasal polyps (CRSwNP). Sinus opacification in this condition is secondary to obstruction from central tissue remodeling. Although the literature supports a unique phenotype of CCAD, controversy persists due to variations in published studies. Standardized diagnostic criteria for CCAD are lacking. The current literature was systematically evaluated to propose a set of diagnostic criteria for CCAD.

Methods

A comprehensive literature review on CCAD was performed. Using current knowledge as the foundation, diagnostic criteria for CCAD are proposed.

Results

Tissue remodeling of the central part of the nasal and sinus cavity in a patient with features of an IgE mediated allergic process is the cornerstone of CCAD. It is a type 2 dominant endotype with an eosinophilic inflammatory profile. While CCAD is represented by unique phenotypic and endotypic features, there are likely geographic variations in CCAD presentation, especially when comparing CCAD to other subtypes of CRS. Previous lack of clear diagnostic criteria for CCAD has likely contributed to conflicting findings in the published literature on CCAD.

Conclusion

Endoscopic confirmation of polypoid changes of central compartment structures (middle/superior turbinate, and/or posterosuperior nasal septum) is critical to the diagnosis of CCAD. Allergic history strongly supports CCAD diagnosis. While CCAD may display some unique radiologic features, radiology alone is insufficient to diagnose CCAD.

Key points

Introduction

Previous studies suggest a relationship between olfactory impairment and frailty but have focused on odor identification (I), potentially overlooking odor threshold sensitivity (T), and discrimination (D). Our objective was to characterize the relationship between olfactory subcomponents and frailty status by establishing optimal thresholds for these measures.

Methods

We conducted a prospective study assessing frailty using the physical frailty phenotype and olfactory function using the Sniffin’ Sticks Test in older adults. T, D, I (range 0–16 for each) and the composite threshold, discrimination, and identification (TDI) score (range −0 to 48) were evaluated using receiver operating characteristic analysis and area under the curve (AUC) measures.

Results

This study included 103 participants, with a mean age of 82.8 ± 5 years, 56% (n = 58) female, and 89% (n = 92) white. Frail participants had significantly lower T (8.5 ± 3.3 vs. 4.4 ± 3.0), D (11.4 ± 2.0 vs. 8.1 ± 2.5), I (11.9 ± 2.4 vs. 9.2 ± 2.9), and TDI scores (31.9 ± 5.9 vs. 21.7 ± 6.6) compared to non-frail individuals (p < 0.001 for all). TDI demonstrated the strongest ability to distinguish between frail and non-frail participants (AUC  =  0.86), followed by D (AUC  =  0.85), T (AUC  =  0.82), and I (AUC  =  0.78). The optimal cutoff for D was a score of 10, yielding a sensitivity of 73% and specificity of 88%. Cutoff values were also established for T (score = 6), I (score = 12), and TDI (score = 29).

Conclusion

Identified cutoff values may enhance risk stratification and inform clinical interventions. Across subdomains, discrimination scores may yield more information than identification. Additional studies in larger samples will be necessary to investigate subdomain differences.

Advancements in synthetic biology provide an opportunity to deliver targeted and controllable precision therapy to address sinonasal diseases. By leveraging the natural microbial ecosystem of the nasal mucosa and its mutability, engineered therapeutic bacteria present a promising treatment modality currently underexplored in this field. Investigating the practical application of this emerging therapeutic option stands to enhance our management of rhinologic diseases. Synthetic biology enables us to address underlying inflammatory processes and sinonasal malignancies with bespoke microorganisms that facilitate localized microbiota modulation and immunomodulation. By harnessing the intrinsic regulatory properties of the native nasal microbiome, we can develop innovative strategies that not only improve treatment efficacy but also open new avenues for managing complex sinonasal conditions.