JAMA otolaryngology-- head & neck surgery最新文献

Importance: Deep learning (DL), a subset of artificial intelligence, uses multilayered neural networks to uncover complex patterns in large datasets without manual feature engineering. Unlike traditional machine learning, DL autonomously learns hierarchical representations from raw data, offering distinct advantages for analyzing images (eg, stroboscopy) and physiologic signals (eg, cochlear implant optimization). Despite these advances, DL remains conceptually difficult for many clinicians to integrate into routine clinical practice. This narrative review sought to synthesize recent DL applications and propose a framework for their integration in otolaryngology.

Observations: A total of 1422 articles (2020-2025) were screened, and 327 original research studies on DL in otolaryngology were included in the analysis. The included articles were categorized into 4 domains: detection and diagnosis (179 [55%]), prediction and prognostics (16; [5%]), image segmentation (93 [28%]), and emerging applications (39 [12%]). Proof-of-concept studies have demonstrated that DL systems can achieve acceptable diagnostic performance comparable to experts, with models accurately identifying nasopharyngeal carcinoma (92%), laryngeal malignant neoplasms (86%), and otologic pathology (>95%). Prognostic applications included survival stratification in oropharyngeal cancer and recurrence prediction in chronic rhinosinusitis. Segmentation models reliably delineated anatomical regions. Emerging uses encompassed hearing aid optimization, surgical instrument tracking, and intraoperative landmark identification. Further progress requires multi-institutional datasets, standardized acquisition protocols, and transparent, interpretable models to improve trust and clinical adoption.

Conclusions and relevance: This narrative review found that DL applications in otolaryngology show potential for improving diagnostic performance, predicting outcomes, and providing intraoperative guidance. Widespread and equitable adoption needs to be supported by harmonized, high-quality, and representative datasets, as well as the mitigation of algorithmic bias and robust model interpretability. Federated learning and explainability are emerging frameworks that support the preservation of privacy and increased clinician trust. Standardized reporting, prospective validation, human-in-the-loop models, and interdisciplinary partnerships can help balance the promise of algorithmic approaches and their clinical utility, ensuring that DL tools contribute meaningfully to patient care.

Importance: Adjuvant radiotherapy can improve locoregional control and survival in patients with olfactory neuroblastoma (ONB), particularly with advanced-stage and histologic-grade disease. Standard radiotherapy treatment is with intensity-modulated radiotherapy (IMRT). Proton beam radiotherapy (PBRT) provides theoretical advantages in greater sparing of dose to uninvolved organs at risk.

Objective: To investigate if there are differences in the effectiveness and radiation treatment-related adverse events (RTAEs) between adjuvant IMRT and PBRT for patients with ONB.

Design, setting, and participants: This propensity score-matched cohort study included patients with ONB treated between February 2005 and April 2021 with either IMRT or PBRT at 9 academic tertiary care centers in North America. Patients were matched 1:2 based on age, modified Kadish stage, and Hyams grade. Data were analyzed from July 2024 to January 2025.

Exposure: Adjuvant IMRT or adjuvant PBRT.

Main outcomes and measures: Local recurrence-free survival (RFS), any RFS, and overall survival (OS). RTAEs, ie, grade 2 events or higher based on Common Terminology Criteria for Adverse Events, were recorded for both modalities.

Results: Of 54 included patients, 27 (50%) were female, and the mean (SD) age was 46.2 (15.4) years. A total of 18 were treated with PBRT and 36 were treated with IMRT. Most patients had modified Kadish stage C disease (33 of 54 [61%]), and 24 patients (44%) had Hyams grade III or IV disease. The RTAE rate was 20% (8 of 40); IMRT had a rate of 21% (6 of 29), and PBRT had a rate of 18% (2 of 11). The difference in the point estimates for 10-year RFS showed a potential clinical benefit favoring IMRT, although the wide confidence interval indicates uncertainty (10-year RFS: IMRT, 63.3%; 95% CI, 44.6-89.8; PBRT, 37.8%; 95% CI, 14.2-100; difference, 25.5 percentage points; 95% CI, -17.6 to 68.6). There were no clinically meaningful differences in 10-year local RFS (IMRT, 75.6%; 95% CI, 59.8-95.4; PBRT, 72.7%; 95% CI, 45.2-100; difference, 2.9 percentage points; 95% CI, -35.9 to 41.7) or 10-year OS (IMRT, 61.8%; 95% CI, 42.8-89.1; PBRT, 57.1%; 95% CI, 24.3-100; difference, 4.7 percentage points; 95% CI, -49.2 to 58.6), although wide confidence intervals indicate considerable uncertainty.

Conclusions and relevance: Due to the imprecision of estimates, no definitive conclusions can be made regarding the comparative effectiveness of IMRT vs PBRT for patients with ONB. These preliminary data may inform the design of appropriately powered prospective studies evaluating the efficacy of PBRT vs IMRT in this population.