Chinese journal of otorhinolaryngology head and neck surgery最新文献

Objective: This study aims to preliminarily explore the impact of cognitive function on 2-digit and 3-digit digit-in-noise tests in the Ningbo dialect version, particularly when used for hearing screening. Methods: A total of 31 older native speakers of the Ningbo dialect were selected. The cohort comprised 13 males and 18 females, with an age range of 60 to 88 years and a mean age of 73.6 years. Participants completed pure-tone audiometry, the Ningbo dialect versions of the 2-digit and 3-digit digit-in-noise tests twice, the Chinese version of the Montreal Cognitive Assessment (MoCA-BC) basic scale, and the auditory version of the digit span test. Results: Spearman's correlation analysis showed that MoCA-BC scores were significantly correlated only with the second SRT (speech reception threshold) of the digit triplet test. There was no significant correlation between digit span scores and the results of any of the digit-in-noise tests. Attention scores from the MoCA-BC were significantly correlated with the first (rs=-0.374, P=0.038) and second (rs=-0.369, P=0.041) SRTs of the 3-digit condition but showed no significant correlation with the first and second SRTs of the 2-digit condition. The effectiveness of the 3-digit and 2-digit in hearing screening was found to be generally consistent. When using an average hearing threshold of 35 dB HL in the better ear as the hearing screening cut-off point, ROC curve analysis revealed that the areas under the curve (AUC) for the first and second trials of the 2-digit and the first and second trials of the 3-digit test were 0.868, 0.864, 0.873, and 0.857, respectively. Conclusion: Cognitive function has little effect on the results of 2-digit and 3-digit digit-in-noise tests. However, compared to the 3-digit, the 2-digit is simpler and less affected by cognitive decline.

Objective: To investigate the clinical characteristics of patients with oropharyngeal squamous cell carcinoma (OPSCC) and the relationship between the clinical characteristics and pre-treatment inflammatory parameters such as neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic inflammation response index (SII), and systemic inflammation response index (SIRI). Methods: A retrospective analysis was conducted on the clinical data of 149 OPSCC patients treated in two tertiary hospitals, Shanxi Province Cancer Hospital and Shanxi Bethune Hospital, between January 2017 and January 2024, including 129 males and 20 females, aged from 37 to 84 years old. The patients were grouped based on HPV infection status (p16 expression), namely p16-positive group and p16-negative group, and statistical analysis was performed to evaluate the correlation between the inflammatory parameters and clinical factors such as clinical stage, treatment regimen, and prognosis in two groups. Statistical analysis was conducted using SPSS 29.0 software. Results: In this cohort, 56.4%(84/149) of OPSCC patients were p16-positive. The total 1-year and 3-year survival rates were 87.0% and 66.5%, respectively. Analysis showed p16 status was an independent prognostic factor (HR=0.444, 95%CI: 0.206-0.957, P=0.038). No significant difference in prognosis was observed between p16-positive and p16-negative groups when NLR, PLR, and SII levels were elevated (all P>0.05). Pre-treatment SII levels were significantly higher in p16-positive patients compared to p16-negative patients (62.4% vs. 37.6%, χ²=8.021, P=0.005). Elevated pre-treatment PLR levels indicated a higher risk of lymph node metastasis (χ²=4.791, P=0.029). Conclusion: Elevated levels of NLR, PLR, and SII may attenuate the prognostic advantage of HPV-positive OPSCC. SII and PLR may play important roles in predicting HPV infection status and the risk of cervical lymph node metastasis in OPSCC patients.

Objective: To develop a nasopharyngeal carcinoma (NPC) diagnostic model based on foundation model transfer learning, aiming to address the limited generalization and diagnostic performance of existing models under small-sample conditions. Methods: A retrospective study was conducted using 27 362 nasopharyngeal endoscopic images from eight regional NPC centers. The images were classified into three groups: NPC, benign hyperplasia (BH), and normal nasopharynx (NOR). The data were randomly split into a training/validation set (85%) and a hold-out test set (15%). To evaluate generalization under small-sample conditions, models were trained on both the full dataset (100%) and a small subset (1%), then tested on the same test set. The model was based on BiomedCLIP, pre-trained on large medical image-text datasets and fine-tuned for classification. The performance of our fine-tuned BiomedCLIP model was systematically compared against several benchmark models, including ResNet50, ViT-Base, and the original CLIP. Performance was assessed using accuracy, the area under the receiver operating characteristic curve (AUC), specificity, and sensitivity, with attention maps used to visualize how the model made its decisions. Results: With the full training data, the BiomedCLIP model demonstrated robust performance. It achieved 95.46% (95%CI: 94.87%-96.08%) accuracy and an AUC of 0.98 (95%CI: 0.98-0.99) for distinguishing normal vs abnormal cases (NAN), and 89.92% (95%CI: 89.04%-90.78%) accuracy and an AUC of 0.90 (95%CI: 0.89-0.90) for distinguishing malignant vs non-malignant cases (MNM), significantly outperforming all comparator models. Even when trained with only 1% of the data, BiomedCLIP still maintained strong performance, with AUCs of 0.89 (95%CI: 0.88-0.90) for NAN and 0.81 (95%CI: 0.79-0.82) for MNM, demonstrating effective generalization in data-scarce scenarios. Conclusions: The endoscopic image-based auxiliary diagnostic model presented in this study accurately differentiates NPC, BH, and NOR under small-sample conditions. The model exhibits high diagnostic accuracy and robust generalization despite limited training data, highlighting its promise for clinical deployment as a screening and decision-support tool.

Objective: To evaluate HE2Signature for predicting inflammatory gene expression and postoperative outcomes in chronic rhinosinusitis with nasal polyps (CRSwNP) directly from whole slide images (WSIs). Methods: In an independent external cohort of 178 CRSwNP patients, HE2Signature was employed to analyze WSIs to predict expression of 33 inflammatory marker genes. Post-operative control was assessed (EPOS 2020). Predicted gene expression was correlated with clinical indicators and compared across control groups. LASSO regression was used to build a prediction model for post-operative outcome, compared with ControlNet. Results: The predicted expression levels of 18 of the 33 marker genes showed significant correlations with clinical indicators (P<0.05). The predicted expression of type 2 genes (e.g., POSTN, FCER2, IL-13) were significantly and positively correlated with eosinophil-related metrics and disease burden (r=0.160-0.244). Conversely, predicted expression of non-type 2 genes (e.g., CSF3, SAA1) was positively associated with tissue neutrophil counts. The uncontrolled disease group was characterized by a significant upregulation of predicted Type 2 inflammatory genes. A model based on the predicted gene signature achieved an area under the receiver operating characteristic curve (AUC) of 0.776 for discriminating uncontrolled status and 0.800 for discriminating control status. This performance was not statistically different from that of the ControlNet model (P>0.05). Conclusion: The HE2Signature model effectively predicts marker gene expression and postoperative outcomes in CRSwNP from routine histology, offering a scalable and intelligent pathway for precision medicine without requiring molecular assays.

Objectives: To investigate the pathological inflammatory features based on artificial intelligence for whole slide image (AI-WSI), and to evaluate its consistency and clinical relevance with the conventional mean of ten random high-power fields (10-HPF). Ultimately, a WSI-based pathological endotype classification for chronic rhinosinusitis with nasal polyps (CRSwNP) was established. Methods: A total of 407 CRSwNP patients admitted to the Department of Otorhinolaryngology-Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University from January 2020 to December 2023 were retrospectively enrolled. The cohort included 288 males and 119 females, aged from 18 to 84 years. Quantitative analysis of inflammatory cells in the pathological sections of these patients was performed using the traditional 10 HPF method and the AI-WSI method, respectively. Subsequently, a typing system was established based on AI-WSI results using unsupervised clustering, discriminant analysis, and classification tree modeling, and the tissue components and clinical characteristics of each subtype were compared. Data analysis was conducted using SPSS 26.0 and R 4.4.2 software. Results: Significant differences were observed in the proportion of inflammatory cells between the AI-WSI and 10 HPF methods, with a Cohen's Kappa coefficient of 0.48. The existing 10 HPF-based typing criterion was not suitable for the inflammatory feature results of AI-WSI. Cluster analysis of AI-WSI data identified four distinct subtypes: eosinophil (Eos)-predominant, plasma cell (Pla)-predominant, lymphocyte (Lym)-predominant, and neutrophil (Neu)-predominant. The Eos-predominant subtype accounted for 28.26%, characterized by the highest recurrence rate (39.13%) and olfactory dysfunction. The Lym-predominant and Pla-predominant subtypes presented milder symptoms, with recurrence rates of 13.63% and 16.13%, respectively. Although the Neu-predominant subtype was associated with significant head and facial pain, it had a lower recurrence rate (11.11%). Conclusions: There are differences in the pathological inflammatory features between the traditional 10 HPF method and the AI-WSI method, and the features derived from AI-WSI are currently difficult to directly apply to existing typing criterion. This study successfully establishes a four-subtype classification system for CRSwNP based on AI-WSI, which demonstrates good stability and discriminative ability.