OBJECTIVE: To explore the risk factors of radiation-induced oral mucositis (RIOM) in patients with head and neck tumors undergoing radiotherapy.
METHODS: A retrospective collection was conducted on patients with head and neck tumors who underwent radiotherapy and chemotherapy in our hospital from April 1, 2015 to April 1, 2019. They were divided into an incidence group (n=48) and a non-incidence group (n=76) based on whether RIOM occurred, and relevant data was collected for comparison.
RESULTS: There were statistically significant differences between the two groups of patients in terms of tumor type, smoking percentage, education level percentage, tumor stage, oral mucosal inflammation stage, radiotherapy dose, mucosal protectants, and oral hygiene condition(P<0.05); The regression analysis results showed that smoking (OR=1.274, 95% CI: 1.095-2.007), high-dose radiotherapy (OR=1.223, 95% CI: 1.098-2.077), and poor oral hygiene (OR=1.367, 95% CI: 1.024-2.890) were risk factors for RIOM.
CONCLUSIONS: Smoking, high-dose radiotherapy, and poor oral hygiene were risk factors for RIOM in head and neck patients after radiotherapy and chemotherapy.

Objective:To investigate the clinical features, imaging findings, pathological phenotype, treatment and prognosis of unicentric Castleman disease in the children\'s neck, in order to improve the understanding of CD among Otolaryngology Head and Neck Surgery. Methods:Retrospective cross-sectional, observational study was undertaken in Kunming Children\'s Hospital, from the archival data between January July 2015 and June 2020. Only 6 cases of CD were identified after studying the histomorphological characteristics and neck mass diagnosed. The imaging and pathological features were summarized and the pathogenesis was discussed. Results:Among the 6 cases of Castleman disease, five were male and one was female. Histopathology: Five cases were hyaline vascular subtype, one was mixed type. The uniform clinicopathologic features seen in all hyaline vascular subtype of CD included atrophic germinal centre with lymphocyte depletion, concentric rings of small lymphocytes, increased vascularity and predominance of high endothelial vessels in interfollicular region. Twinning, in which two or more germinal centers are combined and surrounded by lymphocytes in the mantle zone was observed in two cases with lollipop pattern at the same time. All the cases underwent complete surgical resection, the median follow-up time was 48 months（26, 84）, both of them had good prognosis. Conclusion:Most cases of unicentric type CD in children are diagnosed late, which is clinical showed by painless lymphadenopathy. The most common pathological type is hyaline vascular. The overall prognosis of surgical treatment was good.
目的：探讨儿童颈部单中心型Castleman病的临床特点、影像学表现、病理表型、治疗和预后，以期提高耳鼻咽喉头颈外科医师对Castleman病的认识。 方法：回顾性横断面分析2015年7月-2020年6月在昆明市儿童医院经病理诊断为Castleman病、肿物在颈部的6例患儿临床资料，总结其影像学和病理特征，探讨其发生机制。 结果：6例Castleman病患儿中，男5例，女1例。组织病理学：透明血管型5例，混合型1例。透明血管型中观察到一致的病理特征是：萎缩的生发中心伴淋巴细胞削减、增生的套细胞呈同心圆状排列、大量的血管增生，且在滤泡间区高内皮小静脉的增生占据主导地位。2例患儿表现出生发中心双胞胎/花椰菜形多胞胎的同时，观察到“棒棒糖”外观的形成。所有患儿均接受了病灶完整手术切除治疗，随访中位时间48（26，84）个月，预后良好。 结论：儿童颈部单中心型Castleman病大多数延迟确诊，多表现为无痛性淋巴结肿大，病理类型以透明血管型为主，外科手术治疗整体预后良好。.

Aim: This article aims to identify risk factors for severe radiation-induced oral mucositis (RIOM) in head and neck cancer (HNC) patients. In addition, we intend to establish a predictive model in patients undergoing intensity-modulated radiotherapy. Patients & methods: In this retrospective study, several HNC patients (n = 179) treated at Zhejiang Provincial People\'s Hospital from January 2019 to June 2023 were considered. The recruited subjects were divided into modeling and validation groups. The experimental data on clinical characteristics and treatment were collected and analyzed to identify predictive factors for severe RIOM based on the logistic regression approach. Results: The results indicated that severe RIOM occurred in 55.3% of patients. Accordingly, significant predictors included smoking history, diabetes, concurrent chemotherapy, cumulative radiation dose and weight loss of ≥5% in relative to admission weight. A nomogram based on these factors was validated, showing excellent predictive accuracy. Conclusion: In summary, the predictive model could effectively identify high-risk patients for severe RIOM, enabling the design of targeted interventions and improving patient management during radiotherapy.
[Box: see text].

Intensity-modulated radiation therapy (IMRT) has been widely used in treating head and neck tumors. However, due to the complex anatomical structures in the head and neck region, it is challenging for the plan optimizer to rapidly generate clinically acceptable IMRT treatment plans. A novel deep learning multi-scale Transformer (MST) model was developed in the current study aiming to accelerate the IMRT planning for head and neck tumors while generating more precise prediction of the voxel-level dose distribution. The proposed end-to-end MST model employs the shunted Transformer to capture multi-scale features and learn a global dependency, and utilizes 3D deformable convolution bottleneck blocks to extract shape-aware feature and compensate the loss of spatial information in the patch merging layers. Moreover, data augmentation and self-knowledge distillation are used to further improve the prediction performance of the model. The MST model was trained and evaluated on the OpenKBP Challenge dataset. Its prediction accuracy was compared with three previous dose prediction models: C3D, TrDosePred, and TSNet. The predicted dose distributions of our proposed MST model in the tumor region are closest to the original clinical dose distribution. The MST model achieves the dose score of 2.23 Gy and the DVH score of 1.34 Gy on the test dataset, outperforming the other three models by 8%-17%. For clinical-related DVH dosimetric metrics, the prediction accuracy in terms of mean absolute error (MAE) is 2.04% for D 99 , 1.54% for D 95 , 1.87% for D 1 , 1.87% for D mean , 1.89% for D 0.1 c c , respectively, superior to the other three models. The quantitative results demonstrated that the proposed MST model achieved more accurate voxel-level dose prediction than the previous models for head and neck tumors. The MST model has a great potential to be applied to other disease sites to further improve the quality and efficiency of radiotherapy planning.

Head and neck tumors are malignant tumors that appear in the head and neck. Although much progress has been made in the treatment of head and neck tumors, many challenges remain. The prognosis of some advanced cases remains poor and survival and quality of life after treatment face certain limitations. Therefore, further research into the pathogenesis and treatment options for head and neck tumors is important in order to improve the prognosis and quality of life of patients. The Protein Arginine Methyltransferase (PRMT) family is a class of enzymes that are responsible for adding methyl groups to arginine residues in proteins. PRMT family members play important roles in regulating many cellular processes, such as transcriptional regulation, signaling, and cell cycle regulation. Recent studies have shown that the PRMT family also plays an important function in tumorigenesis and development. Here, we found that PRMT family members are significantly overexpressed in head and neck tumors and that PRMT5 may serve as an independent prognostic factor in head and neck tumors. We found that PRMT5-regulated differential genes were significantly enriched in tumor-associated signaling pathways such as IL-17 and p53. And we also found that the expression of PRMT5 in head and neck tumors was significantly correlated with immune cell infiltration, m6A as well as the expression of ferroptosis-related genes, and drug sensitivity. These results suggest that PRMT may play an important role in the development of head and neck tumors.

UNASSIGNED: Preoperative classification of head and neck (HN) tumors remains challenging, especially distinguishing early cancerogenic masses from benign lesions. Synthetic MRI offers a new way for quantitative analysis of tumors. The present study investigated the application of synthetic MRI and stimulus and fast spin echo diffusion-weighted imaging with periodically rotated overlapping parallel lines with enhanced reconstruction (FSE-PROPELLER DWI) to differentiate malignant from benign HN tumors.
UNASSIGNED: Forty-eight patients with pathologically confirmed HN tumors were retrospectively recruited between August 2022 and October 2022. The patients were divided into malignant (n = 28) and benign (n = 20) groups. All patients were scanned using synthetic MRI and FSE-PROPELLER DWI. T1, T2, and proton density (PD) values were acquired on the synthetic MRI and ADC values on the FSE-PROPELLER DWI.
UNASSIGNED: Benign tumors (ADC: 2.03 ± 0.31 × 10-3 mm2/s, T1: 1741.13 ± 662.64 ms, T2: 157.43 ± 72.23 ms) showed higher ADC, T1, and T2 values compared to malignant tumors (ADC: 1.46 ± 0.37 × 10-3 mm2/s, T1: 1390.06 ± 241.09 ms, T2: 97.64 ± 14.91 ms) (all P<0.05), while no differences were seen for PD values. ROC analysis showed that T2+ADC (cut-off value, > 0.55; AUC, 0.950) had optimal diagnostic performance vs. T1 (cut-off value, ≤ 1675.84 ms; AUC, 0.698), T2 (cut-off value, ≤ 113.24 ms; AUC, 0.855) and PD (cut off value, > 80.67 pu; AUC, 0.568) alone in differentiating malignant from benign lesions (all P<0.05); yet, the difference in AUC between ADC and T2+ADC or T2 did not reach statistical significance.
UNASSIGNED: Synthetic MRI and FSE-PROPELLER DWI can quantitatively differentiate malignant from benign HN tumors. T2 value is comparable to ADC value, and T2+ADC values could improve diagnostic efficacy., apparent diffusion coeffificient, head and neck tumors.

The joint analysis of single-cell transcriptomics, proteomics, lipidomics, metabolomics and spatial metabolomics is continually transforming our understanding of the mechanisms of metabolic reprogramming in tumor cells. Since head and neck tumor is the sixth most common tumor in the world, the study of the metabolic mechanism of its occurrence, development and prognosis is still undeveloped. In the past decade, this field has witnessed tremendous technological revolutions and considerable development that enables major breakthroughs to be made in the study of human tumor metabolism. In this review, a comprehensive comparison of traditional metabolomics and spatial metabolomics has been concluded, and the recent progress and challenges of the application of spatial metabolomics combined multi-omics in the research of metabolic reprogramming in tumors are reviewed. Furthermore, we also highlight the advances of spatial metabolomics in the study of metabolic mechanisms of head and neck tumors, and provide an outlook of its application prospects.

UNASSIGNED: Laryngeal carcinoma is one of the most common types of head and neck tumors. The mortality rate in patients with laryngeal cancer has not declined in recent years. Previous studies have shown that laryngeal cancer mortality is related to the extent of laryngeal cancer, the proportion of lymph node metastases, treatment modalities, and postoperative lifestyle habits. Thus, early identifying patients at high risk of laryngeal cancer-specific death is of great clinical importance. However, in the presence of competing risk, the existing survival models based on Cox proportional hazards model may be biased in estimating tumor-specific mortality. In this study, we developed and validated a nomogram based on competitive risk analysis for patients with laryngeal cancer.
UNASSIGNED: We used SEER*Stat (Version 4.6.1) software to identify patients in the Surveillance, Epidemiology, and End Results (SEER) database who were diagnosed with laryngeal cancer between 2000 and 2019 as study subjects. The collected data included demographic data, the primary site of laryngeal cancer, the histological type of tumor, tumor size, and other variables. After excluding cases with missing information, the entire cohort was randomly split into a training cohort and a validation cohort at a 7:3 ratio. The training cohort was used in building the model while the validation cohort was used to validate the model. Univariate and multivariate Fine&Gray regression analyses were used to screen statistically significant variables, and the model performance was measured by establishing a consistency index, receiver operating characteristic curve (ROC), and calibration curves.
UNASSIGNED: After excluding cases with missing information, 3,805 patients (2,264 in the training cohort and 1,141 in the validation cohort) were included in the study and followed for a median of 16 months. A total of 411 died of laryngeal cancer, and 2,104 patients died from other causes. Among 3,805 patients, the vast majority was male (80.9%), and Caucasian (77.2%), and aged 60-80 years old (58.4%).
UNASSIGNED: Advanced age and keratinized SCC are risk factors for laryngeal cancer-specific death. These high-risk patients should be given more attention and closer monitoring in clinical practice.

UNASSIGNED: To investigate the performance of a model in predicting carotid artery (CA) invasion in patients with head and neck masses using computed tomography (CT).
UNASSIGNED: This retrospective study included patients with head and neck masses who underwent CT and surgery between January 2013 and July 2021. Patient characteristics and ten CT features were assessed by two radiologists. The patients were randomly allocated to a training cohort (n=106) and a validation cohort (n=109). Independent risk factors for CA invasion were assessed by univariate and multivariate logistic regression analyses. The predictive model was established as a nomogram using the training cohort. In addition, the calibration, discrimination, reclassification, and clinical application of the model were assessed in the validation cohort.
UNASSIGNED: A total of 215 patients were evaluated, including 54 patients with CA invasion. Vascular wall deformation (odds ratio [OR], 7.17; p=0.02) and the extent of encasement to the CA (OR, 1.02; p<0.001) were independent predictors of CA invasion in the multivariable analysis in the training cohort. The performance of the model was similar between the training and validation cohort, with an area under the receiver operating characteristic curve of 0.93 (95% confidence intervals [CI], 0.88-0.98) and 0.88 (95% CI, 0.80-0.96) (p=0.07), respectively. The calibration curve showed a good agreement between the predicted and actual probabilities.
UNASSIGNED: A predictive model for carotid artery invasion can be defined based on features that come from patient characteristics and CT data to help in improve surgical planning and invasion evaluation.

UNASSIGNED: Head and neck tumors account for more than 6% of all cancers. The primary treatment for tumors of the head and neck is radiation therapy, which can induce oropharyngeal mucositis as a side effect. At present, there is no widely available therapeutic for the treatment of oropharyngeal mucositis in clinical practice. Based on the traditional prescription Liushen Wan, the pathogenesis and pathology, we developed a new Chinese medicine prescription and made Zhenhuang submicron emulsion (ZHSE) spray, which has an efficacious therapeutic effect for oropharyngeal mucositis. However, its mechanism is unclear.
UNASSIGNED: This research explored the mechanism behind the modulatory effects of ZHSE by a strategy of metabolomics and network pharmacology. Multivariate data analyses, including unsupervised principal component analysis (PCA) and supervised orthogonal partial least squares discriminant analysis (OPLS-DA), were performed. Potential biomarkers were identified depending on the mass-charge ratio of the selected compound. Statistical and pathway enrichment analysis was performed in the KEGG pathway database. Network pharmacology combining metabolomic analyses was conducted to illustrate the key targets and pathways.
UNASSIGNED: Critical metabolic pathways were investigated, 56f biomarkers were enriched and key metabolites such as linoleic acid, 9,10-epoxyoctadecenoic acid, acetoacetic acid and citric acid were identified. A complex network of \"compound-target-potential metabolite\" interactions was drawn to illuminate the regulation of chemical constituents on key metabolites. These findings manifest that ZHSE regulates endogenous metabolite disorders during the treatment of oropharyngeal mucositis by various constituents, interacting with multiple targets associated with inflammation and pain.
UNASSIGNED: In this work, we determined several critical biomarkers and metabolic pathways and identified the possible regulatory mechanism by which ZHSE functions in the treatment of oropharyngeal mucositis. This study provides a new perspective on integrating metabolomics and network pharmacology for exploring improved therapy for head and neck tumors based on the traditional classic prescription of LSW.