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Abstract Backgrounds Preoperative imaging, particularly with magnetic resonance imaging (MRI) and computed tomography (CT) scans, plays a crucial role in distinguishing between benign and malignant parotid gland tumors, while the reliability of Ultrasound-Guided Fine Needle Aspiration (FNA) in diagnosing these masses remains a topic of debate. Methods This two-center retrospective analysis was conducted on 347 patients with parotid gland tumors who had FNA and preoperative imaging (CT or MRI). All patients underwent surgery and final histopathological examination was available, along with complete medical records between January 2008 and May 2023. Results Among the 347 patients, 318 (92%) had benign and 10 (3%) had malignant tumors based on FNA, with 19 (5%) unsatisfactory specimens. Final histological diagnosis revealed 303 (87%) benign and 44 (13%) malignant lesions, with a false-negative rate of 10.6% for FNA. Multivariate analysis identified irregular shape and invasion as independent predictors of malignancy in patient with benign or unsatisfactory FNA results. The odds ratio for irregular shape was 3.06 and for invasion was 12.73. Conclusion Imaging characteristics, such as irregular shape and invasion may indicate towards malignant parotid tumors, even in patients with false-negative benign findings in FNA.

Abstract Background The proportional trends of HPV-associated oropharyngeal squamous cell carcinoma (OPSCC) according to various factors have not been analyzed in detail in previous studies. We aimed to evaluate the trends of HPV-associated OPSCC in the United States. Methods This retrospective cohort study included 13,081 patients with OPSCC from large population-based data using Surveillance, Epidemiology, and End Results (SEER) 2010–2017 database, 17 Registries. Patients were diagnosed with OPSCC primarily in the base of tongue (BOT), posterior pharyngeal wall (PPW), soft palate (SP), and tonsil and were tested for HPV infection status. We analyzed how the proportional trends of patients with OPSCC changed according to various demographic factors. Additionally, we forecasted and confirmed the trend of HPV (+) and (−) patients with OPSCC using the autoregressive integrated moving average (ARIMA) model. Results The proportion of patients who performed the HPV testing increased every year, and it has exceeded 50% since 2014 (21.95% and 51.37% at 2010 and 2014, respectively). The HPV-positive rates tended to increase over past 7 years (66.37% and 79.32% at 2010 and 2016, respectively). Positivity rates of HPV were significantly higher in OPSCC located in the tonsil or BOT than in those located in PPW or SP. The ARIMA (2,1,0) and (0,1,0) models were applied to forecast HPV (+) and (−) patients with OPSCC, respectively, and the predicted data generally matched the actual data well. Conclusion This large population-based study suggests that the proportional trends of HPV (+) patients with OPSCC has increased and will continue to increase. However, the trends of HPV (+) and (−) patients differed greatly according to various demographic factors. These results present a direction for establishing appropriate preventive measures to deal with HPV-related OPSCC in more detail.

BackgroundIntractable xerostomia is defined as the subjective perception of dry mouth and persistent salivary gland hypofunction. Pilocarpine is an approved salivary sialagogue that is frequently prescribed for the treatment of intractable xerostomia; however, it often exhibits more side effects at high-doses and limited effectiveness at low-doses. Baekhogainsam-Tang (BIT) is a common herbal formula used by patients complaining of sore throats and thirst. It seems that BIT can compensate for the insufficient effect of low-dose pilocarpine. However, no clinical trials have studied the efficacy of combined administration of BIT and low-dose pilocarpine for intractable xerostomia. We aim to assess the non-inferior efficacy and fewer side effects of combined administration of BIT and low-dose pilocarpine compared with the administration of high-dose pilocarpine.MethodsA randomized, open-label, parallel-group, multi-center trial will be conducted. A total of 120 patients with Sjogren's syndrome having an unstimulated salivary flow rate (SFR) ≤0.1 mL/min or who have undergone radiotherapy to the head and neck with an unstimulated SFR ≤0.25 mL/min will be recruited competitively. They will be randomly allocated to either the experimental or control groups. The experimental group will receive BIT herbal granules three times and pilocarpine (2.5-mg) four times daily; meanwhile, the control group will receive only 5-mg pilocarpine four times daily for 12 weeks. The primary outcome is unstimulated SFR after 12 weeks of treatment. Secondary outcomes are stimulated SFR after 12 weeks of medication, as well as differences and mean percentage changes in unstimulated and stimulated SFR, visual analog scale, salivary scintigraphy, and questionnaires for both oral symptoms and quality of life during the clinical trial. An independent T test or Mann-Whitney U test will be performed to compare values between the two groups. The Paired T test or Wilcoxon signed-rank test will be performed to compare intragroup continuous values.ConclusionThis trial will be significant evidence on the efficacy and safety of combined use of BIT and low-dose pilocarpine to treat intractable xerostomia.Clinical trial registrationThe Clinical Research Information Service of the Republic of Korea (ISRCTN, KCT0005982). Registered on 10 February 2021.

Abstract Background Various cancer stem cell (CSC) biomarkers and the genes encoding them in head and neck squamous cell carcinoma (HNSCC) have been identified and evaluated. However, the validity of these factors in the prognosis of HNSCC has been questioned and remains unclear. In this study, we examined the clinical significance of CSC biomarker genes in HNSCC, using five publicly available HNSCC cohorts. Methods To predict the prognosis of patients with HNSCC, we developed and validated the expression signatures of CSC biomarker genes whose mRNA expression levels correlated with at least one of the four CSC genes (CD44, MET, ALDH1A1, and BMI1). Results Patients in The Cancer Genome Atlas (TCGA) HNSCC cohort were classified into CSC gene expression-associated high-risk (CSC-HR; n = 285) and CSC gene expression-associated low-risk (CSC-LR; n = 281) subgroups. The 5-year overall survival and recurrence-free survival rates were significantly lower in the CSC-HR subgroup than in the CSC-LR subgroup (p = 0.04 and 0.02, respectively). The clinical significance of the CSC gene expression signature was validated using four independent cohorts. Analysis using Cox proportional hazards models showed that the CSC gene expression signature was an independent prognostic factor of non-oropharyngeal HNSCC which mostly indicates HPV (–) status. Furthermore, the CSC gene expression signature was associated with the prognosis of HNSCC patients who received radiotherapy. Conclusion The CSC gene expression signature is associated with the prognosis of HNSCC and may help in personalized treatments for patients with HNSCC, especially in cases with HPV (–) status who were classified in more detail.

The FAT1 gene functions as a tumor suppressor or promoter and remains incompletely understood. We examined the clinical significance of FAT1 in head and neck squamous cell carcinoma (HNSCC) using four publicly available HNSCC cohorts and one HNSCC cohort enrolled at a tertiary medical center. We developed FAT1 signatures reflecting FAT1 mutations and mRNA expression using one cohort. Patients with HNSCC were classified into FAT1‐associated low risk (FAT1‐LR; n = 195) and FAT1‐associated high risk (FAT1‐HR; n = 371) subgroups. The five‐year overall survival and recurrence‐free survival rates were significantly lower in the FAT1‐HR subgroup than in the FAT1‐LR subgroup (P = 0.01 and 0.003, respectively). The clinical significance of FAT1 was validated using four independent cohorts. Cox proportional hazards models showed that the FAT1 signature was an independent prognostic factor for HNSCC patients. In addition, FAT1 signature was associated with the response to radiotherapy, advanced stage, and human papilloma virus (HPV) status in HNSCC patients. In conclusion, the FAT1 gene signature was associated with prognosis of HNSCC and may help to provide personalized treatments for HNSCC patients.

BackgroundWe determined appropriate survival prediction machine learning models for patients with oropharyngeal squamous cell carcinoma (OPSCC) using the “Surveillance, Epidemiology, and End Results” (SEER) database.MethodsIn total, 4039 patients diagnosed with OPSCC between 2004 and 2016 were enrolled in this study. In particular, 13 variables were selected and analyzed: age, sex, tumor grade, tumor size, neck dissection, radiation therapy, cancer directed surgery, chemotherapy, T stage, N stage, M stage, clinical stage, and human papillomavirus (HPV) status. The T-, N-, and clinical staging were reconstructed based on the American Joint Committee on Cancer (AJCC) Staging Manual, 8th Edition. The patients were randomly assigned to a development or test dataset at a 7:3 ratio. The extremely randomized survival tree (EST), conditional survival forest (CSF), and DeepSurv models were used to predict the overall and disease-specific survival in patients with OPSCC. A 10-fold cross-validation on a development dataset was used to build the training and internal validation data for all models. We evaluated the predictive performance of each model using test datasets.ResultsA higher c-index value and lower integrated Brier score (IBS), root mean square error (RMSE), and mean absolute error (MAE) indicate a better performance from a machine learning model. The C-index was the highest for the DeepSurv model (0.77). The IBS was also the lowest in the DeepSurv model (0.08). However, the RMSE and RAE were the lowest for the CSF model.ConclusionsWe demonstrated various machine-learning-based survival prediction models. The CSF model showed a better performance in predicting the survival of patients with OPSCC in terms of the RMSE and RAE. In this context, machine learning models based on personalized survival predictions can be used to stratify various complex risk factors. This could help in designing personalized treatments and predicting prognoses for patients.