Objective: To define clear clinical characteristics and management strategies of herniation of temporomandibular joint (TMJ) into the external auditory canal (EAC). Data Source: MEDLINE, PubMed, and EMBASE databases. Study Selection: A search was conducted using the keywords “temporomandibular joint” and “herniation” with all of their synonyms. Literature selection criteria included articles published in English, and articles dating back no further than 1970. Results: Forty articles regarding 51 cases were eligible for critical appraisal. According to the previously published papers, TMJ herniation has following characteristics; symptoms are nonspecific, but a distinguishable feature is a protruding mass into the EAC that can be seen to appear and disappear as the mouth opens and closes. High-resolution computed tomography scans are sensitive to the bony defect and are helpful in diagnosing TMJ herniation. In the surgical treatment of TMJ herniation, wall reconstruction rather than simple mass excision could be a safe and long-lasting strategy. Conclusions: Herniation of TMJ into the EAC is a rare condition, but can be encountered in the clinic at any time. This literature review could be helpful in the diagnosis and treatment of TMJ herniation into the EAC.

The role of systemic inflammation has not been clearly defined in thyroid cancers. There have been conflicting reports on whether systemic inflammatory markers have predictive value for thyroid cancers. We aimed to evaluate the association between systemic inflammatory markers and clinicopathological factors in thyroid cancers and to assess their predictive value for thyroid cancers in detail. Five hundred thirty-one patients who underwent surgery for thyroid nodules were included. The patient population consisted of 99 individuals (18.6%) with benign thyroid nodules and 432 individuals (81.4%) with thyroid cancers. In 432 patients with thyroid cancers, neutrophil-to-lymphocyte ratio (NLR) was significantly higher in the cases with tumors greater than 2 cm than in those with tumors less than 2 cm. (p = 0.027). NLR and platelet-to-lymphocyte ratio (PLR) were significantly higher in cases with lateral lymph node metastasis (LNM) than in those without LNM (p = 0.007 and 0.090, respectively). The nodule size was significantly higher in benign thyroid nodules than in thyroid cancers (p < 0.001). When the cases were stratified by tumor size, NLR was a significant predictor of thyroid cancers in cases with nodules greater than 2 cm (Exp(B) = 1.85, 95% CI = 1.15–2.97, p = 0.011), but not in those with nodules less than 2 cm. In thyroid cancers, preoperative NLR was associated with pathological prognosticators such as tumor size and lateral lymph node metastasis. When the size difference between thyroid cancers and benign thyroid nodules was adjusted, NLR could be a significant predictor of thyroid cancers.

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Purpose The immunologic properties of tumors can change during the clinical course. We aimed to compare the expression of PD-L1 and the infiltration of CD8+ tumor-infiltrating lymphocytes (TILs) in the tumor microenvironment between initial and recurrent head and neck squamous-cell carcinomas (HNSCCs). We also evaluated whether the changes of those immunologic properties in recurrent HNSCCs affect the oncologic outcome. Methods We included 42 patients who had been treated for both initial and recurrent HNSCCs. Pathologic specimens from initial and salvage surgery were obtained for each patient, and IHC staining of CD3, CD8, PD-1, and PD-L1 were done. Also, neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were collected. The change of each immunologic profile was expressed as the recurrent-to-initial ratio (R/I ratio). Results The change of CD3+, CD8+, PD-1, and PD-L1 positive cells varied widely. For more than half the patients, those values decreased in recurrent tumors. The median R/I ratio was 0.5 for CD3+ TILs, 0.6 for CD8+ TILs, 0.5 for PD-1 and 0.4 for PD-L1. In contrast, NLR and PLR increased in recurrent tumors for more than half the patients. The median R/I ratio of NLR and PLR was 1.6 and 1.4, respectively. In multivariate analysis, increased CD8 (R/I ratio &gt;1) was the independent prognostic factor for better OS (hazard ratio 0.228; 95% CI 0.067 – 0.777; p= 0.018). Conclusion The change of immunologic properties along with the recurrence of HNSCC varied widely from patient to patient. Generally, the intratumoral biomarkers decreased, while the systemic inflammatory markers increased. The increased CD8+ TILs in recurrent HNSCCs was the significant prognostic factor for better overall survival.

Pretreatment values of the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) are well-established prognosticators in various cancers, including head and neck cancers. However, there are no studies on whether temporal changes in the NLR and PLR values after treatment are related to the development of recurrence. Therefore, in this study, we aimed to develop a deep neural network (DNN) model to discern cancer recurrence from temporal NLR and PLR values during follow-up after concurrent chemoradiotherapy (CCRT) and to evaluate the model’s performance compared with conventional machine learning (ML) models. Along with conventional ML models such as logistic regression (LR), random forest (RF), and gradient boosting (GB), the DNN model to discern recurrences was trained using a dataset of 778 consecutive patients with primary head and neck cancers who received CCRT. There were 16 input features used, including 12 laboratory values related to the NLR and the PLR. Along with the original training dataset (N = 778), data were augmented to split the training dataset (N = 900). The model performance was measured using ROC-AUC and PR-AUC values. External validation was performed using a dataset of 173 patients from an unrelated external institution. The ROC-AUC and PR-AUC values of the DNN model were 0.828 ± 0.032 and 0.663 ± 0.069, respectively, in the original training dataset, which were higher than the ROC-AUC and PR-AUC values of the LR, RF, and GB models in the original training dataset. With the recursive feature elimination (RFE) algorithm, five input features were selected. The ROC-AUC and PR-AUC values of the DNN-RFE model were higher than those of the original DNN model (0.883 ± 0.027 and 0.778 ± 0.042, respectively). The ROC-AUC and PR-AUC values of the DNN-RFE model trained with a split dataset were 0.889 ± 0.032 and 0.771 ± 0.044, respectively. In the external validation, the ROC-AUC values of the DNN-RFE model trained with the original dataset and the same model trained with the split dataset were 0.710 and 0.784, respectively. The DNN model with feature selection using the RFE algorithm showed the best performance among the ML models to discern a recurrence after CCRT in patients with head and neck cancers. Data augmentation by splitting training data was helpful for model performance. The performance of the DNN-RFE model was also validated with an external dataset.