Introduction: The incidence of occult cervical lymph node metastases (OCLNM) is reported to be 20–30% in early-stage oral cancer and oropharyngeal cancer. There is a lack of an accurate diagnostic method to predict occult lymph node metastasis and to help surgeons make precise treatment decisions. Aim: To construct and evaluate a preoperative diagnostic method to predict OCLNM in early-stage oral and oropharyngeal squamous cell carcinoma (OC and OP SCC) based on deep learning features (DLFs) and radiomics features. Methods: A total of 319 patients diagnosed with early-stage OC or OP SCC were retrospectively enrolled and divided into training, test and external validation sets. Traditional radiomics features and DLFs were extracted from their MRI images. The least absolute shrinkage and selection operator (LASSO) analysis was employed to identify the most valuable features. Prediction models for OCLNM were developed using radiomics features and DLFs. The effectiveness of the models and their clinical applicability were evaluated using the area under the curve (AUC), decision curve analysis (DCA), and survival analysis. Results: Seventeen prediction models were constructed. The Resnet50 deep learning (DL) model based on the combination of radiomics and DL features achieves the optimal performance, with AUC values of 0.928 (95% CI: 0.881–0.975), 0.878 (95% CI: 0.766–0.990), 0.796 (95% CI: 0.666–0.927), and 0.834 (95% CI: 0.721–0.947) in the training, test, external validation set1, and external validation set2, respectively. Moreover, the Resnet50 model has great prediction value of prognosis in patients with early-stage OC and OP SCC. Conclusion: The proposed MRI-based Resnet50 DL model demonstrated high capability in diagnosis of OCLNM and prognosis prediction in the early-stage OC and OP SCC. The Resnet50 model could help refine the clinical diagnosis and treatment of the early-stage OC and OP SCC.

Objective: To screen the radiomic features of simple bone cysts of the jaws and explore the potential application of radiomics in pre-operative diagnosis of jaw simple bone cysts. Methods: The investigators designed and implemented a case–control study. 19 patients with simple bone cysts who were admitted to the Department of Maxillofacial Surgery, Sun Yat-sen University Affiliated Stomatology Hospital from 2013 to 2019 were included in this study. Their clinical data and cone-beam computed tomography (CBCT) images were examined. The control group consisted of patients with odontogenic keratocyst. CBCT imaging features were analyzed and compared between the patient and control groups. Results: Overall, 10,323 image features were extracted through feature analysis. A subset of 25 radiomic features obtained after feature selection were analyzed further. These 25 features were significantly different between the 2 groups (p < 0.05). The absolute value of correlation coefficient was 0.487–0.775. Gray-level co-occurrence matrix (GLCM) contrast, neighborhood gray tone difference matrix (NGTDM) contrast, and GLCM variance were the features with the highest correlation coefficients. Conclusions: Pre-operative radiomics analysis showed the differences between simple bone cysts and odontogenic keratocysts, can help to diagnose simple bone cysts. Three specific texture features—GLCM contrast, NGTDM contrast, and GLCM variance—may be the characteristic imaging features of simple bone cysts of the jaw.

Background: Substantial studies reveal that tertiary lymphoid structure (TLS) correlate with prognosis and immunotherapy response in various types of cancers. However, the predictive value of TLS, the specific immune cell subtype within TLS and their anti-tumor mechanisms remain unclear.Methods: Based on 23 TLS-related genes (TLSRGs), we utilized bioinformatics methods to construct a scoring system, named TLSscore. By integrating RNA and single-cell sequencing data, we assessed the utility of TLSscore in head and neck squamous cell carcinoma (HNSCC). Flow cytometric sorting was used to isolate specific T cells subtypes, in vivo and in vitro experiments were conducted to demonstrate its anti-tumor effects.Results: The TLSscore model was constructed and specific TLSscore-genes were found to consistently align with the spatial location of TLS. TLSscore has proven to be a robust predictive model for predicting survival prognosis, immune cell infiltration, somatic mutation and immunotherapy response. Notably, a specific PD1⁺CXCL13⁺CD8⁺T cell subtype was identified within TLS. Both in vivo and in vitro experiments demonstrated that PD1⁺CXCL13⁺CD8⁺T cell might represent a functional cell subtype exerting anti-tumor effects during the process of immune surveillance.Conclusions: Our study presents a predictive model for TLS, which can evaluate its presence and predicts survival prognosis and immunotherapy response in HNSSC patients. Additionally, we identify a specific subtype of T cells that might elucidate the mechanism of TLS function in anti-tumor activities. This T cell subtype holds the potential to be a prognostic marker and a target for adoptive cell therapy (ACT) in the future.

Mast cells play complex and context‐dependent roles within the tumor microenvironment, yet their molecular characteristics and functional diversity across human cancers remain poorly defined. In this study, single‐cell RNA sequencing and spatial transcriptomics data are integrated to comprehensively map the transcriptional and spatial heterogeneity of mast cells across ten cancer types. A distinct proinflammatory mast cell (PMC) population is identified, characterized by strong antigen‐presenting features and immune‐activating potential. Mechanistic analyses show that retinoic acid (RA) signaling drives the polarization of PMCs through activation of RARα, which promotes CIITA‐mediated MHC‐II expression, CXCL16 secretion, and T cell recruitment and activation. Across multiple cancer types, tumors with higher PMC abundance are associated with more favorable clinical outcomes. These findings reveal the pivotal role of RA–RARα–CIITA signaling in mast cell reprogramming and suggest that pharmacologic induction of proinflammatory mast cells may represent a promising approach to enhance antitumor immunity.

To compare the postoperative complications, speech function, quality of life (QoL), overall clinical outcomes and flap volume loss of tongue reconstruction

Background: Neoadjuvant chemoimmunotherapy (NACI) has become one of the most widely adopted therapies for head and neck squamous cell carcinoma (HNSCC) before surgery. However, the accurate prediction of patients responding to this therapy has been challenging owing to the lack of predictive biomarkers. Methods: In the present study, histologically confirmed HNSCC patients with complete clinicopathological data, who received chemotherapy plus programmed cell death protein 1 inhibitor as the NACI regimen for 2–3 cycles before radical surgery between 2021 and 2023, were screened, and both clinicopathological and magnetic resonance imaging (MRI) data were collected and divided into training, testing, and external validation cohorts. Both traditional radiomics and deep-learning techniques were employed to extract features from MRI, followed by feature selection using both Spearman correlation and least absolute shrinkage and selection operator analysis. The selected features were incorporated into predictive models using a logistic regression classifier for pathologic complete response. Results: The results demonstrated that three out of seven features extracted from MRI were deep-learning features. Notably, the integration of deep-learning features with clinicopathological and radiomics features increased the area under the curve in the training, testing, and external validation cohorts to 0.781, 0.759, and 0.740, respectively. Moreover, multimodal prediction for patient stratification can significantly improve the prognosis of HNSCC patients undergoing NACI. Conclusions: In conclusion, deep-learning features from MRI can augment traditional imaging analysis to uncover hidden predictive patterns reflecting responsiveness to NACI in HNSCC patients, and integration of different data types provides a more robust prediction strategy.