Search

Abstract Background The cellular origin of hypopharyngeal diseases is crucial for further diagnosis and treatment, and the microenvironment in tissues may also be associated with specific cell types at the same time. Normal adjacent tissues (NATs) of hypopharyngeal carcinoma differ from non-tumor-bearing tissues, and can influenced by the tumor. However, the heterogeneity in kinds of disease samples remains little known, and the transcriptomic profile about biological information associated with disease occurrence and clinical outcome contained in it has yet to be fully evaluated. For these reasons, we should quickly investigate the taxonomic and transcriptomic information of NATs in human hypopharynx. Results Single-cell suspensions of normal adjacent tissues (NATs) of hypopharyngeal carcinoma were obtained and single-cell RNA sequencing (scRNA-seq) was performed. We present scRNA-seq data from 39,315 high-quality cells in the hypopharyngeal from five human donors, nine clusters of normal adjacent human hypopharyngeal cells were presented, including epithelial cells, endothelial cells (ECs), mononuclear phagocyte system cells (MPs), fibroblasts, T cells, plasma cells, B cells, mural cells and mast cells. Nonimmune components in the microenvironment, including epithelial cells, endothelial cells, fibroblasts and the subpopulations of them were performed. Conclusions Our data provide a solid basis for the study of single-cell landscape in human normal adjacent hypopharyngeal tissues biology and related diseases.

Summary: Background: The occult lymph node metastasis (LNM) of laryngeal squamous cell carcinoma (LSCC) affects the treatment and prognosis of patients. This study aimed to comprehensively compare the performance of the three-dimensional and two-dimensional deep learning models, radiomics model, and the fusion models for predicting occult LNM in LSCC. Methods: In this retrospective diagnostic study, a total of 553 patients with clinical N0 stage LSCC, who underwent surgical treatment without distant metastasis and multiple primary cancers, were consecutively enrolled from four Chinese medical centres between January 01, 2016 and December 30, 2020. The participant data were manually retrieved from medical records, imaging databases, and pathology reports. The study cohort was divided into a training set (n = 300), an internal test set (n = 89), and two external test sets (n = 120 and 44, respectively). The three-dimensional deep learning (3D DL), two-dimensional deep learning (2D DL), and radiomics model were developed using CT images of the primary tumor. The clinical model was constructed based on clinical and radiological features. Two fusion strategies were utilized to develop the fusion model: the feature-based DLRad_FB model and the decision-based DLRad_DB model. The discriminative ability and correlation of 3D DL, 2D DL and radiomics features were analysed comprehensively. The performances of the predictive models were evaluated based on the pathological diagnosis. Findings: The 3D DL features had superior discriminative ability and lower internal redundancy compared to 2D DL and radiomics features. The DLRad_DB model achieved the highest AUC (0.89–0.90) among all the study sets, significantly outperforming the clinical model (AUC = 0.73–0.78, P = 0.0001–0.042, Delong test). Compared to the DLRad_DB model, the AUC values for the DLRad_FB, 3D DL, 2D DL, and radiomics models were 0.82–0.84 (P = 0.025–0.46), 0.86–0.89 (P = 0.75–0.97), 0.83–0.86 (P = 0.029–0.66), and 0.79–0.82 (P = 0.0072–0.10), respectively in the study sets. Additionally, the DLRad_DB model exhibited the best sensitivity (82–88%) and specificity (79–85%) in the test sets. Interpretation: The decision-based fusion model DLRad_DB, which combines 3D DL, 2D DL, radiomics, and clinical data, can be utilized to predict occult LNM in LSCC. This has the potential to minimize unnecessary lymph node dissection and prophylactic radiotherapy in patients with cN0 disease. Funding: National Natural Science Foundation of China, Natural Science Foundation of Shandong Province.