Central neurocytomas (CN) are rare intraventricular tumors with prominent neuronal differentiation. CN commonly arise in the lateral ventricles of young adults who predominantly present with raised intracranial pressure. Few studies have described the clinical, pathological, and radiological features of these tumors, and those that have are typically single case reports. Herein, we report ten patients with CN with variable clinical and pathological features and discuss the management of these tumors. Nine tumors occupied the lateral ventricle and only one was located in the sellar region. On MRI, all 10 tumors showed heterogeneous hypo-or iso-intensity on T1-weighted and hyperintensity on T2-weighted MRI. Contrast enhancement varied greatly from very slight to intense. All patients were surgically treated by macroscopic total or subtotal removal. Postoperative radiotherapy was given to six patients (four of whom had undergone subtotal resection and two of whom had undergone total resection). The surgical and histopathological data of these patients were reviewed and analyzed. No recurrences were noted although we were unable to contact two patients for follow-up. A brief review of the literature concerning differential diagnosis and therapeutic aspects of these tumors is also presented.

Background: Available evidence indicates that kinetochore-localized astrin/SPAG5-binding protein (KNSTRN) is an oncogene in skin carcinoma. This study aimed to evaluate the prognostic value of KNSTRN in lung adenocarcinoma (LUAD) underlying the Cancer Genome Atlas (TCGA) database. Methods: The relationship between clinicopathological features and KNSTRN was analyzed with the Wilcoxon signed-rank test and logistic regression. The clinicopathological characteristics associated with overall survival (OS) were evaluated using Cox regression and the Kaplan–Meier method. Gene ontology (GO) analysis, gene set enrichment analysis (GSEA), and single-sample GSEA (ssGSEA) were performed using TCGA data.Results: The KNSTRN expression level was found to be significantly higher in LUAD tissue than in normal lung tissue. Also, it correlated significantly with advanced clinicopathological characteristics. The Kaplan–Meier survival curve revealed a significant relationship of high expression of KNSTRN with poor OS in patients with LUAD. The multivariate Cox regression hazard model demonstrated the KNSTRN expression level as an independent prognostic factor for patients with LUAD. GO and GSEA analyses indicated the involvement of KNSTRN in cell cycle checkpoints, DNA replication, and G2-M checkpoint M phase. Based on ssGSEA analysis, KNSTRN had a positive relationship with Th2 cells and CD56dim natural killer cells. The KNSTRN expression levels in several types of immune cells were significantly different.Conclusion: The findings suggested that the increased expression level of KNSTRN was significantly associated with the progression of LUAD and could also serve as a novel prognostic biomarker for patients with LUAD.

Background To predict the risk of radiation pneumonitis (RP), deep learning (DL) models were built to stratify lung cancer patients. Our study also investigated the impact of RP on survival. Methods This study retrospectively collected 100 RP and 99 matched non-RP lung cancer patients treated with radiotherapy from two independent centers. These patients were randomly divided into training (n = 175) and validation cohorts (n = 24). The radiomics and dosiomics features were extracted from radiation planning computed tomography (CT). Clinical information was retrospectively collected from the electronic medical record database. All features were screened by LASSO cox regression. A multi-omics prediction model was developed by the optimal algorithm and estimated the area under the receiver operating characteristic curve (AUC). Overall survival (OS) between RP, non-RP, mild-RP, and severe-RP groups was analyzed by the Kaplan-Meier method. Results There were eventually selected 16 radiomics features, 2 dosiomics features, and 1 clinical feature to build the best multi-omics model. GLRLM_Gray Level Non Uniformity Normalized and GLCM_MCC from PTV were essential dosiomics features, and T stage was a paramount clinical feature. The optimal performance for predicting RP was the AUC of testing set [0.94, 95% confidence interval (CI) (0.939-1.000)] and the AUC of external validation set [0.92, 95% CI (0.80-1.00)]. All RP patients were divided into mild-RP and severe-RP group according to RP grade (≤ 2 grade and > 2 grade). The median OS was 31 months (95% CI, 28–39) for non-RP group compared with 49 months (95% CI, 36-NA) for RP group (HR = 0.53, P = 0.0022). Among RP subgroup, the median OS was 57months (95% CI, 47-NA) for mild-RP and 25 months (95% CI, 29-NA) for severe-RP, and mild-RP group exhibited a longer OS (HR = 3.72, P < 0.0001). Conclusion The multi-omics model contributed to improvement in the accuracy of the RP prediction. Interestingly, this study also demonstrated that compared with non-RP patients, RP patients displayed longer OS, especially mild-RP.