Immunohistochemical Profiling and Staging in Esthesioneuroblastoma: A Single-Center Cohort Study and Systematic Review

The nomogram for predicting nasal bleeding after endoscopic transsphenoidal resection of pituitary adenomas: a retrospective study

Objective

This study aimed to develop and validate a dynamic nomogram to assess the risk of nasal bleeding after endoscopic transnasal transsphenoidal pituitary tumor resection.

Methods

A retrospective analysis was conducted on patients who underwent endoscopic transnasal transsphenoidal pituitary tumor resection from June 2019 to June 2021. Univariate and multivariate logistic regression analyses were used to screen for independent risk factors for nasal bleeding from the training set. A multivariate logistic regression model was established, a nomogram was plotted, and it was validated in an internal validation set. The performance of the nomogram was evaluated based on the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

Results

The nomogram indicators included anticoagulant use, sphenoid sinus artery injury, nasal irrigation, platelet count (PLT), and constipation. The predictive model had an area under the ROC curve of 0.932 (95% CI: 0.873-0.990) and 0.969 (95% CI: 0.940-0.997) for the training and validation sets, respectively, indicating good discrimination. The calibration curve showed good consistency between the actual and predicted incidence of nasal bleeding (p > 0.05). DCA indicated that the nomogram had good clinical net benefit in predicting postoperative nasal bleeding in patients.

Conclusion

In summary, this study explored the incidence and influencing factors of nasal bleeding after endoscopic transnasal transsphenoidal pituitary tumor resection and established a predictive model to assist clinical decision-making.

Characterizing Immune Infiltration in Esthesioneuroblastoma Subtypes Through Gene Expression Deconvolution

BACKGROUND

Esthesioneuroblastoma (ENB) is a rare cancer deriving from the olfactory mucosa. Among the basal or neural genomic subtypes, the basal subtype is associated with poorer survival, poor differentiation, and higher levels of tumor-infiltrating immune cells (TIICs). The immune microenvironment of these ENB subtypes remains unclear. We used an established machine learning algorithm on ENB transcriptomic profiles.

METHODS

The authors characterized 22 immune cell populations using the CIBERSORTx deconvolutional machine learning pipeline on RNA sequencing data from 18 ENB cases. The characterization aimed to elucidate differences in relative proportions and populations of TIICs between basal and neural ENB.

RESULTS

No differences in age, Hyams, Dulguerov, IDH2 mutation, or PD-L1 expression were seen between basal and neural subtypes of ENB (P > 0.05). Also, no difference in median overall survival was appreciated (52.0 ± 13.1 months vs. 50.0 ± 43.2 months, P = 0.5). As a cohort, M2 macrophages were the most abundant subpopulation (14%) followed by naïve B cells (13%) and CD4 memory resting T cells (12%). No gross differences in CD20, CD4, or CD8 cells/mm2 were apparent on gross histology (P > 0.05). However, further analysis showed that activated CD4 memory T cells were significantly increased in the basal ENBs, whereas resting dendritic cells were increased in the neural ENB subtype. The TIIC profiles alone could not differentiate between basal and neural ENB, but did suggest immunoprofile differences.

CONCLUSIONS

Basal and neural subtypes display distinct TIIC involvement, which may impact their difference in outcome. These findings provide the framework for further investigation in novel immunomodulation strategies for ENB.