Influence of gender and sexual hormones on outcomes after pituitary surgery: a systematic review and meta-analysis

Radiological and Immunohistochemical Characteristics of PitNETs in 79 Patients Undergoing Neurosurgery

The human pituitary is a gland located within a small bony box, the sella turcica, under the base of the brain [...].

Machine Learning Prediction of Pituitary Macroadenoma Consistency: Utilizing Demographic Data and Brain MRI Parameters

Consistency of pituitary macroadenomas is a key determinant in surgical outcomes, with non-soft consistency linked to more complications and incomplete resections. This study aimed to develop a machine learning model to predict the consistency of pituitary macroadenomas to improve surgical planning and outcomes. A retrospective study of patients with pituitary macroadenomas was conducted. Data included brain magnetic resonance imaging findings (diameter and apparent diffusion coefficient), patient demographics (age and sex), and tumor consistency. Seventy patients were evaluated, 59 with soft consistency and 11 with non-soft consistency. The support vector machine (SVM) was the best model with ROC AUC score of 83.3% [95% CI 65.8, 97.6], AP AUC of 69.8% [95% CI 41.3, 91.1], sensitivity of 73.1% [95% CI 44.4, 100], specificity of 89.8% [95% CI 82, 96.7], F1 score of 0.63 [95% CI 0.36, 0.83], and Matthews correlation coefficient score of 0.57 [95% CI 0.29, 0.79]. These findings indicate a significant improvement over random classification, as confirmed by a permutation test (p < 0.05). Additionally, the model had a 67.4% probability of outperforming the second-best model in cross-validation, as determined through Bayesian analysis, and demonstrated statistical significance (p < 0.05) compared to non-ensemble models. Using explainability heuristics, both 2D and 3D probability maps highlighted areas with a higher probability of non-soft consistency. The attributes most influential in the correct classification by our best model were male sex and age ≤ 42.25 years. Despite some limitations, the SVM model showed promise in predicting tumor consistency, which could aid in surgical planning. To address concerns about generalizability, we have created an open-access repository to promote future external validation studies and collaboration with other research centers, with the goal of enhancing model prediction through transfer learning.

The Application of Pneumatic Arm in Neuroendoscopic Transsphenoidal Pituitary Adenoma Resection

OBJECTIVE

To summarize the application experience of the pneumatic arm in transnasal sphenoidal pituitary adenoma resection under neuroendoscope.

METHODS

A retrospective analysis was conducted on the clinical data of 52 patients with pituitary adenoma who underwent endoscopic transsphenoidal surgery with pneumatic arm fixation in the Neurosurgery Department of the First Affiliated Hospital of Anhui Medical University from July 2021 to March 2024. Among them, there were 5 cases of pituitary microadenoma, 35 cases of macroadenoma, and 12 cases of giant adenoma. Head CT and a full set of hormones were re-examined within 24 hours after surgery to evaluate the surgical effect. Follow-up was conducted by the outpatient department after surgery to assess the clinical symptoms, hormone level, and imaging of all patients.

RESULTS

Among 52 patients, gross total resection was achieved in 48 cases (92.3%), subtotal resection in 3 cases (5.8%), and partial resection in 1 case (1.9%). Preoperatively, 43 patients had diminished vision, with 40 showing improvement postoperatively, 1 worsening, and 2 having no significant improvement. Thirty-eight patients had headaches preoperatively, and all showed varying degrees of improvement postoperatively. Routine hormone examination within 24 hours after surgery showed that all 20 prolactinoma patients had restored normal hormone levels, 10 of 12 growth hormone-secreting adenoma patients normalized, and 4 of 6 cases of adrenocorticotropic hormone-secreting adenoma immediately relieved after surgery. Postoperative complications included intracranial hematoma in 1 case, cerebrospinal fluid leakage in 2 cases, transient diabetes insipidus in 6 cases, intracranial infection in 1 case, and no death cases. The median follow-up time of 52 patients was 18.6 months (range: 1-32 mo). During the follow-up period, the initial clinical symptoms of all patients improved to varying degrees, and they were able to work and live normally. At the last follow-up, 1 patient had recurrent tumor and 1 patient had progression.

CONCLUSION

Transnasal sphenoidal resection of pituitary adenoma using a pneumatic arm-fixed neuroendoscope allows the operator to perform the surgery with both hands, resulting in satisfactory overall tumor resection and fewer surgical complications. This technique has good clinical value for promotion.