Meyer, Fredric B.

OBJECTIVE While endonasal transsphenoidal surgery (ETS) is the standard treatment for nonfunctioning pituitary adenomas (NFPAs), the postoperative recovery rates of secondary adrenal insufficiency and their predictive factors remain poorly understood. The authors investigated the relationship between the tumor resection rate and adrenal function recovery in patients with NFPAs. METHODS This retrospective study included 450 patients with NFPAs who underwent ETS between January 2013 and November 2023. The rates of preoperative hypopituitarism and postoperative recovery rates of secondary adrenal insufficiency and their associated factors were analyzed. Recovery was defined as normalization of serum cortisol levels without replacement therapy for at least 24 hours, accompanied by symptom resolution. The tumor resection rate was calculated as the percentage of the tumor volume resected. RESULTS The median follow-up period for the entire cohort was 52 months (range 1–131 months). A total of 246 (55%) patients had dysfunction in one or more pituitary axes. Fifty-three (47%) of 114 patients with preoperative adrenal insufficiency showed postoperative adrenal insufficiency recovery. The cumulative adrenal insufficiency recovery rates were 12% at 1 month, 19% at 3 months, 32% at 6 months, and 41% at 12 months. Among the 114 patients with preoperative adrenal insufficiency, the factors associated with postoperative adrenal insufficiency recovery were investigated. Patients with smaller tumor sizes of < 20 mm (log-rank test, p = 0.001) and those with tumor resection rates of more than 85% (log-rank test, p = 0.032) demonstrated better postoperative adrenal insufficiency improvement. Bivariate Cox proportional hazards analysis demonstrated that smaller tumor sizes (continuous, HR 0.97, 95% CI 0.93–0.98; p = 0.043) and higher tumor resection rates (HR 1.03, 95% CI 1.01–1.07; p = 0.001) were significantly associated with better postoperative adrenal insufficiency recovery. Multivariable analysis confirmed that only higher tumor resection rates were significantly associated with better postoperative adrenal insufficiency recovery (HR 1.03, 95% CI 1.01–1.06; p = 0.019). CONCLUSIONS Nearly half of the patients with preoperative adrenal insufficiency achieved recovery after ETS for NFPAs. The enhanced extent of tumor removal was an independent factor associated with better postoperative recovery, indicating that optimizing safe maximal resection procedures may be crucial in enhancing adrenal function.

OBJECTIVE Nonfunctioning pituitary adenomas (NFPAs) often present with associated mild hyperprolactinemia, described in literature as "stalk effect." The presence of preoperative hyperprolactinemia caused by tumor compression of the pituitary stalk and endocrinological outcomes has been noted; however, a detailed investigation on the impact on perioperative endocrinological outcome is lacking. The authors aimed to determine whether elevated preoperative serum prolactin concentrations are associated with increased risk of perioperative pituitary dysfunction in patients with NFPAs undergoing endonasal transsphenoidal surgery (ETS). METHODS The authors conducted a single-center retrospective cohort study of patients who underwent ETS for NFPAs between January 2013 and April 2023. Patients were divided into two groups based on preoperative prolactin concentrations: normal or hyperprolactinemic (prolactin values above upper limit of reference range, adjusted for sex). Outcomes included perioperative endocrinological function and surgical outcomes. Multivariable analysis was conducted to identify factors associated with perioperative endocrinological function outcomes. RESULTS Of 244 patients (140 [57%] males; median age 59 years) with a median follow-up of 65 months, 131 (54%) had hyperprolactinemia. Compared with patients with normal preoperative prolactin levels (n = 113), the hyperprolactinemia group had similar tumor size (median diameter 2.5 cm vs 2.3 cm, p = 0.102) but higher rates of cavernous sinus invasion (99% vs 91%, p = 0.035), pituitary stalk deviation (95% vs 74%, p = 0.001), stalk bending (14% vs 1%, p = 0.001), preoperative secondary hypothyroidism (39% vs 22%, p = 0.005), and adrenal insufficiency (AI; 34% vs 22%, p = 0.048). Postoperatively, the hyperprolactinemia group showed higher rates of new secondary hypothyroidism (16% vs 7%, p = 0.048) and new permanent AI (16% vs 6%, p = 0.046). Multivariable analysis revealed preoperative high prolactin as an independent predictor of preoperative secondary hypothyroidism (OR 1.83, 95% CI 1.17–3.44; p = 0.046) and its postoperative deterioration (OR 2.65, 95% CI 1.02–7.69; p = 0.042). Maximum tumor diameter was also a significant predictor of various endocrinological dysfunctions both pre- and postoperatively. CONCLUSIONS Preoperative hyperprolactinemia in patients with NFPA is associated with higher risks of specific perioperative endocrinological dysfunction and pronounced pituitary stalk deviation. These findings highlight the utility of preoperative prolactin levels as a biomarker for predicting postoperative endocrinological outcomes, potentially enhancing risk assessment and guiding personalized perioperative management.

OBJECTIVE Patients with growth hormone (GH)–secreting pituitary adenomas (PAs) experience various symptoms and comorbidities, which can ultimately lead to increased mortality. This study aimed to develop and validate a machine learning (ML) model for predicting long-term outcomes in patients with GH-secreting PAs following endonasal transsphenoidal surgery (ETS). METHODS The authors conducted a retrospective three-institution cohort study that included patients with GH-secreting PAs treated with ETS between 2013 and 2023. Clinical, radiological, and biochemical data were collected. The main outcome of interest was the intervention-free rate (IFR) after primary ETS. Supervised ML algorithms, including decision trees and random forests, were developed to predict the IFR. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC) and Shapley Additive Explanations (SHAP) values. RESULTS The median follow-up for 100 patients with GH-secreting PAs (53% female) was 64 months (range 1–130 months). Additional intervention for persistent or recurrent acromegaly was required in 32% of patients. Following primary ETS alone, the 3-year IFR was 70% and the 5-year IFR was 67%. Multiple ML models were developed and evaluated using AUROCs. The decision tree analysis achieved an accuracy of 81% and emphasized the importance of both gross-total resection (GTR) and patient age in determining the long-term IFR. To better understand the factors that contributed to model performance, SHAP analysis was applied to the best-performing model. The SHAP dependence plots showed that key factors associated with a longer IFR included tumor size < 9 mm, GTR, patient age > 65 years, and Knosp grade 0. CONCLUSIONS This ML model offers a more nuanced and potentially more accurate approach to identify patients more likely to develop recurrent or persistent acromegaly following primary ETS and require additional treatment. Following external validation, this ML model could improve personalized treatment planning and follow-up strategies and enhance patient care and resource allocation in clinical practice.

OBJECTIVE In this study, the authors aimed to establish a supervised machine learning (ML) model based on multiple tree-based algorithms to predict long-term biochemical outcomes and intervention-free survival (IFS) after endonasal transsphenoidal surgery (ETS) in patients with Cushing’s disease (CD). METHODS The medical records of patients who underwent ETS for CD between 2013 and 2023 were reviewed. Data were collected on the patient’s baseline characteristics, intervention details, histopathology, surgical outcomes, and postoperative endocrine functions. The study’s primary outcome was IFS, and the therapeutic outcomes were labeled as "under control" or "treatment failure," depending on whether additional therapeutic interventions after primary ETS were required. The decision tree and random forest classifiers were trained and tested to predict long-term IFS based on unseen data, using an 80/20 cohort split. RESULTS Data from 150 patients, with a median follow-up period of 56 months, were extracted. In the cohort, 42 (28%) patients required additional intervention for persistent or recurrent CD. Consequently, the IFS rates following ETS alone were 83% at 3 years and 78% at 5 years. Multivariable Cox proportional hazards analysis demonstrated that a smaller tumor diameter that could be detected by MRI (hazard ratio 0.95, 95% CI 0.90–0.99; p = 0.047) was significantly associated with greater IFS. However, the lack of tumor detection on MRI was a poor predictor. The ML-based model using a decision tree model displayed 91% accuracy (95% CI 0.70–0.94, sensitivity 87.0%, specificity 89.0%) in predicting IFS in the unseen test dataset. Random forest analysis revealed that tumor size (mean minimal depth 1.67), Knosp grade (1.75), patient age (1.80), and BMI (1.99) were the four most significant predictors of long-term IFS. CONCLUSIONS The ML algorithm could predict long-term postoperative endocrinological remission in CD with high accuracy, indicating that prognosis may vary not only with previously reported factors such as tumor size, Knosp grade, gross-total resection, and patient age but also with BMI. The decision tree flowchart could potentially stratify patients with CD before ETS, allowing for the selection of personalized treatment options and thereby assisting in determining treatment plans for these patients. This ML model may lead to a deeper understanding of the complex mechanisms of CD by uncovering patterns embedded within the data.

OBJECTIVE Patients with autosomal dominant polycystic kidney disease (ADPKD) have been identified to have a significantly increased risk of developing intracranial aneurysms (IAs). These patients are diagnosed at younger ages and are also at increased risk for IA rupture. The objective of this systematic review and meta-analysis was to identify risk factors associated with IA detection during screening of asymptomatic ADPKD patients. METHODS The authors searched for studies reporting the prevalence of IA among patients with ADPKD screened using MRA, CTA, or DSA on PubMed, Embase, Google Scholar, Scopus, Web of Science, Cochrane Database of Systematic Reviews, and ClinicalTrials.gov prior to August 2023 by using individualized standardized search strategies. Eligibility of all retrieved studies was assessed according to the PRISMA guidelines. Risk of bias of the included observational studies was assessed using the Newcastle-Ottawa Scale. Meta-analysis was performed using a random-effects model. RESULTS Twenty-four observational studies involving 5907 patients with ADPKD were identified for quantitative analysis. The authors observed an overall prevalence of IA of 12.6%. They identified several risk factors for IA with statistically significantly increased odds for unruptured IA (UIA) detection, including female sex (18 studies, OR 1.63, 95% CI 1.37–1.94, I 2 = 0%), family history of either IA or subarachnoid hemorrhage (SAH) (12 studies, OR 2.17, 95% CI 1.68–2.81, I 2 = 0%), family history of SAH (3 studies, OR 2.67, 95% CI 1.25–5.71, I 2 = 66.0%), stage 3 or greater chronic kidney disease (CKD) (4 studies, OR 2.55, 95% CI 1.84–3.54, I 2 = 0%), and hypertension (13 studies, OR 1.41, 95% CI 1.04–1.91, I 2 = 34%). Age > 45 years, family history of ADPKD, smoking status, and presence of liver cysts did not reach statistical significance as risk factors during this meta-analysis. CONCLUSIONS The results of the authors’ meta-analysis suggest increased risk of UIA detection in screening of ADPKD patients who are female, have a family history of IA or SAH, have hypertension, or have moderate or more severe CKD. Given the increased prevalence of IA in ADPKD patients with these risk factors, these patients may stand to benefit from screening for IA early in treatment or at time of diagnosis, even if neurologically asymptomatic.

Abstract Background While serial sampling of glioma tissue is rarely performed prior to recurrence, cerebrospinal fluid (CSF) is an underutilized longitudinal source of candidate glioma biomarkers for understanding therapeutic impacts. However, the impact of key variables to consider in longitudinal CSF samples for monitoring biomarker discovery, including anatomical location and post-surgical changes, remains unknown. Methods Aptamer-based proteomics was performed on 147 CSF samples from 74 patients; 71 of whom had grade 2–4 astrocytomas or grade 2–3 oligodendrogliomas. This included pre- versus post-resection intracranial CSF samples obtained at early (1–16 days; n = 20 patients) or delayed (86–153 days; n = 11 patients) time points for patients with glioma. Paired lumbar versus intracranial glioma CSF samples were also obtained (n = 14 patients). Results Significant differences were identified in the CSF proteome between lumbar, subarachnoid, and ventricular CSF in patients with gliomas. Importantly, we found that resection had a significant, evolving longitudinal impact on the CSF proteome, with distinct sets of proteins present at different time points since resection. Our analysis of serial intracranial CSF samples suggests the early potential for disease monitoring and evaluation of pharmacodynamic impact of targeted therapies, such as bevacizumab and immunotherapies. Conclusions The intracranial glioma CSF proteome serves as a rich and dynamic reservoir of potential biomarkers that can be used to evaluate the effects of resection and other therapies over time. All data within this study, including detailed individual clinical annotations, are shared as a resource for the neuro-oncology community to collectively address these unanswered questions and further understand glioma biology through CSF proteomics.

Abstract BACKGROUND Pineocytoma is a rare pineal parenchymal WHO grade 1 tumor, typically well-managed with surgical resection alone. With gross total resection (GTR), 91% 1-year and 84% 5-year survival rates have been reported. Recurrent pineocytoma is rare and optimal management in that setting is unknown, in part owing to the rarity of the condition. METHODS We report an atypical course of pineocytoma in a 58-year-old male involving metastatic disease. RESULTS A pineal mass was identified upon MRI of the brain obtained for vision changes. Patient underwent GTR, pathology was consistent with pineocytoma, WHO grade 1. Ki-67 labeling index was low at 1-2%. Despite low-grade pathology, two years later, surveillance imaging demonstrated recurrence in the pineal region and a second lesion in the right thalamus. The patient underwent gamma knife surgery (GKRS) to both lesions. Three years later, he developed three new lesions in the right cerebellum and again underwent GKRS. Subsequent surveillance imaging identified new lesions in the right cerebellum and along ventricular walls, treated with further GKRS. Nine years after initial diagnosis, focal nodular leptomeningeal metastases were discovered, along with progression at the primary tumor site. Patient was treated with GKRS to the nodular deposits followed by pembrolizumab. Further disease progression as noted despite immunotherapy. Patient was treated with 6 cycles of temozolomide chemotherapy and at further progression 3 cycles of lomustine. Upon additional progression, further radiation therapy was administered with bevacizumab to reduce risk of radiation necrosis. Patient remains on surveillance 13 years after initial diagnosis with visual changes and mild headache representing only clinical issues. CONCLUSIONS Although exceedingly rare, pineocytoma is capable of metastasizing to brain parenchyma and the leptomeninges, as demonstrated in our patient. However, our case demonstrates prolonged survival is possible even in the setting of relentlessly progressive metastatic disease with utilization of multimodality treatment.

Abstract While serial sampling of glioma tissue is rarely performed before recurrence, cerebrospinal fluid (CSF) can be acquired serially to evaluate candidate monitoring and pharmacodynamic biomarkers in patients with gliomas. However, the impact of key variables to consider in longitudinal CSF, including anatomical location and post-surgical changes, remains unknown. To that end, pre- versus post-resection intracranial CSF samples from patients with gliomas were obtained at early (1-16 days; n=20) or delayed (86-153 days; n=11) timepoints, as well as lumbar-versus-intracranial glioma CSF samples (n=14). Using aptamer-based proteomics, we identified significant differences in the glioma CSF proteome between lumbar, subarachnoid, and ventricular CSF. Indeed, intracranial CSF in paired lumbar/intracranial samples had nearly 1600 unique aptamers, regardless of which intracranial location was compared to the paired lumbar CSF, including in enrichment style analyses that evaluated the overlap between protein states. Importantly, resection had a significant, evolving longitudinal impact on the CSF proteome, with subsets of proteins associated with early versus delayed post-resection changes, many of which were enriched for inflammatory-related pathways. Interestingly, despite increased contamination from surgical debris, enrichment for plasma-associated proteins decreased with resection (FDR: 0.000), suggesting that a portion of the baseline glioma panel could be derived from plasma-originating analytes due to blood-brain barrier disruption. Despite these challenges, our data still revealed preliminary candidate monitoring biomarkers, such as brevican, that increased with progression (example patient: fold-change of 3.58x and 2.08x through lomustine and bevacizumab, respectively), but not pseudoprogression (Fold change less than 1.5). Serial intracranial CSF samples suggested the early potential for disease monitoring and evaluation of pharmacodynamic impact of targeted therapies, including bevacizumab and pembrolizumab. In conclusion, our available data to date suggest that the intracranial glioma CSF proteome provides an abundant and evolving source of candidate biomarkers to longitudinally discern the impact of resection and systemic treatments.