Nguyen, Ryan

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.

Background/Objectives: Spinal astrocytomas (SA) represent 30–40% of all intramedullary spinal cord tumors (IMSCTs) and present significant clinical challenges due to their aggressive behavior and potential for recurrence. We aimed to pool the evidence on SA and investigate predictors of regrowth or recurrence after surgical resection. Methods: A systematic review and meta-analysis were conducted on peer-reviewed human studies from several databases covering the field of SA. Data were collected including sex, age, tumor location, extent of resection, histopathological diagnosis, and adjuvant therapy to identify predictors of SA recurrence. Recurrence was defined as failure of local tumor control or regrowth after treatment. Results: A total of 53 studies with 1365 patients were included in the meta-analysis. A postoperative deterioration in neurological outcomes, as assessed by the modified McCormick scale, was noted in most of the patients. The overall recurrence rate amounted to 41%. On meta-analysis, high-grade WHO tumors were associated with higher odds of recurrence (OR = 2.65; 95% CI: 1.87, 3.76; p = 0.001). Similarly, GTR was associated with lower odds of recurrence compared to STR (OR = 0.33; 95% CI: 0.18, 0.60; p = 0.0003). Sex (p = 0.5848) and tumor location (p = 0.3693) did not show any significant differences in the odds of recurrence. Intraoperative neurophysiological monitoring was described in 8 studies and adjuvant radiotherapy in 41 studies. Conclusions: The results highlight the significant importance of tumor grade and extent of resection in patient prognosis. The role of adjuvant radiotherapy remains unclear, with most studies suggesting no differences in outcomes, with limitations due to potential confounders.