A Surgical Perspective on the Association between Cystic Lesions of the Pineal Gland (Descartes' Seat of the Soul) and the Pituitary (the Master Gland).

Introduction Rene Descartes (1596-1650), the famous philosopher and scientist, identified the pineal gland as the only cerebral structure not represented bilaterally, the "seat of the soul"; and the source of rational thought. Pineal cysts (PCs) are often incidentally identified in MRI studies, with a reported prevalence of 1 to 4.3%. Rathke cleft cysts (RCCs) are pituitary lesions accounting for <1% of intracranial masses. There are scant data in the literature addressing any association between these two midline cystic lesions. Methods We reviewed the medical records of patients presenting at our institution from April 2008 through February 2020, whose records indicated a diagnosis of RCC, and those whose records included pineal lesions. Our objective was to evaluate the association between these two midline lesions. Brain MRI studies were reviewed for the presence of PCs; only patients with PCs that measured ≥5 mm in diameter were included. Results We identified 116 patients with RCCs, and 34 patients with PCs, treated from April 2008 through February 2020. Among the RCC group, 14/116 patients (12%) had PCs. Among the PC group, 3/34 patients (8.8%) had RCCs. Overall, 17 patients (11.3%) had concomitant RCCs and PCs. The mean maximal diameter of the PCs was 7.5 mm (range = 5-17 mm), whereas the mean maximal diameter of RCCs was 13 mm (range = 5-40 mm). Conclusion The incidental diagnosis of cystic lesions of the pineal and pituitary gland is increasingly reported, primarily because of advances in current diagnostic modalities. Our data demonstrated no clear consensual association between pineal and pituitary cysts.

Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice.

Accurate brain meningioma segmentation and volumetric assessment are critical for serial patient follow-up, surgical planning and monitoring response to treatment. Current gold standard of manual labeling is a time-consuming process, subject to inter-user variability. Fully-automated algorithms for meningioma segmentation have the potential to bring volumetric analysis into clinical and research workflows by increasing accuracy and efficiency, reducing inter-user variability and saving time. Previous research has focused solely on segmentation tasks without assessment of impact and usability of deep learning solutions in clinical practice. Herein, we demonstrate a three-dimensional convolutional neural network (3D-CNN) that performs expert-level, automated meningioma segmentation and volume estimation on MRI scans. A 3D-CNN was initially trained by segmenting entire brain volumes using a dataset of 10,099 healthy brain MRIs. Using transfer learning, the network was then specifically trained on meningioma segmentation using 806 expert-labeled MRIs. The final model achieved a median performance of 88.2% reaching the spectrum of current inter-expert variability (82.6-91.6%). We demonstrate in a simulated clinical scenario that a deep learning approach to meningioma segmentation is feasible, highly accurate and has the potential to improve current clinical practice.

Contemporary assessment of extent of resection in molecularly defined categories of diffuse low-grade glioma: a volumetric analysis.

OBJECTIVE: While the effect of increased extent of resection (EOR) on survival in diffuse infiltrating low-grade glioma (LGG) patients is well established, there is still uncertainty about the influence of the new WHO molecular subtypes. The authors designed a retrospective analysis to assess the interplay between EOR and molecular classes. METHODS: The authors retrospectively reviewed the records of 326 patients treated surgically for hemispheric WHO grade II LGG at Brigham and Women's Hospital and Massachusetts General Hospital (2000-2017). EOR was calculated volumetrically and Cox proportional hazards models were built to assess for predictive factors of overall survival (OS), progression-free survival (PFS), and malignant progression-free survival (MPFS). RESULTS: There were 43 deaths (13.2%; median follow-up 5.4 years) among 326 LGG patients. Median preoperative tumor volume was 31.2 cm3 (IQR 12.9-66.0), and median postoperative residual tumor volume was 5.8 cm3 (IQR 1.1-20.5). On multivariable Cox regression, increasing postoperative volume was associated with worse OS (HR 1.02 per cm3; 95% CI 1.00-1.03; p = 0.016), PFS (HR 1.01 per cm3; 95% CI 1.00-1.02; p = 0.001), and MPFS (HR 1.01 per cm3; 95% CI 1.00-1.02; p = 0.035). This result was more pronounced in the worse prognosis subtypes of IDH-mutant and IDH-wildtype astrocytoma, for which differences in survival manifested in cases with residual tumor volume of only 1 cm3. In oligodendroglioma patients, postoperative residuals impacted survival when exceeding 8 cm3. Other significant predictors of OS were age at diagnosis, IDH-mutant and IDH-wildtype astrocytoma classes, adjuvant radiotherapy, and increasing preoperative volume. CONCLUSIONS: The results corroborate the role of EOR in survival and malignant transformation across all molecular subtypes of diffuse LGG. IDH-mutant and IDH-wildtype astrocytomas are affected even by minimal postoperative residuals and patients could potentially benefit from a more aggressive surgical approach.