Despite advances in the surgical management of brain tumors, achieving optimal surgical results and identification of tumor remains a challenge. Raman spectroscopy, a laser-based technique that can be used to nondestructively differentiate molecules based on the inelastic scattering of light, is being applied toward improving the accuracy of brain tumor surgery. Here, the authors systematically review the application of Raman spectroscopy for guidance during brain tumor surgery. Raman spectroscopy can differentiate normal brain from necrotic and vital glioma tissue in human specimens based on chemical differences, and has recently been shown to differentiate tumor-infiltrated tissues from noninfiltrated tissues during surgery. Raman spectroscopy also forms the basis for coherent Raman scattering (CRS) microscopy, a technique that amplifies spontaneous Raman signals by 10,000-fold, enabling real-time histological imaging without the need for tissue processing, sectioning, or staining. The authors review the relevant basic and translational studies on CRS microscopy as a means of providing real-time intraoperative guidance. Recent studies have demonstrated how CRS can be used to differentiate tumor-infiltrated tissues from noninfiltrated tissues and that it has excellent agreement with traditional histology. Under simulated operative conditions, CRS has been shown to identify tumor margins that would be undetectable using standard bright-field microscopy. In addition, CRS microscopy has been shown to detect tumor in human surgical specimens with near-perfect agreement to standard H & E microscopy. The authors suggest that as the intraoperative application and instrumentation for Raman spectroscopy and imaging matures, it will become an essential component in the neurosurgical armamentarium for identifying residual tumor and improving the surgical management of brain tumors.
Todd Hollon, Spencer Lewis, Christian W. Freudiger, X. Sunney Xie and Daniel A. Orringer
Zhe Guan, Todd Hollon, J. Nicole Bentley and Hugh J. L. Garton
Epidermoid cysts (ECs) are uncommon pediatric tumors that often occur in the cerebellopontine angle. Although cyst rupture is a recognized complication, the radiographic evolution of an EC following rupture and the resultant parenchymal brainstem edema have not been reported. The authors present the case of a 13-year-old female with a newly diagnosed cerebellopontine angle EC who presented with worsening headaches, photophobia, and emesis. Magnetic resonance imaging demonstrated significant pericystic brainstem edema and mass effect with effacement of the fourth ventricle. Refractory symptoms prompted repeat imaging, revealing cyst enlargement and dense rim enhancement. Resection of the EC resolved both her symptoms and the brainstem edema. This case documents the radiographic evolution of EC rupture and subsequent clinical course.
Luis E. Savastano, Todd C. Hollon, Ariel L. Barkan and Stephen E. Sullivan
Korsakoff syndrome is a chronic memory disorder caused by a severe deficiency of thiamine that is most commonly observed in alcoholics. However, some have proposed that focal structural lesions disrupting memory circuits—in particular, the mammillary bodies, the mammillothalamic tract, and the anterior thalamus—can give rise to this amnestic syndrome. Here, the authors present 4 patients with reversible Korsakoff syndromes caused by suprasellar retrochiasmatic lesions compressing the mammillary bodies and adjacent caudal hypothalamic structures.
Three of the patients were found to have large pituitary macroadenomas in their workup for memory deficiency and cognitive decline with minimal visual symptoms. These tumors extended superiorly into the suprasellar region in a retrochiasmatic position and caused significant mass effect in the bilateral mammillary bodies in the base of the brain. These 3 patients had complete and rapid resolution of amnestic problems shortly after initiation of treatment, consisting of resection in 1 case of nonfunctioning pituitary adenoma or cabergoline therapy in 2 cases of prolactinoma. The fourth patient presented with bizarre and hostile behavior along with significant memory deficits and was found to have a large cystic craniopharyngioma filling the third ventricle and compressing the midline diencephalic structures. This patient underwent cyst fenestration and tumor debulking, with a rapid improvement in his mental status. The rapid and dramatic memory improvement observed in all of these cases is probably due to a reduction in the pressure imposed by the lesions on structures contiguous to the third ventricle, rather than a direct destructive effect of the tumor, and highlights the essential role of the caudal diencephalic structures—mainly the mammillary bodies—in memory function.
In summary, large pituitary lesions with suprasellar retrochiasmatic extension and third ventricular craniopharyngiomas can cause severe Korsakoff-like amnestic syndromes, probably because of bilateral pressure on or damage to mammillary bodies, anterior thalamic nuclei, or their major connections. Neuropsychiatric symptoms may rapidly and completely reverse shortly after initiation of therapy via surgical decompression of tumors or pharmacological treatment of prolactinomas. Early identification of these lesions with timely treatment can lead to a favorable prognosis for this severe neuropsychiatric disorder.
Todd Hollon, Vincent Nguyen, Brandon W. Smith, Spencer Lewis, Larry Junck and Daniel A. Orringer
Survival rates and prognostic factors for supratentorial hemispheric ependymomas have not been determined. The authors therefore designed a retrospective study to determine progression-free survival (PFS), overall survival (OS), and prognostic factors for hemispheric ependymomas.
The study population consisted of 8 patients from our institution and 101 patients from the literature with disaggregated survival information (n = 109). Patient age, sex, tumor side, tumor location, extent of resection (EOR), tumor grade, postoperative chemotherapy, radiation, time to recurrence, and survival were recorded. Kaplan-Meier survival analyses and Cox proportional hazard models were completed to determine survival rates and prognostic factors.
Anaplastic histology/WHO Grade III tumors were identified in 62% of cases and correlated with older age. Three-, 5-, and 10-year PFS rates were 57%, 51%, and 42%, respectively. Three-, 5-, and 10-year OS rates were 77%, 71%, and 58%, respectively. EOR and tumor grade were identified on both Kaplan-Meier log-rank testing and univariate Cox proportional hazard models as prognostic for PFS and OS. Both EOR and tumor grade remained prognostic on multivariate analysis. Subtotal resection (STR) predicted a worse PFS (hazard ratio [HR] 4.764, p = 0.001) and OS (HR 4.216, p = 0.008). Subgroup survival analysis of patients with STR demonstrated a 5- and 10-year OS of 28% and 0%, respectively. WHO Grade III tumors also had worse PFS (HR 10.2, p = 0.004) and OS (HR 9.1, p = 0.035). Patients with WHO Grade III tumors demonstrated 5- and 10-year OS of 61% and 46%, respectively. Postoperative radiation was not prognostic for PFS or OS.
A high incidence of anaplastic histology was found in hemispheric ependymomas and was associated with older age. EOR and tumor grade were prognostic factors for PFS and OS on multivariate analysis. STR or WHO Grade III pathology, or both, predicted worse overall prognosis in patients with hemispheric ependymoma.
Todd C. Hollon, Adish Parikh, Balaji Pandian, Jamaal Tarpeh, Daniel A. Orringer, Ariel L. Barkan, Erin L. McKean and Stephen E. Sullivan
Pituitary adenomas occur in a heterogeneous patient population with diverse perioperative risk factors, endocrinopathies, and other tumor-related comorbidities. This heterogeneity makes predicting postoperative outcomes challenging when using traditional scoring systems. Modern machine learning algorithms can automatically identify the most predictive risk factors and learn complex risk-factor interactions using training data to build a robust predictive model that can generalize to new patient cohorts. The authors sought to build a predictive model using supervised machine learning to accurately predict early outcomes of pituitary adenoma surgery.
A retrospective cohort of 400 consecutive pituitary adenoma patients was used. Patient variables/predictive features were limited to common patient characteristics to improve model implementation. Univariate and multivariate odds ratio analysis was performed to identify individual risk factors for common postoperative complications and to compare risk factors with model predictors. The study population was split into 300 training/validation patients and 100 testing patients to train and evaluate four machine learning models using binary classification accuracy for predicting early outcomes.
The study included a total of 400 patients. The mean ± SD patient age was 53.9 ± 16.3 years, 59.8% of patients had nonfunctioning adenomas and 84.7% had macroadenomas, and the mean body mass index (BMI) was 32.6 ± 7.8 (58.0% obesity rate). Multivariate odds ratio analysis demonstrated that age < 40 years was associated with a 2.86 greater odds of postoperative diabetes insipidus and that nonobese patients (BMI < 30) were 2.2 times more likely to develop postoperative hyponatremia. Using broad criteria for a poor early postoperative outcome—major medical and early surgical complications, extended length of stay, emergency department admission, inpatient readmission, and death—31.0% of patients met criteria for a poor early outcome. After model training, a logistic regression model with elastic net (LR-EN) regularization best predicted early postoperative outcomes of pituitary adenoma surgery on the 100-patient testing set—sensitivity 68.0%, specificity 93.3%, overall accuracy 87.0%. The receiver operating characteristic and precision-recall curves for the LR-EN model had areas under the curve of 82.7 and 69.5, respectively. The most important predictive variables were lowest perioperative sodium, age, BMI, highest perioperative sodium, and Cushing’s disease.
Early postoperative outcomes of pituitary adenoma surgery can be predicted with 87% accuracy using a machine learning approach. These results provide insight into how predictive modeling using machine learning can be used to improve the perioperative management of pituitary adenoma patients.