Faculty Bibliography

In low-grade glioma surgery, depicting tumor margins is challenging. 7 - Bowden 2018 - Sodium Fluorescein Facilitates Guided Sampling of Diagnostic Tumor Tissue.pdf Several tools have emerged to assist surgical decision-making. Intraoperative MRI, albeit expensive and time-consuming, can provide useful information during surgery. Fluorescence-guidance with 5-aminolevulinic acid (5-ALA) helps provide real-time information during surgery regardless of brain-shift, assists in finding anaplastic foci in low-grade tumors, and enables diagnosis of malignant tissue. Raman histology has potential for detecting viable tumor in biopsied tissue and for identifying tumor infiltration in vivo. This article analyzes and discusses these surgical adjuncts.

OBJECTIVEPituitary 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.METHODSA 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.RESULTSThe 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.CONCLUSIONSEarly 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.

OBJECTIVES:The postoperative management of patients who undergo brain tumor resection frequently occurs in an ICU. However, the routine admission of all patients to an ICU following surgery is controversial. This study seeks to identify the frequency with which patients undergoing elective supratentorial tumor resection require care, aside from frequent neurologic checks, that is specific to an ICU and to determine the frequency of new complications during ICU admission. Additionally, clinical predictors of ICU-specific care are identified, and a scoring system to discriminate patients most likely to require ICU-specific treatment is validated. DESIGN:Retrospective observational cohort study. SETTING:Academic neurosurgical center. PATIENTS:Two-hundred consecutive adult patients who underwent supratentorial brain tumor surgery. An additional 100 consecutive patients were used to validate the prediction score. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:Univariate statistics and multivariable logistic regression were used to identify clinical characteristics associated with ICU-specific treatment. Eighteen patients (9%) received ICU-specific care, and 19 (9.5%) experienced new complications or underwent emergent imaging while in the ICU. Factors significantly associated with ICU-specific care included nonelective admission, preoperative Glasgow Coma Scale, and volume of IV fluids. A simple clinical scoring system that included Karnofsky Performance Status less than 70 (1 point), general endotracheal anesthesia (1 point), and any early postoperative complications (2 points) demonstrated excellent ability to discriminate patients who required ICU-specific care in both the derivation and validation cohorts. CONCLUSIONS:Less than 10% of patients required ICU-specific care following supratentorial tumor resection. A simple clinical scoring system may aid clinicians in stratifying the risk of requiring ICU care and could inform triage decisions when ICU bed availability is limited.

IDH mutation is of central importance in the diagnosis and treatment of gliomas. Fourier-transform infrared spectroscopy, in combination with a supervised machine-learning approach, can be used to detect metabolic alterations induced by IDH1 mutations in a fraction of the time of conventional techniques. Clin Cancer Res; 24(11); 2467-9. ©2018 AACRSee related article by Uckermann et al., p. 2530.

We hypothesized elderly patients with good Karnofsky Performance Status (KPS) treated with standard dose or dose-escalated radiation therapy (SDRT/DERT) and concurrent temozolomide (TMZ) would have favorable overall survival (OS) compared to historical elderly patients treated with hypofractionated RT (HFRT). From 2004 to 2015, 66 patients age ≥ 60 with newly diagnosed, pathologically proven glioblastoma were treated with SDRT/DERT over 30 fractions with concurrent/adjuvant TMZ at a single institution. Kaplan-Meier methods and the log-rank test were used to assess OS and progression-free survival (PFS). Multivariate analysis (MVA) was performed using Cox Proportional-Hazards. Median follow-up was 12.6 months. Doses ranged from 60 to 81 Gy (median 66). Median KPS was 90 (range 60-100) and median age was 67 years (range 60-81), with 29 patients ≥ 70 years old. 32% underwent gross total resection (GTR). MGMT status was known in 28%, 42% of whom were methylated. Median PFS was 8.3 months (95% CI 6.9-11.0) and OS was 12.7 months (95% CI 9.7-14.1). Patients age ≥ 70 with KPS ≥ 90 had a median OS of 12.4 months. Median OS was 27.1 months for MGMT methylated patients. On MVA controlling for age, dose, KPS, MGMT, GTR, and adjuvant TMZ, younger age (HR 0.9, 95% CI 0.8-0.9, p < 0.01), MGMT methylation (HR:0.2, 95% CI 0.1-0.7, p = 0.01), and GTR (HR:0.5, 95% CI 0.3-0.9, p = 0.01) were associated with improved OS. Our findings do not support routine use of a standard 6-week course of radiation therapy in elderly patients with glioblastoma. However, a select group of elderly patients with excellent performance status and MGMT methylation or GTR may experience favorable survival with a standard 6-week course of treatment.

Primary melanocytic disease of the central nervous system is a rarely encountered condition currently without consensus on treatment and lacking major guidelines for management. Understanding the nature of the disease and differentiating primary melanocytic disease from the much more commonly encountered secondary (metastatic) melanoma is important in identifying the condition and pursuing appropriate treatment.

Accurate histopathologic diagnosis is essential for providing optimal surgical management of pediatric brain tumors. Current methods for intraoperative histology are time- and labor-intensive and often introduce artifacts that limit interpretation. Stimulated Raman histology (SRH) is a novel label-free imaging technique that provides intraoperative histologic images of fresh, unprocessed surgical specimens. Here we evaluate the capacity of SRH for use in the intraoperative diagnosis of pediatric type brain tumors. SRH revealed key diagnostic features in fresh tissue specimens collected from 33 prospectively enrolled pediatric type brain tumor patients, preserving tumor cytology and histoarchitecture in all specimens. We simulated an intraoperative consultation for 25 patients with specimens imaged using both SRH and standard hematoxylin and eosin histology. SRH-based diagnoses achieved near-perfect diagnostic concordance (Cohen's kappa, kappa > 0.90) and an accuracy of 92-96%. We then developed a quantitative histologic method using SRH images based on rapid image feature extraction. Nuclear density, tumor-associated macrophage infiltration, and nuclear morphology parameters from 3337 SRH fields of view were used to develop and validate a decision-tree machine-learning model. Using SRH image features, our model correctly classified 25 fresh pediatric type surgical specimens into normal versus lesional tissue and low-grade versus high-grade tumors with 100% accuracy. Our results provide insight into how SRH can deliver rapid diagnostic histologic data that could inform the surgical management of pediatric brain tumors.