Faculty Bibliography

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.

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.

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.

Objective We aim to describe a novel, label-free, real-time imaging technique, coherent Raman scattering (CRS) microscopy, for histopathological evaluation of head and neck cancer. We evaluated the ability of CRS microscopy to delineate between tumor and nonneoplastic tissue in tissue samples from patients with head and neck cancer. Study Design Prospective case series. Setting Tertiary care medical center. Subjects and Methods Patients eligible were surgical candidates with biopsy-proven, previously untreated head and neck carcinoma and were consented preoperatively for participation in this study. Tissue was collected from 50 patients, and after confirmation of tumor and normal specimens by hematoxylin and eosin (H&E), there were 42 tumor samples and 42 normal adjacent controls. Results There were 42 confirmed carcinoma specimens on H&E, and CRS microscopy identified 37 as carcinoma. Of the 42 normal specimens, CRS microscopy identified 40 as normal. This resulted in a sensitivity of 88.1% and specificity of 95.2% in distinguishing between neoplastic and nonneoplastic images. Conclusion CRS microscopy is a unique label-free imaging technique that can provide rapid, high-resolution images and can accurately determine the presence of head and neck carcinoma. This holds potential for implementation into standard practice, allowing frozen margin evaluation even at institutions without a histopathology laboratory.

BACKGROUND:Extent of resection (EOR) correlates with glioblastoma outcomes. Resectability and EOR depend on anatomical, clinical, and surgeon factors. Resectability likely influences outcome in and of itself, but an accurate measurement of resectability remains elusive. An understanding of resectability and the factors that influence it may provide a means to control a confounder in clinical trials and provide reference for decision making. OBJECTIVE:To provide proof of concept of the use of the collective wisdom of experienced brain tumor surgeons in assessing glioblastoma resectability. METHODS:We surveyed 13 academic tumor neurosurgeons nationwide to assess the resectability of newly diagnosed glioblastoma. Participants reviewed 20 cases, including digital imaging and communications in medicine-formatted pre- and postoperative magnetic resonance images and clinical vignettes. The selected cases involved a variety of anatomical locations and a range of EOR. Participants were asked about surgical goal, eg, gross total resection, subtotal resection (STR), or biopsy, and rationale for their decision. We calculated a "resectability index" for each lesion by pooling responses from all 13 surgeons. RESULTS:Neurosurgeons' individual surgical goals varied significantly ( P = .015), but the resectability index calculated from the surgeons' pooled responses was strongly correlated with the percentage of contrast-enhancing residual tumor ( R = 0.817, P < .001). The collective STR goal predicted intraoperative decision of intentional STR documented on operative notes ( P < .01) and nonresectable residual ( P < .01), but not resectable residual. CONCLUSION/CONCLUSIONS:In this pilot study, we demonstrate the feasibility of measuring the resectability of glioblastoma through crowdsourcing. This tool could be used to quantify resectability, a potential confounder in neuro-oncology clinical trials.

Conventional methods for intraoperative histopathologic diagnosis are labour- and time-intensive, and may delay decision-making during brain-tumour surgery. Stimulated Raman scattering (SRS) microscopy, a label-free optical process, has been shown to rapidly detect brain-tumour infiltration in fresh, unprocessed human tissues. Here, we demonstrate the first application of SRS microscopy in the operating room by using a portable fibre-laser-based microscope and unprocessed specimens from 101 neurosurgical patients. We also introduce an image-processing method - stimulated Raman histology (SRH) - which leverages SRS images to create virtual haematoxylin-and-eosin-stained slides, revealing essential diagnostic features. In a simulation of intraoperative pathologic consultation in 30 patients, we found a remarkable concordance of SRH and conventional histology for predicting diagnosis (Cohen's kappa, κ > 0.89), with accuracy exceeding 92%. We also built and validated a multilayer perceptron based on quantified SRH image attributes that predicts brain-tumour subtype with 90% accuracy. Our findings provide insight into how SRH can now be used to improve the surgical care of brain tumour patients.

The ability to detect neuronal currents with high spatiotemporal resolution using magnetic resonance imaging (MRI) is important for studying human brain function in both health and disease. While significant progress has been made, we still lack evidence showing that it is possible to measure an MR signal time-locked to neuronal currents with a temporal waveform matching concurrently recorded local field potentials (LFPs). Also lacking is evidence that such MR data can be used to image current distribution in active tissue. Since these two results are lacking even in vitro, we obtained these data in an intact isolated whole cerebellum of turtle during slow neuronal activity mediated by metabotropic glutamate receptors using a gradient-echo EPI sequence (TR=100ms) at 4.7T. Our results show that it is possible (1) to reliably detect an MR phase shift time course matching that of the concurrently measured LFP evoked by stimulation of a cerebellar peduncle, (2) to detect the signal in single voxels of 0.1mm(3), (3) to determine the spatial phase map matching the magnetic field distribution predicted by the LFP map, (4) to estimate the distribution of neuronal current in the active tissue from a group-average phase map, and (5) to provide a quantitatively accurate theoretical account of the measured phase shifts. The peak values of the detected MR phase shifts were 0.27-0.37°, corresponding to local magnetic field changes of 0.67-0.93nT (for TE=26ms). Our work provides an empirical basis for future extensions to in vivo imaging of neuronal currents.

OBJECTIVE 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. METHODS 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. RESULTS 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. CONCLUSIONS 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.