Faculty Bibliography

BACKGROUND AND OBJECTIVES/OBJECTIVE:Dose selection for brain metastases stereotactic radiosurgery (SRS) classically has been based on tumor diameter with a reduction of dose in the settings of prior brain irradiation, larger tumor volumes, and critical brain location. However, retrospective series have shown local control rates to be suboptimal with reduced doses. We hypothesized that lower doses could be effective for specific tumor biologies with concomitant systemic therapies. This study aims to report the local control (LC) and toxicity when using low-dose SRS in the era of modern systemic therapy. METHODS:We reviewed 102 patients with 688 tumors managed between 2014 and 2021 who had low-margin dose radiosurgery, defined as ≤14 Gy. Tumor control was correlated with demographic, clinical, and dosimetric data. RESULTS:The main primary cancer types were lung in 48 (47.1%), breast in 31 (30.4%), melanoma in 8 (7.8%), and others in 15 patients (11.7%). The median tumor volume was 0.037cc (0.002-26.31 cm3), and the median margin dose was 14 Gy (range 10-14). The local failure (LF) cumulative incidence at 1 and 2 years was 6% and 12%, respectively. On competing risk regression analysis, larger volume, melanoma histology, and margin dose were predictors of LF. The 1-year and 2-year cumulative incidence of adverse radiation effects (ARE: an adverse imaging-defined response includes increased enhancement and peritumoral edema) was 0.8% and 2%. CONCLUSION/CONCLUSIONS:It is feasible to achieve acceptable LC in BMs with low-dose SRS. Volume, melanoma histology, and margin dose seem to be predictors for LF. The value of a low-dose approach may be in the management of patients with higher numbers of small or adjacent tumors with a history of whole brain radio therapy or multiple SRS sessions and in tumors in critical locations with the aim of LC and preservation of neurological function.

PURPOSE/OBJECTIVE:Patients with EGFR-mutated NSCLC represent a unique subset of lung cancer patients with distinct clinical and molecular characteristics. Previous studies have shown a higher incidence of brain metastases (BM) in this subgroup of patients, and neurologic death has been reported to be as high as 40% and correlates with leptomeningeal disease (LMD). METHODS:Between 2012 and 2021, a retrospective review of our prospective registry identified 606 patients with BM from NSCLC, with 170 patients having an EGFR mutation. Demographic, clinical, radiographic, and treatment characteristics were correlated to the incidence of LMD and survival. RESULTS:LMD was identified in 22.3% of patients (n = 38) at a median follow-up of 19 (2-98) months from initial SRS. Multivariate regression analysis showed targeted therapy and a cumulative number of metastases as significant predictors of LMD (p = 0.034, HR = 0.44), (p = .04, HR = 1.02). The median survival time after SRS of the 170 patients was 24 months (CI 95% 19.1-28.1). In a multivariate Cox regression analysis, RPA, exon 19 deletion, and osimertinib treatment were significant predictors of overall survival. The cumulative incidence of neurological death at 2 and 4 years post initial stereotactic radiosurgery (SRS) was 8% and 11%, respectively, and correlated with LMD. CONCLUSION/CONCLUSIONS:The study shows that current-generation targeted therapy for EGFR-mutated NSCLC patients may prevent the development and progression of LMD, leading to improved survival outcomes. Nevertheless, LMD is associated with poor outcomes and neurologic death, making innovative strategies to treat LMD essential.

BACKGROUND AND PURPOSE:Most multinodular and vacuolating neuronal tumors (MVNTs) are diagnosed and followed radiologically without any change across time. There are no surveillance guidelines or quantitative volumetric assessments of these tumors. We evaluated MVNT volumes during long follow-up periods using segmentation tools with the aim of quantitative assessment. MATERIALS AND METHODS:All patients with MVNTs in a brain MR imaging report in our system were reviewed. Patients with only 1 brain MR imaging or in whom MVNT was not clearly the most likely diagnosis were excluded. All MVNTs were manually segmented. For all follow-up examinations, absolute and percentage volume change from immediately prior and initial examinations were calculated. RESULTS:= .67), respectively. CONCLUSIONS:MVNT segmentation across follow-up brain MR imaging examinations did not demonstrate significant volume differences, suggesting that these tumors do not enlarge with time. Hence, frequent surveillance imaging of newly diagnosed MVNTs may not be necessary.

OBJECTIVE:In certain cases, clinicians may consider continued observation of a vestibular schwannoma after initial growth is detected. The aim of the current work was to determine if patients with growing sporadic vestibular schwannomas could be stratified by the likelihood of subsequent growth based on initial growth behavior. STUDY DESIGN/METHODS:Slice-by-slice volumetric tumor measurements from 3,505 serial magnetic resonance imaging studies were analyzed from 952 consecutively treated patients. SETTING/METHODS:Three tertiary-referral centers. PATIENTS/METHODS:Adults with sporadic vestibular schwannoma. INTERVENTIONS/METHODS:Wait-and-scan. MAIN OUTCOME MEASURES/METHODS:Composite end point of subsequent growth- or treatment-free survival rates, where growth is defined as an additional increase of at least 20% in tumor volume from the volume at the time of initial growth. RESULTS:Among 405 patients who elected continued observation despite documented growth, stratification, of volumetric growth rate into less than 25% (reference: n = 107), 25 to less than 50% (hazard ratio [HR], 1.39; p = 0.06; n = 96), 50 to less than 100% (HR, 1.71; p = 0.002; n = 112), and at least 100% (HR, 2.01; p < 0.001; n = 90) change per year predicted the likelihood of future growth or treatment. Subsequent growth- or treatment-free survival rates (95% confidence interval) at year 5 after detection of initial growth were 31% (21-44%) for those with less than 25% growth per year, 18% (10-32%) for those with 25 to less than 50%, 15% (9-26%) for those with 50 to less than 100%, and 6% (2-16%) for those with at least 100%. Neither patient age ( p = 0.15) nor tumor volume at diagnosis ( p = 0.95) significantly differed across stratification groups. CONCLUSIONS:At the time of diagnosis, clinical features cannot consistently predict which tumors will ultimately display aggressive behavior. Stratification by volumetric growth rate at the time of initial growth results in a stepwise progression of increasing likelihood of subsequent growth. When considering continued observation after initial growth detection, almost 95% of patients who have tumors that double in volume between diagnosis and the first detection of growth demonstrate further tumor growth or undergo treatment if observed to 5 years.

BACKGROUND:Brain metastases (BM) have long been considered a terminal diagnosis with management mainly aimed at palliation and little hope for extended survival. Use of brain stereotactic radiosurgery (SRS) and/or resection, in addition to novel systemic therapies, has enabled improvements in overall and progression-free (PFS) survival. OBJECTIVE:To explore the possibility of extended survival in patients with non-small-cell lung cancer (NSCLC) BM in the current era. METHODS:During the years 2008 to 2020, 606 patients with NSCLC underwent their first Gamma Knife SRS for BM at our institution with point-of-care data collection. We reviewed clinical, molecular, imaging, and treatment parameters to explore the relationship of such factors with survival. RESULTS:The median overall survival was 17 months (95% CI, 13-40). Predictors of increased survival in a multivariable analysis included age <65 years (P < .001), KPS ≥80 (P < .001), absence of extracranial metastases (P < .001), fewer BM at first SRS (≤3, P = .003), and targeted therapy (P = .005), whereas chemotherapy alone was associated with shorter survival (P = .04). In a subgroup of patients managed before 2016 (n = 264), 38 (14%) were long-term survivors (≥5 years), of which 16% required no active cancer treatment (systemic or brain) for ≥3 years by the end of their follow-up. CONCLUSION/CONCLUSIONS:Long-term survival in patients with brain metastases from NSCLC is feasible in the current era of SRS when combined with the use of effective targeted therapeutics. Of those living ≥5 years, the chance for living with stable disease without the need for active treatment for ≥3 years was 16%.

INTRODUCTION:Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. Access to timely molecular diagnostic testing for brain tumor patients is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. METHODS:By combining stimulated Raman histology (SRH), a rapid, label-free, non-consumptive, optical imaging method, and deep learning-based image classification, we are able to predict the molecular genetic features used by the World Health Organization (WHO) to define the adult-type diffuse glioma taxonomy, including IDH-1/2, 1p19q-codeletion, and ATRX loss. We developed a multimodal deep neural network training strategy that uses both SRH images and large-scale, public diffuse glioma genomic data (i.e. TCGA, CGGA, etc.) in order to achieve optimal molecular classification performance. RESULTS:One institution was used for model training (University of Michigan) and four institutions (NYU, UCSF, Medical University of Vienna, and University Hospital Cologne) were included for patient enrollment in the prospective testing cohort. Using our system, called DeepGlioma, we achieved an average molecular genetic classification accuracy of 93.2% and identified the correct diffuse glioma molecular subgroup with 91.5% accuracy within 2 minutes in the operating room. DeepGlioma outperformed conventional IDH1-R132H immunohistochemistry (94.2% versus 91.4% accuracy) as a first-line molecular diagnostic screening method for diffuse gliomas and can detect canonical and non-canonical IDH mutations. CONCLUSIONS:Our results demonstrate how artificial intelligence and optical histology can be used to provide a rapid and scalable alternative to wet lab methods for the molecular diagnosis of brain tumor patients during surgery.

Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic testing for patients with brain tumors is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. In this study, we developed DeepGlioma, a rapid (<90 seconds), artificial-intelligence-based diagnostic screening system to streamline the molecular diagnosis of diffuse gliomas. DeepGlioma is trained using a multimodal dataset that includes stimulated Raman histology (SRH); a rapid, label-free, non-consumptive, optical imaging method; and large-scale, public genomic data. In a prospective, multicenter, international testing cohort of patients with diffuse glioma (n = 153) who underwent real-time SRH imaging, we demonstrate that DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy (IDH mutation, 1p19q co-deletion and ATRX mutation), achieving a mean molecular classification accuracy of 93.3 ± 1.6%. Our results represent how artificial intelligence and optical histology can be used to provide a rapid and scalable adjunct to wet lab methods for the molecular screening of patients with diffuse glioma.

BACKGROUND/UNASSIGNED:Central nervous system (CNS) cancer is the 10th leading cause of cancer-associated deaths for adults, but the leading cause in pediatric patients and young adults. The variety and complexity of histologic subtypes can lead to diagnostic errors. DNA methylation is an epigenetic modification that provides a tumor type-specific signature that can be used for diagnosis. METHODS/UNASSIGNED:We performed a prospective study using DNA methylation analysis as a primary diagnostic method for 1921 brain tumors. All tumors received a pathology diagnosis and profiling by whole genome DNA methylation, followed by next-generation DNA and RNA sequencing. Results were stratified by concordance between DNA methylation and histopathology, establishing diagnostic utility. RESULTS/UNASSIGNED:Of the 1602 cases with a World Health Organization histologic diagnosis, DNA methylation identified a diagnostic mismatch in 225 cases (14%), 78 cases (5%) did not classify with any class, and in an additional 110 (7%) cases DNA methylation confirmed the diagnosis and provided prognostic information. Of 319 cases carrying 195 different descriptive histologic diagnoses, DNA methylation provided a definitive diagnosis in 273 (86%) cases, separated them into 55 methylation classes, and changed the grading in 58 (18%) cases. CONCLUSIONS/UNASSIGNED:DNA methylation analysis is a robust method to diagnose primary CNS tumors, improving diagnostic accuracy, decreasing diagnostic errors and inconclusive diagnoses, and providing prognostic subclassification. This study provides a framework for inclusion of DNA methylation profiling as a primary molecular diagnostic test into professional guidelines for CNS tumors. The benefits include increased diagnostic accuracy, improved patient management, and refinements in clinical trial design.