Calcified meningiomas demonstrate equivocal grade, proliferation, and immediate surgical outcomes.

BACKGROUND: Meningioma calcification is thought to predict lessened growth potential and aggression. However, historical studies have mostly focused on correlating calcification of small (diameter < 2.5 cm) meningiomas, rather than analyzing traits of calcified meningiomas across all sizes. OBJECTIVE: To investigate the pathologic and clinical implications of meningioma calcification. METHODS: We utilized a historical database of 342 consecutive, newly diagnosed intracranial meningiomas with preoperative CT and MRI scans treated at a single institution from 2005 to 2019. We correlated the presence of calcification with patient demographics, grade, MIB-1 index, location, volume, Simpson grade, and recurrence with both univariate and multivariate generalized linear models. RESULTS: On univariate analysis, no single variable correlated with tumor calcification. Notably, neither tumor WHO grade (p = 0.91) nor MIB-1index (p = 0.62) predicted calcification. After accounting for demographic characteristics and tumor volume and location, there was no significant association between WHO grade (p = 0.52) and MIB-1index (p = 0.54) and calcification. Calcification had no influence on rate resection grade (p = 0.59) or recurrence (p = 0.80). CONCLUSION: In this series, calcified meningiomas exhibited equal WHO grading distribution, proliferation indexes, and immediate surgical outcomes than their noncalcified counterparts. These findings question the historical role of using meningioma calcification as an independent guide to their management.

Network targets for therapeutic brain stimulation: towards personalized therapy for pain.

Precision neuromodulation of central brain circuits is a promising emerging therapeutic modality for a variety of neuropsychiatric disorders. Reliably identifying in whom, where, and in what context to provide brain stimulation for optimal pain relief are fundamental challenges limiting the widespread implementation of central neuromodulation treatments for chronic pain. Current approaches to brain stimulation target empirically derived regions of interest to the disorder or targets with strong connections to these regions. However, complex, multidimensional experiences like chronic pain are more closely linked to patterns of coordinated activity across distributed large-scale functional networks. Recent advances in precision network neuroscience indicate that these networks are highly variable in their neuroanatomical organization across individuals. Here we review accumulating evidence that variable central representations of pain will likely pose a major barrier to implementation of population-derived analgesic brain stimulation targets. We propose network-level estimates as a more valid, robust, and reliable way to stratify personalized candidate regions. Finally, we review key background, methods, and implications for developing network topology-informed brain stimulation targets for chronic pain.

Nurturing diversity and inclusion in AI in Biomedicine through a virtual summer program for high school students.

Artificial Intelligence (AI) has the power to improve our lives through a wide variety of applications, many of which fall into the healthcare space; however, a lack of diversity is contributing to limitations in how broadly AI can help people. The UCSF AI4ALL program was established in 2019 to address this issue by targeting high school students from underrepresented backgrounds in AI, giving them a chance to learn about AI with a focus on biomedicine, and promoting diversity and inclusion. In 2020, the UCSF AI4ALL three-week program was held entirely online due to the COVID-19 pandemic. Thus, students participated virtually to gain experience with AI, interact with diverse role models in AI, and learn about advancing health through AI. Specifically, they attended lectures in coding and AI, received an in-depth research experience through hands-on projects exploring COVID-19, and engaged in mentoring and personal development sessions with faculty, researchers, industry professionals, and undergraduate and graduate students, many of whom were women and from underrepresented racial and ethnic backgrounds. At the conclusion of the program, the students presented the results of their research projects at the final symposium. Comparison of pre- and post-program survey responses from students demonstrated that after the program, significantly more students were familiar with how to work with data and to evaluate and apply machine learning algorithms. There were also nominally significant increases in the students' knowing people in AI from historically underrepresented groups, feeling confident in discussing AI, and being aware of careers in AI. We found that we were able to engage young students in AI via our online training program and nurture greater diversity in AI. This work can guide AI training programs aspiring to engage and educate students entirely online, and motivate people in AI to strive towards increasing diversity and inclusion in this field.

WHO Grade I Meningioma Recurrence: Identifying High Risk Patients Using Histopathological Features and the MIB-1 Index.

Objective: In this study, we identify clinical, radiographic, and histopathologic prognosticators of overall, early, and post-median recurrence in World Health Organization (WHO) grade I meningiomas. We also determine a clinically relevant cutoff for MIB-1 to identify patients at high risk for recurrence. Method: A retrospective review of WHO grade I meningioma patients with available MIB-1 index data who underwent treatment at our institution from 2007 to 2017 was performed. Univariate and multivariate analyses, and recursive partitioning analysis (RPA), were used to identify risk factors for overall, early (within 24 months), and post-median (>24 months post-treatment) recurrence. Result: A total of 239 patients were included. The mean age was 60.0 years, and 69.5% of patients were female. The average follow-up was 41.1 months. All patients received surgery and 2 patients each received either adjuvant radiotherapy (2/239) or gamma knife treatment (2/239). The incidence of recurrence was 10.9% (26/239 patients), with an average time to recurrence of 33.2 months (6-105 months). Posterior fossa tumor location (p = 0.004), MIB-1 staining (p = 0.008), nuclear atypia (p = 0.003), and STR (p < 0.001) were independently associated with an increased risk of recurrence on cox-regression analysis. RPA for overall recurrence highlighted extent of resection, and after gross total resection (GTR), a MIB-1 index cutoff of 4.5% as key prognostic factors for recurrence. Patients with a GTR and MIB-1 >4.5% had a similar incidence of recurrence as those with STR (18.8 vs. 18.6%). Variables independently associated with early recurrence on binary logistic regression modeling included STR (p = 0.002) and nuclear atypia (p = 0.019). RPA confirmed STR as associated with early recurrence. Conclusion: STR, posterior fossa location, nuclear atypia, and elevated MIB-1 index are prognostic factors for WHO grade I meningioma recurrence. Moreover, MIB-1 index >4.5% is prognostic for recurrence in patients with GTR. Verification of our findings in larger, multi-institutional studies could enable risk stratification and recommendations for adjuvant radiotherapy following resection of WHO grade I meningiomas.

Postoperative Delirium in Glioblastoma Patients: Risk Factors and Prognostic Implications.

BACKGROUND: Delirium is a postoperative neurological morbidity in glioblastoma whose risk factors, incidence, and prognostic implications remain undefined. OBJECTIVE: To develop an algorithm using preoperative factors to predict postoperative delirium. METHODS: Retrospective analysis of 554 consecutive patients (mean age = 61.5 yr; 42% female) undergoing first glioblastoma procedure at our institution 2005 to 2011. RESULTS: Postoperative delirium occurred in 7% of patients (n = 38). Patients undergoing biopsy (10%; n = 54) did not experience delirium. In patients undergoing resection (n = 500), multivariate logistic regression identified 5 factors independently predicting postoperative delirium: age, chronic pulmonary disease, psychiatric history, bihemispheric tumors, and tumor size. We developed a score function entitled "GRAD" (Glioblastoma Risk Assessment for Delirium) to stratify patients into risk categories by assigning point(s) to each preoperative factor based on the relative magnitude of its regression coefficient. Point totals were summed for each patient: patients with 0 to 2 (n = 227) and 3 to 7 (n = 221) points were designated as low and high risk with postoperative delirium rates of 2% vs 15%, respectively (chi-square; P < .001), with the model validated using a separate patient cohort. Postoperative delirium lengthened hospital stays (P < .001), decreased likelihood of discharge home (P < .001), and was independently associated with decreased survival (4.5 vs 13.4 mo; hazard ratio = 1.9 [1.2-2.8]) in multivariate analysis. CONCLUSION: We developed a model to predict development of postoperative delirium using 2 tumor-specific (bihemispheric tumors and tumor size) and 3 patient-specific (age, psychiatric history, and chronic pulmonary disease) factors. High-risk patients and their families should be counseled preoperatively, and this risk could be considered in the choice of biopsy vs resection, and resection patients should be monitored closely postoperatively.

Indications and Efficacy of Gamma Knife Stereotactic Radiosurgery for Recurrent Glioblastoma: 2 Decades of Institutional Experience.

BACKGROUND: The role of stereotactic radiosurgery (SRS) for recurrent glioblastoma and the radionecrosis risk in this setting remain unclear. OBJECTIVE: To perform a large retrospective study to help inform proper indications, efficacy, and anticipated complications of SRS for recurrent glioblastoma. METHODS: We retrospectively analyzed patients who underwent Gamma Knife SRS between 1991 and 2013. We used the partitioning deletion/substitution/addition algorithm to identify potential predictor covariate cut points and Kaplan-Meier and proportional hazards modeling to identify factors associated with post-SRS and postdiagnosis survival. RESULTS: One hundred seventy-four glioblastoma patients (median age, 54.1 years) underwent SRS a median of 8.7 months after initial diagnosis. Seventy-five percent had 1 treatment target (range, 1-6), and median target volume and prescriptions were 7.0 cm 3 (range, 0.3-39.0 cm 3 ) and 16.0 Gy (range, 10-22 Gy), respectively. Median overall survival was 10.6 months after SRS and 19.1 months after diagnosis. Kaplan-Meier and multivariable modeling revealed that younger age at SRS, higher prescription dose, and longer interval between original surgery and SRS are significantly associated with improved post-SRS survival. Forty-six patients (26%) underwent salvage craniotomy after SRS, with 63% showing radionecrosis or mixed tumor/necrosis vs 35% showing purely recurrent tumor. The necrosis/mixed group had lower mean isodose prescription compared with the tumor group (16.2 vs 17.8 Gy; P = .003) and larger mean treatment volume (10.0 vs 5.4 cm 3 ; P = .009). CONCLUSION: Gamma Knife may benefit a subset of focally recurrent patients, particularly those who are younger with smaller recurrences. Higher prescriptions are associated with improved post-SRS survival and do not seem to have greater risk of symptomatic treatment effect.