Despite improved surgical and adjuvant treatment options, malignant brain tumors remain non-curable to date. The thin line between treatment effectiveness and patient harms underpins the importance... Show moreDespite improved surgical and adjuvant treatment options, malignant brain tumors remain non-curable to date. The thin line between treatment effectiveness and patient harms underpins the importance of tailoring clinical management to the individual brain tumor patient. Over the past decades, the volume and complexity of clinically-derived patient data (i.e., imaging, genomics, free-text etc.) is increasing exponentially. Machine learning provides a vast range of algorithms that can learn from this data and guide clinical decision-making by providing accurate patient-level predictions. The current thesis describes several studies along the continuum of the machine learning spectrum as it applies to neurosurgical oncology. Part I investigates postoperative complications and risk factors in patients operated for a primary malignant brain tumor. Part II describes de development of a model for the prediction of individual-patient survival in glioblastoma patients. Part III encompasses the development of a natural language processing framework for automated medical text analysis. Machine learning algorithms should be considered as an extension to statistical approaches and exist along a continuum determined by how much is specified by humans and how much is learnt by the machine. Although machine learning algorithms can produce highly accurate predictions based on high-dimensional data, clinicians and researchers should interpret the clinical implications of these predictions on case-by-case basis. Show less
Purpose Although standard-of-care has been defined for the treatment of glioblastoma patients, substantial practice variation exists in the day-to-day clinical management. This study aims to... Show morePurpose Although standard-of-care has been defined for the treatment of glioblastoma patients, substantial practice variation exists in the day-to-day clinical management. This study aims to compare the use of laboratory tests in the perioperative care of glioblastoma patients between two tertiary academic centers-Brigham and Women's Hospital (BWH), Boston, USA, and University Medical Center Utrecht (UMCU), Utrecht, the Netherlands. Methods All glioblastoma patients treated according to standard-of-care between 2005 and 2013 were included. We compared the number of blood drawings and laboratory tests performed during the 70-day perioperative period using a Poisson regression model, as well as the estimated laboratory costs per patient. Additionally, we compared the likelihood of an abnormal test result using a generalized linear mixed effects model. Results After correction for age, sex, IDH1 status, postoperative KPS score, length of stay, and survival status, the number of blood drawings and laboratory tests during the perioperative period were 3.7-fold (p < 0.001) and 4.7-fold (p < 0.001) higher, respectively, in BWH compared to UMCU patients. The estimated median laboratory costs per patient were 82 euros in UMCU and 256 euros in BWH. Furthermore, the likelihood of an abnormal test result was lower in BWH (odds ratio [OR] 0.75, p < 0.001), except when the prior test result was abnormal as well (OR 2.09, p < 0.001). Conclusions Our results suggest a substantially lower clinical threshold for ordering laboratory tests in BWH compared to UMCU. Further investigating the clinical consequences of laboratory testing could identify over and underuse, decrease healthcare costs, and reduce unnecessary discomfort that patients are exposed to. Show less
Glioblastoma is associated with a poor prognosis. Even though survival statistics are well-described at the population level, it remains challenging to predict the prognosis of an individual... Show moreGlioblastoma is associated with a poor prognosis. Even though survival statistics are well-described at the population level, it remains challenging to predict the prognosis of an individual patient despite the increasing number of prognostic models. The aim of this study is to systematically review the literature on prognostic modeling in glioblastoma patients. A systematic literature search was performed to identify all relevant studies that developed a prognostic model for predicting overall survival in glioblastoma patients following the PRISMA guidelines. Participants, type of input, algorithm type, validation, and testing procedures were reviewed per prognostic model. Among 595 citations, 27 studies were included for qualitative review. The included studies developed and evaluated a total of 59 models, of which only seven were externally validated in a different patient cohort. The predictive performance among these studies varied widely according to the AUC (0.58-0.98), accuracy (0.69-0.98), and C-index (0.66-0.70). Three studies deployed their model as an online prediction tool, all of which were based on a statistical algorithm. The increasing performance of survival prediction models will aid personalized clinical decision-making in glioblastoma patients. The scientific realm is gravitating towards the use of machine learning models developed on high-dimensional data, often with promising results. However, none of these models has been implemented into clinical care. To facilitate the clinical implementation of high-performing survival prediction models, future efforts should focus on harmonizing data acquisition methods, improving model interpretability, and externally validating these models in multicentered, prospective fashion. Show less
Maas, S.L.N.; Solinge, T.S. van; Schnoor, R.; Yekula, A.; Senders, J.T.; Vrij, J. de; ... ; Broekman, M.L.D. 2020
Simple SummaryIn Glioblastoma (GB), a malignant tumor of the central nervous system, diagnosis can currently only be obtained via tissue biopsy. In this study we were able to isolate GB derived... Show moreSimple SummaryIn Glioblastoma (GB), a malignant tumor of the central nervous system, diagnosis can currently only be obtained via tissue biopsy. In this study we were able to isolate GB derived extracellular vesicles (EVs) in the blood, after patients received 5-aminolevulinic acid (5-ALA) before surgery. This isolation is based on fluorescence caused by the accumulation of fluorescent protoporphyrin IX (PpIX) in these EVs. We show that these EVs contain various GB-related micro RNAs. While there are many ways in which our technique needs to be improved before being able to be implemented in the clinic, this study shows that detecting and analyzing circulating GB-derived EVs based on PpIX fluorescence is feasible. In the future, our technique could be developed to diagnose and monitor GB via blood samples instead of a brain biopsy.Background: In glioblastoma (GB), tissue is required for accurate diagnosis and subtyping. Tissue can be obtained through resection or (stereotactic) biopsy, but these invasive procedures provide risks for patients. Extracellular vesicles (EVs) are small, cell-derived vesicles that contain miRNAs, proteins, and lipids, and possible candidates for liquid biopsies. GB-derived EVs can be found in the blood of patients, but it is difficult to distinguish them from circulating non-tumor EVs. 5-aminolevulinic acid (5-ALA) is orally administered to GB patients to facilitate tumor visualization and maximal resection, as it is metabolized to fluorescent protoporphyrin IX (PpIX) that accumulates in glioma cells. In this study, we assessed whether PpIX accumulates in GB-derived EVs and whether these EVs could be isolated and characterized to enable a liquid biopsy in GB. Methods: EVs were isolated from the conditioned media of U87 cells treated with 5-ALA by differential ultracentrifugation. Blood samples were collected and processed from healthy controls and patients undergoing 5-ALA guided surgery for GB. High-resolution flow cytometry (hFC) enabled detection and sorting of PpIX-positive EVs, which were subsequently analyzed by digital droplet PCR (ddPCR). Results: PpIX-positive EVs could be detected in conditioned cell culture media as well as in patient samples after administration of 5-ALA. By using hFC, we could sort the PpIX-positive EVs for further analysis with ddPCR, which indicated the presence of EVs and GB-associated miRNAs. Conclusion: GB-derived EVs can be isolated from the plasma of GB patients by using 5-ALA induced fluorescence. Although many challenges remain, our findings show new possibilities for the development of blood-based liquid biopsies in GB patients. Show less
Given the median survival of 15 months after diagnosis, novel treatment strategies are needed for glioblastoma. Beta-blockers have been demonstrated to inhibit angiogenesis and tumor cell... Show moreGiven the median survival of 15 months after diagnosis, novel treatment strategies are needed for glioblastoma. Beta-blockers have been demonstrated to inhibit angiogenesis and tumor cell proliferation in various cancer types. The aim of this study was to systematically review the evidence on the effect of beta-blockers on glioma growth. A systematic literature search was performed in the PubMed, Embase, Google Scholar, Web of Science, and Cochrane Central to identify all relevant studies. Preclinical studies concerning the pharmacodynamic effects of beta-blockers on glioma growth and proliferation were included, as well as clinical studies that studied the effect of beta-blockers on patient outcomes according to PRISMA guidelines. Among the 980 citations, 10 preclinical studies and 1 clinical study were included after title/abstract and full-text screening. The following potential mechanisms were identified: reduction of glioma cell proliferation (n = 9), decrease of glioma cell migration (n = 2), increase of drug sensitivity (n = 1), induction of glioma cell death (n = 1). Beta-blockers affect glioma proliferation by inducing a brief reduction of cAMP and a temporary cell cycle arrest in vitro.Contrasting results were observed concerning glioma cell migration. The identified clinical study did not find an association between beta-blockers and survival in glioma patients. Although preclinical studies provide scarce evidence for the use of beta-blockers in glioma, they identified potential pathways for targeting glioma. Future studies are needed to clarify the effect of beta-blockers on clinical endpoints including survival outcomes in glioma patients to scrutinize the value of beta-blockers in glioma care. Show less
PURPOSE The aim of this study was to develop an open-source natural language processing (NLP) pipeline for text mining of medical information from clinical reports. We also aimed to provide insight... Show morePURPOSE The aim of this study was to develop an open-source natural language processing (NLP) pipeline for text mining of medical information from clinical reports. We also aimed to provide insight into why certain variables or reports are more suitable for clinical text mining than others.MATERIALS AND METHODS Various NLP models were developed to extract 15 radiologic characteristics from free-text radiology reports for patients with glioblastoma. Ten-fold cross-validation was used to optimize the hyperparameter settings and estimate model performance. We examined how model performance was associated with quantitative attributes of the radiologic characteristics and reports.RESULTS In total, 562 unique brain magnetic resonance imaging reports were retrieved. NLP extracted 15 radiologic characteristics with high to excellent discrimination (area under the curve, 0.82 to 0.98) and accuracy (78.6% to 96.6%). Model performance was correlated with the inter-rater agreement of the manually provided labels (rho = 0.904; P<.001) but not with the frequency distribution of the variables of interest (rho = 0.179; P =.52). All variables labeled with a near perfect inter-rater agreement were classified with excellent performance (area under the curve > 0.95). Excellent performance could be achieved for variables with only 50 to 100 observations in the minority group and class imbalances up to a 9:1 ratio. Report-level classification accuracy was not associated with the number of words or the vocabulary size in the distinct text documents.CONCLUSION This study provides an open-source NLP pipeline that allows for text mining of narratively written clinical reports. Small sample sizes and class imbalance should not be considered as absolute contraindications for text mining in clinical research. However, future studies should report measures of inter-rater agreement whenever ground truth is based on a consensus label and use this measure to identify clinical variables eligible for text mining. Show less
Background Fluorescence-guided surgery (FGS) is a technique used to enhance visualization of tumor margins in order to increase the extent of tumor resection in glioma surgery. In this paper, we... Show moreBackground Fluorescence-guided surgery (FGS) is a technique used to enhance visualization of tumor margins in order to increase the extent of tumor resection in glioma surgery. In this paper, we systematically review all clinically tested fluorescent agents for application in FGS for glioma and all preclinically tested agents with the potential for FGS for glioma.Methods We searched the PubMed and Embase databases for all potentially relevant studies through March 2016. We assessed fluorescent agents by the following outcomes: rate of gross total resection (GTR), overall and progression-free survival, sensitivity and specificity in discriminating tumor and healthy brain tissue, tumor-to-normal ratio of fluorescent signal, and incidence of adverse events.Results The search strategy resulted in 2155 articles that were screened by titles and abstracts. After full-text screening, 105 articles fulfilled the inclusion criteria evaluating the following fluorescent agents: 5-aminolevulinic acid (5-ALA) (44 studies, including three randomized control trials), fluorescein (11), indocyanine green (five), hypericin (two), 5aminofluorescein- human serum albumin (one), endogenous fluorophores (nine) and fluorescent agents in a pre-clinical testing phase (30). Three meta-analyses were also identified.Conclusions 5-ALA is the only fluorescent agent that has been tested in a randomized controlled trial and results in an improvement of GTR and progression-free survival in highgrade gliomas. Observational cohort studies and case series suggest similar outcomes for FGS using fluorescein. Molecular targeting agents (e.g., fluorophore/nanoparticle labeled with anti-EGFR antibodies) are still in the pre-clinical phase, but offer promising results and may be valuable future alternatives. Show less
