Diagnosis Of Pediatric Brain Tumors Research Articles

Delay in the Diagnosis of Pediatric Brain Tumors in Low- and Middle-Income Countries: A Systematic Review and Meta-Analysis.

Vague symptoms and a lack of pathognomonic features hinder the timely diagnosis of pediatric brain tumors (PBTs). However, patients in low- and middle-income countries (LMICs) must also bear the brunt of a multitude of additional factors contributing to diagnostic delays and subsequently affecting survival. Therefore, this study aims to assess these factors and quantify the durations associated with diagnostic delays for PBTs in LMICs. A systematic review of extant literature regarding children from LMICs diagnosed with brain tumors was conducted. Articles published before June 2023 were identified using PubMed, Google Scholar, Scopus, Embase, Cumulative Index to Nursing and Allied Health Literature, and Web of Science. A meta-analysis was conducted using a random-effects model through R Statistical Software. Quality was assessed using the Newcastle Ottawa Scale. A total of 40 studies including 2483 patients with PBT from 21 LMICs were identified. Overall, nonspecific symptoms (62.5%) and socioeconomic status (45.0%) were the most frequently reported factors contributing to diagnostic delays. Potential sources of patient-associated delay included lack of parental awareness (45.0%) and financial constraints (42.5%). Factors contributing to health care system delays included misdiagnoses (42.5%) and improper referrals (32.5%). A pooled mean prediagnostic symptomatic interval was calculated to be 230.77 days (127.58-333.96), the patient-associated delay was 146.02 days (16.47-275.57), and the health care system delay was 225.05 days (-64.79 to 514.89). A multitude of factors contribute to diagnostic delays in LMICs. The disproportionate effect of these factors is demonstrated by the long interval between symptom onset and the definitive diagnosis of PBTs in LMICs, when compared with high-income countries. While evidence-based policy recommendations may improve the pace of diagnosis, policy makers will need to be cognizant of the unique challenges patients and health care systems face in LMICs.

IMG-18. DIFFERENTIAL DIAGNOSIS OF POSTERIOR FOSSA TUMORS USING RADIOMICS; A FIRST-LINE DIAGNOSTIC STEP REQUIRED FOR DOWNSTREAM ANALYSES

Abstract BACKGROUND Accurate radiographic diagnosis of pediatric brain tumors (PBTs) that originate in the brainstem and posterior fossa, including medulloblastoma (MB), pilocytic astrocytoma (PA), ependymoma (EPN), atypical teratoid/rhabdoid tumor (ATRT), and diffuse intrinsic pontine glioma (DIPG), is crucial for optimizing surgical approaches and enhancing neoadjuvant therapies. Existing research on the radiographic differential diagnosis of posterior fossa tumors has limitations, including small sample sizes, lack of inclusion of certain histologies especially rarer tumors such as ATRTs, and incomplete analysis of the whole tumor, including peritumoral edema. In this study, we aimed to perform a comprehensive analysis using radiomics and machine learning to differentiate among the common posterior fossa and brainstem tumors. METHODS We employed 927 radiomic features extracted from whole tumor regions within treatment-naïve, standard multiparametric MRI sequences (pre-/post-contrast T1-weighted, T2-weighted, FLAIR) of 264 patients (106 MBs, 78 PAs, 28 EPNs, 27 ATRTs, and 25 DIPGs), collected from the Children’s Brain Tumor Network (CBTN). We adopted a one-versus-rest classification strategy, employing Support Vector Machines combined with the Least Absolute Shrinkage and Selection Operator (LASSO) for feature selection, and implementing nested cross-validation for robustness. RESULTS The performances of the classifiers were evaluated using the Area Under the Receiver Operating Characteristic Curve, yielding values of 0.84 for MBs, 0.84 for PAs, 0.70 for EPNs, 0.75 for ATRTs, and 0.71 for DIPGs. CONCLUSIONS Our method effectively differentiates between various tumor types in the posterior fossa and brainstem, paving the path towards the development of comprehensive diagnostic and prognostic AI tools for pre-treatment histological diagnosis of these tumors. These AI tools can lead to more tailored, risk-adjusted treatments for PBTs, reducing morbidities and improving patient outcomes. Based on our promising initial results, we will expand our dataset to include more samples and incorporate rarer tumor types, as well as piloting in different molecular subclassifications.

Deciphering Machine Learning Decisions to Distinguish between Posterior Fossa Tumor Types Using MRI Features: What Do the Data Tell Us?

Machine learning (ML) models have become capable of making critical decisions on our behalf. Nevertheless, due to complexity of these models, interpreting their decisions can be challenging, and humans cannot always control them. This paper provides explanations of decisions made by ML models in diagnosing four types of posterior fossa tumors: medulloblastoma, ependymoma, pilocytic astrocytoma, and brainstem glioma. The proposed methodology involves data analysis using kernel density estimations with Gaussian distributions to examine individual MRI features, conducting an analysis on the relationships between these features, and performing a comprehensive analysis of ML model behavior. This approach offers a simple yet informative and reliable means of identifying and validating distinguishable MRI features for the diagnosis of pediatric brain tumors. By presenting a comprehensive analysis of the responses of the four pediatric tumor types to each other and to ML models in a single source, this study aims to bridge the knowledge gap in the existing literature concerning the relationship between ML and medical outcomes. The results highlight that employing a simplistic approach in the absence of very large datasets leads to significantly more pronounced and explainable outcomes, as expected. Additionally, the study also demonstrates that the pre-analysis results consistently align with the outputs of the ML models and the clinical findings reported in the existing literature.

The surgical intervention for pineal region tumors.

Histological and molecular characterization is essential for the diagnosis of pediatric brain tumors. In the pineal region tumors, it is necessary to remove a sufficient tumor volume to make a diagnosis. However, surgery in this region is challenging due to its deep anatomical location and surrounded by critical structures and complex venous system. Knowledge of the anatomy and function of the pineal region and tumor histological types is imperative for the successful management of pineal region tumors. This article describes surgical approaches to pineal tumors, focusing on the occipital transtentorial approach and adding the author's experience to what has been known in the literature. Recent innovations have made this approach more popular and can be applied to occipital fossa lesions.

Neurocognitive development after pediatric brain tumor - a longitudinal, retrospective cohort study

ABSTRACT Survivors of Pediatric Brain Tumors (PBTs) treated with cranial radiation therapy (CRT) often experience a decline in neurocognitive test scores. Less is known about the neurocognitive development of non-irradiated survivors of PBTs. The aim of this study was to statistically model neurocognitive development after PBT in both irradiated and non-irradiated survivors and to find clinical variables associated with the rate of decline in neurocognitive scores. A total of 151 survivors were included in the study. Inclusion criteria: Diagnosis of PBT between 2001 and 2013 or earlier diagnosis of PBT and turning 18 years of age between 2006 and 2013. Exclusion criteria: Death within a year from diagnosis, neurocutaneous syndromes, severe intellectual disability. Clinical neurocognitive data were collected retrospectively from medical records. Multilevel linear modeling was used to evaluate the rate of decline in neurocognitive measures and factors associated with the same. A decline was found in most measures for both irradiated and non-irradiated survivors. Ventriculo-peritoneal (VP) shunting and treatment with whole-brain radiation therapy (WBRT) were associated with a faster decline in neurocognitive scores. Male sex and supratentorial lateral tumor were associated with lower scores. Verbal learning measures were either stable or improving. Survivors of PBTs show a pattern of decline in neurocognitive scores irrespective of treatment received, which suggests the need for routine screening for neurocognitive rehabilitation. However, survivors treated with WBRT and/or a VP shunt declined at a faster rate and appear to be at the highest risk of negative neurocognitive outcomes and to have the greatest need for neurocognitive rehabilitation.

Rapid intraoperative diagnosis of pediatric brain tumors using Raman spectroscopy: A machine learning approach

BackgroundSurgical resection is a mainstay in the treatment of pediatric brain tumors to achieve tissue diagnosis and tumor debulking. While maximal safe resection of tumors is desired, it can be challenging to differentiate normal brain from neoplastic tissue using only microscopic visualization, intraoperative navigation, and tactile feedback. Here, we investigate the potential for Raman spectroscopy (RS) to accurately diagnose pediatric brain tumors intraoperatively.MethodsUsing a rapid acquisition RS device, we intraoperatively imaged fresh ex vivo brain tissue samples from 29 pediatric patients at the Lucile Packard Children’s Hospital between October 2018 and March 2020 in a prospective fashion. Small tissue samples measuring 2-4 mm per dimension were obtained with each individual tissue sample undergoing multiple unique Raman spectra acquisitions. All tissue samples from which Raman spectra were acquired underwent individual histopathology review. A labeled dataset of 678 unique Raman spectra gathered from 160 samples was then used to develop a machine learning model capable of (1) differentiating normal brain from tumor tissue and (2) normal brain from low-grade glioma (LGG) tissue.ResultsTrained logistic regression model classifiers were developed using our labeled dataset. Model performance was evaluated using leave-one-patient-out cross-validation. The area under the curve (AUC) of the receiver-operating characteristic (ROC) curve for our tumor vs normal brain model was 0.94. The AUC of the ROC curve for LGG vs normal brain was 0.91.ConclusionsOur work suggests that RS can be used to develop a machine learning-based classifier to differentiate tumor vs non-tumor tissue during resection of pediatric brain tumors.

The role of diffusion tensor imaging metrics in machine learning-based characterisation of paediatric brain tumors and their practicality for multicentre clinical assessment

Abstract Aims Magnetic resonance imaging (MRI) is a valuable tool for non-invasive diagnosis of paediatric brain tumours. The rarity of the disease dictates multi-centre studies and imaging biomarkers that are robust to protocol variability. We investigated diffusion tensor MRI (DT-MRI), combined with machine learning, as an aid to diagnosis and evaluated the robustness of the imaging metrics. Method A multi-centre cohort of 52 clinical DT-MRI scans (20 medulloblastomas (MB), 21 pilocytic astrocytomas (PA), 11 ependymomas (EP)) were analysed retrospectively. Histograms for regions of solid tumour for fractional anisotropy (FA), mean diffusivity (MD), pure anisotropic diffusion (q) and pure isotropic diffusion (p) were compared to assess diagnostic capability. Linear discriminate analysis (LDA) was used for classification and validated using leave-one-out-cross-validation (LOOCV). Results Histogram medians for FA, MD, q and p were all different between tumor groups (P<.0001, Kruskal Wallis test). Median MD, p and q values were highest in PA, then EP and lowest in MB (P<.0001, Pairwise Wilcox test). FA median was higher for EP than PA (P=.004) with no significant difference between EP and MB (P=.591). ROC analysis showed that median MD, q and p perform best as a diagnostic marker (AUC= 0.92 to 0.99). LOOCV showed an overall accuracy of the LDA classification, ranging between 67% - 87%. FA values were highly dependent on protocol parameters, whereas pure anisotropic diffusion, q, was not. Conclusion DT-MRI metrics from multi-centre acquisitions can classify paediatric brain tumours. FA is the least robust metric to protocol variability and q provides the most robust quantification of anisotropic behaviour.

Neuropathology of Pediatric Brain Tumors: A Concise Review.

Pediatric brain tumors are an incredibly diverse group of neoplasms and neuropathological tumor classification is an essential part of patient care. Classification of pediatric brain tumors has changed considerably in recent years as molecular diagnostics have become incorporated with routine histopathology in the diagnostic process. This article will focus on the fundamental major histologic, immunohistochemical, and molecular features that neuropathologists use to make an integrated diagnosis of pediatric brain tumors. This concise review will focus on tumors that are integral to the central nervous system in pediatric patients including: embryonal tumors, low and high grade gliomas, glioneuronal tumors, ependymomas, and choroid plexus tumors.

PATH-20. METHYLATION ARRAY PROFILING OF PEDIATRIC BRAIN TUMORS; SINGLE CENTRE EXPERIENCE

BACKGROUNDSignificant heterogeneity of pediatric brain tumors poses major challenge on diagnostics. Therefore, we aimed to evaluate feasibility of methylation array in the diagnostic process.METHODSMethylation array (Infinium MethylationEPIC, Illumina) was performed on DNA extracted from fresh frozen tissue from prospective newly diagnosed and selected retrospective patients. Results from Heidelberg classifier (www.molecularneuropathology.org) were compared to the histological diagnosis and further genetic testing was performed to establish integrated morphological/molecular diagnosis.RESULTSWithin years 2018–2019, we performed methylation array profiling of 102 samples consisting mainly of ependymoma, medulloblastoma high-grade and low-grade glioma. High calibrated score (>0.9) was achieved in 62 patients (61%). In 46 cases (74%) with score >0.9, the histological diagnosis matched the methylation class (MC). In the remaining cases (16) that were classified by histopathology mainly as ependymomas, the methylation profiles were classified as novel molecular entities (HGNET_BCOR, HGNET_MN1, etc.) or different tumor type. In 40 cases (39%) with the score <0.9, six were found to have high normal tissue content. Nine cases had no match in the classifier and 25 were assigned MC with score 0.3 to 0.89. In 20 out of 34 cases with low score, the molecular diagnosis could be confirmed based on copy number variants inferred from the methylation array or using additional testing for gene fusions and mutations.CONCLUSIONSOur experience on the first 100+ cases demonstrated that methylation array could be integral part of diagnostic process in order to establish integrated morphological and molecular diagnosis of pediatric brain tumors.

Magnetic resonance spectroscopy in pediatric brain tumors: how to make a more confident diagnosis

BackgroundNon-invasive diagnosis of pediatric brain tumors can be challenging due to diverse tumor pathologies and similar imaging appearances. Magnetic resonance spectroscopy (MRS), when combined with high spatial resolution anatomic imaging obtained with conventional magnetic resonance imaging (MRI), provides metabolic information within the lesion as well as the surrounding tissue. The differentiation of neoplastic from non-neoplastic lesions and low-grade from high-grade neoplasms is essential for determining the choice of treatment and the best treatment plan. We aimed to measure specific metabolic ratios and evaluate metabolic profiles of various lesions by MRS to assist in making a more confident diagnosis.ResultsThe choline/creatine (Cho/Cr), choline/N-acetylaspartate (Cho/NAA), and Cho/NAA+Cr ratios all had statistically significant values for the differentiation between neoplastic and non-neoplastic lesions at cutoffs 1.8, 2, and 0.8 respectively. The Cho/NAA, Cho/Cr, Cho/NAA+Cr, and myo-inositol/creatine (mI/Cr) ratios all had statistically significant values for the differentiation of high-grade from low-grade neoplasms at cutoffs 3.3, 3.5, 1.3, and 1.5 respectively. The presence of a lipid lactate peak was only significant for differentiating high-grade from low-grade neoplasms. Medulloblastomas, diffuse pontine gliomas, and choroid plexus carcinoma all showed characteristic metabolic profiles on MRS. Metastasis showed lower Cho/NAA and Cho/Cr ratios outside the tumor margin than high-grade neoplasms.ConclusionThe use of certain metabolite ratios with high sensitivity and specificity to distinguish neoplastic from non-neoplastic lesions and low-grade from high-grade neoplasms while assessing the metabolic profile of the lesion aids in the non-invasive diagnosis of pediatric brain tumors. MRS facilitates earlier treatment planning by determining tumor spatial extent and predicting tumor behavior with potential to solve sampling problems of inaccessible and heterogenous lesions as well as unnecessary sampling of benign lesions.

Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: A multi-site study.

The imaging and subsequent accurate diagnosis of paediatric brain tumours presents a radiological challenge, with magnetic resonance imaging playing a key role in providing tumour specific imaging information. Diffusion weighted and perfusion imaging are commonly used to aid the non-invasive diagnosis of children's brain tumours, but are usually evaluated by expert qualitative review. Quantitative studies are mainly single centre and single modality. The aim of this work was to combine multi-centre diffusion and perfusion imaging, with machine learning, to develop machine learning based classifiers to discriminate between three common paediatric tumour types. The results show that diffusion and perfusion weighted imaging of both the tumour and whole brain provide significant features which differ between tumour types, and that combining these features gives the optimal machine learning classifier with >80% predictive precision. This work represents a step forward to aid in the non-invasive diagnosis of paediatric brain tumours, using advanced clinical imaging.

Diagnostic accuracy and added value of qualitative radiological review of 1H-magnetic resonance spectroscopy in evaluation of childhood brain tumors.

1H-magnetic resonance spectroscopy (MRS) facilitates noninvasive diagnosis of pediatric brain tumors by providing metabolite profiles. Prospective studies of diagnostic accuracy and comparisons with conventional MRI are lacking. We aimed to evaluate diagnostic accuracy of MRS for childhood brain tumors and determine added clinical value compared with conventional MRI. Children presenting to a tertiary pediatric center with brain lesions from December 2015 through 2017 were included. MRI and single-voxel MRS were acquired on 52 tumors and sequentially interpreted by 3 radiologists, blinded to histopathology. Proportions of correct diagnoses and interrater agreement at each stage were compared. Cases were reviewed to determine added value of qualitative radiological review of MRS through increased certainty of correct diagnosis, reduced number of differentials, or diagnosis following spectroscopist evaluation. Final diagnosis was agreed by the tumor board at study end. Radiologists' principal MRI diagnosis was correct in 69%, increasing to 77% with MRS. MRI + MRS resulted in significantly more additional correct diagnoses than MRI alone (P = .035). There was a significant increase in interrater agreement when correct with MRS (P = .046). Added value following radiologist interpretation of MRS occurred in 73% of cases, increasing to 83% with additional spectroscopist review. First histopathological diagnosis was available a median of 9.5 days following imaging, with 25% of all patients managed without conclusive histopathology. MRS can improve the accuracy of noninvasive diagnosis of pediatric brain tumors and add value in the diagnostic pathway. Incorporation into practice has the potential to facilitate early diagnosis, guide treatment planning, and improve patient care.

Can apparent diffusion coefficient values help distinguish between different types of pediatric brain tumors?

Rationale and objectivesClassifying brain tumors is challenging, but recently developed imaging techniques offer the opportunity for neuroradiologists and neurosurgeons to diagnose, differentiate, and manage different types of brain tumors. Such advances will be reflected in improvements in patients’ life expectancy and quality of life. Among the newest techniques, the apparent diffusion coefficient (ADC), which tracks the rate of microscopic water diffusion within tissues, has become a focus of investigation. Recently, ADC has been used as a preoperative diffusion-weighted magnetic resonance imaging (MRI) parameter that facilitates tumor diagnosis and grading. Here, we aimed to determine the ADC cutoff values for pediatric brain tumors (PBTs) categorized according to the World Health Organization (WHO) classification of brain tumors. Materials and methodsWe retrospectively reviewed 80 cases, and assessed them based on their MRI-derived ADC. These results were compared with those of WHO classification-based histopathology. ResultsWhole-lesion ADC values ranged 0.225–1.240 × 10−3 mm2/s for ependymal tumors, 0.107–1.571 × 10−3 mm2/s for embryonal tumors, 0.1065–2.37801 × 10−3 mm2/s for diffuse astrocytic and oligodendroglial tumors, 0.5220–0.7840 × 10−3 mm2/s for other astrocytic tumors, and 0.1530–0.8160 × 10−3 mm2/s for meningiomas. These findings revealed the usefulness of ADC in the differential diagnosis of PBT, as it was able to discriminate between five types of PBTs. ConclusionThe application of an ADC diagnostic criterion would reduce the need for spectroscopic analysis. However, further research is needed to implement ADC in the differential diagnosis of PBT.

Time to diagnosis of pediatric brain tumors: a report from the Pediatric Hematology and Oncology Center in Rabat, Morocco.

Delayed diagnosis of pediatric brain tumors is known to occur worldwide but is not well studied in developing countries. Here, we examined the extent of delayed pediatric brain tumor diagnoses in Rabat, Morocco, and consider its potential causes and possible solutions. We conducted a survey and interviews of the parents of children who were admitted to the Department of Hematology and Pediatric Oncology of Rabat Children's Hospital from January 1, 2016 to June 30, 2016. The families of 27 patients (14 girls and 13 boys) participated in the survey and interview. The median patient age was 7years (range, 1-15years). The most common presenting symptoms were vomiting (n = 18) and headache (n = 17). The tumor locations were supratentorial in 13 cases and infratentorial in 14 cases. The median time to diagnosis was 2months (range, 0.25-20months). The longest times to diagnosis occurred in children older than 5years and in patients with supratentorial tumors or low-grade glioma. We did not observe any differences in the time to diagnosis according to sex, socioeconomic status, or urban or rural origin. Delayed diagnosis of pediatric brain tumors is a universal problem, evidenced by many studies in different countries. We propose that a paradigm shift in medical curricula addressing the delayed diagnosis of pediatric brain tumors should occur in medical schools and clinical training programs.

Rapid Intraoperative Diagnosis of Pediatric Brain Tumors Using Stimulated Raman Histology.

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 artifact 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, κ > 0.90) and an accuracy of 92% to 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.Significance: A new imaging method simplifies diagnosis and informs decision making during pediatric brain tumor surgery. Cancer Res; 78(1); 278-89. ©2017 AACR.

Radiomics in paediatric neuro-oncology: A multicentre study on MRI texture analysis.

Brain tumours are the most common solid cancers in children in the UK and are the most common cause of cancer deaths in this age group. Despite current advances in MRI, non-invasive diagnosis of paediatric brain tumours has yet to find its way into routine clinical practice. Radiomics, the high-throughput extraction and analysis of quantitative image features (e.g. texture), offers potential solutions for tumour characterization and decision support. In the search for diagnostic oncological markers, the primary aim of this work was to study the application of MRI texture analysis (TA) for the classification of paediatric brain tumours. A multicentre study was carried out, within a supervised classification framework, on clinical MR images, and a support vector machine (SVM) was trained with 3D textural attributes obtained from conventional MRI. To determine the cross-centre transferability of TA, an assessment of how SVM performs on unseen datasets was carried out through rigorous pairwise testing. The study also investigated the nature of features that are most likely to train classifiers that can generalize well with the data. Finally, the issue of class imbalance, which arises due to some tumour types being more common than others, was explored. For each of the tests carried out through pairwise testing, the optimal area under the receiver operating characteristic curve ranged between 76% and 86%, suggesting that the model was able to capture transferable tumour information. Feature selection results suggest that similar aspects of tumour texture are enhanced by MR images obtained at different hospitals. Our results also suggest that the availability of equally represented classes has enabled SVM to better characterize the data points. The findings of the study presented here support the use of 3D TA on conventional MR images to aid diagnostic classification of paediatric brain tumours.

Clinical Application of Whole Genome Array Improves the Diagnosis of Pediatric Brain Tumors.

Pediatric brain tumors are the leading cause of childhood cancer mortality. Recurring genetic abnormalities play an essential role in the diagnosis and prognosis of pediatric brain tumors. However, clinical workup has not routinely included whole genome assessment. Here, we present high resolution whole genome array results in 11 pediatric brain tumors. Array identified clinically relevant abnormalities in all samples. Copy number aberrations with targeted therapy implication, GOPC-ROS1 fusion, CDK4 amplification, and NF1 deletion, were detected in 3 cases. In addition, array detected recurring genetic abnormalities, including KIAA1549-BRAF fusion, 19q13.42 amplification, i(17q), and monosomy 6, which assisted accurate histological diagnosis in pediatric brain tumors. In conclusion, our results show that whole genome high-resolution array detects diagnostic and treatment-relevant copy number abnormalities in pediatric brain tumors.

Pediatric cancer pathology review from a single institution: Neuropathology expert opinion is essential for accurate diagnosis of pediatric brain tumors.

Second pathology review has been reported to improve accuracy in oncologic diagnoses, including pediatric malignancies. We assessed the impact of second review on the diagnosis of pediatric malignancies at a tertiary care referral center in Beirut, Lebanon. Pathology reports of patients treated at the Children's Cancer Institute in Lebanon were retrospectively reviewed for the period 2008-2016 and compared with same samples' diagnoses at St. Jude Children's Research Hospital. Diagnostic disagreements were divided into major, minor, and none based on their effect on diagnosis and/or patient management. Second review was requested for 171 cases, accounting for 19% of all cases during that period. Second opinion was mostly requested for brain tumors (62% of all brain tumor cases) and neuroblastoma for NMYC testing (65% of all neuroblastoma), while hematologic malignancies had the fewest referrals (3% of all hematologic cases). Major disagreements in second review occurred in 20 cases (12% of total), and minor disagreements in 21 cases (12% of total). The largest proportion of major disagreements (71%) occurred in pediatric brain tumors, and novel molecular tests contributed to the diagnosis in 55% of these cases. The availability of a specialized pediatric neuropathologist and a basic panel of relevant molecular testing are essential for appropriate diagnosis of pediatric brain tumors. Centers that do not have the available infrastructure in place can benefit greatly from second review referrals for this challenging subset of tumors.

Evaluation of the added value of 1H-magnetic resonance spectroscopy for the diagnosis of pediatric brain lesions in clinical practice.

BackgroundMagnetic resonance spectroscopy (MRS) aids noninvasive diagnosis of pediatric brain tumors, but use in clinical practice is not well documented. We aimed to review clinical use of MRS, establish added value in noninvasive diagnosis, and investigate potential impact on patient care.MethodsSixty-nine children with lesions imaged using MRS and reviewed by the tumor board from 2014 to 2016 met inclusion criteria. Contemporaneous MRI diagnosis, spectroscopy analysis, histopathology, and clinical information were reviewed. Final diagnosis was agreed on by the tumor board at study end.ResultsFive cases were excluded for lack of documented MRI diagnosis. The principal MRI diagnosis by pediatric radiologists was correct in 59%, increasing to 73% with addition of MRS. Of the 73%, 19.1% (95% CI, 9.1%-33.3%) were incorrectly diagnosed with MRI alone. MRS led to a significant improvement in correct diagnosis over all tumor types (P = .012). Of diagnoses correctly made with MRI, confidence increased by 37% when adding MRS, with no patients incorrectly re-diagnosed. Indolent lesions were diagnosed noninvasively in 85% of cases, with MRS a major contributor to 91% of these diagnoses. Of all patients, 39% were managed without histopathological diagnosis. MRS contributed to diagnosis in 68% of this group, modifying it in 12%. MRS influenced management in 33% of cases, mainly through avoiding and guiding biopsy and aiding tumor characterization.ConclusionMRS can improve accuracy and confidence in noninvasive diagnosis of pediatric brain lesions in clinical practice. There is potential to improve outcomes through avoiding biopsy of indolent lesions, aiding tumor characterization, and facilitating earlier family discussions and treatment planning.

Short-term mortality following surgical procedures for the diagnosis of pediatric brain tumors: outcome analysis in 5533 children from SEER, 2004-2011.

OBJECT Thirty-day mortality is increasingly a reference metric regarding surgical outcomes. Recent data estimate a 30-day mortality rate of 1.4-2.7% after craniotomy for tumors in children. No detailed analysis of short-term mortality following a diagnostic neurosurgical procedure (e.g., resection or tissue biopsy) for tumor in the US pediatric population has been conducted. METHODS The Surveillance, Epidemiology and End Results (SEER) data sets identified patients ≤ 21 years who underwent a diagnostic neurosurgical procedure for primary intracranial tumor from 2004 to 2011. One- and two-month mortality was estimated. Standard statistical methods estimated associations between independent variables and mortality. RESULTS A total of 5533 patients met criteria for inclusion. Death occurred within the calendar month of surgery in 64 patients (1.16%) and by the conclusion of the calendar month following surgery in 95 patients (1.72%). Within the first calendar month, patients < 1 year of age (n = 318) had a risk of death of 5.66%, while those from 1 to 21 years (n = 5215) had a risk of 0.88% (p < 0.0001). By the end of the calendar month following surgery, patients < 1 year (n = 318) had a risk of death of 7.23%, while those from 1 to 21 years (n = 5215) had a risk of 1.38% (p < 0.0001). Children < 1 year at diagnosis were more likely to harbor a high-grade lesion than older children (OR 1.9, 95% CI 1.5-2.4). CONCLUSIONS In the SEER data sets, the risk of death within 30 days of a diagnostic neurosurgical procedure for a primary pediatric brain tumor is between 1.16% and 1.72%, consistent with contemporary data from European populations. The risk of mortality in infants is considerably higher, between 5.66% and 7.23%, and they harbor more aggressive lesions.