Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings.

We applied machine learning algorithms for differentiation of posterior fossa tumors using apparent diffusion coefficient (ADC) histogram analysis and structural MRI findings. A total of 256 patients with intra-axial posterior fossa tumors were identified, of whom 248 were included in machine learning analysis, with at least 6 representative subjects per each tumor pathology. The ADC histograms of solid components of tumors, structural MRI findings, and patients' age were applied to construct decision models using Classification and Regression Tree analysis. We also compared different machine learning classification algorithms (i.e., naïve Bayes, random forest, neural networks, support vector machine with linear and polynomial kernel) for dichotomized differentiation of the 5 most common tumors in our cohort: metastasis (n = 65), hemangioblastoma (n = 44), pilocytic astrocytoma (n = 43), ependymoma (n = 27), and medulloblastoma (n = 26). The decision tree model could differentiate seven tumor histopathologies with terminal nodes yielding up to 90% accurate classification rates. In receiver operating characteristics (ROC) analysis, the decision tree model achieved greater area under the curve (AUC) for differentiation of pilocytic astrocytoma (p = 0.020); and atypical teratoid/rhabdoid tumor ATRT (p = 0.001) from other types of neoplasms compared to the official clinical report. However, neuroradiologists' interpretations had greater accuracy in differentiating metastases (p = 0.001). Among different machine learning algorithms, random forest models yielded the highest accuracy in dichotomized classification of the 5 most common tumor types; and in multiclass differentiation of all tumor types random forest yielded an averaged AUC of 0.961 in training datasets, and 0.873 in validation samples. Our study demonstrates the potential application of machine learning algorithms and decision trees for accurate differentiation of brain tumors based on pretreatment MRI. Using easy to apply and understandable imaging metrics, the proposed decision tree model can help radiologists with differentiation of posterior fossa tumors, especially in tumors with similar qualitative imaging characteristics. In particular, our decision tree model provided more accurate differentiation of pilocytic astrocytomas from ATRT than by neuroradiologists in clinical reads

Constitutional mismatch repair deficiency (CMMRD) presenting with high-grade glioma, multiple developmental venous anomalies and malformations of cortical development-a multidisciplinary/multicentre approach and neuroimaging clues to clinching the diagnosis

Constitutional mismatch repair deficiency syndrome (CMMRD) is a rare cancer-predisposition syndrome associated with a high risk of developing a spectrum of malignancies in childhood and adolescence, including brain tumours. In this report, we present the case of an 8-year-old boy with acute headache, vomiting and an episode of unconsciousness in whom brain imaging revealed a high-grade glioma (HGG). The possibility of an underlying diagnosis of CMMRD was suspected radiologically on the basis of additional neuroimaging findings, specifically the presence of multiple supratentorial and infratentorial developmental venous anomalies (DVAs) and malformations of cortical development (MCD), namely, heterotopic grey matter. The tumour was debulked and confirmed to be a HGG on histopathology. The suspected diagnosis of CMMRD was confirmed on immunohistochemistry and genetic testing which revealed mutations in PMS2 and MSH6. The combination of a HGG, multiple DVAs and MCD in a paediatric or young adult patient should prompt the neuroradiologist to suggest an underlying diagnosis of CMMRD. A diagnosis of CMMRD has an important treatment and surveillance implications not only for the child but also the family in terms of genetic counselling

Surgical Outcome of Posteriour Fossa Tumors in Children

Posterior fossa tumors are most common in children than adults accounting for 54 to 70% of all childhood brain tumours. Out of them 30% are brain stem tumors. Most common posterior fossa tumors are Medulloblastoma, Astrocytoma, Ependymoma, and Brain Stem tumours. While dermoid, epidermoide and teratoma are the rare tumours. All focal and Cystic brain stem tumors in posterior fossa show better results with redical surgery than more diffused tumours that had stereotactic biopsy. Key words:  Posterior Fossa Tumors

Molecularly defined diffuse leptomeningeal glioneuronal tumor (DLGNT) comprises two subgroups with distinct clinical and genetic features

Diffuse leptomeningeal glioneuronal tumors (DLGNT) represent rare CNS neoplasms which have been included in the 2016 update of the WHO classification. The wide spectrum of histopathological and radiological features can make this enigmatic tumor entity difficult to diagnose. In recent years, large-scale genomic and epigenomic analyses have afforded insight into key genetic alterations occurring in multiple types of brain tumors and provide unbiased, complementary tools to improve diagnostic accuracy. Through genome-wide DNA methylation screening of > 25,000 tumors, we discovered a molecularly distinct class comprising 30 tumors, mostly diagnosed histologically as DLGNTs. Copy-number profiles derived from the methylation arrays revealed unifying characteristics, including loss of chromosomal arm 1p in all cases. Furthermore, this molecular DLGNT class can be subdivided into two subgroups [DLGNT methylation class (MC)-1 and DLGNT methylation class (MC)-2], with all DLGNT-MC-2 additionally displaying a gain of chromosomal arm 1q. Co-deletion of 1p/19q, commonly seen in IDH-mutant oligodendroglioma, was frequently observed in DLGNT, especially in DLGNT-MC-1 cases. Both subgroups also had recurrent genetic alterations leading to an aberrant MAPK/ERK pathway, with KIAA1549:BRAF fusion being the most frequent event. Other alterations included fusions of NTRK1/2/3 and TRIM33:RAF1, adding up to an MAPK/ERK pathway activation identified in 80% of cases. In the DLGNT-MC-1 group, age at diagnosis was significantly lower (median 5 vs 14 years, p < 0.01) and clinical course less aggressive (5-year OS 100, vs 43% in DLGNT-MC-2). Our study proposes an additional molecular layer to the current histopathological classification of DLGNT, of particular use for cases without typical morphological or radiological characteristics, such as diffuse growth and radiologic leptomeningeal dissemination. Recurrent 1p deletion and MAPK/ERK pathway activation represent diagnostic biomarkers and therapeutic targets, respectively—laying the foundation for future clinical trials with, e.g., MEK inhibitors that may improve the clinical outcome of patients with DLGNT

DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology

Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumors

BACKGROUND AND PURPOSE: Qualitative radiologic MR imaging review affords limited differentiation among types of pediatric posterior fossa brain tumors and cannot detect histologic or molecular subtypes, which could help to stratify treatment. This study aimed to improve current posterior fossa discrimination of histologic tumor type by using support vector machine classifiers on quantitative MR imaging features. MATERIALS AND METHODS: This retrospective study included preoperative MRI in 40 children with posterior fossa tumors (17 medulloblastomas, 16 pilocytic astrocytomas, and 7 ependymomas). Shape, histogram, and textural features were computed from contrast-enhanced T2WI and T1WI and diffusivity (ADC) maps. Combinations of features were used to train tumor-type-specific classifiers for medulloblastoma, pilocytic astrocytoma, and ependymoma types in separation and as a joint posterior fossa classifier. A tumor-subtype classifier was also produced for classic medulloblastoma. The performance of different classifiers was assessed and compared by using randomly selected subsets of training and test data. RESULTS: ADC histogram features (25th and 75th percentiles and skewness) yielded the best classification of tumor type (on average >95.8% of medulloblastomas, >96.9% of pilocytic astrocytomas, and >94.3% of ependymomas by using 8 training samples). The resulting joint posterior fossa classifier correctly assigned >91.4% of the posterior fossa tumors. For subtype classification, 89.4% of classic medulloblastomas were correctly classified on the basis of ADC texture features extracted from the Gray-Level Co-Occurence Matrix. CONCLUSIONS: Support vector machine–based classifiers using ADC histogram features yielded very good discrimination among pediatric posterior fossa tumor types, and ADC textural features show promise for further subtype discrimination. These findings suggest an added diagnostic value of quantitative feature analysis of diffusion MR imaging in pediatric neuro-oncology

The added value of advanced multi-modal magnetic resonance imaging in the diagnosis and management of childhood cancer

Background: Magnetic Resonance Imaging (MRI) provides images with excellent structural detail, but imparts limited information about the characteristics of paediatric tumours. MRI based functional imaging probes tissue properties to provide clinically important information about metabolites, structure and cellularity. Aim: To determine the added value of advanced MRI, particularly diffusion-weighted imaging (DWI) and magnetic resonance spectroscopy (MRS), in non-invasive diagnosis and management of paediatric tumours, and facilitate integration into clinical practice. Methods: Children with newly diagnosed body and brain tumours were imaged using multi b-value DWI and MRS respectively. Imaging data was used to develop a clinical decision support system for presentation to clinicians. Added diagnostic and clinical value of additional information was ascertained through retrospective and prospective evaluation. Results: Quantitative DWI confers added diagnostic accuracy beyond conventional MRI, allowing discrimination of benign from malignant body tumours through morphological and quantifiably significant differences in Apparent Diffusion Coefficient (ADC) histograms. Chemotherapeutic response is reflected through visually apparent and quantifiably significant histogram changes. Review of MRS improves diagnostic accuracy of paediatric brain tumours, adding therapeutic value through avoiding biopsy of indolent lesions, aiding tumour characterisation, and allowing earlier treatment planning and clinical decision-making. Conclusion: Advanced MRI adds value to non-invasive diagnosis and management of paediatric tumours in a real-time clinical setting. Presentation of complex information through a decision support system makes it accessible and comprehensible for clinicians, overcoming barriers precluding clinical use. Multicentre assessment is needed to promote integration of these techniques into the clinical workflow to improve care of children with cancer