Dataset Information

Predicting Deletion of Chromosomal Arms 1p/19q in Low-Grade Gliomas from MR Images Using Machine Intelligence.

ABSTRACT: Several studies have linked codeletion of chromosome arms 1p/19q in low-grade gliomas (LGG) with positive response to treatment and longer progression-free survival. Hence, predicting 1p/19q status is crucial for effective treatment planning of LGG. In this study, we predict the 1p/19q status from MR images using convolutional neural networks (CNN), which could be a non-invasive alternative to surgical biopsy and histopathological analysis. Our method consists of three main steps: image registration, tumor segmentation, and classification of 1p/19q status using CNN. We included a total of 159 LGG with 3 image slices each who had biopsy-proven 1p/19q status (57 non-deleted and 102 codeleted) and preoperative postcontrast-T1 (T1C) and T2 images. We divided our data into training, validation, and test sets. The training data was balanced for equal class probability and was then augmented with iterations of random translational shift, rotation, and horizontal and vertical flips to increase the size of the training set. We shuffled and augmented the training data to counter overfitting in each epoch. Finally, we evaluated several configurations of a multi-scale CNN architecture until training and validation accuracies became consistent. The results of the best performing configuration on the unseen test set were 93.3% (sensitivity), 82.22% (specificity), and 87.7% (accuracy). Multi-scale CNN with their self-learning capability provides promising results for predicting 1p/19q status non-invasively based on T1C and T2 images. Predicting 1p/19q status non-invasively from MR images would allow selecting effective treatment strategies for LGG patients without the need for surgical biopsy.

SUBMITTER: Akkus Z

PROVIDER: S-EPMC5537096 | biostudies-other | 2017 Aug

REPOSITORIES: biostudies-other

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Project description:High-grade gliomas are malignant neuroepithelial tumors. The current WHO classification for adult-type diffuse gliomas is based on IDH1/2 mutational and 1p/19q-codeletion status, which can be refined by emerging genomics, transcriptomics, and methylomics approaches. Nevertheless, therapy has been dominated by radiochemotherapy for about 15 years and progressed little in efficacy or precision through advanced molecular patient stratification. Glioma proteome alterations remain undercharacterized despite their promise for a better stratification and, in particular, the identification of therapeutic targets. Here, we used mass spectrometry (MS) to characterize 42 formalin-fixed, paraffin-embedded (FFPE) samples from IDH-wildtype (IDHwt), IDH-mutant (IDHmut) gliomas with and without 1p/19q-codeletion, and non-neoplastic brain tissue controls. Based on more than 5500 quantified proteins and 5000 phospho-sites, gliomas separated according to IDH1/2 mutational status and reflected loss of proteins encoded on 1p/19q in IDHmut, gains on chromosome 7 and losses on chromosome 10 in IDHwt. Unexpectedly, the 1p/19q-codeletion resulted in minor proteome changes. Instead, two proteomic subtypes of IDHmut gliomas showed major perturbations of mitochondrial DNA-encoded proteins, aerobic/anaerobic energy metabolism, RNA metabolism, chromatin dynamics, the extracellular matrix as well as tumor suppressor and oncoproteins. Reanalysis of three glioma proteomics studies independently validate the observed hallmarks and associate these proteomic subtypes with the well-established proneural and classic/mesenchymal IDHwt glioma subtypes. These results suggest an alternative stratification of IDHmut gliomas as part of common phenotypic subtypes independent of the IDH status, with broad therapeutic implications for patients with IDHmut gliomas in the future. Altogether, our study provides a rich protein and phospho-site resource restratifying glioma subtypes and supporting future mechanism of action and target discovery investigations.

Project description:BACKGROUND AND PURPOSE:Isocitrate dehydrogenase (IDH)-mutant lower grade gliomas are classified as oligodendrogliomas or diffuse astrocytomas based on 1p/19q-codeletion status. We aimed to test and validate neuroradiologists' performances in predicting the codeletion status of IDH-mutant lower grade gliomas based on simple neuroimaging metrics. MATERIALS AND METHODS:One hundred two IDH-mutant lower grade gliomas with preoperative MR imaging and known 1p/19q status from The Cancer Genome Atlas composed a training dataset. Two neuroradiologists in consensus analyzed the training dataset for various imaging features: tumor texture, margins, cortical infiltration, T2-FLAIR mismatch, tumor cyst, T2* susceptibility, hydrocephalus, midline shift, maximum dimension, primary lobe, necrosis, enhancement, edema, and gliomatosis. Statistical analysis of the training data produced a multivariate classification model for codeletion prediction based on a subset of MR imaging features and patient age. To validate the classification model, 2 different independent neuroradiologists analyzed a separate cohort of 106 institutional IDH-mutant lower grade gliomas. RESULTS:Training dataset analysis produced a 2-step classification algorithm with 86.3% codeletion prediction accuracy, based on the following: 1) the presence of the T2-FLAIR mismatch sign, which was 100% predictive of noncodeleted lower grade gliomas, (n = 21); and 2) a logistic regression model based on texture, patient age, T2* susceptibility, primary lobe, and hydrocephalus. Independent validation of the classification algorithm rendered codeletion prediction accuracies of 81.1% and 79.2% in 2 independent readers. The metrics used in the algorithm were associated with moderate-substantial interreader agreement (κ = 0.56-0.79). CONCLUSIONS:We have validated a classification algorithm based on simple, reproducible neuroimaging metrics and patient age that demonstrates a moderate prediction accuracy of 1p/19q-codeletion status among IDH-mutant lower grade gliomas.

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Predicting Deletion of Chromosomal Arms 1p/19q in Low-Grade Gliomas from MR Images Using Machine Intelligence.

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