Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas.
ABSTRACT: High-grade gliomas with mutations in the isocitrate dehydrogenase (IDH) gene family confer longer overall survival relative to their IDH-wild-type counterparts. Accurate determination of the IDH genotype preoperatively may have both prognostic and diagnostic value. The current study used a machine-learning algorithm to generate a model predictive of IDH genotype in high-grade gliomas based on clinical variables and multimodal features extracted from conventional MRI.Preoperative MRIs were obtained for 120 patients with primary grades III (n = 35) and IV (n = 85) glioma in this retrospective study. IDH genotype was confirmed for grade III (32/35, 91%) and IV (22/85, 26%) tumors by immunohistochemistry, spectrometry-based mutation genotyping (OncoMap), or multiplex exome sequencing (OncoPanel). IDH1 and IDH2 mutations were mutually exclusive, and all mutated tumors were collapsed into one IDH-mutated cohort. Cases were randomly assigned to either the training (n = 90) or validation cohort (n = 30). A total of 2970 imaging features were extracted from pre- and postcontrast T1-weighted, T2-weighted, and apparent diffusion coefficient map. Using a random forest algorithm, nonredundant features were integrated with clinical data to generate a model predictive of IDH genotype.Our model achieved accuracies of 86% (area under the curve [AUC] = 0.8830) in the training cohort and 89% (AUC = 0.9231) in the validation cohort. Features with the highest predictive value included patient age as well as parametric intensity, texture, and shape features.Using a machine-learning algorithm, we achieved accurate prediction of IDH genotype in high-grade gliomas with preoperative clinical and MRI features.
Project description:PURPOSE:Isocitrate dehydrogenase (IDH) and 1p19q codeletion status are importantin providing prognostic information as well as prediction of treatment response in gliomas. Accurate determination of the IDH mutation status and 1p19q co-deletion prior to surgery may complement invasive tissue sampling and guide treatment decisions. METHODS:Preoperative MRIs of 538 glioma patients from three institutions were used as a training cohort. Histogram, shape, and texture features were extracted from preoperative MRIs of T1 contrast enhanced and T2-FLAIR sequences. The extracted features were then integrated with age using a random forest algorithm to generate a model predictive of IDH mutation status and 1p19q codeletion. The model was then validated using MRIs from glioma patients in the Cancer Imaging Archive. RESULTS:Our model predictive of IDH achieved an area under the receiver operating characteristic curve (AUC) of 0.921 in the training cohort and 0.919 in the validation cohort. Age offered the highest predictive value, followed by shape features. Based on the top 15 features, the AUC was 0.917 and 0.916 for the training and validation cohort, respectively. The overall accuracy for 3 group prediction (IDH-wild type, IDH-mutant and 1p19q co-deletion, IDH-mutant and 1p19q non-codeletion) was 78.2% (155 correctly predicted out of 198). CONCLUSION:Using machine-learning algorithms, high accuracy was achieved in the prediction of IDH genotype in gliomas and moderate accuracy in a three-group prediction including IDH genotype and 1p19q codeletion.
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 (? = 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.
Project description:Grade II and III gliomas have variable clinical behaviors, showing the distinct molecular genetic alterations from glioblastoma (GBM), many of which eventually transform into more aggressive tumors. Since the classifications of grade II/III gliomas based on the genetic alterations have been recently emerging, it is now a trend to include molecular data into the standard diagnostic algorithm of glioma.Here we sequenced TERT promoter mutational status (TERTp-mut) in the DNA of 377 grade II/III gliomas and analyzed the clinical factors, molecular aberrations, and transcriptome profiles.We found that TERTp-mut occurred in 145 of 377 grade II and III gliomas (38.5%), mutually exclusive with a TP53 mutation (TP53-mut; P < .001) and coincident with a 1p/19q co-deletion (P = .002). TERTp-mut was an independent predictive factor of a good prognosis in all patients (P = .048). It has been an independent factor associated with a good outcome in the IDH mutation (IDH-mut) subgroup (P = .018), but it has also been associated with a poor outcome in the IDH wild-type (IDH-wt) subgroup (P = .049). Combining TERTp-mut and IDH-mut allowed the grade II/III malignancies to be reclassified into IDH-mut/TERTp-mut, IDH-mut only, TERTp-mut only, and IDH-wt/TERTp-wt. 1p/19q co-deletion, TP53-muts, Ki-67 expression differences, and p-MET expression differences characterized IDH-mut/TERTp-mut, IDH-mut only, TERTp-mut only, and IDH-wt/TERTp-wt subtypes, respectively.Our results showed that TERTp-mut combined with IDH-mut allowed simple classification of grade II/III gliomas for stratifying patients and clarifying diagnostic accuracy by supplementing standard histopathological criteria.
Project description:Diffuse low-grade gliomas (LGG) have been reclassified based on molecular mutations, which require invasive tumor tissue sampling. Tissue sampling by biopsy may be limited by sampling error, whereas non-invasive imaging can evaluate the entirety of a tumor. This study presents a non-invasive analysis of low-grade gliomas using imaging features based on the updated classification. We introduce molecular (MGMT methylation, IDH mutation, 1p/19q co-deletion, ATRX mutation, and TERT mutations) prediction methods of low-grade gliomas with imaging. Imaging features are extracted from magnetic resonance imaging data and include texture features, fractal and multi-resolution fractal texture features, and volumetric features. Training models include nested leave-one-out cross-validation to select features, train the model, and estimate model performance. The prediction models of MGMT methylation, IDH mutations, 1p/19q co-deletion, ATRX mutation, and TERT mutations achieve a test performance AUC of 0.83?±?0.04, 0.84?±?0.03, 0.80?±?0.04, 0.70?±?0.09, and 0.82?±?0.04, respectively. Furthermore, our analysis shows that the fractal features have a significant effect on the predictive performance of MGMT methylation IDH mutations, 1p/19q co-deletion, and ATRX mutations. The performance of our prediction methods indicates the potential of correlating computed imaging features with LGG molecular mutations types and identifies candidates that may be considered potential predictive biomarkers of LGG molecular classification.
Project description:The 2016 World Health Organization Classification of Tumors of the Central Nervous System incorporates the use of molecular information into the classification of brain tumors, including grade II and III gliomas, providing new prognostic information that cannot be delineated based on histopathology alone. We hypothesized that these genomic subgroups may also have distinct imaging features. A retrospective single institution study was performed on 40 patients with pathologically proven infiltrating WHO grade II/III gliomas with a pre-treatment MRI and molecular data on IDH, chromosomes 1p/19q and ATRX status. Two blinded Neuroradiologists qualitatively assessed MR features. The relationship between each parameter and molecular subgroup (IDH-wildtype; IDH-mutant-1p/19q codeleted-ATRX intact; IDH-mutant-1p/19q intact-ATRX loss) was evaluated with Fisher's exact test. Progression free survival (PFS) was also analyzed. A border that could not be defined on FLAIR was most characteristic of IDH-wildtype tumors, whereas IDH-mutant tumors demonstrated either well-defined or slightly ill-defined borders (p?=?0.019). Degree of contrast enhancement and presence of restricted diffusion did not distinguish molecular subgroups. Frontal lobe predominance was associated with IDH-mutant tumors (p?=?0.006). The IDH-wildtype subgroup had significantly shorter PFS than the IDH-mutant groups (p?<?0.001). No differences in PFS were present when separating by tumor grade. FLAIR border patterns and tumor location were associated with distinct molecular subgroups of grade II/III gliomas. These imaging features may provide fundamental prognostic and predictive information at time of initial diagnostic imaging.
Project description:Unravelling the heterogeneity is the central challenge for glioma precession oncology. In this study, we extracted quantitative image features from T2-weighted MR images and revealed that the isocitrate dehydrogenase (IDH) wild type and mutant lower grade gliomas (LGGs) differed in their expression of 146 radiomic descriptors. The logistic regression model algorithm further reduced these to 86 features. The classification model could discriminate the two types in both the training and validation sets with area under the curve values of 1.0000 and 0.9932, respectively. The transcriptome-radiomic analysis revealed that these features were associated with the immune response, biological adhesion, and several malignant behaviors, all of which are consistent with biological processes that are differentially expressed in IDH wild type and IDH mutant LGGs. Finally, a prognostic signature showed an ability to stratify IDH mutant LGGs into high and low risk groups with distinctive outcomes. By extracting a large number of radiomic features, we identified an IDH mutation-specific radiomic signature with prognostic implications. This radiomic signature may provide a way to non-invasively discriminate lower-grade gliomas as with or without the IDH mutation.
Project description:Diagnosis of WHO grade III anaplastic gliomas does not always correspond to its clinical outcome because of the isocitrate dehydrogenase (IDH) gene status. Anaplastic gliomas without IDH mutation result in a poor prognosis, similar to grade IV glioblastomas. However, the malignant features of anaplastic gliomas without IDH mutation are not well understood. The aim of this study was to examine anaplastic gliomas, in particular those without IDH mutation, with regard to their malignant features, recurrence patterns, and association with glioma stem cells.We retrospectively analyzed 86 cases of WHO grade III anaplastic gliomas. Data regarding patient characteristics, recurrence pattern, and prognosis were obtained from medical records. We examined molecular alterations such as IDH mutation, 1p19q loss, TP53 mutation, MGMT promoter methylation, Ki67 labeling index, and CD133, SOX2, and NESTIN expression.Of the 86 patients with anaplastic gliomas, 58 carried IDH mutation, and 40 experienced recurrence. The first recurrence was local in 25 patients and distant in 15. Patients without IDH mutation exhibited significantly higher CD133 and SOX2 expression (P = .025 and .020, respectively) and more frequent distant recurrence than those with IDH mutation (P = .022).Patients with anaplastic gliomas without IDH mutation experienced distant recurrence and exhibited glioma stem cell markers, indicating that this subset may share some malignant characteristics with glioblastomas.
Project description:The latest World Health Organization (WHO) classification of tumors of the central nervous system (CNS) integrates both histological and molecular features in the definition of diagnostic entities. This new approach enrolls novel entities of gliomas. In this study, we aimed to reveal the epidemiological characteristics, including age at diagnosis, gender ratio, tumor distribution and survival, of these new entities. We retrospectively reclassified 1210 glioma samples according to the 2016 CNS WHO diagnostic criteria. In our cohort, glioblastoma multiforme (GBM) with wildtype isocitrate dehydrogenase (IDH) was the most common malignant tumor in the brain. Almost all gliomas were more prevalent in males, especially in the cluster of WHO grade III gliomas and IDH-wildtype GBM. Age at diagnosis was directly proportional to tumor grade. With respect to the distribution by histology, we found that gliomas concurrent with IDH-mutant and 1p/19q-codeleted or with single IDH-mutant were mainly distributed in frontal lobe, while those with IDH-wildtype were dominant in temporal lobe. Lesions located in insular lobe were more likely to be IDH-mutant astrocytoma. In summary, our results elucidated the epidemiological characteristics as well as the regional constituents of these new gliomas entities, which could bring insights into tumorigenesis and personalized treatment of Chinese glioma population.
Project description:BackgroundCombining MRI techniques with machine learning methodology is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such a framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from a multi-center patients into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status.MethodsThree hundred thirty-three patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant?=?151 or IDH-wildtype?=?182) were retrospectively identified. Raw DSC-MRI data was post-processed for normalised leakage-corrected relative cerebral blood volume (rCBV) maps. Shape, intensity distribution (histogram) and rotational invariant Haralick texture features over the tumour mask were extracted. Differences in extracted features across glioma grades and mutation status were tested using the Wilcoxon two-sample test. A random-forest algorithm was employed (2-fold cross-validation, 250 repeats) to predict grades or mutation status using the extracted features.ResultsShape, distribution and texture features showed significant differences across mutation status. WHO grade II-III differentiation was mostly driven by shape features while texture and intensity feature were more relevant for the III-IV separation. Increased number of features became significant when differentiating grades further apart from one another. Gliomas were correctly stratified by mutation status in 71% and by grade in 53% of the cases (87% of the gliomas grades predicted with distance less than 1).ConclusionsDespite large heterogeneity in the multi-center dataset, machine learning assisted DSC-MRI radiomics hold potential to address the inherent variability and presents a promising approach for non-invasive glioma molecular subtyping and grading.
Project description:We hypothesized that machine learning analysis based on texture information from the preoperative MRI can predict IDH mutational status in newly diagnosed WHO grade II and III gliomas. This retrospective study included in total 79 consecutive patients with a newly diagnosed WHO grade II or III glioma. Local binary pattern texture features were generated from preoperative B0 and fractional anisotropy (FA) diffusion tensor imaging. Using a training set of 59 patients, a single hidden layer neural network was then trained on the texture features to predict IDH status. The model was validated based on the prediction accuracy calculated in a previously unseen set of 20 gliomas. Prediction accuracy of the generated model was 92% (54/59 cases; AUC?=?0.921) in the training and 95% (19/20; AUC?=?0.952) in the validation cohort. The ten most important features were comprised of tumor size and both B0 and FA texture information, underlining the joint contribution of imaging data to classification. Machine learning analysis of DTI texture information and tumor size reliably predicts IDH status in preoperative MRI of gliomas. Such information may increasingly support individualized surgical strategies, supplement pathological analysis and highlight the potential of radiogenomics.