Brain Tumor Radiogenomic Classification of O6-Methylguanine-DNA Methyltransferase Promoter Methylation in Malignant Gliomas-Based Transfer Learning.

Artificial Intelligence (AI) is the subject of a challenge and attention in the field of oncology and raises many promises for preventive diagnosis, but also fears, some of which are based on highly speculative visions for the classification and detection of tumors. A brain tumor that is malignant is a life-threatening disorder. Glioblastoma is the most prevalent kind of adult brain cancer and the 1 with the poorest prognosis, with a median survival time of less than a year. The presence of O6 -methylguanine-DNA methyltransferase (MGMT) promoter methylation, a particular genetic sequence seen in tumors, has been proven to be a positive prognostic indicator and a significant predictor of recurrence.This strong revival of interest in AI is modeled in particular to major technological advances which have significantly increased the performance of the predicted model for medical decision support. Establishing reliable forecasts remains a significant challenge for electronic health records (EHRs). By enhancing clinical practice, precision medicine promises to improve healthcare delivery. The goal is to produce improved prognosis, diagnosis, and therapy through evidence-based sub stratification of patients, transforming established clinical pathways to optimize care for each patient's individual requirements. The abundance of today's healthcare data, dubbed "big data," provides great resources for new knowledge discovery, potentially advancing precision treatment. The latter necessitates multidisciplinary initiatives that will use the knowledge, skills, and medical data of newly established organizations with diverse backgrounds and expertise.The aim of this paper is to use magnetic resonance imaging (MRI) images to train and evaluate your model to detect the presence of MGMT promoter methylation in this competition to predict the genetic subtype of glioblastoma based transfer learning. Our objective is to emphasize the basic problems in the developing disciplines of radiomics and radiogenomics, as well as to illustrate the computational challenges from the perspective of big data analytics.

Automated machine learning to predict the co-occurrence of isocitrate dehydrogenase mutations and O6 -methylguanine-DNA methyltransferase promoter methylation in patients with gliomas.

Combining isocitrate dehydrogenase mutation (IDHmut) with O6 -methylguanine-DNA methyltransferase promoter methylation (MGMTmet) has been identified as a critical prognostic molecular marker for gliomas. The aim of this study was to determine the ability of glioma radiomics features from magnetic resonance imaging (MRI) to predict the co-occurrence of IDHmut and MGMTmet by applying the tree-based pipeline optimization tool (TPOT), an automated machine learning (autoML) approach. This was a retrospective study, in which 162 patients with gliomas were evaluated, including 58 patients with co-occurrence of IDHmut and MGMTmet and 104 patients with other status comprising: IDH wildtype and MGMT unmethylated (n = 67), IDH wildtype and MGMTmet (n = 36), and IDHmut and MGMT unmethylated (n = 1). Three-dimensional (3D) T1-weighted images, gadolinium-enhanced 3D T1-weighted images (Gd-3DT1WI), T2-weighted images, and fluid-attenuated inversion recovery (FLAIR) images acquired at 3.0 T were used. Radiomics features were extracted from FLAIR and Gd-3DT1WI images. The TPOT was employed to generate the best machine learning pipeline, which contains both feature selector and classifier, based on input feature sets. A 4-fold cross-validation was used to evaluate the performance of automatically generated models. For each iteration, the training set included 121 subjects, while the test set included 41 subjects. Student's t-test or a chi-square test was applied on different clinical characteristics between two groups. Sensitivity, specificity, accuracy, kappa score, and AUC were used to evaluate the performance of TPOT-generated models. Finally, we compared the above metrics of TPOT-generated models to identify the best-performing model. Patients' ages and grades between two groups were significantly different (p = 0.002 and p = 0.000, respectively). The 4-fold cross-validation showed that gradient boosting classifier trained on shape and textual features from the Laplacian-of-Gaussian-filtered Gd-3DT1 achieved the best performance (average sensitivity = 81.1%, average specificity = 94%, average accuracy = 89.4%, average kappa score = 0.76, average AUC = 0.951). Using autoML based on radiomics features from MRI, a high discriminatory accuracy was achieved for predicting co-occurrence of IDHmut and MGMTmet in gliomas. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 3.

MGMT promoter methylation prediction based on multiparametric MRI via vision graph neural network.

Purpose: Glioblastoma (GBM) is aggressive and malignant. The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in GBM tissue is considered an important biomarker for developing the most effective treatment plan. Although the standard method for assessing the MGMT promoter methylation status is via bisulfite modification and deoxyribonucleic acid (DNA) sequencing of biopsy or surgical specimens, a secondary automated method based on medical imaging may improve the efficiency and accuracy of those tests. Approach: We propose a deep vision graph neural network (ViG) using multiparametric magnetic resonance imaging (MRI) to predict the MGMT promoter methylation status noninvasively. Our model was compared to the RSNA radiogenomic classification winners. The dataset includes 583 usable patient cases. Combinations of MRI sequences were compared. Our multi-sequence fusion strategy was compared with those using single MR sequences. Results: Our best model [Fluid Attenuated Inversion Recovery (FLAIR), T1-weighted pre-contrast (T1w), T2-weighted (T2)] outperformed the winning models with a test area under the curve (AUC) of 0.628, an accuracy of 0.632, a precision of 0.646, a recall of 0.677, a specificity of 0.581, and an F1 score of 0.661. Compared to the winning models with single MR sequences, our ViG utilizing fused-MRI showed a significant improvement statistically in AUC scores, which are FLAIR (p=0.042), T1w (p=0.017), T1wCE (p=0.001), and T2 (p=0.018). Conclusions: Our model is superior to challenge champions. A graph representation of the medical images enabled good handling of complexity and irregularity. Our work provides an automatic secondary check pipeline to ensure the correctness of MGMT methylation status prediction.

Prediction of the Molecular Subtype of IDH Mutation Combined with MGMT Promoter Methylation in Gliomas via Radiomics Based on Preoperative MRI.

BACKGROUND: The molecular subtype of IDH mut combined with MGMT meth in gliomas suggests a good prognosis and potential benefit from TMZ chemotherapy. The aim of this study was to establish a radiomics model to predict this molecular subtype. METHOD: The preoperative MR images and genetic data of 498 patients with gliomas were retrospectively collected from our institution and the TCGA/TCIA dataset. A total of 1702 radiomics features were extracted from the tumour region of interest (ROI) of CE-T1 and T2-FLAIR MR images. Least absolute shrinkage and selection operator (LASSO) and logistic regression were used for feature selection and model building. Receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the predictive performance of the model. RESULTS: Regarding clinical variables, age and tumour grade were significantly different between the two molecular subtypes in the training, test and independent validation cohorts (p < 0.05). The areas under the curve (AUCs) of the radiomics model based on 16 selected features in the SMOTE training cohort, un-SMOTE training cohort, test set and independent TCGA/TCIA validation cohort were 0.936, 0.932, 0.916 and 0.866, respectively, and the corresponding F1-scores were 0.860, 0.797, 0.880 and 0.802. The AUC of the independent validation cohort increased to 0.930 for the combined model when integrating the clinical risk factors and radiomics signature. CONCLUSIONS: radiomics based on preoperative MRI can effectively predict the molecular subtype of IDH mut combined with MGMT meth.

The diagnostic value of ADC histogram and direct ADC measurements for coexisting isocitrate dehydrogenase mutation and O6-methylguanine-DNA methyltransferase promoter methylation in glioma.

Objectives: To non-invasively predict the coexistence of isocitrate dehydrogenase (IDH) mutation and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in adult-type diffuse gliomas using apparent diffusion coefficient (ADC) histogram and direct ADC measurements and compare the diagnostic performances of the two methods. Materials and methods: A total of 118 patients with adult-type diffuse glioma who underwent preoperative brain magnetic resonance imaging (MRI) and diffusion weighted imaging (DWI) were included in this retrospective study. The patient group included 40 patients with coexisting IDH mutation and MGMT promoter methylation (IDHmut/MGMTmet) and 78 patients with other molecular status, including 32 patients with IDH wildtype and MGMT promoter methylation (IDHwt/MGMTmet), one patient with IDH mutation and unmethylated MGMT promoter (IDHmut/MGMTunmet), and 45 patients with IDH wildtype and unmethylated MGMT promoter (IDHwt/MGMTunmet). ADC histogram parameters of gliomas were extracted by delineating the region of interest (ROI) in solid components of tumors. The minimum and mean ADC of direct ADC measurements were calculated by placing three rounded or elliptic ROIs in solid components of gliomas. Receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to evaluate the diagnostic performances of the two methods. Results: The 10th percentile, median, mean, root mean squared, 90th percentile, skewness, kurtosis, and minimum of ADC histogram analysis and minimum and mean ADC of direct measurements were significantly different between IDHmut/MGMTmet and the other glioma group (P < 0.001 to P = 0.003). In terms of single factors, 10th percentile of ADC histogram analysis had the best diagnostic efficiency (AUC = 0.860), followed by mean ADC obtained by direct measurements (AUC = 0.844). The logistic regression model combining ADC histogram parameters and direct measurements had the best diagnostic efficiency (AUC = 0.938), followed by the logistic regression model combining the ADC histogram parameters with statistically significant difference (AUC = 0.916) and the logistic regression model combining minimum ADC and mean ADC (AUC = 0.851). Conclusion: Both ADC histogram analysis and direct measurements have potential value in predicting the coexistence of IDHmut and MGMTmet in adult-type diffuse glioma. The diagnostic performance of ADC histogram analysis was better than that of direct ADC measurements. The combination of the two methods showed the best diagnostic performance.

Molecular Neuropathology in Practice: Clinical Profiling and Integrative Analysis of Molecular Alterations in Glioblastoma.

Molecular profiling of glioblastoma has revealed complex cytogenetic, epigenetic, and molecular abnormalities that are necessary for diagnosis, prognosis, and treatment. Our neuro-oncology group has developed a data-driven, institutional consensus guideline for efficient and optimal workup of glioblastomas based on our routine performance of molecular testing. We describe our institution's testing algorithm, assay development, and genetic findings in glioblastoma, to illustrate current practices and challenges in neuropathology related to molecular and genetic testing. We have found that coordination of test requisition, tissue handling, and incorporation of results into the final pathologic diagnosis by the neuropathologist improve patient care. Here, we present analysis of O6-methylguanine-DNA-methyltransferase promoter methylation and next-generation sequencing results of 189 patients, obtained utilizing our internal processes led by the neuropathology team. Our institutional pathway for neuropathologist-driven molecular testing has streamlined the management of glioblastoma samples for efficient return of results for incorporation of genomic data into the pathological diagnosis and optimal patient care.

Clinical prognostic value of isocitrate dehydrogenase mutation, O-6-methylguanine-DNA methyltransferase promoter methylation, and 1p19q co-deletion in glioma patients.

BACKGROUND: Gliomas are the most frequently occurring malignant brain cancers. Recently, isocitrate dehydrogenase (IDH) mutations, O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation, and 1p/19q co-deletion have been suggested to indicate a favorable prognosis in gliomas. However, the clinical prognostic value of these genetic tests in human gliomas is not fully understood. METHODS: We included glioma patients who accepted genetic testing including IDH, MGMT and 1p/19q at Xiangya Hospital, Central South University in China (Jan 2015 to Jun 2017) and further analyzed the effect of the above gene states in high-grade gliomas. RESULTS: In 103 high-grade glioma patients, IDH mutation, MGMT promoter methylation, and 1p/19q co-deletion had better progression-free survival (PFS) than IDH wild-type (P=0.005), MGMT unmethylated promoter (P=0.002), and without 1p19q co-deletion (P=0.008), respectively. Additionally, we classified the above gliomas into 5 molecular groups, triple-positive, IDH mutation and MGMT methylation, methylation in MGMT only, mutation in IDH only, and triple-negative, according to characteristics of recruited patients. We found that triple-positive gliomas had better PFS than triple-negative cases in high-grade patients (P=0.016). Moreover, the IDH mutation and MGMT methylation groups had prolonged PFS compared to triple-negative (P=0.029). CONCLUSIONS: Our study reinforced the clinical value of biomarkers, including 1p/19q co-deletion, IDH mutation, and the most prominent MGMT methylation, as previously described in glioma prognosis. Further, triple-negative patients have poorer PFS, indicating that the states of these genes can be divided into subgroups as a potential prognostic marker for clinical treatment, which requires a larger, multicenter study to testify.