Imaging scoring systems for preoperative molecular diagnoses of lower-grade gliomas

Oligodendroglioma, IDH-mutant and 1p/19q-codeleted-prognostic factors, standard of care and chemotherapy, and future perspectives with neoadjuvant strategy

Oligodendroglioma, IDH-mutant and 1p/19q-codeleted is known for their relative chemosensitivity and indolent clinical course among diffuse gliomas of adult type. Based on the data from phase 3 clinical trials, the standard of post-surgical care for those tumors is considered to be initial chemoradiotherapy regardless of histopathological grade, particularly with PCV. However, partly due to its renewed definition in late years, prognostic factors in patients with those tumors are not well established. Moreover, the survival rate declines over 15 years, with only a 37% OS rate at 20 years for grade 3 tumors, even with the current standard of care. Given that most of this disease occurs in young or middle-aged adults, further improvements in treatment and management are necessary. Here, we discuss prognostic factors, standard of care and chemotherapy, and future perspectives with neoadjuvant strategy in those tumors.

Imaging-Based Versus Pathologic Survival Stratifications of Diffuse Glioma According to the 2021 WHO Classification System

OBJECTIVE

Imaging-based survival stratification of patients with gliomas is important for their management, and the 2021 WHO classification system must be clinically tested. The aim of this study was to compare integrative imaging- and pathology-based methods for survival stratification of patients with diffuse glioma.

MATERIALS AND METHODS

This study included diffuse glioma cases from The Cancer Genome Atlas (training set: 141 patients) and Asan Medical Center (validation set: 131 patients). Two neuroradiologists analyzed presurgical CT and MRI to assign gliomas to five imaging-based risk subgroups (1 to 5) according to well-known imaging phenotypes (e.g., T2/FLAIR mismatch) and recategorized them into three imaging-based risk groups, according to the 2021 WHO classification: group 1 (corresponding to risk subgroup 1, indicating oligodendroglioma, isocitrate dehydrogenase [IDH]-mutant, and 1p19q-co-deleted), group 2 (risk subgroups 2 and 3, indicating astrocytoma, IDH-mutant), and group 3 (risk subgroups 4 and 5, indicating glioblastoma, IDHwt). The progression-free survival (PFS) and overall survival (OS) were estimated for each imaging risk group, subgroup, and pathological diagnosis. Time-dependent area-under-the receiver operating characteristic analysis (AUC) was used to compare the performance between imaging-based and pathology-based survival model.

RESULTS

Both OS and PFS were stratified according to the five imaging-based risk subgroups (P < 0.001) and three imaging-based risk groups (P < 0.001). The three imaging-based groups showed high performance in predicting PFS at one-year (AUC, 0.787) and five-years (AUC, 0.823), which was similar to that of the pathology-based prediction of PFS (AUC of 0.785 and 0.837). Combined with clinical predictors, the performance of the imaging-based survival model for 1- and 3-year PFS (AUC 0.813 and 0.921) was similar to that of the pathology-based survival model (AUC 0.839 and 0.889).

CONCLUSION

Imaging-based survival stratification according to the 2021 WHO classification demonstrated a performance similar to that of pathology-based survival stratification, especially in predicting PFS.

Imaging-based stratification of adult gliomas prognosticates survival and correlates with the 2021 WHO classification

BACKGROUND

Because of the lack of global accessibility, delay, and cost-effectiveness of genetic testing, there is a clinical need for an imaging-based stratification of gliomas that can prognosticate survival and correlate with the 2021-WHO classification.

METHODS

In this retrospective study, adult primary glioma patients with pre-surgery/pre-treatment MRI brain images having T2, FLAIR, T1, T1 post-contrast, DWI sequences, and survival information were included in TCIA training-dataset (n = 275) and independent validation-dataset (n = 200). A flowchart for imaging-based stratification of adult gliomas(IBGS) was created in consensus by three authors to encompass all adult glioma types. Diagnostic features used were T2-FLAIR mismatch sign, central necrosis with peripheral enhancement, diffusion restriction, and continuous cortex sign. Roman numerals (I, II, and III) denote IBGS types. Two independent teams of three and two radiologists, blinded to genetic, histology, and survival information, manually read MRI into three types based on the flowchart. Overall survival-analysis was done using age-adjusted Cox-regression analysis, which provided both hazard-ratio (HR) and area-under-curve (AUC) for each stratification system(IBGS and 2021-WHO). The sensitivity and specificity of each IBSG type were analyzed with cross-table to identify the corresponding 2021-WHO genotype.

RESULTS

Imaging-based stratification was statistically significant in predicting survival in both datasets with good inter-observer agreement (age-adjusted Cox-regression, AUC > 0.5, k > 0.6, p < 0.001). IBGS type-I, type-II, and type-III gliomas had good specificity in identifying IDHmut 1p19q-codel oligodendroglioma (training - 97%, validation - 85%); IDHmut 1p19q non-codel astrocytoma (training - 80%, validation - 85.9%); and IDHwt glioblastoma (training - 76.5%, validation- 87.3%) respectively (p-value < 0.01).

CONCLUSIONS

Imaging-based stratification of adult diffuse gliomas predicted patient survival and correlated well with 2021-WHO glioma classification.

Conventional MRI features can predict the molecular subtype of adult grade 2-3 intracranial diffuse gliomas

Purpose

Molecular biomarkers are important for classifying intracranial gliomas, prompting research into correlating imaging with genotype (“radiogenomics”). A limitation of the existing radiogenomics literature is the paucity of studies specifically characterizing grade 2–3 gliomas into the three key molecular subtypes. Our study investigated the accuracy of multiple different conventional MRI features for genotype prediction.

Methods

Grade 2–3 gliomas diagnosed between 2007 and 2013 were identified. Two neuroradiologists independently assessed nine conventional MRI features. Features with better inter-observer agreement (κ ≥ 0.6) proceeded to consensus assessment. MRI features were correlated with genotype, classified as IDH-mutant and 1p/19q-codeleted (IDH^mut/1p19q^codel), IDH-mutant and 1p/19q-intact (IDH^mut/1p19q^int), or IDH-wildtype (IDH^wt). For IDH^wt tumors, additional molecular markers of glioblastoma were noted.

Results

One hundred nineteen patients were included. T2-FLAIR mismatch (stratified as > 50%, 25–50%, or < 25%) was the most predictive feature across genotypes (p < 0.001). All 30 tumors with > 50% mismatch were IDH^mut/1p19q^int, and all seven with 25–50% mismatch. Well-defined margins correlated with IDH^mut/1p19q^int status on univariate analysis (p < 0.001), but this related to correlation with T2-FLAIR mismatch; there was no longer an association when considering only tumors with < 25% mismatch (p = 0.386). Enhancement (p = 0.001), necrosis (p = 0.002), and hemorrhage (p = 0.027) correlated with IDH^wt status (especially “molecular glioblastoma”). Calcification correlated with IDH^mut/1p19q^codel status (p = 0.003). A simple, step-wise algorithm incorporating these features, when present, correctly predicted genotype with a positive predictive value 91.8%.

Conclusion

T2-FLAIR mismatch strongly predicts IDH^mut/1p19q^int even with a lower threshold of ≥ 25% mismatch and outweighs other features. Secondary features include enhancement, necrosis and hemorrhage (predicting IDH^wt, especially “molecular glioblastoma”), and calcification (predicting IDH^mut/1p19q^codel).

Maximum 11C-methionine PET uptake as a prognostic imaging biomarker for newly diagnosed and untreated astrocytic glioma

This study aimed whether the uptake of amino tracer positron emission tomography (PET) can be used as an additional imaging biomarker to estimate the prognosis of glioma. Participants comprised 56 adult patients with newly diagnosed and untreated World Health Organization (WHO) grade II–IV astrocytic glioma who underwent surgical excision and were evaluated by 11C-methionine PET prior to the surgical excision at Osaka City University Hospital from July 2011 to March 2018. Clinical and imaging studies were retrospectively reviewed based on medical records at our institution. Preoperative Karnofsky Performance Status (KPS) only influenced progression-free survival (hazard ratio [HR] 0.20; 95% confidence interval [CI] 0.10–0.41, p < 0.0001), whereas histology (anaplastic astrocytoma: HR 5.30, 95% CI 1.23–22.8, p = 0.025; glioblastoma: HR 11.52, 95% CI 2.27–58.47, p = 0.0032), preoperative KPS ≥ 80 (HR 0.23, 95% CI 0.09–0.62, p = 0.004), maximum lesion-to-contralateral normal brain tissue (LN max) ≥ 4.03 (HR 0.24, 95% CI 0.08–0.71, p = 0.01), and isocitrate dehydrogenase (IDH) status (HR 14.06, 95% CI 1.81–109.2, p = 0.011) were factors influencing overall survival (OS) in multivariate Cox regression. OS was shorter in patients with LN max ≥ 4.03 (29.3 months) than in patients with LN max < 4.03 (not reached; p = 0.03). OS differed significantly between patients with IDH mutant/LN max < 4.03 and patients with IDH mutant/LN max ≥ 4.03. LN max using 11C-methionine PET may be used in prognostic markers for newly identified and untreated WHO grade II–IV astrocytic glioma.

Predicting 1p/19q codeletion status using diffusion-, susceptibility-, perfusion-weighted, and conventional MRI in IDH-mutant lower-grade gliomas

BACKGROUND

Isocitrate dehydrogenase (IDH)-mutant lower-grade gliomas (LGGs) are further classified into two classes: with and without 1p/19q codeletion. IDH-mutant and 1p/19q codeleted LGGs have better prognosis compared with IDH-mutant and 1p/19q non-codeleted LGGs.

PURPOSE

To evaluate conventional magnetic resonance imaging (cMRI), diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI), and dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) for predicting 1p/19q codeletion status of IDH-mutant LGGs.

MATERIAL AND METHODS

We retrospectively reviewed cMRI, DWI, SWI, and DSC-PWI in 142 cases of IDH mutant LGGs with known 1p/19q codeletion status. Features of cMRI, relative ADC (rADC), intratumoral susceptibility signals (ITSSs), and the value of relative cerebral blood volume (rCBV) were compared between IDH-mutant LGGs with and without 1p/19q codeletion. Receiver operating characteristic curve and logistic regression were used to determine diagnostic performances.

RESULTS

IDH-mutant and 1p/19q non-codeleted LGGs tended to present with the T2/FLAIR mismatch sign and distinct borders (P < 0.001 and P = 0.038, respectively). Parameters of rADC, ITSSs, and rCBV_max were significantly different between the 1p/19q codeleted and 1p/19q non-codeleted groups (P < 0.001, P = 0.017, and P < 0.001, respectively). A combination of cMRI, SWI, DWI, and DSC-PWI for predicting 1p/19q codeletion status in IDH-mutant LGGs resulted in a sensitivity, specificity, positive predictive value, negative predictive value, and an AUC of 80.36%, 78.57%, 83.30%, 75.00%, and 0.88, respectively.

CONCLUSION

1p/19q codeletion status of IDH-mutant LGGs can be stratified using cMRI and advanced MRI techniques, including DWI, SWI, and DSC-PWI. A combination of cMRI, rADC, ITSSs, and rCBV_max may improve the diagnostic performance for predicting 1p/19q codeletion status.

Reproducible imaging-based prediction of molecular subtype and risk stratification of gliomas across different experience levels using a structured reporting system

OBJECTIVES

To determine reproducible MRI parameters predictive of molecular subtype and risk stratification in glioma and develop a structured reporting system.

METHODS

All study patients were initially diagnosed with glioma, 141 from the Cancer Genome Atlas and 131 from our tertiary institution, as training and validation sets, respectively. Images were analyzed by three neuroradiologists with 1-7 years of experience. MRI features including contrast enhancement pattern, necrosis, margin, edema, T2/FLAIR mismatch, internal cyst, and cerebral blood volume higher than normal cortex were reported using a structured reporting system. The pathology was stratified into five risk types: (1) oligodendroglioma, isocitrate dehydrogenase [IDH]-mutant, 1p19q co-deleted; (2) diffuse astrocytoma, IDH-mutant, grade II-III; (3) glioblastoma, IDH-mutant, grade IV; (4) diffuse astrocytoma, IDH-wild, grade II-III; and (5) glioblastoma, IDH-wild, grade IV. Significant predictors were selected using multivariate logistic regression, and diagnostic performance was tested using a validation set.

RESULTS

Reproducible imaging parameters exhibiting > 50% agreement across readers included the presence of necrosis, T2/FLAIR mismatch, internal cyst, and predominant contrast enhancement. In the validation set, prediction of risk type 5 exhibited the highest diagnostic performance with AUCs of 0.92 (reader 1) and 0.93 (reader 2) with predominant enhancement, followed by risk type 2 with AUCs of 0.95 and 0.95 with T2/FLAIR mismatch sign and no necrosis, and risk type 1 with AUCs of 0.84 and 0.83 with internal cyst or necrosis. Risk types 3 and 4 were difficult to visually predict.

CONCLUSIONS

Imaging parameters with high reproducibility enabling prediction of IDH-wild-type glioblastoma, IDH-mutant/1p19q co-deletion oligodendroglioma, and IDH-mutant diffuse astrocytoma were identified.

KEY POINTS

• Reproducible MRI parameters for determining molecular subtypes of glioma included the presence of necrosis, T2/FLAIR mismatch, internal cyst, and predominant contrast enhancement. • IDH-wild type glioblastoma, IDH-mutant/1p19q co-deletion oligodendroglioma, and IDH-mutant low-grade astrocytoma were identified using MRI parameters with high inter-reader reproducibility. • Identification of IDH-wild type low-grade glioma and IDH-mutant glioblastoma was difficult by visual analysis.

Predictive Role of the Apparent Diffusion Coefficient and MRI Morphologic Features on IDH Status in Patients With Diffuse Glioma: A Retrospective Cross-Sectional Study

Purpose

To evaluate isocitrate dehydrogenase (IDH) status in clinically diagnosed grade II~IV glioma patients using the 2016 World Health Organization (WHO) classification based on MRI parameters.

Materials and Methods

One hundred and seventy-six patients with confirmed WHO grade II~IV glioma were retrospectively investigated as the study set, including lower-grade glioma (WHO grade II, n = 64; WHO grade III, n = 38) and glioblastoma (WHO grade IV, n = 74). The minimum apparent diffusion coefficient (ADCmin) in the tumor and the contralateral normal-appearing white matter (ADCn) and the rADC (ADCmin to ADCn ratio) were defined and calculated. Intraclass correlation coefficient (ICC) analysis was carried out to evaluate interobserver and intraobserver agreement for the ADC measurements. Interobserver agreement for the morphologic categories was evaluated by Cohen’s kappa analysis. The nonparametric Kruskal-Wallis test was used to determine whether the ADC measurements and glioma subtypes were related. By univariable analysis, if the differences in a variable were significant (P<0.05) or an image feature had high consistency (ICC >0.8; κ >0.6), then it was chosen as a predictor variable. The performance of the area under the receiver operating characteristic curve (AUC) was evaluated using several machine learning models, including logistic regression, support vector machine, Naive Bayes and Ensemble. Five evaluation indicators were adopted to compare the models. The optimal model was developed as the final model to predict IDH status in 40 patients with glioma as the subsequent test set. DeLong analysis was used to compare significant differences in the AUCs.

Results

In the study set, six measured variables (rADC, age, enhancement, calcification, hemorrhage, and cystic change) were selected for the machine learning model. Logistic regression had better performance than other models. Two predictive models, model 1 (including all predictor variables) and model 2 (excluding calcification), correctly classified IDH status with an AUC of 0.897 and 0.890, respectively. The test set performed equally well in prediction, indicating the effectiveness of the trained classifier. The subgroup analysis revealed that the model predicted IDH status of LGG and GBM with accuracy of 84.3% (AUC = 0.873) and 85.1% (AUC = 0.862) in the study set, and with the accuracy of 70.0% (AUC = 0.762) and 70.0% (AUC = 0.833) in the test set, respectively.

Conclusion

Through the use of machine-learning algorithms, the accurate prediction of IDH-mutant versus IDH-wildtype was achieved for adult diffuse gliomas via noninvasive MR imaging characteristics, including ADC values and tumor morphologic features, which are considered widely available in most clinical workstations.

Generative adversarial network for glioblastoma ensures morphologic variations and improves diagnostic model for isocitrate dehydrogenase mutant type

Generative adversarial network (GAN) creates synthetic images to increase data quantity, but whether GAN ensures meaningful morphologic variations is still unknown. We investigated whether GAN-based synthetic images provide sufficient morphologic variations to improve molecular-based prediction, as a rare disease of isocitrate dehydrogenase (IDH)-mutant glioblastomas. GAN was initially trained on 500 normal brains and 110 IDH-mutant high-grade astocytomas, and paired contrast-enhanced T1-weighted and FLAIR MRI data were generated. Diagnostic models were developed from real IDH-wild type (n = 80) with real IDH-mutant glioblastomas (n = 38), or with synthetic IDH-mutant glioblastomas, or augmented by adding both real and synthetic IDH-mutant glioblastomas. Turing tests showed synthetic data showed reality (classification rate of 55%). Both the real and synthetic data showed that a more frontal or insular location (odds ratio [OR] 1.34 vs. 1.52; P = 0.04) and distinct non-enhancing tumor margins (OR 2.68 vs. 3.88; P < 0.001), which become significant predictors of IDH-mutation. In an independent validation set, diagnostic accuracy was higher for the augmented model (90.9% [40/44] and 93.2% [41/44] for each reader, respectively) than for the real model (84.1% [37/44] and 86.4% [38/44] for each reader, respectively). The GAN-based synthetic images yield morphologically variable, realistic-seeming IDH-mutant glioblastomas. GAN will be useful to create a realistic training set in terms of morphologic variations and quality, thereby improving diagnostic performance in a clinical model.

Noninvasive Assessment of O(6)-Methylguanine-DNA Methyltransferase Promoter Methylation Status in World Health Organization Grade II-IV Glioma Using Histogram Analysis of Inflow-Based Vascular-Space-Occupancy Combined with Structural Magnetic Resonance Imaging

BACKGROUND

O(6)-methylguanine-DNA methyltransferase (MGMT) promoter methylation is an important prognostic factor for gliomas and is associated with tumor angiogenesis. Arteriolar cerebral blood volume (CBVa) obtained from inflow-based vascular-space-occupancy (iVASO) magnetic resonance imaging (MRI) is assumed to be an indicator of tumor microvasculature. Its preoperative predictive ability for MGMT promoter methylation remains unclear.

PURPOSE

To investigate the role of iVASO-CBVa histogram features in determining MGMT promoter methylation status of grade II-IV gliomas.

STUDY TYPE

Retrospective SUBJECTS: Forty-six patients consisting of 20 MGMT methylated and 26 unmethylated gliomas.

FIELD STRENGTH/SEQUENCE

3.0 T magnetic resonance images containing iVASO MRI, T₁ -weighted image (T₁ WI), T₂ -weighted image, T₂ -weighted fluid attenuated inversion recovery image images, and enhanced T₁ WI.

ASSESSMENT

Sixteen structural imaging features were visually evaluated on structural MRI and 14 CBVa histogram features were extracted from iVASO-CBVa maps.

STATISTICAL TESTS

Imaging features were screened and ranked using Fisher's exact test, Mann-Whitney U-test, and randomforest algorithm. Features with higher importance were selected to develop logistic regression models to determine MGMT methylation status. Receiver operating characteristics (ROC) curve with the area under the curve (AUC) and leave-one-out cross-validation (LOOCV) were used to assess effectiveness and stability.

RESULTS

The top two CBVa histogram features were root mean squared (RMS) and variance. The top two structural imaging features were contrast-enhancing component of the tumor (CET) location and tumor location. Both the CBVa model of RMS and variance (ROC, AUC = 0.867; LOOCV, AUC = 0.819) and the model of structural features (ROC, AUC = 0.882; LOOCV, AUC = 0.802) accurately identified MGMT methylation. The fusion model of CBVa RMS and CET location improved diagnostic performance (ROC, AUC = 0.931; LOOCV, AUC =0.906). DATA CONCLUSION: iVASO-CBVa has potential in evaluating MGMT methylation status in grade II-IV gliomas.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 2.

Conventional MRI features of adult diffuse glioma molecular subtypes: a systematic review

PURPOSE

Molecular parameters have become integral to glioma diagnosis. Much of radiogenomics research has focused on the use of advanced MRI techniques, but conventional MRI sequences remain the mainstay of clinical assessments. The aim of this research was to synthesize the current published data on the accuracy of standard clinical MRI for diffuse glioma genotyping, specifically targeting IDH and 1p19q status.

METHODS

A systematic search was performed in September 2019 using PubMed and the Cochrane Library, identifying studies on the diagnostic value of T1 pre-/post-contrast, T2, FLAIR, T2*/SWI and/or 3-directional diffusion-weighted imaging sequences for the prediction of IDH and/or 1p19q status in WHO grade II-IV diffuse astrocytic and oligodendroglial tumours as defined in the WHO 2016 Classification of CNS Tumours.

RESULTS

Forty-four studies including a total of 5286 patients fulfilled the inclusion criteria. Correlations between key glioma molecular markers, namely IDH and 1p19q, and distinctive MRI findings have been established, including tumour location, signal composition (including the T2-FLAIR mismatch sign) and apparent diffusion coefficient values.

CONCLUSION

Consistent trends have emerged indicating that conventional MRI is valuable for glioma genotyping, particularly in presumed lower grade glioma. However, due to limited interobserver testing, the reproducibility of qualitatively assessed visual features remains an area of uncertainty.

World Health Organization Grade II/III Glioma Molecular Status: Prediction by MRI Morphologic Features and Apparent Diffusion Coefficient

Background A readily implemented MRI biomarker for glioma genotyping is currently lacking. Purpose To evaluate clinically available MRI parameters for predicting isocitrate dehydrogenase (IDH) status in patients with glioma. Materials and Methods In this retrospective study of patients studied from July 2008 to February 2019, untreated World Health Organization (WHO) grade II/III gliomas were analyzed by three neuroradiologists blinded to tissue results. Apparent diffusion coefficient (ADC) minimum (ADC_min) and mean (ADC_mean) regions of interest were defined in tumor and normal appearing white matter (ADC_NAWM). A visual rating of anatomic features (T1 weighted, T1 weighted with contrast enhancement, T2 weighted, and fluid-attenuated inversion recovery) was performed. Interobserver comparison (intraclass correlation coefficient and Cohen κ) was followed by nonparametric (Kruskal-Wallis analysis of variance) testing of associations between ADC metrics and glioma genotypes, including Bonferroni correction for multiple testing. Descriptors with sufficient concordance (intraclass correlation coefficient, >0.8; κ > 0.6) underwent univariable analysis. Predictive variables (P < .05) were entered into a multivariable logistic regression and tested in an additional test sample of patients with glioma. Results The study included 290 patients (median age, 40 years; interquartile range, 33-52 years; 169 male patients) with 82 IDH wild-type, 107 IDH mutant/1p19q intact, and 101 IDH mutant/1p19q codeleted gliomas. Two predictive models incorporating ADC_mean-to-ADC_NAWM ratio, age, and morphologic characteristics, with model A mandating calcification result and model B recording cyst formation, classified tumor type with areas under the receiver operating characteristic curve of 0.94 (95% confidence interval [CI]: 0.91, 0.97) and 0.96 (95% CI: 0.93, 0.98), respectively. In the test sample of 49 gliomas (nine IDH wild type, 21 IDH mutant/1p19q intact, and 19 IDH mutant/1p19q codeleted), the classification accuracy was 40 of 49 gliomas (82%; 95% CI: 71%, 92%) for model A and 42 of 49 gliomas (86%; 95% CI: 76%, 96%) for model B. Conclusion Two algorithms that incorporated apparent diffusion coefficient values, age, and tumor morphologic characteristics predicted isocitrate dehydrogenase status in World Health Organization grade II/III gliomas on the basis of standard clinical MRI sequences alone. © RSNA, 2020 Online supplemental material is available for this article.

Radiological differences between subtypes of WHO 2016 grade II-III gliomas: a systematic review and meta-analysis

BACKGROUND

Isocitrate dehydrogenase (IDH) mutation and 1p/19q-codeletion are oncogenetic alterations with a positive prognostic value for diffuse gliomas, especially grade II and III. Some studies have suggested differences in biological behavior as reflected by radiological characteristics. In this paper, the literature regarding radiological characteristics in grade II and III glioma subtypes was systematically evaluated and a meta-analysis was performed.

METHODS

Studies that addressed the relationship between conventional radiological characteristics and IDH mutations and/or 1p/19q-codeletions in newly diagnosed, grade II and III gliomas of adult patients were included. The "3-group analysis" compared radiological characteristics between the WHO 2016 glioma subtypes (IDH-mutant astrocytoma, IDH-wildtype astrocytoma, and oligodendroglioma), and the "2-group analysis" compared radiological characteristics between 1p/19q-codeleted gliomas and 1p/19q-intact gliomas.

RESULTS

Fourteen studies (3-group analysis: 670 cases, 2-group analysis: 1042 cases) were included. IDH-mutated astrocytomas showed more often sharp borders and less frequently contrast enhancement compared to IDH-wildtype astrocytomas. 1p/19q-codeleted gliomas had less frequently sharp borders, but showed a heterogeneous aspect, calcification, cysts, and edema more frequently. For the 1p/19q-codeleted gliomas, a sensitivity of 96% was found for heterogeneity and a specificity of 88.1% for calcification.

CONCLUSIONS

Significant differences in conventional radiological characteristics exist between the WHO 2016 glioma subtypes, which may reflect differences in biological behavior. However, the diagnostic value of the independent radiological characteristics is insufficient to reliably predict the molecular genetic subtype.

Interrelationships between molecular subtype, anatomical location, and extent of resection in diffuse glioma: a systematic review and meta-analysis

Background

The introduction of the 2016 WHO Classification of Tumors of the Central Nervous System has resulted in tumor groupings with improved prognostic value for diffuse glioma patients. Molecular subtype, primarily based on IDH-mutational status and 1p/19q-status, is a strong predictor of survival. It is unclear to what extent this finding may be mediated by differences in anatomical location and surgical resectability among molecular subgroups. Our aim was to elucidate possible correlations between (1) molecular subtype and anatomical location and (2) molecular subtype and extent of resection.

Methods

We performed a systematic review of literature searching for studies on molecular subtype in relation to anatomical location and extent of resection. Only original data concerning adult participants suffering from cerebral diffuse glioma were included. Studies adopting similar outcomes measures were included in our meta-analysis.

Results

In the systematic analysis for research questions 1 and 2, totals of 20 and 9 studies were included, respectively. Study findings demonstrated that IDH-mutant tumors were significantly more frequently located in the frontal lobe and less often in the temporal lobe compared with IDH-wildtype gliomas. Within the IDH-mutant group, 1p/19q-codeleted tumors were associated with more frequent frontal and less frequent temporal localization compared with 1p/19q-intact tumors. In IDH-mutant gliomas, greater extent of resection was achieved than in IDH-wildtype tumors.

Conclusions

Genetic profile of diffuse cerebral glioma influences their anatomical location and seems to affect tumor resectability.

Magnetic resonance imaging texture analyses in lower-grade gliomas with a commercially available software: correlation of apparent diffusion coefficient and T2 skewness with 1p/19q codeletion

Preoperative prediction of molecular information of lower-grade gliomas (LrGGs) helps to determine the overall treatment strategy as well as the initial surgical strategy. This study aimed to detect magnetic resonance imaging (MRI) texture parameters to predict the molecular signature of LrGGs using a commercially available software and routine MR images. Forty-three patients treated at Keio University Hospital who had World Health Organization grade II or III gliomas were included. All patients having preoperative T1- and T2-weighted, fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted (DW) images were also included. Texture analyses of T2, FLAIR, and apparent diffusion coefficient (ADC) histograms were performed using a commercially available software. Texture parameters including kurtosis, skewness, and entropy were investigated to determine any correlation with the presence or absence of isocitrate dehydrogenase (IDH) mutations, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation. ADC skewness and T2 skewness were significantly associated with 1p/19q codeletion status. ADC skewness of ≥ 0.25 predicted 1p/19q codeletion with a sensitivity and specificity of 80% and 65.2%, respectively (AUC = 0.728). T2 skewness of ≥ - 0.11 predicted 1p/19q codeletion with a sensitivity and specificity of 80% and 91.3%, respectively, (AUC = 0.866). None of the texture parameters were associated with IDH mutation and MGMT promoter methylation. MRI texture analysis using a commercially available software demonstrated that T2 skewness could predict 1p/19q codeletion with high sensitivity and specificity, suggesting a clinical utility.

Neuroimaging-Based Classification Algorithm for Predicting 1p/19q-Codeletion Status in IDH-Mutant Lower Grade Gliomas

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.