Radiological features combined with IDH1 status for predicting the survival outcome of glioblastoma patients

Genetic mutation patterns among glioblastoma patients in the Taiwanese population - insights from a single institution retrospective study

This study utilized Next-Generation Sequencing (NGS) to explore genetic determinants of survival duration in Glioblastoma Multiforme (GBM) patients. We categorized 30 primary GBM patients into two groups based on their survival periods: extended survival (over two years, N = 17) and abbreviated survival (under two years, N = 13). For identifying pathogenic or likely pathogenic variants, we leveraged the ClinVar database. The cohort, aged 23 to 66 (median: 53), included 17 patients in Group A (survival >2 years, 10 males, 7 females), and 13 patients in Group B (survival <2 years, 8 males, 5 females), with a 60% to 40% male-to-female ratio. Identified mutations included CHEK2 (c.1477 G > A, p.E493K), IDH1 (c.395 G > A, p.R132H), and TP53 mutations. Non-coding regions exhibited variants in the TERT promoter (c.-146C > T, c.-124C > T) and TP53 RNA splicing site (c.376-2 A > C, c.376-2 A > G). While Group A had more mutations, statistical significance wasn't reached, likely due to sample size. Notably, TP53, and ATR displayed a trend toward significance. Surprisingly, TP53 mutations were more prevalent in Group A, contradicting Western findings on poorer GBM prognosis. In Taiwanese GBM patients, bevacizumab usage is linked to improved survival rates, affirming its safety and effectiveness. EGFR mutations are infrequent, suggesting potential distinctions in carcinogenic pathways. Further research on EGFR mutations and amplifications is essential for refining therapeutic approaches. TP53 mutations are associated with enhanced survival, but their functional implications necessitate detailed exploration. This study pioneers genetic analysis in Taiwanese GBM patients using NGS, advancing our understanding of their genetic landscape.

Glioblastomas with and without peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity present morphological and microstructural differences on conventional MR images

OBJECTIVES

Glioblastoma (GB) without peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity is atypical and its characteristics are barely known. The aim of this study was to explore the differences in pathological and MRI-based intrinsic features (including morphologic and first-order features) between GBs with peritumoral FLAIR hyperintensity (PFH-bearing GBs) and GBs without peritumoral FLAIR hyperintensity (PFH-free GBs).

METHODS

In total, 155 patients with pathologically diagnosed GBs were retrospectively collected, which included 110 PFH-bearing GBs and 45 PFH-free GBs. The pathological and imaging data were collected. The Visually AcceSAble Rembrandt Images (VASARI) features were carefully evaluated. The first-order radiomics features from the tumor region were extracted from FLAIR, apparent diffusion coefficient (ADC), and T1CE (T1-contrast enhanced) images. All parameters were compared between the two groups of GBs.

RESULTS

The pathological data showed more alpha thalassemia/mental retardation syndrome X-linked (ATRX)-loss in PFH-free GBs compared to PFH-bearing ones (p < 0.001). Based on VASARI evaluation, PFH-free GBs had larger intra-tumoral enhancing proportion and smaller necrotic proportion (both, p < 0.001), more common non-enhancing tumor (p < 0.001), mild/minimal enhancement (p = 0.003), expansive T1/FLAIR ratio (p < 0.001) and solid enhancement (p = 0.009), and less pial invasion (p = 0.010). Moreover, multiple ADC- and T1CE-based first-order radiomics features demonstrated differences, especially the lower intensity heterogeneity in PFH-free GBs (for all, adjusted p < 0.05).

CONCLUSIONS

Compared to PFH-bearing GBs, PFH-free ones demonstrated less immature neovascularization and lower intra-tumoral heterogeneity, which would be helpful in clinical treatment stratification.

CLINICAL RELEVANCE STATEMENT

Glioblastomas without peritumoral FLAIR hyperintensity show less immature neovascularization and lower heterogeneity leading to potential higher treatment benefits due to less drug resistance and treatment failure.

KEY POINTS

• The study explored the differences between glioblastomas with and without peritumoral FLAIR hyperintensity. • Glioblastomas without peritumoral FLAIR hyperintensity showed less necrosis and contrast enhancement and lower intensity heterogeneity. • Glioblastomas without peritumoral FLAIR hyperintensity had less immature neovascularization and lower tumor heterogeneity.

Diagnostic Approaches to Adult-Type Diffuse Glial Tumors: Comparative Literature and Clinical Practice Study

Gliomas are the most frequent intrinsic central nervous system tumors. The new 2021 WHO Classification of Central Nervous System Tumors brought significant changes into the classification of gliomas, that underline the role of molecular diagnostics, with the adult-type diffuse glial tumors now identified primarily by their biomarkers rather than histology. The status of the isocitrate dehydrogenase (IDH) 1 or 2 describes tumors at their molecular level and together with the presence or absence of 1p/19q codeletion are the most important biomarkers used for the classification of adult-type diffuse glial tumors. In recent years terminology has also changed. IDH-mutant, as previously known, is diagnostically used as astrocytoma and IDH-wildtype is used as glioblastoma. A comprehensive understanding of these tumors not only gives patients a more proper treatment and better prognosis but also highlights new difficulties. MR imaging is of the utmost importance for diagnosing and supervising the response to treatment. By monitoring the tumor on followup exams better results can be achieved. Correlations are seen between tumor diagnostic and clinical manifestation and surgical administration, followup care, oncologic treatment, and outcomes. Minimal resection site use of functional imaging (fMRI) and diffusion tensor imaging (DTI) have become indispensable tools in invasive treatment. Perfusion imaging provides insightful information about the vascularity of the tumor, spectroscopy shows metabolic activity, and nuclear medicine imaging displays tumor metabolism. To accommodate better treatment the differentiation of pseudoprogression, pseudoresponse, or radiation necrosis is needed. In this report, we present a literature review of diagnostics of gliomas, the differences in their imaging features, and our radiology's departments accumulated experience concerning gliomas.

Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review

DWI is an imaging technique commonly used for the assessment of acute ischemia, inflammatory disorders, and CNS neoplasia. It has several benefits since it is a quick, easily replicable sequence that is widely used on many standard scanners. In addition to its normal clinical purpose, DWI offers crucial functional and physiological information regarding brain neoplasia and the surrounding milieu. A narrative review of the literature was conducted based on the PubMed database with the purpose of investigating the potential role of DWI in the neuro-oncology field. A total of 179 articles were included in the study.

MRI radiomic features of peritumoral edema may predict the recurrence sites of glioblastoma multiforme

Background and purpose

As one of the most aggressive malignant tumor in the central nervous system, the main cause of poor outcome of glioblastoma (GBM) is recurrence, a non-invasive method which can predict the area of recurrence pre-operation is necessary.To investigate whether there is radiological heterogeneity within peritumoral edema and identify the reproducible radiomic features predictive of the sites of recurrence of glioblastoma(GBM), which may be of value to optimize patients' management.

Materials and methods

The clinical information and MR images (contrast-enhanced T1 weighted and FLAIR sequences) of 22 patients who have been histologically proven glioblastoma, were retrospectively evaluated. Kaplan-Meier methods was used for survival analysis. Oedematous regions were manually segmented by an expert into recurrence region, non-recurrence region. A set of 94 radiomic features were obtained from each region using the function of analyzing MR image of 3D slicer. Paired t test was performed to identify the features existing significant difference. Subsequently, the data of two patients from TCGA database was used to evaluate whether these features have clinical value.

Results

Ten features with significant differences between the recurrence and non-recurrence subregions were identified and verified on two individual patients from the TCGA database with pathologically confirmed diagnosis of GBM.

Conclusions

Our results suggested that heterogeneity does exist in peritumoral edema, indicating that the radiomic features of peritumoral edema from routine MR images can be utilized to predict the sites of GBM recurrence. Our findings may further guide the surgical treatment strategy for GBM.

Noninvasive Determination of the IDH Status of Gliomas Using MRI and MRI-Based Radiomics: Impact on Diagnosis and Prognosis

Gliomas are the most common primary malignant brain tumors in adults. The fifth edition of the WHO Classification of Tumors of the Central Nervous System, published in 2021, provided molecular and practical approaches to CNS tumor taxonomy. Currently, molecular features are essential for differentiating the histological subtypes of gliomas, and recent studies have emphasized the importance of isocitrate dehydrogenase (IDH) mutations in stratifying biologically distinct subgroups of gliomas. IDH plays a significant role in gliomagenesis, and the association of IDH status with prognosis is very clear. Recently, there has been much progress in conventional MR imaging (cMRI), advanced MR imaging (aMRI), and radiomics, which are widely used in the study of gliomas. These advances have resulted in an improved correlation between MR signs and IDH mutation status, which will complement the prediction of the IDH phenotype. Although imaging cannot currently substitute for genetic tests, imaging findings have shown promising signs of diagnosing glioma subtypes and evaluating the efficacy and prognosis of individualized molecular targeted therapy. This review focuses on the correlation between MRI and MRI-based radiomics and IDH gene-phenotype prediction, discussing the value and application of these techniques in the diagnosis and evaluation of the prognosis of gliomas.

Deep learning for prediction of isocitrate dehydrogenase mutation in gliomas: a critical approach, systematic review and meta-analysis of the diagnostic test performance using a Bayesian approach

Background

Conventionally, identifying isocitrate dehydrogenase (IDH) mutation in gliomas is based on histopathological analysis of tissue specimens acquired via stereotactic biopsy or definitive resection. Accurate pre-treatment prediction of IDH mutation status using magnetic resonance imaging (MRI) can guide clinical decision-making. We aim to evaluate the diagnostic performance of deep learning (DL) to determine IDH mutation status in gliomas.

Methods

A systematic search of Cochrane Library, Web of Science, Medline, and Scopus was conducted to identify relevant publications until August 1, 2021. Articles were included if all the following criteria were met: (I) patients with histopathologically confirmed World Health Organization (WHO) grade II, III, or IV gliomas; (II) histopathological examination with the IDH mutation; (III) DL was used to predict the IDH mutation status; (IV) sufficient data for reconstruction of confusion matrices in terms of the diagnostic performance of the DL algorithms; and (V) original research articles. Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) was used to assess the studies' quality. Bayes theorem was utilized to calculate the posttest probability.

Results

Four studies with a total of 1,295 patients were included. In the training set, the pooled sensitivity, specificity, and area under the summary receiver operating characteristic (SROC) curve were 93.9%, 90.9% and 0.958, respectively. In the validation set, the pooled sensitivity, specificity, and area under the SROC curve were 90.8%, 85.5% and 0.939, respectively. With a known pretest probability of 80.2%, the Bayes theorem yielded a posttest probability of 97.6% and 96.0% for a positive test and 27.0% and 30.6% for a negative test for training sets and validation sets, respectively.

Discussion

This is the first meta-analysis that summarizes the diagnostic performance of DL in predicting IDH mutation status in gliomas via the Bayes theorem. DL algorithms demonstrate excellent diagnostic performance in predicting IDH mutation in gliomas. Radiomic features associated with IDH mutation, and its underlying pathophysiology extracted from advanced MRI may improve prediction probability. However, more studies are required to optimize and increase its reliability. Limitations include obtaining some data via email and lack of training and test sets statistics.

Gamma distribution model of diffusion MRI for evaluating the isocitrate dehydrogenase mutation status of glioblastomas

OBJECTIVE

To determine whether the γ distribution (GD) model of diffusion MRI is useful in the evaluation of the isocitrate dehydrogenase (IDH) mutation status of glioblastomas.

METHODS

12 patients with IDH-mutant glioblastomas and 54 patients with IDH-wildtype glioblastomas were imaged with diffusion-weighted imaging using 13 b-values from 0 to 1000 s/mm2. The shape parameter (κ) and scale parameter (θ) were obtained with the GD model. Fractions of three different areas under the probability density function curve (f1, f2, f3) were defined as follows: f1, diffusion coefficient (D) < 1.0×10-3 mm2/s; f2, D > 1.0×10-3 and <3.0×10-3 mm2/s; f3, D > 3.0 × 10-3 mm2/s. The GD model-derived parameters measured in gadolinium-enhancing lesions were compared between the IDH-mutant and IDH-wildtype groups. Receiver operating curve analyses were performed to assess the parameters' diagnostic performances.

RESULTS

The IDH-mutant group's f1 (0.474  ±  0.143) was significantly larger than the IDH-wildtype group's (0.347  ±  0.122, p = 0.0024). The IDH-mutant group's f2 (0.417  ±  0.131) was significantly smaller than the IDH-wildtype group's (0.504  ±  0.126, p = 0.036). The IDH-mutant group's f3 (0.109  ±  0.060) was significantly smaller than the IDH-wildtype group's (0.149  ±  0.063, p = 0.0466). The f1 showed the best diagnostic performance among the GD model-derived parameters with the area under the curve value of 0.753.

CONCLUSION

The GD model could well describe the pathological features of IDH-mutant and IDH-wildtype glioblastomas, and was useful in the differentiation of these tumors.

ADVANCES IN KNOWLEDGE

Diffusion MRI based on the γ distribution model could well describe the pathological features of IDH-mutant and IDH-wildtype glioblastomas, and its use enabled the significant differentiation of these tumors. The γ distribution model may contribute to the non-invasive identification of the IDH mutation status based on histological viewpoint.

The potential of advanced MR techniques for precision radiotherapy of glioblastoma

As microscopic tumour infiltration of glioblastomas is not visible on conventional magnetic resonance (MR) imaging, an isotropic expansion of 1-2 cm around the visible tumour is applied to define the clinical target volume for radiotherapy. An opportunity to visualize microscopic infiltration arises with advanced MR imaging. In this review, various advanced MR biomarkers are explored that could improve target volume delineation for radiotherapy of glioblastomas. Various physiological processes in glioblastomas can be visualized with different advanced MR techniques. Combining maps of oxygen metabolism (CMRO₂), relative cerebral blood volume (rCBV), vessel size imaging (VSI), and apparent diffusion coefficient (ADC) or amide proton transfer (APT) can provide early information on tumour infiltration and high-risk regions of future recurrence. Oxygen consumption is increased 6 months prior to tumour progression being visible on conventional MR imaging. However, presence of the Warburg effect, marking a switch from an infiltrative to a proliferative phenotype, could result in CMRO₂ to appear unaltered in high-risk regions. Including information on biomarkers representing angiogenesis (rCBV and VSI) and hypercellularity (ADC) or protein concentration (APT) can omit misinterpretation due to the Warburg effect. Future research should evaluate these biomarkers in radiotherapy planning to explore the potential of advanced MR techniques to personalize target volume delineation with the aim to improve local tumour control and/or reduce radiation-induced toxicity.

Multiple Survival Outcome Prediction of Glioblastoma Patients Based on Multiparametric MRI

PURPOSE

Construction of radiomics models for the individualized estimation of multiple survival stratification in glioblastoma (GBM) patients using the multiregional information extracted from multiparametric MRI that could facilitate clinical decision-making for GBM patients.

MATERIALS AND METHODS

A total of 134 eligible GBM patients were selected from The Cancer Genome Atlas. These patients were separated into the long-term and short-term survival groups according to the median of individual survival indicators: overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS). Then, the patients were divided into a training set and a validation set in a ratio of 2:1. Radiomics features (n = 5,152) were extracted from multiple regions of the GBM using multiparametric MRI. Then, radiomics signatures that are related to the three survival indicators were respectively constructed using the analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) regression for each patient in the training set. Based on a Cox proportional hazards model, the radiomics model was further constructed by combining the signature and clinical risk factors.

RESULTS

The constructed radiomics model showed a promising discrimination ability to differentiate in the training set and validation set of GBM patients with survival indicators of OS, PFS, and DSS. Both the four MRI modalities and five tumor subregions have different effects on the three survival indicators of GBM. The favorable calibration and decision curve analysis indicated the clinical decision value of the radiomics model. The performance of models of the three survival indicators was different but excellent; the best model achieved C indexes of 0.725, 0.677, and 0.724, respectively, in the validation set.

CONCLUSION

Our results show that the proposed radiomics models have favorable predictive accuracy on three survival indicators and can provide individualized probabilities of survival stratification for GBM patients by using multiparametric and multiregional MRI features.

A Review of Radiomics and Deep Predictive Modeling in Glioma Characterization

Recent developments in glioma categorization based on biological genotypes and application of computational machine learning or deep learning based predictive models using multi-modal MRI biomarkers to assess these genotypes provides potential assurance for optimal and personalized treatment plans and efficacy. Artificial intelligence based quantified assessment of glioma using MRI derived hand-crafted or auto-extracted features have become crucial as genomic alterations can be associated with MRI based phenotypes. This survey integrates all the recent work carried out in state-of-the-art radiomics, and Artificial Intelligence based learning solutions related to molecular diagnosis, prognosis, and treatment monitoring with the aim to create a structured resource on radiogenomic analysis of glioma. Challenges such as inter-scanner variability, requirement of benchmark datasets, prospective validations for clinical applicability are discussed with further scope for designing optimal solutions for glioma stratification with immediate recommendations for further diagnostic decisions and personalized treatment plans for glioma patients.

The Prognostic Role of Peritumoral Edema in Patients with Newly Diagnosed Glioblastoma: A Retrospective Analysis

OBJECTIVE

Previous studies on glioblastomas (GBMs) have not reached a consensus on peritumoral edema (PTE)'s influence on survival. This study evaluated the PTE index's prognostic role in newly diagnosed GBMs using a well-designed method.

METHODS

Selected patients were reviewed after a rigorous screening process. Their general information was obtained from electronic medical records. The imaging metrics (MTD, TTM, TTE) representing tumor diameter, laterality, and PTE extent were obtained by manual measurement in Syngo FastView software. The PTE index was a ratio of TTE to MTD. Multiple variables were evaluated using analysis of variance and Cox regression model.

RESULTS

Of 143 patients, 62 were included in this study. MGMT promoter methylation and tumor laterality were both independent prognostic factors (p = 0.020, 0.042; HR = 0.272, 2.630). The lateral tumors' index was higher than that of the medial tumors (57.7% vs. 42.6%, p = 0.027). Low-index tumors were located in relatively medial positions compared with high-index tumors (TTM, 4.9 vs. 12.8, p = 0.032). This finding indicated that the PTE index tended to increase with tumor laterality. Moreover, the patients with low-index tumors had a significant survival disadvantage in the univariate analysis but not in the multivariate analysis (p = 0.023, 0.220). However, further analysis found that the combination of tumor laterality and PTE statistically stratified the survival outcome. The patients with lateral high-index tumors survived significantly longer (p = 0.022, HR = 1.927).

CONCLUSIONS

In contrast with the previous studies, this study recommends combining PTE and tumor laterality for survival stratification in newly diagnosed GBMs.

MRI-Based Radiomics and Radiogenomics in the Management of Low-Grade Gliomas: Evaluating the Evidence for a Paradigm Shift

Low-grade gliomas (LGGs) are tumors that affect mostly adults. These neoplasms are comprised mainly of oligodendrogliomas and diffuse astrocytomas. LGGs remain vexing to current management and therapeutic modalities although they exhibit more favorable survival rates compared with high-grade gliomas (HGGs). The specific genetic subtypes that these tumors exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of an LGG pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). The introduction of radiomics as a high throughput quantitative imaging technique that allows for improved diagnostic, prognostic and predictive indices has created more interest for such techniques in cancer research and especially in neurooncology (MRI-based classification of LGGs, predicting Isocitrate dehydrogenase (IDH) and Telomerase reverse transcriptase (TERT) promoter mutations and predicting LGG associated seizures). Radiogenomics refers to the linkage of imaging findings with the tumor/tissue genomics. Numerous applications of radiomics and radiogenomics have been described in the clinical context and management of LGGs. In this review, we describe the recently published studies discussing the potential application of radiomics and radiogenomics in LGGs. We also highlight the potential pitfalls of the above-mentioned high throughput computerized techniques and, most excitingly, explore the use of machine learning artificial intelligence technologies as standalone and adjunct imaging tools en route to enhance a personalized MRI-based tumor diagnosis and management plan design.

Fluid attenuation in non-contrast-enhancing tumor (nCET): an MRI Marker for Isocitrate Dehydrogenase (IDH) mutation in Glioblastoma

PURPOSE

The WHO 2016 update classifies glioblastomas (WHO grade IV) according to isocitrate dehydrogenase (IDH) gene mutation status. We aimed to determine MRI-based metrics for predicting IDH mutation in glioblastoma.

METHODS

This retrospective study included glioblastoma cases (n = 199) with known IDH mutation status and pre-operative MRI (T1WI, T2WI, FLAIR, contrast-enhanced T1W1 at minimum). Two neuroradiologists determined the following MRI metrics: (1) primary lobe of involvement (frontal or non-frontal); (2) presence/absence of contrast-enhancement; (3) presence/absence of necrosis; (4) presence/absence of fluid attenuation in the non-contrast-enhancing tumor (nCET); (5) maximum width of peritumoral edema (cm); (6) presence/absence of multifocal disease. Inter-reader agreement was determined. After resolving discordant measurements, multivariate association between consensus MRI metrics/patient age and IDH mutation status was determined.

RESULTS

Among 199 glioblastomas, 16 were IDH-mutant. Inter-reader agreement was calculated for contrast-enhancement (ĸ = 0.49 [- 0.11-1.00]), necrosis (ĸ = 0.55 [0.34-0.76]), fluid attenuation in nCET (ĸ = 0.83 [0.68-0.99]), multifocal disease (ĸ = 0.55 [0.39-0.70]), and primary lobe (ĸ = 0.85 [0.80-0.91]). Mean difference for peritumoral edema width between readers was 0.3 cm [0.2-0.5], p < 0.001. Multivariate analysis uncovered significant associations between IDH-mutation and fluid attenuation in nCET (OR 82.9 [19.22, ∞], p < 0.001), younger age (OR 0.93 [0.86, 0.98], p = 0.009), frontal lobe location (OR 11.08 [1.14, 352.97], p = 0.037), and less peritumoral edema (OR 0.15 [0, 0.65], p = 0.044).

CONCLUSIONS

Conventional MRI metrics and patient age predict IDH-mutation status in glioblastoma. Among MRI markers, fluid attenuation in nCET represents a novel marker with high inter-reader agreement that is strongly associated with Glioblastoma, IDH-mutant.

Basic research in childhood cancer: Progress and future directions in China

Childhood cancer is a leading cause of death in children. Some childhood cancers have a particularly high mortality rate. Following the World Health Organization's emphasis on child health, most governments worldwide have taken measures to facilitate childhood cancer research. Thus, the scientific community is showing increasing interest in this area. Chinese government has prominence in building a system for the diagnosis and treatment of childhood cancer, thereby promoting the development of childhood cancer research. This review summarizes the research progress, challenges, and perspectives in childhood cancer, and the increasing contributions of National Natural Science Foundation of China (NSFC) in the past decade (2008-2018).

A Quantified Risk-Scoring System for the Recurrence of Meningiomas: Results From a Retrospective Study of 392 Patients

Aim: This study aimed to identify the independent risk factors of recurrence in patients undergoing primary resection of meningioma and construct a scoring system for the prediction of the risk of postoperative recurrence. Materials and Methods: The clinical data of 591 patients who underwent primary surgical resection for meningioma at the First Affiliated Hospital of Wenzhou Medical University between November 2010 and December 2016 were retrospectively reviewed. The clinical, radiological, and pathological characteristics were evaluated, and the independent risk factors for recurrence were identified via receiver operating characteristic (ROC) curve and logistic analyses. A scoring system that included these independent risk factors was used to construct a risk-predicting model that was evaluated via a ROC curve analysis. The recurrences of different subgroups were observed by Kaplan-Meier's curves. Results: The clinical data of 392 patients with meningioma were used to construct the scoring system. The logistic analysis showed that sex (OR = 2.793, 95% CI = 1.076-7.249, P = 0.035), heterogeneous tumor enhancement (OR = 4.452, 95% CI = 1.714-11.559, P = 0.002), brain invasion (OR = 2.650, 95% CI = 1.043-6.733, P = 0.041), Simpson's removal grade (OR = 5.139, 95% CI = 1.355-19.489, P = 0.016), and pathological grade (OR = 3.282, 95% CI = 1.123-9.595, P = 0.030) were independent risk factors for recurrence. A scoring system was developed and used to divide the patients into the following four subgroups: subgroup 1 with scores of 0-75 (n = 249), subgroup 2 with scores of 76-154 (n = 88), subgroup 3 with scores of 155-215 (n = 46), and subgroup 4 with scores of 216-275 (n = 9). The incidences of recurrence in each subgroup were as follows: subgroup 1, 1.2%; subgroup 2, 5.7%; subgroup 3, 26.1%; and subgroup 4, 66.7% (P < 0.001). The scoring system reliably predicted the postoperative recurrence of meningioma with a high area under the ROC curve. Conclusions: Our scoring system is a simple and reliable instrument for identifying meningioma patients at risk of postoperative recurrence and could help in optimizing individualized clinical treatment.

A radiomics-clinical nomogram for preoperative prediction of IDH1 mutation in primary glioblastoma multiforme

AIM

To develop and validate an individualised radiomics-clinical nomogram for the prediction of the isocitrate dehydrogenase 1 (IDH1) mutation status in primary glioblastoma multiforme (GBM) based on radiomics features and clinical variables.

MATERIALS AND METHODS

In a retrospective study, preoperative magnetic resonance imaging (MRI) images were obtained of 122 patients with primary glioblastoma (development cohort = 101; validation cohort = 21). Radiomics features were extracted from total tumour based on the post-contrast high-resolution three-dimensional (3D) T1-weighted MRI images. Radiomics features were selected by using a least absolute shrinkage and selection operator (LASSO) binomial regression model with nested cross-validation. Then, a radiomics-clinical nomogram was constructed by combining relevant radiomics features and clinical variables and subsequently tested by using the independent validation cohort.

RESULTS

A total of 105 features were quantified on the 3D MRI images of each patient, and eight were selected to construct the radiomics model for predicting IDH1 mutation status. The mean classification accuracy and mean κ value achieved with the model were 88.4±3% and 0.701±0.08, respectively. The radiomics-clinical nomogram, which combines eight radiomics features and three clinical variables (patient age, sex and tumour location), demonstrated good discrimination (C-index 0.934 [95% CI, 0.874 to 0.994]; F1 score 0.78) and performed well with the validation cohort (C-index 0.963 [95% CI, 0.957 to 0.969]; F1 score 0.91; AUC 0.956).

CONCLUSIONS

A radiomics-clinical nomogram was developed and proved to be valuable in the non-invasive, individualised prediction of the IDH1 mutation status in patients with primary GBM. The nomogram can be applied using clinical conditions to facilitate preoperative patient evaluation.

Normative Baseline for Radiomics in Brain MRI: Evaluating the Robustness, Regional Variations, and Reproducibility on FLAIR Images

BACKGROUND

Radiomics in neuroimaging has gained momentum as a noninvasive prediction tool not only to differentiate between types of brain tumors, but also to create phenotypic signatures in neurological and neuropsychiatric disorders. However, there is currently little understating about the robustness and reproducibility of radiomic features in a baseline normative population.

PURPOSE

To investigate the intra- and interscanner reproducibility, spatial robustness, and sensitivity of radiomics on fluid attenuation inversion recovery (FLAIR) images, which are widely used in neuro-oncology investigations.

STUDY TYPE

Retrospective.

POPULATION

Three separate datasets of healthy controls: 1) 87 subjects (age range 12-64 years), 2) intrascanner three timepoints, four subjects, and 3) interscanner, eight subjects at three different sites.

FIELD STRENGTH/SEQUENCE

T₂ -weighted FLAIR at 1.5T and 3.0T.

ASSESSMENT

Spatial variance across lobes, and their relation with age/gender, intra- and inter-scanner reproducibility (with and without site harmonization) of radiomics.

STATISTICAL TESTS

Analysis of variance (ANOVA), interclass correlation (ICC), coefficient of variation (CoV), Bland-Altman analysis.

RESULTS

Analysis of data revealed no differences between genders; however, multiple radiomic features were highly associated with age (P < 0.05). Spatial variability was also evaluated where only 29.04% gray matter and 38.7% white matter features demonstrated an ICC >0.5. Furthermore, the results demonstrated intra-scanner reliability (ICC >0.5); however, inter-scanner reproducibility was poor, with ICC < 0.5 for 82% gray matter and 78.5% white matter features. The inter-scanner reliability improved (ICC < 0.5 for 39.67% gray matter and 38% white matter features) using site-harmonization techniques.

DATA CONCLUSION

These findings suggest that, accounting for age, spatial locations in radiomics-based analysis and use of intersite radiomics harmonization is crucial before interpreting these features for pathological inference. Level of Evidence 3. Technical Efficacy Stage 1. J. MAGN. RESON. IMAGING 2021;53:394-407.

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.

Individualized discrimination of tumor recurrence from radiation necrosis in glioma patients using an integrated radiomics-based model

PURPOSE

To develop and validate an integrated model for discriminating tumor recurrence from radiation necrosis in glioma patients.

METHODS

Data from 160 pathologically confirmed glioma patients were analyzed. The diagnostic model was developed in a primary cohort (n = 112). Textural features were extracted from postoperative ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET), ¹¹C-methionine (¹¹C-MET) PET, and magnetic resonance images. The least absolute shrinkage and selection operator regression model was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop a model for predicting tumor recurrence. The radiomics signature, quantitative PET parameters, and clinical risk factors were incorporated in the model. The clinical value of the model was then assessed in an independent validation cohort using the remaining 48 glioma patients.

RESULTS

The integrated model consisting of 15 selected features was significantly associated with postoperative tumor recurrence (p < 0.001 for both primary and validation cohorts). Predictors contained in the individualized diagnosis model included the radiomics signature, the mean of tumor-background ratio (TBR) of ¹⁸F-FDG, maximum of TBR of ¹¹C-MET PET, and patient age. The integrated model demonstrated good discrimination, with an area under the curve (AUC) of 0.988, with a 95% confidence interval (CI) of 0.975-1.000. Application in the validation cohort showed good differentiation (AUC of 0.914 and 95% CI of 0.881-0.945). Decision curve analysis showed that the integrated diagnosis model was clinically useful.

CONCLUSIONS

Our developed model could be used to assist the postoperative individualized diagnosis of tumor recurrence in patients with gliomas.

IDH mutation-specific radiomic signature in lower-grade gliomas

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.

Intratumoral Susceptibility Signals Reflect Biomarker Status in Gliomas

Susceptibility-weighted imaging (SWI) can be a useful tool to depict vascular structures in brain tumors as well as micro-bleedings, which represent tumor invasion to blood vessels and could also be representative of tumoral angiogenesis. In this study, we investigated the relationship between SWI features and glioma grades, and the expression of key molecular markers isocitrate dehydrogenase 1 (IDH1), O-6-methylguanine-DNA methyltransferase (MGMT), and 1p19q. The gliomas were graded according to the intratumoral susceptibility signals (ITSS). We used the Mann-Whitney test to analyze the relationship between ITSS grades and the pathological level and status of these markers. Additionally, the area under the curve (AUC) was used to determine the predictive value of glioma SWI characteristics for the molecular marker status. In these cases, the ITSS grades of low-grade gliomas (LGG) were significantly lower than those of high-grade gliomas (HGG). Similarly, the ITSS grades of gliomas with IDH1 mutations and MGMT methylation were significantly lower than those of gliomas with Wild-type IDH1 and unmethylated MGMT. However, ITSS grades showed no relationship with 1p19q deletion status, while they did show significant predictive ability for glioma grade, IDH1 mutation, and MGMT methylation. These findings indicate an association between some molecular markers and cerebral microbleeds in gliomas, providing a new avenue for non-invasive prediction of molecular genetics in gliomas and an important basis for preoperative personalized surgical treatment based on molecular pathology.

Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction

Objective: We aimed to identify a radiomic signature to be used as a noninvasive biomarker of prognosis in patients with lower-grade gliomas (LGGs) and to reveal underlying biological processes through comprehensive radiogenomic investigation. Methods: We extracted 55 radiomic features from T2-weighted images of 233 patients with LGGs (training cohort: n = 85; validation cohort: n = 148). Univariate Cox regression and linear risk score formula were applied to generate a radiomic-based signature. Gene ontology analysis of highly expressed genes in the high-risk score group was conducted to establish a radiogenomic map. A nomogram was constructed for individualized survival prediction.

Results: The six-feature radiomic signature stratified patients in the training cohort into low- or high-risk groups for overall survival (P = 0.0018). This result was successfully verified in the validation cohort (P = 0.0396). Radiogenomic analysis revealed that the prognostic radiomic signature was associated with hypoxia, angiogenesis, apoptosis, and cell proliferation. The nomogram resulted in high prognostic accuracy (C-index: 0.92, C-index: 0.70) and favorable calibration for individualized survival prediction in the training and validation cohorts.

Conclusions: Our results suggest a great potential for the use of radiomic signature as a biological surrogate in providing prognostic information for patients with LGGs.

Contrast-enhancement in supratentorial low-grade gliomas: a classic prognostic factor in the molecular age

BACKGROUND

Contrast enhancement (CE) is found in 10-60% of low-grade gliomas. Its prognostic significance is controversial, and its correlation with IDH mutations and 1p/19q codeletion is elusive. The aim of this study is to investigate whether CE is associated with molecular characteristics of low-grade gliomas and uncover its prognostic value.

MATERIALS AND METHODS

All confirmed histological cases of low-grade gliomas diagnosed at our institution between years 2000-2016 were reviewed (n = 102). Spinal and brainstem localization, only-biopsied tumours with ring-like enhancement and incomplete medical records were excluded.

RESULTS

Mean age was 42 years ( ± 13.9 years), and 63.6% were male. The median follow-up time was 79.8 months. CE was present on 25% of preoperative MRI, and 25% of patients were considered high-risk according to Pignatti score. Most were astrocytomas (67%) and 87.2% were surgically removed. IDH mutation was found in 64.6% of tumour samples, and 18.8% had a 1p/19q codeletion. No subgroup differences were observed according to CE except for presurgical performance status and postoperative chemotherapy. IDH status and 1p/19q codeletion were evenly distributed. On univariate analysis, age, size > 6 cm, CE, extent of resection, Pignatti score, IDH mutation and 1p/19q codeletion were significantly associated to OS. On multivariate analysis, only CE and IDH status were independently associated to OS. CE remained a significant prognostic factor in IDH-mutant non-codeleted tumours when analysed by tumour subtype.

CONCLUSION

CE in low-grade gliomas provides prognostic information in IDH-mutant non-codeleted tumours, although its meaning remains uncertain in IDH-wildtype gliomas.

A radiomics nomogram based on multiparametric MRI might stratify glioblastoma patients according to survival

OBJECTIVES

To construct a radiomics nomogram for the individualized estimation of the survival stratification in glioblastoma (GBM) patients using the multiregional information extracted from multiparametric MRI, which could facilitate the clinical decision-making for GBM patients.

MATERIALS AND METHODS

A total of 105 eligible GBM patients (57 in the long-term and 48 in the short-term survival groups, separated by an overall survival of 12 months) were selected from the Cancer Genome Atlas. These patients were divided into a training set (n = 70) and a validation set (n = 35). Radiomics features (n = 4000) were extracted from multiple regions of the GBM using multiparametric MRI. Then, a radiomics signature was constructed using least absolute shrinkage and selection operator regression for each patient in the training set. Combined with clinical risk factors, a radiomics nomogram was constructed based on a multivariate logistic regression model. The performance of this radiomics nomogram was assessed by calibration, discrimination, and clinical usefulness.

RESULTS

The radiomics signature consisted of 25 selected features and performed better than clinical risk factors (i.e., age, Karnofsky performance status, and treatment strategy) in survival stratification. When the radiomics signature and clinical risk factors were combined, the radiomics nomogram exhibited promising discrimination in the training (C-index, 0.971) and validation (C-index, 0.974) sets. The favorable calibration and decision curve analysis indicated the clinical usefulness of the radiomics nomogram.

CONCLUSIONS

The presented radiomics nomogram, as a non-invasive prediction tool, could exhibit a favorable predictive accuracy and provide individualized probabilities of survival stratification for GBM patients.

KEY POINTS

• Non-invasive survival stratification of GBM patients can be obtained with a radiomics nomogram. • The proposed nomogram constructed by radiomics signature selected from 4000 radiomics features, combined with independent clinical risk factors such as age, Karnofsky performance status, and treatment strategy. • The proposed radiomics nomogram exhibited good calibration and discrimination for survival stratification of GBM patients in both training (C-index, 0.971) and validation (C-index, 0.974) sets.

Prognostic value of subventricular zone involvement in relation to tumor volumes defined by fused MRI and O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET imaging in glioblastoma multiforme

BACKGROUND

Subventricular zone (SVZ) involvement is associated with a dismal prognosis in patients with glioblastoma multiforme (GBM). Dual-time point (dtp) O-(2-[¹⁸F]fluoroethyl)-L-tyrosine (FET) PET/CT (PET) may be a time- and cost-effective alternative to dynamic FET PET, but its prognostic value, particularly with respect to SVZ involvement, is unknown.

METHODS

Thirty-five patients had two scans 5-15 and 50-60 min after i.v. FET injection to define tumor volumes and SVZ involvement before starting radiotherapy. Associations between clinical progression markers, MRI- and dtp FET PET-based tumor volumes, or SVZ involvement and progression-free (PFS) and overall survival (OS) were assessed in univariable and multivariable analyses.

RESULTS

The extent of resection was not related to outcomes. Albeit non-significant, dtp FET PET detected more SVZ infiltration than MRI (60% vs. 51%, p = 0.25) and was significantly associated with poor survival (p < 0.03), but PET-T1-Gad volumes were larger in this group (p < 0.002). Survival was shorter in patients with larger MRI tumor volumes, larger PET tumor volumes, and worse Karnofsky performance status (KPS), with fused PET-T1-Gad and KPS significant in multivariable analysis (p < 0.03). Uptake kinetics was not associated with treatment outcomes.

CONCLUSIONS

FET PET-based tumor volumes may be useful for predicting worse prognosis glioblastoma. Although the presence of SVZ infiltration is linked to higher PET/MRI-based tumor volumes, the independent value of dtp FET PET parameters and SVZ infiltration as prognostic markers pre-irradiation has not been confirmed.

Improving the estimation of prognosis for glioblastoma patients by MR based hemodynamic tissue signatures

Advanced MRI and molecular markers have been raised as crucial to improve prognostic models for patients having glioblastoma (GBM) lesions. In particular, different MR perfusion based markers describing vascular intrapatient heterogeneity have been correlated with tumor aggressiveness, and represent key information to understand tumor resistance against effective therapies of these neoplasms. Recently, hemodynamic tissue signature (HTS) markers based on MR perfusion images have been demonstrated to be useful for describing the heterogeneity of GBM at the voxel level, as well as demonstrating significant correlations with the patient's overall survival. In this work, we analyze the abilities of these markers to improve the conventional prognostic models based on clinical, morphological, and demographic features. Our results, in both the regression and classification tests, show that inclusion of the HTS markers improves the reliability of prognostic models. The HTS method is fully automatic and it is available for research use at http://www.oncohabitats.upv.es.

A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas

Objective

The aim of this study was to develop a radiomics signature for prediction of progression-free survival (PFS) in lower-grade gliomas and to investigate the genetic background behind the radiomics signature.

Methods

In this retrospective study, training (n = 216) and validation (n = 84) cohorts were collected from the Chinese Glioma Genome Atlas and the Cancer Genome Atlas, respectively. For each patient, a total of 431 radiomics features were extracted from preoperative T2-weighted magnetic resonance images. A radiomics signature was generated in the training cohort, and its prognostic value was evaluated in both the training and validation cohorts. The genetic characteristics of the group with high-risk scores were identified by radiogenomic analysis, and a nomogram was established for prediction of PFS.

Results

There was a significant association between the radiomics signature (including 9 screened radiomics features) and PFS, which was independent of other clinicopathologic factors in both the training (P < 0.001, multivariable Cox regression) and validation (P = 0.045, multivariable Cox regression) cohorts. Radiogenomic analysis revealed that the radiomics signature was associated with the immune response, programmed cell death, cell proliferation, and vasculature development. A nomogram established using the radiomics signature and clinicopathologic risk factors demonstrated high accuracy and good calibration for prediction of PFS in both the training (C-index, 0.684) and validation (C-index, 0.823) cohorts.

Conclusions

PFS can be predicted non-invasively in patients with LGGs by a group of radiomics features that could reflect the biological processes of these tumors.

•

We developed a non-invasive model for the prediction of PFS in patients with lower-grade gliomas.

•

We further revealed the biological processes underlying the radiomic signature by using comprehensive radiogenomic analysis.

•

PFS of lower-grade gliomas could be predicted effectively based on the radiomics model.

Molecular profiles of tumor contrast enhancement: A radiogenomic analysis in anaplastic gliomas

The presence of contrast enhancement (CE) on magnetic resonance (MR) imaging is conventionally regarded as an indicator for tumor malignancy. However, the biological behaviors and molecular mechanism of enhanced tumor are not well illustrated. The aim of this study was to investigate the molecular profiles associated with anaplastic gliomas (AGs) presenting CE on postcontrast T1‐weighted MR imaging. In this retrospective database study, RNA sequencing and MR imaging data of 91 AGs from the Cancer Genome Atlas (TCGA) and 64 from the Chinese Glioma Genome Atlas (CGGA) were collected. Gene set enrichment analysis (GSEA), significant analysis of microarray, generalized linear models, and Least absolute shrinkage and selection operator algorithm were used to explore radiogenomic and prognostic signatures of AG patients. GSEA indicated that angiogenesis and epithelial‐mesenchymal transition were significantly associated with post‐CE. Genes driving immune system response, cell proliferation, and focal adhesions were also significantly enriched. Gene ontology of 237 differential genes indicated consistent results. A 48‐gene signature for CE was identified in TCGA and validated in CGGA dataset (area under the curve = 0.9787). Furthermore, seven genes derived from the CE‐specific signature could stratify AG patients into two subgroups based on overall survival time according to corresponding risk score. Comprehensive analysis of post‐CE and genomic characteristics leads to a better understanding of radiology‐pathology correlations. Our gene signature helps interpret the occurrence of radiological traits and predict clinical outcomes. Additionally, we found nine prognostic quantitative radiomic features of CE and investigated the underlying biological processes of them.

Imaging prediction of isocitrate dehydrogenase (IDH) mutation in patients with glioma: a systemic review and meta-analysis

OBJECTIVES

To evaluate the imaging features of isocitrate dehydrogenase (IDH) mutant glioma and to assess the diagnostic performance of magnetic resonance imaging (MRI) for prediction of IDH mutation in patients with glioma.

METHODS

A systematic search of Ovid-MEDLINE and EMBASE up to 10 October 2017 was conducted to find relevant studies. The search terms combined synonyms for 'glioma', 'IDH mutation' and 'MRI'. Studies evaluating the imaging features of IDH mutant glioma and the diagnostic performance of MRI for prediction of IDH mutation in patients with glioma were selected. The pooled summary estimates of sensitivity and specificity and their 95% confidence intervals (CIs) were calculated using a bivariate random-effects model. The results of multiple subgroup analyses are reported.

RESULTS

Twenty-eight original articles in a total of 2,146 patients with glioma were included. IDH mutant glioma showed frontal lobe predominance, less contrast enhancement, well-defined border, high apparent diffusion coefficient (ADC) value and low relative cerebral blood volume (rCBV) value. For the meta-analysis that included 18 original articles, the summary sensitivity was 86% (95% CI, 79%-91%) and the summary specificity was 87% (95% CI, 78-92%). In a subgroup analysis, the summary sensitivity of 2-hydroxyglutarate magnetic resonance spectroscopy (MRS) [96% (95% CI, 91-100%)] was higher than the summary sensitivities of other imaging modalities.

CONCLUSIONS

IDH mutant glioma consistently demonstrated less aggressive imaging features than IDH wild-type glioma. Despite the variety of different MRI techniques used, MRI showed the potential to non-invasively predict IDH mutation in patients with glioma. 2-Hydroxyglutarate MRS shows higher pooled sensitivity than other imaging modalities.

KEY POINTS

• IDH mutant glioma showed frontal lobe predominance, less contrast enhancement, well-defined border, high ADC value, and low rCBV value. • The diagnostic performance of MRI for prediction of IDH mutation in patients with glioma is within a clinically acceptable range, the summary sensitivity was 86% (95% CI, 79-91%) and the summary specificity was 87% (95% CI, 78-92%). • In a subgroup analysis, the summary sensitivity of 2-hydroxyglutarate MRS [96% (95% CI, 91-100%)] was higher than the summary sensitivities of other imaging modalities.

Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis

With targeted treatments playing an increasing role in oncology, the need arises for fast non-invasive genotyping in clinical practice. Radiogenomics is a rapidly evolving field of research aimed at identifying imaging biomarkers useful for non-invasive genotyping. Radiogenomic genotyping has the advantage that it can capture tumor heterogeneity, can be performed repeatedly for treatment monitoring, and can be performed in malignancies for which biopsy is not available. In this systematic review of 187 included articles, we compiled a database of radiogenomic associations and unraveled networks of imaging groups and gene pathways oncology-wide. Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma, relevant for prognosis and disease profiling. In non-small cell lung cancer, FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR-mutations and ALK-rearrangements. Oncology-wide gene pathway analysis revealed an association between contrast enhancement (imaging) and the targetable VEGF-signalling pathway. Although the need of independent validation remains a concern, radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection. Quantitative imaging enhanced the potential of multiparametric radiogenomic models. A wealth of data has been compiled for guiding future research towards robust non-invasive genomic profiling.

Radiomics Strategy for Molecular Subtype Stratification of Lower-Grade Glioma: Detecting IDH and TP53 Mutations Based on Multimodal MRI

BACKGROUND

Noninvasive detection of isocitrate dehydrogenase (IDH) and TP53 mutations are meaningful for molecular stratification of lower-grade gliomas (LrGG).

PURPOSE

To explore potential MRI features reflecting IDH and TP53 mutations of LrGG, and propose a radiomics strategy for detecting them.

STUDY TYPE

Retrospective, radiomics.

POPULATION/SUBJECTS

A total of 103 LrGG patients were separated into development (n = 73) and validation (n = 30) cohorts.

FIELD STRENGTH/SEQUENCE

T₁ -weighted (before and after contrast-enhanced), T₂ -weighted, and fluid-attenuation inversion recovery images from 1.5T (n = 37) or 3T (n = 66) scanners.

ASSESSMENT

After data preprocessing, high-throughput features were derived from patients' volumes of interests of different sequences. The support vector machine-based recursive feature elimination (SVM-RFE) was adopted to find the optimal features for IDH and TP53 mutation detection. SVM models were trained and tested on development and validation cohort. The commonly used metric was used for assessing the efficiency.

STATISTICAL TESTS

One-way analysis of variance (ANOVA), chi-square, or Fisher's exact test were applied on clinical characteristics to confirm whether significant differences exist between three molecular subtypes decided by IDH and TP53 status. Intraclass correlation coefficients were calculated to assess the robustness of the radiomics features.

RESULTS

The constituent ratio of histopathologic subtypes was significantly different among three molecular subtypes (P = 0.017). SVM models for detecting IDH and TP53 mutation were established using 12 and 22 optimal features selected by SVM-RFE. The accuracies and area under the curves for IDH and TP53 mutations on the development cohort were 84.9%, 0.830, and 92.0%, 0.949, while on the validation cohort were 80.0%, 0.792, and 85.0%, 0.869, respectively. Furthermore, the stratified accuracies of three subtypes were 72.8%, 71.9%, and 70%, respectively.

DATA CONCLUSION

Using a radiomics approach integrating SVM model and multimodal MRI features, molecular subtype stratification of LGG patients was implemented through detecting IDH and TP53 mutations. The results suggested that the proposed approach has promising detecting efficiency and T₂ -weighted image features are more important than features from other images.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:916-926.

MRI Features and IDH Mutational Status of Grade II Diffuse Gliomas: Impact on Diagnosis and Prognosis

OBJECTIVE

Grade II diffuse gliomas (DGs) with isocitrate dehydrogenase (IDH) mutations are associated with better prognosis than their IDH wild-type counterparts. We sought to determine the MRI characteristics associated with IDH mutational status and ascertain whether MRI considered in combination with IDH mutational status can better predict the clinical outcomes of grade II DGs.

MATERIALS AND METHODS

Preoperative MRI examinations were retrospectively studied for qualitative tumor characteristics, including location, extent, cortical involvement, margin sharpness, cystic component, mineralization or hemorrhage, and contrast enhancement. Quantitative diffusion and perfusion metrics were also assessed. Logistic regression and ROC analyses were used to evaluate the relationship between MRI features and IDH mutational status. The association between IDH mutational status, 1p19q codeletion, MRI features, extent of resection, and clinical outcomes was assessed by Kaplan-Meier and Cox proportional hazards models.

RESULTS

Of 100 grade II DGs, 78 were IDH mutant and 22 were IDH wild type. IDH wild-type tumors were associated with older age, multifocality, brainstem involvement, lack of cystic change, and a lower apparent diffusion coefficient (ADC). Multivariable regression showed that age older than 45 years as well as low minimum ADC (ADC_min), mean ADC, and maximum ADC values were independently associated with IDH mutational status. Of these, an ADC_min threshold of 0.9 × 10^-3 mm²/s or less provided the greatest sensitivity and specificity (91% and 76%, respectively) in defining IDH wild-type grade II DGs. Combining low ADC_min with IDH wild-type status conferred worse outcomes than did IDH wild-type status alone.

CONCLUSION

IDH wild-type grade II DGs are associated with a lower ADC and poor clinical outcomes. Combining IDH mutational status and ADC may allow more accurate prediction of clinical outcomes for patients with grade II DGs.

The extent of edema and tumor synchronous invasion into the subventricular zone and corpus callosum classify outcomes and radiotherapy strategies of glioblastomas

BACKGROUND AND PURPOSE

Irradiating glioblastoma preoperative edema (PE) remains controversial. We investigated the associations between tumors' PE extent with invasion into synchronous subventricular zone and corpus callosum (sSVZCC) and treatment outcomes to provide the clinical evidence for radiotherapy decision-making.

MATERIAL AND METHODS

Extensive PE (EPE) was defined as PE extending ≥2 cm from the tumor edge and extensive progressive disease (EPD) as tumors spreading ≥2 cm from the preoperative tumor edge along PE. The survival and progression patterns were analyzed according to EPE and sSVZCC invasion.

RESULTS

In total, 136 patients were followed for a median of 74.9 (range, 47.6-102.1) months. The median overall survival and progression-free survival were 19.7 versus 28.6 months (p = 0.005) and 11.0 versus 17.4 months (p = 0.011) in patients with EPE+ versus EPE-, and were 18.7 versus 25.4 months (p = 0.021) and 10.7 versus 14.6 months (p = 0.020) in those with sSVZCC+ versus sSVZCC-. The EPD rates for tumors with EPE-/sSVZCC-, EPE-/sSVZCC+, EPE+/sSVZCC-, and EPE+/sSVZCC+ were 2.8%, 7.1%, 37.0%, and 71.9%, respectively. In EPE+/sSVZCC+, tumor migration was associated with the PE extending along the corpus callosum (77.8%) and subventricular zone (50.0%).

CONCLUSIONS

Our results support the need for developing individualized irradiation strategies for glioblastomas according to EPE and sSVZCC.

A new diagnostic marker for differentiating multicentric gliomas from multiple intracranial diffuse large B-cell lymphomas on 18F-FDG PET images

Intracranial gliomas and lymphomas may share similar radiological manifestations, while the treatment strategies for them are different. The aim of the study was to investigate the diagnostic value of fluorine-18-fluoro-2-deoxy-D-glucose (F-FDG) positron emission computed tomography (PET) for differentiation of multicentric gliomas and intracranial multiple diffuse large B-cell lymphomas (DLBCLs) as a study of diagnostic accuracy.A total of 32 patients with multiple intracranial tumors visualized on contrast-enhanced magnetic resonance imaging (MRI) were retrospectively evaluated. Histopathological findings confirmed multicentric gliomas and multiple DLBCLs in 17 and 15 patients, respectively. All patients underwent F-FDG PET with or without C-methionine PET. Maximum standardized uptake values (SUVmax) and tumor-to-normal tissue (T/N) ratios were compared between the 2 tumors. The diagnostic value of F-FDG PET for differentiating multicentric gliomas from multiple DLBCLs was evaluated by receiver operating characteristic (ROC) analysis.The SUVmax of multiple DLBCLs was significantly higher than that of multicentric gliomas (P = .009). However, the percentage of maximum difference-value of SUVmax (or T/N ratio) of multiple DLBCLs was significant lower than that of multicentric gliomas (P < .001). The ROC curve demonstrated that the percentage of maximum difference-value of SUVmax (or T/N ratio) on F-FDG PET images could effectively differentiate multicentric gliomas from multiple DLBCLs, with a cut-off value of 44.4%, sensitivity of 64.7%, and specificity of 100% (P < .001).Percentage of maximum difference-value of SUVmax (or T/N ratio) on F-FDG PET images might be a potential indicator for distinguishing multicentric gliomas from intracranial multiple DLBCLs, which might help determine the treatment strategy.

Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma

Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. The performance of DLR for predicting the mutation status of isocitrate dehydrogenase 1 (IDH1) was validated in a dataset of 151 patients with low-grade glioma. A modified convolutional neural network (CNN) structure with 6 convolutional layers and a fully connected layer with 4096 neurons was used to segment tumors. Instead of calculating image features from segmented images, as typically performed for normal radiomics approaches, image features were obtained by normalizing the information of the last convolutional layers of the CNN. Fisher vector was used to encode the CNN features from image slices of different sizes. High-throughput features with dimensionality greater than 1.6*10⁴ were obtained from the CNN. Paired t-tests and F-scores were used to select CNN features that were able to discriminate IDH1. With the same dataset, the area under the operating characteristic curve (AUC) of the normal radiomics method was 86% for IDH1 estimation, whereas for DLR the AUC was 92%. The AUC of IDH1 estimation was further improved to 95% using DLR based on multiple-modality MR images. DLR could be a powerful way to extract deep information from medical images.

EGFR Amplification and IDH Mutations in Glioblastoma Patients of the Northeast of Morocco

Glioblastomas are the most frequent and aggressive primary brain tumors which are expressing various evolutions, aggressiveness, and prognosis. Thus, the 2007 World Health Organization classification based solely on the histological criteria is no longer sufficient. It should be complemented by molecular analysis for a true histomolecular classification. The new 2016 WHO classification of tumors of the central nervous system uses molecular parameters in addition to histology to reclassify these tumors and reduce the interobserver variability. The aim of this study is to determine the prevalence of IDH mutations and EGFR amplifications in the population of the northeast region of Morocco and then to compare the results with other studies. Methods. IDH1 codon 132 and IDH2 codon 172 were directly sequenced and the amplification of exon 20 of EGFR gene was investigated by qPCR in 65 glioblastoma tumors diagnosed at the University Hospital of Fez between 2010 and 2014. Results. The R132H IDH1 mutation was observed in 8 of 65 tumor samples (12.31%). No mutation of IDH2 was detected. EGFR amplification was identified in 17 cases (26.15%). Conclusion. A systematic search of both histological and molecular markers should be requisite for a good diagnosis and a better management of glioblastomas.

Shedding Light on the 2016 World Health Organization Classification of Tumors of the Central Nervous System in the Era of Radiomics and Radiogenomics

The new World Health Organization classification of brain tumors depends on combining the histologic light microscopy features of central nervous system (CNS) tumors with canonical genetic alterations. This integrated diagnosis is redrawing the pedigree chart of brain tumors with rearrangement of tumor groups on the basis of geno-phenotypical behaviors into meaningful groups. Multiple radiogenomic studies provide a bridge between imaging features and tumor microenvironment. An overlap that can be integrated within the genophenotypical classification of CNS tumors for a better understanding of different clinically relevant entities.

Role of Imaging in the Era of Precision Medicine

Precision medicine is an emerging approach for treating medical disorders, which takes into account individual variability in genetic and environmental factors. Preventive or therapeutic interventions can then be directed to those who will benefit most from targeted interventions, thereby maximizing benefits and minimizing costs and complications. Precision medicine is gaining increasing recognition by clinicians, healthcare systems, pharmaceutical companies, patients, and the government. Imaging plays a critical role in precision medicine including screening, early diagnosis, guiding treatment, evaluating response to therapy, and assessing likelihood of disease recurrence. The Association of University Radiologists Radiology Research Alliance Precision Imaging Task Force convened to explore the current and future role of imaging in the era of precision medicine and summarized its finding in this article. We review the increasingly important role of imaging in various oncological and non-oncological disorders. We also highlight the challenges for radiology in the era of precision medicine.

Clinical parameters outweigh diffusion- and perfusion-derived MRI parameters in predicting survival in newly diagnosed glioblastoma

BACKGROUND

The purpose of this study was to determine the relevance of clinical data, apparent diffusion coefficient (ADC), and relative cerebral blood volume (rCBV) from dynamic susceptibility contrast (DSC) perfusion and the volume transfer constant (k^trans) from dynamic contrast-enhanced (DCE) perfusion for predicting overall survival (OS) and progression-free survival (PFS) in newly diagnosed treatment-naïve glioblastoma patients.

METHODS

Preoperative MR scans including standardized contrast-enhanced T1 (cT1), T2 - fluid-attenuated inversion recovery (FLAIR), ADC, DSC, and DCE of 125 patients with subsequent histopathologically confirmed glioblastoma were performed on a 3 Tesla MRI scanner. ADC, DSC, and DCE parameters were analyzed in semiautomatically segmented tumor volumes on contrast-enhanced (CE) cT1 and hyperintense signal changes on T2 FLAIR (ED). Univariate and multivariable Cox regression analyses including age, sex, extent of resection (EOR), and KPS were performed to assess the influence of each parameter on OS and PFS.

RESULTS

Univariate Cox regression analysis demonstrated a significant association of age, KPS, and EOR with PFS and age, KPS, EOR, lower ADC, and higher rCBV with OS. Multivariable analysis showed independent significance of male sex, KPS, EOR, and increased rCBV_CE for PFS, and age, sex, KPS, and EOR for OS.

CONCLUSIONS

MRI parameters help to predict OS in a univariate Cox regression analysis, and increased rCBV_CE is associated with shorter PFS in the multivariable model. In summary, however, our findings suggest that the relevance of MRI parameters is outperformed by clinical parameters in a multivariable analysis, which limits their prognostic value for survival prediction at the time of initial diagnosis.