Neuroimaging characteristics in subgroup of GBMs with p53 overexpression

The Relationship Between the Molecular Phenotypes of Brain Gliomas and the Imaging Features and Sensitivity of Radiotherapy and Chemotherapy

Gliomas are the most common primary malignant tumors of the brain, accounting for about 80% of all central nervous system malignancies. With the development of molecular biology, the molecular phenotypes of gliomas have been shown to be closely related to the process of diagnosis and treatment. The molecular phenotype of glioma also plays an important role in guiding treatment plans and evaluating treatment effects and prognosis. However, due to the heterogeneity of the tumors and the trauma associated with the surgical removal of tumor tissue, the application of molecular phenotyping in glioma is limited. With the development of imaging technology, functional magnetic resonance imaging (MRI) can provide structural and function information about tumors in a noninvasive and radiation-free manner. MRI is very important for the diagnosis of intracranial lesions. In recent years, with the development of the technology for tumor molecular diagnosis and imaging, the use of molecular phenotype information and imaging procedures to evaluate the treatment outcome of tumors has become a hot topic. By reviewing the related literature on glioma treatment and molecular typing that has been published in the past 20 years, and referring to the latest 2020 NCCN treatment guidelines, summarizing the imaging characteristic and sensitivity of radiotherapy and chemotherapy of different molecular phenotypes of glioma. In this article, we briefly review the imaging characteristics of different molecular phenotypes in gliomas and their relationship with radiosensitivity and chemosensitivity of gliomas.

Non-invasive prediction of p53 and Ki-67 labelling indices and O-6-methylguanine-DNA methyltransferase promoter methylation status in adult patients with isocitrate dehydrogenase wild-type glioblastomas using diffusion-weighted imaging and dynamic susceptibility contrast-enhanced perfusion-weighted imaging combined with conventional MRI

AIM

To assess whether conventional magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI) could non-invasively predict p53 and Ki-67 labelling index (LI) and O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in adult isocitrate dehydrogenase (IDH) wild-type glioblastomas.

METHODS

The conventional MRI, DWI, and DSC-PWI results of 120 adult patients with IDH wild-type glioblastomas were reviewed retrospectively and their efficacy was analysed using chi-square tests or Fisher's exact test. Relative minimum apparent diffusion coefficient (rADCmin) and relative maximum cerebral blood volume (rCBVmax) values were compared between glioblastomas with different molecular statuses using the Mann-Whitney U-test. Receiver operating characteristic (ROC) curves and logistic regression were used to evaluate predictive performance.

RESULTS

Glioblastomas with a high p53 LI were more likely to show a well-defined enhancement margin (p=0.047). Glioblastomas in the high-Ki-67-LI group demonstrated significantly lower rADCmin (p<0.001) and higher rCBVmax (p=0.001) values than those in the low-Ki-67-LI group. Tumours without MGMT promoter methylation showed lower rADCmin (p<0.001) and higher rCBVmax (p<0.001) values than those with it. The rCBVmax value exhibited a greater efficacy in predicting the MGMT promoter methylation status of adult IDH wild-type glioblastomas than the rADCmin value (p=0.001).

CONCLUSIONS

The present results suggest that conventional and DWI and DSC-PWI results are influenced by the molecular status of the glioblastoma and indicate that DWI and DSC-PWI may help to identify regions of high invasiveness within heterogeneous glioblastomas.

Pretreatment structural and arterial spin labeling MRI is predictive for p53 mutation in high-grade gliomas

OBJECTIVES

To determine the performance of pretreatment structural and arterial spin labelling (ASL) MRI in predicting p53 mutation in patients with high-grade gliomas (HGGs).

METHODS

Pre-treatment structural and ASL MRI were performed in 57 patients with histologically confirmed HGGs and information of p53 status. Whole-lesion histogram analysis of cerebral blood flow (CBF) images of the enhancing tumour and the peritumoral oedema in the HGGs were performed. Visually AcceSAble Rembrandt Images features were used as qualitative analysis. The differences of ASL histogram parameters and Visually AcceSAble Rembrandt Images features between HGGs with or without p53 mutation were analyzed with post hoc correction for multiple comparisons. LASSO regression was performed to select the optimal features that could predict p53 mutation, followed by receiver operating characteristic analysis to determine the predictive efficacy.

RESULTS

A total of 33 HGGs with p53 mutation and 24 without p53 mutation were included. HGGs with mutant p53 showed lower CBF_percentile5 and CBF_uniformity of the enhancing tumour (p < 0.05) and higher prevalence of the qualitative MRI feature of enhancing tumour crossing midline (ETCM) (p < 0.05) as compared with HGGs with wild-type p53. LASSO regression showed that the CBF_uniformity of the enhancing tumour and ETCM were predictive features for p53 mutation. CBF_uniformity showed an acceptable performance in predicting p53 mutation (area under the curve = 0.721), when combined with the feature of ETCM, its predictive efficacy was significantly improved (area under the curve = 0.814, p = 0.012).

CONCLUSION

An integrated pre-treatment structural and ASL MRI can help to predict p53 mutation in HGGs.

MRI features predict p53 status in lower-grade gliomas via a machine-learning approach

Background

P53 mutation status is a pivotal biomarker for gliomas. Here, we developed a machine-learning model to predict p53 status in lower-grade gliomas based on radiomic features extracted from conventional magnetic resonance (MR) images.

Methods

Preoperative MR images were retrospectively obtained from 272 patients with primary grade II/III gliomas. The patients were randomly allocated in a 2:1 ratio to a training (n = 180) or validation (n = 92) set. A total of 431 radiomic features were extracted from each patient. The lest absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomic signature construction. Subsequently, a machine-learning model to predict p53 status was established using the selected features and a Support Vector Machine classifier. The predictive performance of all individual features and the model was calculated using receiver operating characteristic curves in both the training and validation sets.

Results

The p53-related radiomic signature was built using the LASSO algorithm; this procedure consisted of four first-order statistics or related wavelet features (including Maximum, Median, Minimum, and Uniformity), a shape and size-based feature (Spherical Disproportion), and ten textural features or related wavelet features (including Correlation, Run Percentage, and Sum Entropy). The prediction accuracies based on the area under the curve were 89.6% in the training set and 76.3% in the validation set, which were better than individual features.

Conclusions

These results demonstrate that MR image texture features are predictive of p53 mutation status in lower-grade gliomas. Thus, our procedure can be conveniently used to facilitate presurgical molecular pathological diagnosis.

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We established a p53-related radiomic signature in lower-grade gliomas based on LASSO algorithm.

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We developed a machine-learning model using the radiomic signature and a support vector machine.

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P53 mutation status of lower-grade gliomas was predicted effectively based on our machine-learning model.

Imaging Genomics of Glioblastoma: Biology, Biomarkers, and Breakthroughs

Glioblastoma is regarded as the most aggressive and most common primary malignant brain tumor in adults. Despite advancements in chemotherapy and radiotherapy, prognosis and overall survival of glioblastoma patients remain dismal. Recently, progresses in genetic profiling have increased our understanding of the underlying heterogenous molecular nature of this aggressive tumor. Several prognostic and predictive molecular biomarkers have been identified that have been linked to patient's survival and response to treatment, respectively. Imaging genomics represents a novel entity in clinical sciences that bidirectionally links imaging features with underlying molecular profile and thus can serve as a surrogate for noninvasive genomic correlation, prediction, and identification.

Imaging genomics of Glioblastoma: state of the art bridge between genomics and neuroradiology

Glioblastoma (GBM) is the most common and most aggressive primary malignant tumor of the central nervous system. Recently, researchers concluded that the "one-size-fits-all" approach for treatment of GBM is no longer valid and research should be directed toward more personalized and patient-tailored treatment protocols. Identification of the molecular and genomic pathways underlying GBM is essential for achieving this personalized and targeted therapeutic approach. Imaging genomics represents a new era as a noninvasive surrogate for genomic and molecular profile identification. This article discusses the basics of imaging genomics of GBM, its role in treatment decision-making, and its future potential in noninvasive genomic identification.

Contrast-enhanced MR imaging in neuroimaging

MR imaging without and with gadolinium-based contrast agents (GBCAs) is an important imaging tool for defining normal anatomy and characteristics of lesions. GBCAs have been used in contrast-enhanced MR imaging in defining and characterizing lesions of the central nervous system for more than 20 years. The combination of unenhanced and GBCA-enhanced MR imaging is the clinical gold standard for the noninvasive detection and delineation of most intracranial and spinal lesions. MR imaging has a high predictive value that rules out neoplasm and most inflammatory and demyelinating processes of the central nervous system.

Biology, genetics and imaging of glial cell tumours

Despite advances in therapy, gliomas remain associated with poor prognosis. Clinical advances will be achieved through molecularly targeted biological therapies, for which knowledge of molecular genetic and gene expression characteristics in relation to histopathology and in vivo imaging are essential. Recent research supports the molecular classification of gliomas based on genetic alterations or gene expression profiles, and imaging data supports the concept that molecular subtypes of glioma may be distinguished through non-invasive anatomical, physiological and metabolic imaging techniques, suggesting differences in the baseline biology of genetic subtypes of infiltrating glioma. Furthermore, MRI signatures are now being associated with complex gene expression profiles and cellular signalling pathways through genome-wide microarray studies using samples obtained by image guidance which may be co-registered with clinical imaging. In this review we describe the pathobiology, molecular pathogenesis, stem cells and imaging characteristics of gliomas with emphasis on astrocytomas and oligodendroglial neoplasms.

The biology underlying molecular imaging in oncology: from genome to anatome and back again

Cancers are complex, evolving, multiscale ecosystems that are characterized by profound spatial and temporal heterogeneity. The interactions in cancer are non-linear in that small changes in one variable can have large changes on another. These multiple interacting phenotypes and spatial scales can best be understood with appropriate mathematical and computational models. Imaging is central to this investigation because it can non-destructively and longitudinally characterize spatial variations in the tumour phenotype and environment so that the system dynamics over time can be captured quantitatively.

Long-Term Survival of a Patient with Giant Cell Glioblastoma: Case Report and Review of the Literature

Glioblastoma multiforme (GBM) is the most common glial tumor of the central nervous system. Overall survival is less than a year in most of the cases in spite of multimodal treatment approaches. A 45-year-old female with histologically confirmed giant cell GBM was treated at our institution. Subtotal excision of the lesion situated in the right precentral area was performed during the initial stay in August 2005. The patient improved after the procedure with no hypertension and additional neurological deficit. Radiotherapy plus concomitant and adjuvant temozolomide was performed. The patient was symptom-free for 35 months after initial surgery. From July 2008 the patient developed partial motor seizures in the left side of the body and progressive hemiparesis. Local tumor progression was demonstrated on the neuroimaging studies. In December 2008, a second operative intervention was performed with subtotal excision of the tumor. Forty-five months after the initial diagnosis the patient is still alive with moderate neurological deficit. Microarray analysis of the tumor found the following numeric chromosomal aberrations: monosomy 8, 10, 13, 22, and trisomy 21, as well as amplifications in 4q34.1, 4q28.2, 6q16.3, 7q36.1, 7p21.3, and deletions in 1q42.12, 1q32.2, 1q25.2, 1p33, 2q37.2, 18q22.3, 19p13.2, Xq28, and Xq27.3. GBMs seem to be a heterogeneous group of glial tumors with different clinical course and therapeutic response. Microarray analysis is a useful method to establish a number of possible molecular predictors.