Diffuse glioma, not otherwise specified: imaging-based risk stratification achieves histomolecular-level prognostication

Jang, Eun Bee; Kim, Ho Sung; Park, Ji Eun; Park, Seo Young; Nam, Yeo Kyung; Nam, Soo Jung; Kim, Young-Hoon; Kim, Jeong Hoon

doi:10.1007/s00330-022-08850-z

Cited by 7 publications

(5 citation statements)

References 30 publications

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“…220 patients classified as diffuse glioma, NOS. [124] important prognostic factor. nonmutated R132HIDH1 and radiographically only diagnosed gliomas.…”

Section: Major Findingmentioning

confidence: 99%

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Advancements in Image-Based Models for High-Grade Gliomas Might Be Accelerated

Frosina

2024

Cancers

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The first half of 2022 saw the publication of several major research advances in image-based models and artificial intelligence applications to optimize treatment strategies for high-grade gliomas, the deadliest brain tumors. We review them and discuss the barriers that delay their entry into clinical practice; particularly, the small sample size and the heterogeneity of the study designs and methodologies used. We will also write about the poor and late palliation that patients suffering from high-grade glioma can count on at the end of life, as well as the current legislative instruments, with particular reference to Italy. We suggest measures to accelerate the gradual progress in image-based models and end of life care for patients with high-grade glioma.

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“…220 patients classified as diffuse glioma, NOS. [124] important prognostic factor. nonmutated R132HIDH1 and radiographically only diagnosed gliomas.…”

Section: Major Findingmentioning

confidence: 99%

“…Three risk categories for NOS gliomas were identified. The predictive capacity was particularly high for long-survival prognoses (three or five years of survival) [124] (Table 2). This prognostic imaging model could also be useful to direct molecular investigations for these types of gliomas.…”

Section: Clinical Studiesmentioning

confidence: 99%

Advancements in Image-Based Models for High-Grade Gliomas Might Be Accelerated

Frosina

2024

Cancers

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“…All patients were independently analyzed in a blinded fashion by two experienced neuroradiologists (X.CQ and H.T with 10 and 5 years of brain MRI experience, respectively). Tumor location (super cial cerebral location/invasion deep cerebral location), tumor maximal diameter, margin(regular/irregular), multifocal lesion, peritumoral edema (minor, edema extending ≤1 cm from the tumor margin; and major, edema extending >1 cm from the tumor margin), and contrast enhancement pattern (no or partial/ predominant) were recorded [7,24].…”

Section: Image Analysismentioning

confidence: 99%

Expression of p53 Combined with Ki-67 in IDH-wildtype Glioblastomas: Prediction by Apparent Diffusion Coefficient Values

Zhang

Xue

Qin

et al. 2023

Preprint

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The presence of p53 mutationscombined with high Ki-67 expression can predict poor prognosis and overall survival in IDH-wildtypeglioblastoma patients. This study aims to use magnetic resonance imaging (MRI) features and apparent diffusion coefficient(ADC) values to predict the expression of p53 and Ki-67 in IDH-wildtype glioblastomas to informclinical treatment. We retrospectively collected clinical and imaging data for 103 IDH-wildtype glioblastoma patients in our hospital between June 2021 and February 2023. The datasets were divided into four groups: p53(+)/Ki-67(+), p53(+)/Ki67(-), p53(-)/Ki67(+), and p53(-)/Ki67(-) according to mutant status of p53 and expression level of Ki-67. Six MRI morphological features and four ADC values for the tumor parenchyma were analyzed. In the 103 patients, 21 had p53(-)/Ki-67(-), 12 had p53(+)/Ki-67(-), 28 had p53(-)/Ki-67(+), and 42 had p53(+)/Ki-67(+). Tumor location showed statistically significant differences among the 4 groups (P < 0.05). ADC parameters were negatively correlated with p53 and Ki-67 expression status and could distinguish the co-expression of p53 and Ki-67 (P < 0.05). In distinguishing between the p53(+)/Ki-67(+) and p53(-)/Ki-67(-) groups, when ADCmin was 0.85×10-3 mm2/s, the sensitivity, specificity and accuracy reached their maximum values of 61.90%, 92.90% and 82.54%, respectively. In conclusion, the expression of p53 and Ki-67 in IDH-wildtypeglioblastoma can be evaluated by ADC values. ADC values can be used as a non-invasive quantitative parameter to assist in the clinical diagnosis of IDH-wildtype glioblastoma molecular states.

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“…Prognostication based on MRI findings remains feasible and may be helpful even in these NOS-classified patients. 27 Pediatric high-grade diffuse gliomas have some distinct imaging characteristics. Diffuse Midline Glioma, H3-K27 altered (DMG), primarily manifests in the brainstem, thalamus, and spinal cord, often presenting with a rapid onset of symptoms (usually less than three months).…”

Section: Structural Imaging: Magnetic Resonance Imaging (Mri) and Com...mentioning

confidence: 99%

Advances in diffuse glial tumors diagnosis

Godoy,

Paes,

Ayres

et al. 2023

Arq Neuropsiquiatr

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In recent decades, there have been significant advances in the diagnosis of diffuse gliomas, driven by the integration of novel technologies. These advancements have deepened our understanding of tumor oncogenesis, enabling a more refined stratification of the biological behavior of these neoplasms. This progress culminated in the fifth edition of the WHO classification of central nervous system (CNS) tumors in 2021. This comprehensive review article aims to elucidate these advances within a multidisciplinary framework, contextualized within the backdrop of the new classification. This article will explore morphologic pathology and molecular/genetics techniques (immunohistochemistry, genetic sequencing, and methylation profiling), which are pivotal in diagnosis, besides the correlation of structural neuroimaging radiophenotypes to pathology and genetics. It briefly reviews the usefulness of tractography and functional neuroimaging in surgical planning. Additionally, the article addresses the value of other functional imaging techniques such as perfusion MRI, spectroscopy, and nuclear medicine in distinguishing tumor progression from treatment-related changes. Furthermore, it discusses the advantages of evolving diagnostic techniques in classifying these tumors, as well as their limitations in terms of availability and utilization. Moreover, the expanding domains of data processing, artificial intelligence, radiomics, and radiogenomics hold great promise and may soon exert a substantial influence on glioma diagnosis. These innovative technologies have the potential to revolutionize our approach to these tumors. Ultimately, this review underscores the fundamental importance of multidisciplinary collaboration in employing recent diagnostic advancements, thereby hoping to translate them into improved quality of life and extended survival for glioma patients.

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Diffuse glioma, not otherwise specified: imaging-based risk stratification achieves histomolecular-level prognostication

Cited by 7 publications

References 30 publications

Advancements in Image-Based Models for High-Grade Gliomas Might Be Accelerated

Advancements in Image-Based Models for High-Grade Gliomas Might Be Accelerated

Expression of p53 Combined with Ki-67 in IDH-wildtype Glioblastomas: Prediction by Apparent Diffusion Coefficient Values

Advances in diffuse glial tumors diagnosis

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