Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: a systematic review

Seow, Pohchoo; Wong, Jeannie Hsiu Ding; Ahmad‐Annuar, Azlina; Mahajan, Abhishek; Abdullah, Nor Aniza; Ramli, Norlisah

doi:10.1259/bjr.20170930

Cited by 45 publications

(37 citation statements)

References 84 publications

(127 reference statements)

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“…Several refinements must be taken into account in future studies. First, the data used to calculate the MATH values were obtained from a single location in tumor tissues deposited in the TCGA database, and an increasing number of studies confirm that the gene mutations found in different locations within various tumor regions are heterogeneous (46,47). Therefore, whether the MATH values found in a single location in a tumor are representative of the state of the whole tumor should be further investigated by multiple locus sequencing.…”

Section: Discussionmentioning

confidence: 99%

Mutant‑allele tumor heterogeneity in malignant glioma effectively predicts neoplastic recurrence

Yang

et al. 2019

Oncol Lett

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Intra-tumor heterogeneity (ITH) is one of the most important causes of therapy resistance, which eventually leads to the poor outcomes observed in patients with glioma. Mutant-allele tumor heterogeneity (MATH) values are based on whole-exon sequencing and precisely reflect genetic ITH. However, the significance of MATH values in predicting glioma recurrence remains unclear. Information of patients with glioma was obtained from The Cancer Genome Atlas database. The present study calculated the MATH value for each patient, analyzed the distributions of MATH values in different subtypes and investigated the rates of clinical recurrence in patients with different MATH values. Gene enrichment and Cox regression analyses were performed to determine which factors influenced recurrence. A nomogram table was established to predict 1-, 2-and 5-year recurrence probabilities. MATH values were increased in patients with glioma with the wild-type isocitrate dehydrogenase (NADP (+)) (IDH)1/2 (IDH-wt) gene (P=0.001) and glioblastoma (GBM; P=0.001). MATH values were negatively associated with the 2-and 5-year recurrence-free survival (RFS) rates in patients with glioma, particularly in the IDH1/2-wt and GBM cohorts (P=0.001 and P=0.017, respectively). Furthermore, glioma cases with different MATH levels had distinct patterns of gene mutation frequencies and gene expression enrichment. Finally, a nomogram table that contained MATH values could be used to accurately predict the probabilities of the 1-, 2-and 5-year RFS of patients with glioma. In conclusion, the MATH value of a patient may be an independent predictor that influences glioma recurrence. The nomogram model presented in the current study was an appropriate method to predict 1-, 2-and 5-year RFS probabilities in patients with glioma.

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Section: Discussionmentioning

confidence: 99%

Mutant‑allele tumor heterogeneity in malignant glioma effectively predicts neoplastic recurrence

Yang

et al. 2019

Oncol Lett

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“…The process that extracts various quantitative features on the basis of intensity, volume, shape, and textural variations from radiographic images and design predictive algorithms to find the association of these vast features to the survival and outcome of the patient is known as radiomics (Chaddad et al, 2019b ). Radiomics incorporates several essential disciplines, including radiology for imaging interpretation, computer vision for quantitative feature extraction, and machine learning for classifier evaluation and regression (Seow et al, 2018 ; Vaidya et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Overall Survival Prediction in Glioblastoma With Radiomic Features Using Machine Learning

Baid¹,

Rane

Talbar³

et al. 2020

Front. Comput. Neurosci.

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Glioblastoma is a WHO grade IV brain tumor, which leads to poor overall survival (OS) of patients. For precise surgical and treatment planning, OS prediction of glioblastoma (GBM) patients is highly desired by clinicians and oncologists. Radiomic research attempts at predicting disease prognosis, thus providing beneficial information for personalized treatment from a variety of imaging features extracted from multiple MR images. In this study, first-order, intensity-based volume and shape-based and textural radiomic features are extracted from fluid-attenuated inversion recovery (FLAIR) and T1ce MRI data. The region of interest is further decomposed with stationary wavelet transform with low-pass and high-pass filtering. Further, radiomic features are extracted on these decomposed images, which helped in acquiring the directional information. The efficiency of the proposed algorithm is evaluated on Brain Tumor Segmentation (BraTS) challenge training, validation, and test datasets. The proposed approach achieved 0.695, 0.571, and 0.558 on BraTS training, validation, and test datasets. The proposed approach secured the third position in BraTS 2018 challenge for the OS prediction task.

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“…Supervised machine learning of MRI texture features might be used to predict MGMT methylation status in glioblastoma patients 45,49,50. Further genes potentially found to be correlated with respective imaging phenotypes in quantitative MRI analyses include EGFR , VEGF , PDGF , TP53 , and PTEN 40,48,51. Transcriptomics, correlating transcriptome patterns with imaging features, revealed that glioblastomas exhibiting the proneural gene expression subtype most frequently occur in the frontal lobe 48.…”

Section: Introductionmentioning

confidence: 99%

<p>The surgical perspective in precision treatment of diffuse gliomas</p>

Thon¹,

Tonn²,

Kreth³

2019

OTT

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Over the last decade, advances in molecular and imaging-based biomarkers have induced a more versatile diagnostic classification and prognostic evaluation of glioma patients. This, in combination with a growing therapeutic armamentarium, enables increasingly individualized, risk-benefit-optimized treatment strategies. This path to precision medicine in glioma patients requires surgical procedures to be reassessed within multidimensional management considerations. This article attempts to integrate the surgical intervention into a dynamic network of versatile diagnostic characterization, prognostic assessment, and multimodal treatment options in the light of the latest 2016 World Health Organization (WHO) classification of diffuse brain tumors, WHO grade II, III, and IV. Special focus is set on surgical aspects such as resectability, extent of resection, and targeted surgical strategies including minimal invasive stereotactic biopsy procedures, convection enhanced delivery, and photodynamic therapy. Moreover, the influence of recent advances in radiomics/radiogenimics on the process of surgical decisionmaking will be touched.

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Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: a systematic review

Abstract: Radiogenomics can offer clinicians better insight into diagnosis, prognosis, and prediction of therapeutic responses of glioma.

Cited by 45 publications

References 84 publications

Mutant‑allele tumor heterogeneity in malignant glioma effectively predicts neoplastic recurrence

Mutant‑allele tumor heterogeneity in malignant glioma effectively predicts neoplastic recurrence

Overall Survival Prediction in Glioblastoma With Radiomic Features Using Machine Learning

<p>The surgical perspective in precision treatment of diffuse gliomas</p>

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