Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures

Liu, Dongming; Chen, Jiu; Hu, Xinhua; Yang, Kun; Liu, Yong; Hu, Guanjie; Ge, Honglin; Zhang, Wenbin; Liu, Hongyi

doi:10.3389/fonc.2021.699265

Cited by 16 publications

(7 citation statements)

References 97 publications

(183 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accurately classifying the different types of gliomas by combining specific genetic and molecular features is critical to mitigating the difficulties of treating such a heterogeneous group of malignancies [16,17] and maximizing the chances of success [18].…”

Section: Molecular Signature Of Gliomasmentioning

confidence: 99%

Advancements in Glioma Care: Focus on Emerging Neurosurgical Techniques

De Simone,

Conti,

Palermo

et al. 2023

Biomedicines

View full text Add to dashboard Cite

Background: Despite significant advances in understanding the molecular pathways of glioma, translating this knowledge into effective long-term solutions remains a challenge. Indeed, gliomas pose a significant challenge to neurosurgical oncology because of their diverse histopathological features, genetic heterogeneity, and clinical manifestations. Relevant sections: This study focuses on glioma complexity by reviewing recent advances in their management, also considering new classification systems and emerging neurosurgical techniques. To bridge the gap between new neurosurgical approaches and standards of care, the importance of molecular diagnosis and the use of techniques such as laser interstitial thermal therapy (LITT) and focused ultrasound (FUS) are emphasized, exploring how the integration of molecular knowledge with emerging neurosurgical approaches can personalize and improve the treatment of gliomas. Conclusions: The choice between LITT and FUS should be tailored to each case, considering factors such as tumor characteristics and patient health. LITT is favored for larger, complex tumors, while FUS is standard for smaller, deep-seated ones. Both techniques are equally effective for small and superficial tumors. Our study provides clear guidance for treating pediatric low-grade gliomas and highlights the crucial roles of LITT and FUS in managing high-grade gliomas in adults. This research sets the stage for improved patient care and future developments in the field of neurosurgery.

show abstract

Section: Molecular Signature Of Gliomasmentioning

confidence: 99%

Advancements in Glioma Care: Focus on Emerging Neurosurgical Techniques

De Simone,

Conti,

Palermo

et al. 2023

Biomedicines

View full text Add to dashboard Cite

show abstract

“…Texture analysis: Tumour heterogeneity as a prognostic factor, can help in grading tumours 25,26 . A limitation of histogram analysis is the inability to retain the spatial arrangement of the voxels.…”

Section: Histogram Analysismentioning

confidence: 99%

Mapping Spatiotemporal Heterogeneity in Tumour Progression by Integrating High-Throughput Imaging and Omics Analysis

Patkulkar¹,

Subbalakshmi²,

Jolly³

et al. 2022

Preprint

View full text Add to dashboard Cite

Intratumoral heterogeneity associates with more aggressive disease progression and worse patient outcomes. Understanding the reasons enabling the emergence of such heterogeneity remains incomplete, which restricts our ability to manage it from a therapeutic perspective. Technological advancements such as high-throughput molecular imaging, single-cell omics and spatial transcriptomics now allow recording the patterns of spatiotemporal heterogeneity in a longitudinal manner, thus offering insights into the multi-scale dynamics of its evolution. Here, we review latest technological trends and biological insights from molecular diagnostics as well as spatial transcriptomics, both of which have witnessed a burgeoning growth in recent past in terms of mapping heterogeneity within tumor cell types as well as stromal constitution. We also discuss ongoing challenges, indicating possible ways to integrate insights across these methods to have a systems-level spatiotemporal map of heterogeneity in each tumor, and a more systematic investigation of implications of heterogeneity for the patient outcomes.

show abstract

“…In the current research, investigators employ multimodal MRI images to depict glioblastoma’s intrinsic heterogeneity and phenotype (Cui et al, n.d.; Ye et al, 2021). Regions exhibiting hypo/hyper-intensity in various MRI modalities are crucial in providing complementary profiles of glioblastoma subregions (Ellingson, 2015; Liu et al, 2021). Extracting radiomic features from the tumor portion is imperative to obtain clinically relevant information for early diagnosis and treatment planning.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Benchmarking of CNN-Based Tumor Segmentation Methods Using Multimodal MRI Data

Kundal,

Rao,

Majumdar

et al. 2024

Preprint

View full text Add to dashboard Cite

Magnetic resonance imaging (MRI) is become an essential and a frontline technique in the detection of brain tumor. However, manual segmentation of tumor from MRI scans is a time-consuming and labour-intensive process. There is a prevalent trend in employing fully automated methods for accurate tumor segmentation using MRI scans. The precision in brain tumor segmentation is essential for the better diagnosis, treatment and prognosis. This study focuses on benchmarking and evaluating the performance of four widely used convolutional neural network (CNN) based brain tumor segmentation methods CaPTk, 2DVNet, EnsembleUNets, and ResNet50. We used 1251 multimodal MRI scans from the BraTS2021 dataset, which encompasses T1, T2, T1ce, and Flair imaging modalities. We compared the performance of these CNN-based methods against a reference set of previously segmented images obtained with the assistance of radiologists. The evaluation encompasses direct utilization of segmented outputs and also by employing radiomic features. Performance evaluation with direct segments method using Dice Similarity Coefficient score (DSC) and Hausdorff Distance (HD) suggested better performance of EnsembleUNets with DSC and HD of 0.93 and 18 respectively, outperforming the other methods. Comparative analysis using radiomics features also revealed that EnsembleUNets is the most precise segmentation method as compared to CaPTk, 2DVNet, and ResNet50. EnsembleUNets achieved Concordance Correlation Coefficient (CCC), Total Deviation Index (TDI), and Root Mean Square Error (RMSE) of 0.79, 1.14 and 0.53 respectively and outperformed its counterparts. These findings contribute valuable insight into the comparative efficacy of EnsembleUNets, facilitating informed decision for accurate brain tumor segmentation.

show abstract

Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures

Cited by 16 publications

References 97 publications

Advancements in Glioma Care: Focus on Emerging Neurosurgical Techniques

Advancements in Glioma Care: Focus on Emerging Neurosurgical Techniques

Mapping Spatiotemporal Heterogeneity in Tumour Progression by Integrating High-Throughput Imaging and Omics Analysis

Comprehensive Benchmarking of CNN-Based Tumor Segmentation Methods Using Multimodal MRI Data

Contact Info

Product

Resources

About