Biologic Pathways Underlying Prognostic Radiomics Phenotypes from                     Paired MRI and RNA Sequencing in Glioblastoma

Sun, Qinjun; Chen, Yinsheng; Liang, Chaofeng; Zhao, Yuanshen; Lv, Xiaofei; Zou, Yan; Yan, Kai; Zheng, Hairong; Liang, Dong; Li, Zhicheng

doi:10.1148/radiol.2021203281

Cited by 56 publications

(43 citation statements)

References 32 publications

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“…Five studies had a more unique assessment of radiomics and genomics in glioma patients [ 12 , 14 , 17 , 24 , 25 ]. One concerning GBM patients used six metagenes ( WDR72, C14orf39, TIMP1, CHIT1, ROS1, EREG ) derived from a differentially expressed gene (DEG) analysis and four machine learning algorithms on radiomic features to examine a correlation with survival [ 14 ].…”

Section: Resultsmentioning

confidence: 99%

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What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies

Mombiela

Arildskov

Bruun

et al. 2022

IJMS

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(1) Background: Radiogenomics is motivated by the concept that biomedical images contain information that reflects underlying pathophysiology. This review focused on papers that used genetics to validate their radiomics models and outcomes and assess their contribution to this emerging field. (2) Methods: All original research with the words radiomics and genomics in English and performed in humans up to 31 January 2022, were identified on Medline and Embase. The quality of the studies was assessed with Radiomic Quality Score (RQS) and the Cochrane recommendation for diagnostic accuracy study Quality Assessment 2. (3) Results: 45 studies were included in our systematic review, and more than 50% were published in the last two years. The studies had a mean RQS of 12, and the studied tumors were very diverse. Up to 83% investigated the prognosis as the main outcome, with the rest focusing on response to treatment and risk assessment. Most applied either transcriptomics (54%) and/or genetics (35%) for genetic validation. (4) Conclusions: There is enough evidence to state that new science has emerged, focusing on establishing an association between radiological features and genomic/molecular expression to explain underlying disease mechanisms and enhance prognostic, risk assessment, and treatment response radiomics models in cancer patients.

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Section: Resultsmentioning

confidence: 99%

“…ED and TV were the strongest predictors of subtypes, but the overall prediction of subgroups by features performed poorly. A similar study also used pathway analysis to classify subgroups [ 25 ] and extracted 30 key genes for genomic analysis. The genes are not specified in the article but are divided into red and blue modules.…”

Section: Resultsmentioning

confidence: 99%

What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies

Mombiela

Arildskov

Bruun

et al. 2022

IJMS

View full text Add to dashboard Cite

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“…Yeh et al( 18) correlated MRI radiomic features with genomic analyses and showed that the enhanced texture of intratumor heterogeneity is associated with the Janus kinase-signal transducer and activator of transcription signaling pathway, which plays an important role in immune regulation (19). Most radiogenomic studies have focused on MRI (20)(21)(22)(23), rather than US imaging of HER2-positive BRCA.…”

Section: Discussionmentioning

confidence: 99%

Radiogenomic Analysis of Prediction HER2 Status in Breast Cancer by Linking Ultrasound Radiomic Feature Modules with Biological Functions

et al. 2022

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“…The prognostic radiomic characteristics reflect the key biological processes related to immune regulation, tumor proliferation, treatment response, and cellular functions that contribute to patient survival outcomes. 100 Recent research further combined multiparametric MRI and ML to predict patient survival. 101 The radiomic features around tumors captured from multisequence MRI (contrast enhanced T1WI, T2WI, and Flair) were found to be the most predictive, compared to features from enhancing tumor, necrotic regions, and known clinical factors, the around tumors intensity heterogeneity and textural patterns could predict long-and short-term survival of GBM patients.…”

Section: Mri-based ML Predicts Prognosismentioning

confidence: 99%

“…Correlation of MRI and RNA sequencing data revealed the biological significance of the prognostic radiomic phenotype of GBM individuals. The prognostic radiomic characteristics reflect the key biological processes related to immune regulation, tumor proliferation, treatment response, and cellular functions that contribute to patient survival outcomes 100 …”

Section: Prognosis/survival Predictionmentioning

confidence: 99%

Treatment Response and Prognosis Evaluation in High‐Grade Glioma: An Imaging Review Based on MRI

Qin

Xue

et al. 2022

Magnetic Resonance Imaging

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In recent years, the development of advanced magnetic resonance imaging (MRI) technology and machine learning (ML) have created new tools for evaluating treatment response and prognosis of patients with high-grade gliomas (HGG); however, patient prognosis has not improved significantly. This is mainly due to the heterogeneity between and within HGG tumors, resulting in standard treatment methods not benefitting all patients. Moreover, the survival of patients with HGG is not only related to tumor cells, but also to noncancer cells in the tumor microenvironment (TME). Therefore, during preoperative diagnosis and follow-up treatment of patients with HGG, noninvasive imaging markers are needed to characterize intratumoral heterogeneity, and then to evaluate treatment response and predict prognosis, timeously adjust treatment strategies, and achieve individualized diagnosis and treatment. In this review, we summarize the research progress of conventional MRI, advanced MRI technology, and ML in evaluation of treatment response and prognosis of patients with HGG. We further discuss the significance of the TME in the prognosis of HGG patients, associate imaging features with the TME, indirectly reflecting the heterogeneity within the tumor, and shifting treatment strategies from tumor cells alone to systemic therapy of the TME, which may be a major development direction in the future.

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Biologic Pathways Underlying Prognostic Radiomics Phenotypes from Paired MRI and RNA Sequencing in Glioblastoma

Cited by 56 publications

References 32 publications

What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies

What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies

Radiogenomic Analysis of Prediction HER2 Status in Breast Cancer by Linking Ultrasound Radiomic Feature Modules with Biological Functions

Treatment Response and Prognosis Evaluation in High‐Grade Glioma: An Imaging Review Based on MRI

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