Construction of a prognostic immune signature for lower grade glioma that can be recognized by MRI radiomics features to predict survival in LGG patients

Li, Zi-zhuo; Liu, Pengfei; An, Tingting; Yang, Haichao; Zhang, Wei; Wang, Jia-xu

doi:10.1016/j.tranon.2021.101065

Cited by 28 publications

(20 citation statements)

References 71 publications

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“…Radiomics involves the exploitation of MRI data to extract high-dimensional quantitative imaging features, which can be used to support clinical decision-making [ 5 6 ]. Although previous radiomics studies in the field of neuro-oncology have mainly focused on gliomas [ 7 8 9 10 11 12 ], there has been an increase in the number of studies on brain metastases. Indeed, radiomics studies have demonstrated promising results in the discrimination of brain metastasis from other tumors [ 13 14 15 16 17 18 19 20 ], identification of primary tumor types in patients with brain metastases [ 21 22 23 24 ], prediction of specific genetic mutations [ 25 26 27 28 29 ], prediction of survival [ 30 31 ], differentiation between radiation necrosis and brain metastasis [ 32 33 34 35 ], and prediction of outcome after radiosurgery [ 36 37 38 39 40 41 ].…”

Section: Introductionmentioning

confidence: 99%

Quality of Radiomics Research on Brain Metastasis: A Roadmap to Promote Clinical Translation

Park

Ahn

et al. 2022

Korean J Radiol

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Objective Our study aimed to evaluate the quality of radiomics studies on brain metastases based on the radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, and the Image Biomarker Standardization Initiative (IBSI) guidelines. Materials and Methods PubMed MEDLINE, and EMBASE were searched for articles on radiomics for evaluating brain metastases, published until February 2021. Of the 572 articles, 29 relevant original research articles were included and evaluated according to the RQS, TRIPOD checklist, and IBSI guidelines. Results External validation was performed in only three studies (10.3%). The median RQS was 3.0 (range, -6 to 12), with a low basic adherence rate of 50.0%. The adherence rate was low in comparison to the “gold standard” (10.3%), stating the potential clinical utility (10.3%), performing the cut-off analysis (3.4%), reporting calibration statistics (6.9%), and providing open science and data (3.4%). None of the studies involved test-retest or phantom studies, prospective studies, or cost-effectiveness analyses. The overall rate of adherence to the TRIPOD checklist was 60.3% and low for reporting title (3.4%), blind assessment of outcome (0%), description of the handling of missing data (0%), and presentation of the full prediction model (0%). The majority of studies lacked pre-processing steps, with bias-field correction, isovoxel resampling, skull stripping, and gray-level discretization performed in only six (20.7%), nine (31.0%), four (3.8%), and four (13.8%) studies, respectively. Conclusion The overall scientific and reporting quality of radiomics studies on brain metastases published during the study period was insufficient. Radiomics studies should adhere to the RQS, TRIPOD, and IBSI guidelines to facilitate the translation of radiomics into the clinical field.

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Section: Introductionmentioning

confidence: 99%

Quality of Radiomics Research on Brain Metastasis: A Roadmap to Promote Clinical Translation

Park

Ahn

et al. 2022

Korean J Radiol

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“…Etv4 was identified in the present study as possibly one of the most important regulators of oligodendrocytes. Previous studies have found that mutations in CIC promote malignant progression of gliomas and that Etv4 is implicated in the transcriptional regulation of CIC [30,31]. Interestingly, fibroblasts were covered in all three main modules (M1, M2, and M3).…”

Section: Discussionmentioning

confidence: 89%

Single-Cell Transcriptomics-Based Study of Transcriptional Regulatory Features in the Mouse Brain Vasculature

Lin

Chen

et al. 2021

BioMed Research International

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Background. The critical role of vascular health on brain function has received much attention in recent years. At the single-cell level, studies on the developmental processes of cerebral vascular growth are still relatively few. Techniques for constructing gene regulatory networks (GRNs) based on single-cell transcriptome expression data have made significant progress in recent years. Herein, we constructed a single-cell transcriptional regulatory network of mouse cerebrovascular cells. Methods. The single-cell RNA-seq dataset of mouse brain vessels was downloaded from GEO (GSE98816). This cell clustering was annotated separately using singleR and CellMarker. We then used a modified version of the SCENIC method to construct GRNs. Next, we used a mouse version of SEEK to assess whether genes in the regulon were coexpressed. Finally, regulatory module analysis was performed to complete the cell type relationship quantification. Results. Single-cell RNA-seq data were used to analyze the heterogeneity of mouse cerebrovascular cells, whereby four cell types including endothelial cells, fibroblasts, microglia, and oligodendrocytes were defined. These subpopulations of cells and marker genes together characterize the molecular profile of mouse cerebrovascular cells. Through these signatures, key transcriptional regulators that maintain cell identity were identified. Our findings identified genes like Lmo2, which play an important role in endothelial cells. The same cell type, for instance, fibroblasts, was found to have different regulatory networks, which may influence the functional characteristics of local tissues. Conclusions. In this study, a transcriptional regulatory network based on single-cell analysis was constructed. Additionally, the study identified and profiled mouse cerebrovascular cells using single-cell transcriptome data as well as defined TFs that affect the regulatory network of the mouse brain vasculature.

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“…Radiomics derived from MRI have been proposed as a reliable tool for accurate diagnosis and risk assessment in several cancer types, e.g. in cervical [ 37 ], brain [ 38 ], and breast [ 39 ]. Similarly, a study has reported that radiomics analysis based on MRI was an effective tool for preoperative risk stratification in EC [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

MRI-based radiomics and ADC values are related to recurrence of endometrial carcinoma: a preliminary analysis

et al. 2021

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Background To identify predictive value of apparent diffusion coefficient (ADC) values and magnetic resonance imaging (MRI)-based radiomics for all recurrences in patients with endometrial carcinoma (EC). Methods One hundred and seventy-four EC patients who were treated with operation and followed up in our institution were retrospectively reviewed, and the patients were divided into training and test group. Baseline clinicopathological features and mean ADC (ADCmean), minimum ADC (ADCmin), and maximum ADC (ADCmax) were analyzed. Radiomic parameters were extracted on T2 weighted images and screened by logistic regression, and then a radiomics signature was developed to calculate the radiomic score (radscore). In training group, Kaplan–Meier analysis was performed and a Cox regression model was used to evaluate the correlation between clinicopathological features, ADC values and radscore with recurrence, and verified in the test group. Results ADCmean showed inverse correlation with recurrence, while radscore was positively associated with recurrence. In univariate analyses, FIGO stage, pathological types, myometrial invasion, ADCmean, ADCmin and radscore were associated with recurrence. In the training group, multivariate Cox analysis showed that pathological types, ADCmean and radscore were independent risk factors for recurrence, which were verified in the test group. Conclusions ADCmean value and radscore were independent predictors of recurrence of EC, which can supplement prognostic information in addition to clinicopathological information and provide basis for individualized treatment and follow-up plan.

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Construction of a prognostic immune signature for lower grade glioma that can be recognized by MRI radiomics features to predict survival in LGG patients

Abstract: Highlights The IMriskScore can predict the prognosis and immunotherapy efficacy of LGG patients. Based on deep learning models, MRI radiomics can be used to predict the IMriskScore. Mutations in CIC improve the prognosis of patients in the high IMriskScore group.

Cited by 28 publications

References 71 publications

Quality of Radiomics Research on Brain Metastasis: A Roadmap to Promote Clinical Translation

Quality of Radiomics Research on Brain Metastasis: A Roadmap to Promote Clinical Translation

Single-Cell Transcriptomics-Based Study of Transcriptional Regulatory Features in the Mouse Brain Vasculature

MRI-based radiomics and ADC values are related to recurrence of endometrial carcinoma: a preliminary analysis

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