MRI Radiogenomics of Pediatric Medulloblastoma: A Multicenter                     Study

Zhang, Michael; Wong, Samuel W; Wright, Jason N.; Wagner, Matthias; Toescu, Sebastian; Han, Michelle; Tam, Lydia; Zhou, Quan; Ahmadian, Saman S.; Shpanskaya, Katie; Lummus, Seth; Lai, Hollie; Eghbal, Azam; Radmanesh, Alireza; Nemelka, Jordan; Harward, Stephen C.; Malinzak, Michael; Laughlin, Suzanne; Perreault, Sébastien; Braun, Kristina R M; Lober, Robert M.; Cho, Yoon Jae; Ertl‐Wagner, Birgit; Ho, Chang Yueh; Mankad, Kshitij; Vogel, Hannes; Cheshier, Samuel; Jacques, Thomas S.; Aquilina, Kristian; Fisher, Paul G.; Taylor, Michael D.; Poussaint, Tina Young; Vitanza, Nicholas A.; Grant, Gerald A.; Pfister, Stefan M.; Thompson, Eric M.; Jaju, Alok; Ramaswamy, Vijay; Yeom, Kristen W.

doi:10.1148/radiol.212137

Cited by 43 publications

(42 citation statements)

References 33 publications

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“…Non-invasive methods have made great advances with the identification of molecular subtypes based on DNA methylation. Similarly, as another clinical evaluation method, magnetic resonance imaging (MRI) also helps the differentiation of medulloblastoma from other pediatric brain tumors and risk stratification based on different features of T1 and T2-weighted MRI ( Duc et al, 2020 ; Minh Thong and Minh Duc, 2020 ; Zhang et al, 2022 ). In addition, cellular proliferation plays an essential role in tumor content, especially in highly malignant cancers ( Gu et al, 2021 ; McCarthy et al, 2021 ; Newman et al, 2021 ; Qiu et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Systematic analysis of MCM3 in pediatric medulloblastoma via multi-omics analysis

Cao

Zhao

Liang

et al. 2022

Front. Mol. Biosci.

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Minichromosome maintenance proteins are DNA-dependent ATPases that bind to replication origins and allow a single round of DNA replication. One member of this family, MCM3, is reportedly active in most cancers. To systematically elucidate the mechanisms affected by aberrant MCM3 expression and evaluate its clinical significance, we analyzed multi-omics data from the GEO database and validated them in cell lines and tumor samples. First, we showed the upregulation of MCM3 in medulloblastoma (MB) at bulk and single-cell RNA sequence levels and revealed the potential role of MCM3 via DNA replication. Then we found the dysregulation of MCM3 might result from abnormal methylation of MCM3. Moreover, we discovered that MCM3 might affect varied biological processes such as apoptosis, autophagy, and ferroptosis and that MCM3 was correlated with immune components such as fibroblast and neutrophils, which were associated with overall survival in different medulloblastoma subtypes. Furthermore, we found that MCM3 expression was correlated with the IC50 values of cisplatin and etoposide. The nomogram of MCM3-related genes showed the reliable and better prediction of 1- and 5-year survival compared to current histological and molecular classifications. Overall, the results of our study demonstrated that MCM3 might serve as a potential biomarker with clinical significance and better guidance than current histological and molecular classifications for clinical decision-making.

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Section: Introductionmentioning

confidence: 99%

Systematic analysis of MCM3 in pediatric medulloblastoma via multi-omics analysis

Cao

Zhao

Liang

et al. 2022

Front. Mol. Biosci.

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“…So far, the application of the proposed radiogenomics approaches in real clinical practice is still hampered by the several concerns, therefore limiting the role of the radiogenomics to research purposes by mostly reconstructing predictive models with a potential use to develop individualized treatments to ultimately affect patients’ prognosis [ 81 , 82 ]. In BC, the radiological imaging and the plethora of “omics” science would represent a significant improvement for patients if combined with genomic data, which have been already strictly correlated with the prognosis [ 83 ].…”

Section: Discussionmentioning

confidence: 99%

A Focus on the Synergy of Radiomics and RNA Sequencing in Breast Cancer

Bellini

Milan

Bordin

et al. 2023

IJMS

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Radiological imaging is currently employed as the most effective technique for screening, diagnosis, and follow up of patients with breast cancer (BC), the most common type of tumor in women worldwide. However, the introduction of the omics sciences such as metabolomics, proteomics, and molecular genomics, have optimized the therapeutic path for patients and implementing novel information parallel to the mutational asset targetable by specific clinical treatments. Parallel to the “omics” clusters, radiological imaging has been gradually employed to generate a specific omics cluster termed “radiomics”. Radiomics is a novel advanced approach to imaging, extracting quantitative, and ideally, reproducible data from radiological images using sophisticated mathematical analysis, including disease-specific patterns, that could not be detected by the human eye. Along with radiomics, radiogenomics, defined as the integration of “radiology” and “genomics”, is an emerging field exploring the relationship between specific features extracted from radiological images and genetic or molecular traits of a particular disease to construct adequate predictive models. Accordingly, radiological characteristics of the tissue are supposed to mimic a defined genotype and phenotype and to better explore the heterogeneity and the dynamic evolution of the tumor over the time. Despite such improvements, we are still far from achieving approved and standardized protocols in clinical practice. Nevertheless, what can we learn by this emerging multidisciplinary clinical approach? This minireview provides a focused overview on the significance of radiomics integrated by RNA sequencing in BC. We will also discuss advances and future challenges of such radiomics-based approach.

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“…In our study, 17 optimal radiomics features were selected, containing 13 texture features and 4 intensity features. A multicenter study of 263 patients from 12 children’s hospitals reported that texture features and first-order intensity features contributed the most to improving the predictive efficacy of the model ( Zhang et al, 2022 ). In the study by Yan et al (2020) , three texture features and eight intensity features were extracted to construct the model.…”

Section: Discussionmentioning

confidence: 99%

Preoperative prediction of sonic hedgehog and group 4 molecular subtypes of pediatric medulloblastoma based on radiomics of multiparametric MRI combined with clinical parameters

Wang

Qin

et al. 2023

Front. Neurosci.

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PurposeTo construct a machine learning model based on radiomics of multiparametric magnetic resonance imaging (MRI) combined with clinical parameters for predicting Sonic Hedgehog (SHH) and Group 4 (G4) molecular subtypes of pediatric medulloblastoma (MB).MethodsThe preoperative MRI images and clinical data of 95 patients with MB were retrospectively analyzed, including 47 cases of SHH subtype and 48 cases of G4 subtype. Radiomic features were extracted from T1-weighted imaging (T1), contrast-enhanced T1 weighted imaging (T1c), T2-weighted imaging (T2), T2 fluid-attenuated inversion recovery imaging (T2FLAIR), and apparent diffusion coefficient (ADC) maps, using variance thresholding, SelectKBest, and Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithms. The optimal features were filtered using LASSO regression, and a logistic regression (LR) algorithm was used to build a machine learning model. The receiver operator characteristic (ROC) curve was plotted to evaluate the prediction accuracy, and verified by its calibration, decision and nomogram. The Delong test was used to compare the differences between different models.ResultsA total of 17 optimal features, with non-redundancy and high correlation, were selected from 7,045 radiomics features, and used to build an LR model. The model showed a classification accuracy with an under the curve (AUC) of 0.960 (95% CI: 0.871−1.000) in the training cohort and 0.751 (95% CI: 0.587−0.915) in the testing cohort, respectively. The location of the tumor, pathological type, and hydrocephalus status of the two subtypes of patients differed significantly (p < 0.05). When combining radiomics features and clinical parameters to construct the combined prediction model, the AUC improved to 0.965 (95% CI: 0.898−1.000) in the training cohort and 0.849 (95% CI: 0.695−1.000) in the testing cohort, respectively. There was a significant difference in the prediction accuracy, as measured by AUC, between the testing cohorts of the two prediction models, which was confirmed by Delong’s test (p = 0.0144). Decision curves and nomogram further validate that the combined model can achieve net benefits in clinical work.ConclusionThe combined prediction model, constructed based on radiomics of multiparametric MRI and clinical parameters can potentially provide a non-invasive clinical approach to predict SHH and G4 molecular subtypes of MB preoperatively.

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MRI Radiogenomics of Pediatric Medulloblastoma: A Multicenter Study

Cited by 43 publications

References 33 publications

Systematic analysis of MCM3 in pediatric medulloblastoma via multi-omics analysis

Systematic analysis of MCM3 in pediatric medulloblastoma via multi-omics analysis

A Focus on the Synergy of Radiomics and RNA Sequencing in Breast Cancer

Preoperative prediction of sonic hedgehog and group 4 molecular subtypes of pediatric medulloblastoma based on radiomics of multiparametric MRI combined with clinical parameters

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