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  • 1
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    NIMG-22. AN AI-BASED COORDINATE SYSTEM ELUCIDATES RADIOGENOMIC HETEROGENEITY OF GLIOBLASTOMA VIA DEEP LEARNING AND MANIFOLD EMBEDDINGS
    Oxford University Press (OUP) ; 2022
    In:  Neuro-Oncology Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii166-vii166
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    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii166-vii166
    Abstract: There is evidence that molecular heterogeneity of glioblastoma is associated with heterogeneity of MR imaging signatures. Modern machine learning models, such as deep neural networks, provide a tool for capturing such complex relationships in high-dimensional datasets. This study leverages recent advances in visualizing neural networks to construct a radiogenomics coordinate system whose axes reflect the expression of imaging signatures of genetic mutations commonly found in glioblastoma. METHODS Multi-parametric MRI (mpMRI) scans (T1, T1-Gd, T2, T2-FLAIR, DSC, DTI) of 254 subjects with glioblastoma were retrospectively collected. Radiomics features, including histograms, morphologic and textural descriptors, were derived. Genetic markers were obtained through next generation sequencing (NGS) panel. A multi-label classification deep neural network was trained for predicting mutation status in key driver genes, EGFR, PTEN, NF1, TP53 and RB1. We utilized a nonlinear manifold learning method called Intensive Principal Component Analysis (InPCA), to visualize the output probability distributions from the trained model. The first three principal components (PCs) were selected for constructing the coordinate system. RESULTS The axes derived from InPCA analysis were associated with molecular pathways known to be implicated in glioblastoma: (1) Increasing values of PC1 were associated with primary involvement of P53 then RB1 then MAPK then RTK/PI3K; (2) Increasing values of PC2 were associated with primary involvement of RTK then RB1/P53/MAPK then PI3K; (3) Increasing values of PC3 were associated with primary involvement of MAPK then RB1/P53/RTK/PI3K. Imaging features significantly associated with each of three PCs (p & lt; 0.05) were identified by Pearson correlation analysis. CONCLUSION Deep learning followed by nonlinear manifold embedding identifies a radiogenomics coordinate system spanned by three components which were associated with different molecular pathways of glioblastoma. The heterogeneity of radiogenomic signatures captured by this coordinate system offers in vivo biomarkers of the molecular heterogeneity of glioblastoma.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noac209.640
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
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  • 2
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    NIMG-40. RADIOGENOMIC SIGNATURES OF DRIVER GENES IN NEWLY DIAGNOSED GLIOBLASTOMA PATIENTS BASED ON PRE-OPERATIVE MULTI-PARAMETRIC MRI
    Oxford University Press (OUP) ; 2020
    In:  Neuro-Oncology Vol. 22, No. Supplement_2 ( 2020-11-09), p. ii156-ii157
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    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 22, No. Supplement_2 ( 2020-11-09), p. ii156-ii157
    Abstract: Non-invasive and quantitative biomarkers of somatic mutations derived from multi-parametric MRI (MP-MRI) could potentially help in predicting the response of patients to therapy, leading to development of targeted and personalized treatments. In this study, we developed radiogenomic signatures of multiple driver genes using artificial intelligence (AI) methods. METHODS In this study, 2740 radiomic features, including shape and volumetric measures computed for different tumorous regions, and characteristics derived from histograms and gray-level co-occurrence matrix (GLCM), were extracted from pre-operative MP-MRI (T1, T1Gd, T2, T2-FLAIR, DTI, and DSC-MRI) scans of 161 patients with newly diagnosed glioblastoma. The tumor samples, collected surgically from these patients, were sequenced using an in-house targeted next generation sequencing (NGS) panel of genes. We constructed quantitative imaging signatures of somatic mutations in several genes from 161 IDH-wildtype glioblastoma patients, including ATRX, FGFR2, EGFR, MET, NF1, PDGFRA, PIK3CA, PTEN, RB1, TP53, using cross-validated SVM classifiers. RESULTS The cross-validated classification performance for each signature was assessed by area under the receiver operating characteristic (ROC) curve (AUC), indicating the following results: PTEN (n = 69, AUC = 0.64), EGFR (n = 52, AUC = 0.72), TP53 (n = 51, AUC = 0.67), NF1 (n = 33, AUC = 0.74), ATRX (n = 22; AUC = 0.74), FGFR2 (n = 6, AUC = 0.82), MET (n = 26, AUC = 0.77), PDGFRA (n = 14, AUC = 0.82), PIK3CA (n = 14, AUC = 0.78), RB1 (n = 14, AUC = 0.81). CONCLUSION Using multi-parametric MRI, we developed quantitative non-invasive in vivo signatures with the potential for pre-operative assessment of a glioblastoma’s molecular characteristics. These non-invasive radiogenomic biomarkers may be useful for understanding the molecular composition of a glioblastoma prior to surgical resection, thus enabling earlier selection of patients for targeted therapy trials and possible neoadjuvant treatment.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noaa215.653
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
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  • 3
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    NIMG-37. JOINT LEARNING OF IMAGING AND GENOMIC DATA REVEALS DISTINCT GLIOBLASTOMA SUBTYPES
    Oxford University Press (OUP) ; 2022
    In:  Neuro-Oncology Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii171-vii171
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    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii171-vii171
    Abstract: The significant heterogeneity of glioblastoma is typically displayed on both phenotypical and molecular levels. Non-invasive in vivo approaches to characterize this heterogeneity would potentially facilitate personalized therapies. Here we leverage advanced unsupervised machine learning to integrate radiomic imaging features and genomics to identify distinct subtypes of glioblastoma. METHODS A retrospective cohort of 571 IDH-wildtype glioblastoma patients were collected with pre-operative multi-parametric MRI (T1, T1CE, T2, T2-FLAIR, DSC, DTI) scans (available in 462/571 patients) and targeted next-generation sequencing (NGS) data (available in 355/571 patients). Radiomic features (n= 971) were extracted from these MRI scans and a subset of 12 features were selected by L21-norm minimization. A total of 14 key driver genes in the 5 main pathways that are most frequently altered in glioblastoma were chosen. Subtypes were identified by a joint learning approach called Anchor-based Partial Multi-modal Clustering (APMC) on both radiomic and genomic modalities. RESULTS Three distinct glioblastoma subtypes were discovered by APMC based on 14-dimension NGS data together with 12 selected radiomic features representing characteristics from histograms, shape, and volumetric measures for different tumor sub-regions. The identified subtypes were 1) high-risk; 2) medium-risk; and 3) low-risk, in terms of their overall survival outcome in Kaplan-Meier analysis (p= 5.52e-6, log-rank test; HR= 1.51, 95%CI:1.20-1.74, Cox proportional hazard model). The three subtypes also displayed different molecular characteristics: subtype 1 exhibited increased frequency of mutation in [EGFR, PIK3CA, PTEN, NF1], subtype 3 showed frequently mutated [PDGFRA, ATRX] , while subtype 2 did not show significant differences for mutations in any of these genes. CONCLUSION Our results revealed the synergistic value of integrated radiomic signatures and molecular characteristics for glioblastoma subtyping. Joint learning on both modalities could help better understand the molecular basis of phenotypical signatures of glioblastoma and further provide insights into the biologic underpinnings of tumor formation and progression.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noac209.655
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
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  • 4
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    NIMG-82. RADIOGENOMIC SIGNATURES OF KEY DRIVER GENES IN GBM REVEAL MOLECULAR HETEROGENEITY OF THE TUMOR MICROENVIRONMENT LINKED TO SPATIAL DISTRIBUTION: IMPACT ON THE TRAJECTORY OF GLIOMA EVOLUTION
    Oxford University Press (OUP) ; 2022
    In:  Neuro-Oncology Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii184-vii184
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii184-vii184
    Abstract: Somatic genomic alterations acquired during GBM growth enhance adaptation of tumor cells to their microenvironment and give rise to molecular heterogeneity. Radiogenomics could facilitate exploration of the underlying pathobiology of tumor growth in specific microenvironments and thereby, promote precision medicine for the patients. We derived radiogenomic signatures of key driver genes and evaluated molecular compositions of tumor groups with predisposition to specific brain regions. Pre-operative multiparametric conventional MRI scans of 357 IDH-wildtype GBM patients with available targeted NGS data were jointly segmented and registered into a common template. We constructed spatial distribution atlases for tumors harboring mutations in driver genes and identified four distinct groups of tumor locations with predilection to the left frontal cingulate region (Group1), right temporal (Group2), right parietal (Group3), and occipital pole (Group4). Evaluation of the differences in molecular features of the tumor groups included: (1) exploring similarities of genomic profiles across all four groups by evaluating cosine similarity metric (CSM) between mutational signatures; (2) quantification of molecular heterogeneity based on Mutant Allele Tumor Heterogeneity (MATH) scores; and (3) inference of the evolutionary trajectories. Groups 1 and 4 were the most different, and Groups 2 and 3 were the most similar tumors, molecularly. The mutational signatures between Groups 1 and 4 revealed a CSM of 0.35. Group1 showed significantly lower MATH score (less heterogeneity) compared to Group4 (p & lt; 0.05). Evaluation of evolutionary patterns suggested NF1 mutation as an early event in Group1, without subsequent gain of function or mutation in EGFR. In contrast, in Group4, EGFR mutations were early events triggering PTEN mutations later in the evolutionary trajectory. Radiogenomic signatures revealed distinct molecular underpinnings for the tumors with predilection towards specific brain regions that may suggest existence of different tumor microenvironments in different brain regions that cause intra- and inter-patient heterogeneity in the molecular tumor composition.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noac209.700
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
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  • 5
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    EPCO-25. MULTI-OMICS DISEASE STRATIFICATION IN PATIENTS WITH IDH-WILDTYPE GLIOBLASTOMA: SYNERGISTIC VALUE OF CLINICAL MEASURES, CONVENTIONAL AND DEEP RADIOMICS, AND GENOMICS FOR PREDICTION OF OVERALL SURVIVAL
    Oxford University Press (OUP) ; 2021
    In:  Neuro-Oncology Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi7-vi7
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi7-vi7
    Abstract: Multi-omics data integration captures tumor characteristics at multiple scales [i.e., microscopic (genomics and epigenetics), macroscopic (radiomics), clinical manifestation], provides a more comprehensive assessment of patient’s risk, and facilitates personalized therapies. In this work, we investigated the synergistic value of such multiple data sources for risk stratification and prediction of overall survival in IDH-wildtype glioblastoma tumors. METHODS Quantitative conventional and deep radiomics were extracted from pre-operative multi-parametric structural MRI (T1, T1Gd, T2, T2-FLAIR) of 501 patients with newly diagnosed glioblastoma. 389/501 and 112/501 patients formed our discovery and replication cohorts, respectively. Conventional radiomics were extracted from CaPTk, and deep radiomics from a pre-trained VGG-19 model. Multivariate SVM classification was performed on the discovery cohort to stratify patients in high, medium, and low-risk groups, using recursive feature elimination and 5-fold cross-validation. This model was independently tested on the replication cohort, and a radiomic-based survival prediction index (SPIradiomics) was calculated for each patient. Multi-stage integration of omics data, i.e., clinical (age, gender, extent of resection (EOR)), SPIradiomics, epigenetics (MGMT promoter methylation), and genomics (27 clinically relevant gene mutations via next-generation sequencing (NGS)), was performed using multivariate Cox proportional hazards (Cox-PH) model for stratification of the risk in the replication cohort. RESULTS Cox-PH modeling resulted in a concordance index (c-index) of 0.65 (95% CI:0.6–0.7) for clinical data, 0.67 (95% CI:0.62–0.72) for clinical and epigenetics, 0.70 (95% CI:0.65–0.75) for clinical and radiomics, 0.72 (95% CI:0.68–0.77) for clinical, epigenetics, and radiomics, and 0.75 (95% CI:0.71 – 0.78) for the multi-omics combination of all data; highlighting the added value of each layer of information in prediction of the patient’s risk. CONCLUSION Our results reinforce the synergistic value of integrated diagnostic methods for improving risk assessment of patients with glioblastoma that may pave the path towards a more personalized treatment planning.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noab196.024
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
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  • 6
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    NIMG-58. CANONICAL CORRELATION ANALYSIS IN GLIOBLASTOMA REVEALS ASSOCIATIONS BETWEEN EXPRESSION OF RADIOMIC SIGNATURES AND GENOMICS
    Oxford University Press (OUP) ; 2021
    In:  Neuro-Oncology Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi142-vi142
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi142-vi142
    Abstract: Understanding the molecular underpinnings of imaging signatures of glioblastoma can provide insights into the biologic basis of tumor formation and progression as well as in vivo surrogate markers of molecular events driving the tumor’s phenotype. Through machine learning (ML), this study demonstrates that distinct imaging subtypes of glioblastoma are related to specific molecular alterations. METHODS Pre-operative multi-parametric MRI (T1, T2, T1CE, T2-FLAIR, DSC-MRI, DTI-MRI) of 669 IDH-wildtype subjects with glioblastoma were retrospectively collected and radiomic features, including descriptors of morphology, intensity, histogram, and texture, were extracted. Imaging subtypes were identified by a feature selection and clustering approach. Genomic markers, obtained using next generation sequencing (NGS) panel of 27 key glioblastoma genes, were available in 358/669 patients. Canonical correlation analysis (CCA) was conducted within each imaging subtype between the selected imaging features and genetic variables to seek maximum correlations between combinations of variables in imaging and genomic sets, and hence elucidate the molecular drivers of respective subtypes. RESULTS Three distinct imaging subtypes were identified by clustering on 50 selected features, representing characteristics of morphology, tumor neo-angiogenesis (DSC-derived features), and cellular density (DTI-derived features). These subtypes yielded differentiable overall survival based on Kaplan-Meier analysis. The canonical coefficients of each subtype revealed the distinction of the underlying genomic characteristics: one exhibited frequently mutated [ARID2, NTRK1], another subtype showed increased frequency of mutation in [ATRX, EGFR, PIK3R1] , while the third was associated with all these genes and [NF1, PIK3CA, RB1], additionally. CONCLUSION We derived three distinct radiomic MRI subtypes for glioblastoma that highly correlate with the patients' survival and molecular genetic characteristics. Investigating the relationship between imaging and genomic information may enable identification of molecularly- and phenotypically-consistent tumor subtypes, which would offer non-invasive approaches for characterizing heterogeneity of glioblastoma, further facilitating patient stratification and treatment planning.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noab196.556
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
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  • 7
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    NIMG-52. RADIOGENOMICS SIGNATURES IN KEY DRIVER GENES IN GLIOBLASTOMA EVALUATED WITH AND WITHOUT THE PRESENCE OF CO-OCCURRING MUTATIONS
    Oxford University Press (OUP) ; 2021
    In:  Neuro-Oncology Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi141-vi141
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi141-vi141
    Abstract: Glioblastomas display significant heterogeneity on the molecular level, typically harboring several co-occurring mutations, which likely contributes to failure of molecularly targeted therapeutic approaches. Radiogenomics has emerged as a promising tool for in vivo characterization of this heterogeneity. We derive radiogenomic signatures of four mutations via machine learning (ML) analysis of multiparametric MRI (mpMRI) and evaluate them in the presence and absence of other co-occurring mutations. METHODS We identified a retrospective cohort of 359 IDH-wildtype glioblastoma patients, with available pre-operative mpMRI (T1, T1Gd, T2, T2-FLAIR) scans and targeted next generation sequencing (NGS) data. Radiomic features, including morphologic, histogram, texture, and Gabor wavelet descriptors, were extracted from the mpMRI. Multivariate predictive models were trained using cross-validated SVM with LASSO feature selection to predict mutation status in key driver genes, EGFR, PTEN, TP53, and NF1. ML models and spatial population atlases of genetic mutations were generated for stratification of the tumors (1) with co-occurring mutations versus wildtypes, (2) with exclusive mutations in each driver gene versus the tumors without any mutations in the pathways associated with these genes. RESULTS ML models yielded AUCs of 0.75 (95%CI:0.62-0.88) / 0.87 (95%CI:0.70-1) for co-occurring / exclusive EGFR mutations, 0.69 (95%CI:0.58-0.80) / 0.80 (95%CI:0.61-0.99) for co-occurring / exclusive PTEN mutations, and 0.77 (95%CI:0.65-0.88) / 0.86 (95%CI:0.69-1) for co-occurring / exclusive TP53 cases. Spatial atlases revealed a predisposition of left temporal lobe for NF1 and right frontotemporal region for TP53 in mutually exclusive tumors, which was not observed in the co-occurring mutation atlases. CONCLUSION Our results suggest the presence of distinct radiogenomic signatures of several glioblastoma mutations, which become even more pronounced when respective mutations do not co-occur with other mutations. These in vivo signatures can contribute to pre-operative stratification of patients for molecular targeted therapies, and potentially longitudinal monitoring of mutational changes during treatment.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noab196.550
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
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  • 8
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    NIMG-73. CAPTURING GLIOBLASTOMA HETEROGENEITY USING IMAGING AND DEEP LEARNING: APPLICATION TO MGMT PROMOTER METHYLATION
    Oxford University Press (OUP) ; 2021
    In:  Neuro-Oncology Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi146-vi146
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi146-vi146
    Abstract: Intratumor heterogeneity is frequent in glioblastoma (GB), giving rise to the tumor’s resistance to standard therapies and, ultimately, poorer clinical outcomes. Yet heterogeneity is often not quantified when assessing the genomic or methylomic profile of a tumor, when a single tissue sample is analyzed. This study proposes a novel approach to non-invasively characterize heterogeneity across glioblastoma using deep learning analysis MRI scans, using MGMT promoter methylation (MGMTpm) as a test-case, and validates the imaging-derived heterogeneity maps with MGMTpm heterogeneity measured via multiple tissue samples. METHODS Multi-parametric MRI (mpMRI) scans (T1, T1-Gd, T2, T2-FLAIR) of 181 patients with newly diagnosed glioblastoma, who underwent surgical tumor resection and had MGMT methylation assessment results, were retrospectively collected. We trained a 5-fold cross-validated deep convolutional neural network with six convolutional layers for a discovery cohort of 137 patients by placing overlapping regional patches over the whole tumor on mpMRI scans to capture spatial heterogeneity of MGMTpm status in different regions within the tumor. Our approach effectively hypothesized that despite heterogeneity in the training examples, dominant imaging patterns would be captured by deep learning. Trained model was independently applied to an unseen replication cohort of 44 patients, with multiple tissue specimens chosen from different spatial regions within the tumor, allowing us to compare imaging- and tissue-based MGMTpm estimates. RESULTS Our model yielded AUC of 0.75 (95% CI: 0.65–0.79) for global MGMT status prediction, which reflected the heterogeneity in MGMTpm, but also that a dominant imaging pattern of MGMT methylation seemed to emerge. In methylated patients with multiple tissue samples, a significant Pearson's correlation coefficient of 0.64 (p & lt; 0.05) was found between imaging-based heterogeneity maps and MGMTpm heterogeneity. CONCLUSION A novel method based on mpMRI and deep neural networks yielded imaging-based heterogeneity maps that strongly associated with intratumor molecular heterogeneity in MGMT promoter methylated tumors.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noab196.570
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
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  • 9
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    Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma
    Springer Science and Business Media LLC ; 2022
    In:  Scientific Reports Vol. 12, No. 1 ( 2022-05-24)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2022-05-24)
    Abstract: Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in isocitrate dehydrogenase (IDH)-wildtype GBM patients, by combining conventional and deep learning methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. Support vector machine (SVM) classifiers were trained on the radiomic features in the discovery cohort (n = 404) to categorize patient groups of high-risk (OS  〈  6 months) vs all, and low-risk (OS ≥ 18 months) vs all. The trained radiomic model was independently tested in the replication cohort (n = 112) and a patient-wise survival prediction index was produced. Multivariate Cox-PH models were generated for the replication cohort, first based on clinical measures solely, and then by layering on radiomics and molecular information. Evaluation of the high-risk and low-risk classifiers in the discovery/replication cohorts revealed area under the ROC curves (AUCs) of 0.78 (95% CI 0.70–0.85)/0.75 (95% CI 0.64–0.79) and 0.75 (95% CI 0.65–0.84)/0.63 (95% CI 0.52–0.71), respectively. Cox-PH modeling showed a concordance index of 0.65 (95% CI 0.6–0.7) for clinical data improving to 0.75 (95% CI 0.72–0.79) for the combination of all omics. This study signifies the value of integrated diagnostics for improved prediction of OS in GBM.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-022-12699-z
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
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