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  • 1
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    Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training
    Elsevier BV ; 2020
    In:  NeuroImage Vol. 220 ( 2020-10), p. 117081-
    Details
    In: NeuroImage, Elsevier BV, Vol. 220 ( 2020-10), p. 117081-
    Type of Medium: Online Resource
    ISSN: 1053-8119
    URL: Article
    DOI: 10.1016/j.neuroimage.2020.117081
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2020
    detail.hit.zdb_id: 1471418-8
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  • 2
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    A Multidimensional Neural Maturation Index Reveals Reproducible Developmental Patterns in Children and Adolescents
    Society for Neuroscience ; 2020
    In:  The Journal of Neuroscience Vol. 40, No. 6 ( 2020-02-05), p. 1265-1275
    Details
    In: The Journal of Neuroscience, Society for Neuroscience, Vol. 40, No. 6 ( 2020-02-05), p. 1265-1275
    Abstract: Adolescence is a time of extensive neural restructuring, leaving one susceptible to atypical development. Although neural maturation in humans can be measured using functional and structural MRI, the subtle patterns associated with the initial stages of abnormal change may be difficult to identify, particularly at an individual level. Brain age prediction models may have utility in assessing brain development in an individualized manner, as deviations between chronological age and predicted brain age could reflect one's divergence from typical development. Here, we built a support vector regression model to summarize high-dimensional neuroimaging as an index of brain age in both sexes. Using structural and functional MRI data from two large pediatric datasets and a third clinical dataset, we produced and validated a two-dimensional neural maturation index (NMI) that characterizes typical brain maturation patterns and identifies those who deviate from this trajectory. Examination of brain signatures associated with NMI scores revealed that elevated scores were related to significantly lower gray matter volume and significantly higher white matter volume, particularly in high-order regions such as the prefrontal cortex. Additionally, those with higher NMI scores exhibited enhanced connectivity in several functional brain networks, including the default mode network. Analysis of data from a sample of male and female patients with schizophrenia revealed an association between advanced NMI scores and schizophrenia diagnosis in participants aged 16–22, confirming the NMI's utility as a marker of atypicality. Altogether, our findings support the NMI as an individualized, interpretable measure by which neural development in adolescence may be assessed. SIGNIFICANCE STATEMENT The substantial neural restructuring that occurs during adolescence increases one's vulnerability to aberration. A brain index that is capable of capturing one's conformance with typical development will allow for individualized assessment and enhance our understanding of typical and atypical development. In this analysis, we produce a neural maturation index (NMI) using support vector regression and a large pediatric sample. This index generalizes across multiple cohorts and shows potential in the identification of clinical groups. We also implement a novel method for examining the developmental trajectory through data-driven analysis. The signatures identified by the NMI reflect key stages of the extensive neural development that occurs during adolescence and support its utility as a metric of typical brain development.
    Type of Medium: Online Resource
    ISSN: 0270-6474 , 1529-2401
    URL: Article
    DOI: 10.1523/JNEUROSCI.2092-19.2019
    Language: English
    Publisher: Society for Neuroscience
    Publication Date: 2020
    detail.hit.zdb_id: 1475274-8
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  • 3
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    Enhancing the REMBRANDT MRI collection with expert segmentation labels and quantitative radiomic features
    Springer Science and Business Media LLC ; 2022
    In:  Scientific Data Vol. 9, No. 1 ( 2022-06-14)
    Details
    In: Scientific Data, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2022-06-14)
    Abstract: Malignancy of the brain and CNS is unfortunately a common diagnosis. A large subset of these lesions tends to be high grade tumors which portend poor prognoses and low survival rates, and are estimated to be the tenth leading cause of death worldwide. The complex nature of the brain tissue environment in which these lesions arise offers a rich opportunity for translational research. Magnetic Resonance Imaging (MRI) can provide a comprehensive view of the abnormal regions in the brain, therefore, its applications in the translational brain cancer research is considered essential for the diagnosis and monitoring of disease. Recent years has seen rapid growth in the field of radiogenomics , especially in cancer, and scientists have been able to successfully integrate the quantitative data extracted from medical images (also known as radiomics) with genomics to answer new and clinically relevant questions. In this paper, we took raw MRI scans from the REMBRANDT data collection from public domain, and performed volumetric segmentation to identify subregions of the brain. Radiomic features were then extracted to represent the MRIs in a quantitative yet summarized format. This resulting dataset now enables further biomedical and integrative data analysis, and is being made public via the NeuroImaging Tools & Resources Collaboratory (NITRC) repository ( https://www.nitrc.org/projects/rembrandt_brain/ ).
    Type of Medium: Online Resource
    ISSN: 2052-4463
    URL: Article
    DOI: 10.1038/s41597-022-01415-1
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2775191-0
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  • 4
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    Author Correction: Enhancing the REMBRANDT MRI collection with expert segmentation labels and quantitative radiomic features
    Springer Science and Business Media LLC ; 2022
    In:  Scientific Data Vol. 9, No. 1 ( 2022-07-07)
    Details
    In: Scientific Data, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2022-07-07)
    Type of Medium: Online Resource
    ISSN: 2052-4463
    URL: Article
    DOI: 10.1038/s41597-022-01518-9
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
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  • 5
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    Cancer Imaging Phenomics via CaPTk: Multi-Institutional Prediction of Progression-Free Survival and Pattern of Recurrence in Glioblastoma
    American Society of Clinical Oncology (ASCO) ; 2020
    In:  JCO Clinical Cancer Informatics , No. 4 ( 2020-11), p. 234-244
    Details
    In: JCO Clinical Cancer Informatics, American Society of Clinical Oncology (ASCO), , No. 4 ( 2020-11), p. 234-244
    Abstract: To construct a multi-institutional radiomic model that supports upfront prediction of progression-free survival (PFS) and recurrence pattern (RP) in patients diagnosed with glioblastoma multiforme (GBM) at the time of initial diagnosis. PATIENTS AND METHODS We retrospectively identified data for patients with newly diagnosed GBM from two institutions (institution 1, n = 65; institution 2, n = 15) who underwent gross total resection followed by standard adjuvant chemoradiation therapy, with pathologically confirmed recurrence, sufficient follow-up magnetic resonance imaging (MRI) scans to reliably determine PFS, and available presurgical multiparametric MRI (MP-MRI). The advanced software suite Cancer Imaging Phenomics Toolkit (CaPTk) was leveraged to analyze standard clinical brain MP-MRI scans. A rich set of imaging features was extracted from the MP-MRI scans acquired before the initial resection and was integrated into two distinct imaging signatures for predicting mean shorter or longer PFS and near or distant RP. The predictive signatures for PFS and RP were evaluated on the basis of different classification schemes: single-institutional analysis, multi-institutional analysis with random partitioning of the data into discovery and replication cohorts, and multi-institutional assessment with data from institution 1 as the discovery cohort and data from institution 2 as the replication cohort. RESULTS These predictors achieved cross-validated classification performance (ie, area under the receiver operating characteristic curve) of 0.88 (single-institution analysis) and 0.82 to 0.83 (multi-institution analysis) for prediction of PFS and 0.88 (single-institution analysis) and 0.56 to 0.71 (multi-institution analysis) for prediction of RP. CONCLUSION Imaging signatures of presurgical MP-MRI scans reveal relatively high predictability of time and location of GBM recurrence, subject to the patients receiving standard first-line chemoradiation therapy. Through its graphical user interface, CaPTk offers easy accessibility to advanced computational algorithms for deriving imaging signatures predictive of clinical outcome and could similarly be used for a variety of radiomic and radiogenomic analyses.
    Type of Medium: Online Resource
    ISSN: 2473-4276
    URL: Article
    DOI: 10.1200/CCI.19.00121
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2020
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  • 6
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    NIMG-22. INTEGRATION OF A RADIOMIC SIGNATURE, CLINICAL VARIABLES AND PLASMA CELL-FREE DNA IN ADULT PATIENTS WITH NEWLY DIAGNOSED GLIOBLASTOMA PREDICTS PATIENT SURVIVAL AND IMPROVES DISEASE STRATIFICATION
    Oxford University Press (OUP) ; 2020
    In:  Neuro-Oncology Vol. 22, No. Supplement_2 ( 2020-11-09), p. ii151-ii152
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 22, No. Supplement_2 ( 2020-11-09), p. ii151-ii152
    Abstract: We have previously demonstrated the potential role of liquid biopsy, specifically plasma cell-free DNA (cfDNA), as a non-invasive biomarker for prognostication in patients with glioblastoma. In separate prior studies, we have also developed MRI-based radiomic signatures to predict survival outcomes in glioblastoma. In this study, for the first time, we evaluated the potential of combining radiomic signatures, epidemiological and clinical variables, and plasma cfDNA quantification for upfront prediction of overall survival (OS) in patients with newly diagnosed glioblastoma. METHODS Quantitative radiomic features were extracted from multiparametric MRI (T1, T1Gd, T2, T2-FLAIR) scans of a discovery cohort of 505 and an independent replication cohort of 50 IDH-wildtype glioblastoma patients. For the independent replication cohort, pre-surgical plasma cfDNA was extracted and quantified. In the first stage, a radiomic signature was created for stratification of patients into categories of short (OS ≤ 6 months) and long (OS ≥ 18 months) survivors using a cross-validated XGBoost method based on the discovery cohort, which was tested independently on the replication cohort. In the second stage, the radiomic signature and clinical variables were integrated to build a second-stage signature using a cross-validated support vector machine (SVM) classifier to stratify the patients into short and long survivor categories. In the third stage, the value of the second-stage signature integrated with cfDNA concentration was assessed through a cross-validated SVM regression method. RESULTS The combination of radiomic, clinical, and cfDNA variables resulted in the best overall predictive accuracy, with Pearson’s correlation coefficient of 0.59 (p & lt; 0.0001) between actual and predicted OS. CONCLUSION In this study, we evaluated the value of combining plasma cfDNA, radiomic, and clinical variables for predicting OS, and showed that it could act as an effective non-invasive prognostic and patient stratification tool in patients with newly diagnosed glioblastoma.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noaa215.635
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
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  • 7
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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
    Details
    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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  • 8
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    TMOD-34. A RADIOMIC SIGNATURE OF INFILTRATION IN PERITUMORAL EDEMA PREDICTS SUBSEQUENT RECURRENCE IN GLIOBLASTOMA
    Oxford University Press (OUP) ; 2019
    In:  Neuro-Oncology Vol. 21, No. Supplement_6 ( 2019-11-11), p. vi270-vi270
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 21, No. Supplement_6 ( 2019-11-11), p. vi270-vi270
    Abstract: The malignant parenchyma in glioblastoma extends beyond the enhancing borders of tumor on postcontrast T1-weighted magnetic resonance imaging (MRI), which is the primary target of treatment. Such non-enhancing tumor invasion into the peri-tumoral edema (PED) is, however, not usually distinguishable on conventional MRI. The aim of this study was to evaluate pre-operative MRI in the PED to assess whether areas of tumor infiltration and early recurrence can be detected. METHODS A cohort of 90 de novo glioblastoma patients from a single institution (Penn) was selected. All included patients had preoperative multi-parametric MRI (mpMRI;T1,T1Gd,T2,T2-FLAIR,ADC), underwent initial gross-total-resection followed by standard chemoradiation, and had pathologically-confirmed recurrence. An extensive panel of handcrafted features, including shape, volume, intensity distributions, texture, was extracted from mpMRI scans. Predictive modeling for estimation of PED infiltration was performed using sequential feature selection approach designed with a support vector machine classifier and through a Leave-one-out cross-validation approach in the Penn cohort. Generalizability of the model was evaluated by applying it on a cohort of 20 patients from a second institution (Case), and predicted probability distributions in PED were compared in both the cohorts. RESULTS Spatial probability maps, representing the likelihood of tumor infiltration and eventual recurrence, were binarized at 50% cutoff, and compared with actual recurrence on post-recurrence scans. The cross-validated accuracy of our model within Penn cohort was 81.35% (odds-ratio=3.62, sensitivity/specificity=78.26/81.35). The model trained on the Penn cohort, when applied on the Case cohort, produced almost similar intensity distribution in the PED, suggesting that the method has potential for robust performance across institutions. The comparison of intensity distributions revealed higher ADC and T2-FLAIR in non-recurrent regions compared to recurrent ones. CONCLUSION Multi-parametric pattern analysis of mpMRI across multiple institutions generates similar, accurate estimates of spatial extent and patterns of recurrence in PED, which may guide strategies for treatment intensification in glioblastoma.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noz175.1133
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
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  • 9
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    NIMG-39. RADIOMIC ANALYSIS FOR NON-INVASIVE IN VIVO PROGNOSTIC STRATIFICATION OF DE NOVO GLIOBLASTOMA PATIENTS: A MULTI-INSTITUTIONAL EVALUATION FOR GENERALIZABILITY IN THE RESPOND CONSORTIUM
    Oxford University Press (OUP) ; 2021
    In:  Neuro-Oncology Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi137-vi137
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi137-vi137
    Abstract: Multi-parametric MRI based radiomic signatures have highlighted the promise of artificial intelligence (AI) in neuro-oncology. However, inter-institution heterogeneity hinders generalization to data from unseen clinical institutions. To this end, we formulated the ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium for glioblastoma. Here, we seek non-invasive generalizable radiomic signatures from routine clinically-acquired MRI for prognostic stratification of glioblastoma patients. METHODS We identified a retrospective cohort of 606 patients with near/gross total tumor resection ( & gt;90%), from 13 geographically-diverse institutions. All pre-operative structural MRI scans (T1,T1-Gd,T2,T2-FLAIR) were aligned to a common anatomical atlas. An automatic algorithm segmented the whole tumors (WTs) into 3 sub-compartments, i.e., enhancing (ET), necrotic core (NC), and peritumoral T2-FLAIR signal abnormality (ED). The combination of ET+NC defines the tumor core (TC). Quantitative radiomic features were extracted to generate our AI model to stratify patients into short- ( & lt; 14mts) and long-survivors ( & gt;14mts). The model trained on 276 patients from a single institution was independently validated on 330 unseen patients from 12 left-out institutions, using the area-under-the-receiver-operating-characteristic-curve (AUC). RESULTS Each feature individually offered certain (limited but reproducible) value for identifying short-survivors: 1) TC closer to lateral ventricles (AUC=0.62); 2) larger ET/brain (AUC=0.61); 3) larger TC/brain (AUC=0.59); 4) larger WT/brain (AUC=0.55); 5) larger ET/WT (AUC=0.59); 6) smaller ED/WT (AUC=0.57); 7) larger ventricle deformations (AUC=0.6). Integrating all features and age, through a multivariate AI model, resulted in higher accuracy (AUC=0.7; 95% C.I.,0.64-0.77). CONCLUSION Prognostic stratification using basic radiomic features is highly reproducible across diverse institutions and patient populations. Multivariate integration yields relatively more accurate and generalizable radiomic signatures, across institutions. Our results offer promise for generalizable non-invasive in vivo signatures of survival prediction in patients with glioblastoma. Extracted features from clinically-acquired imaging, renders these signatures easier for clinical translation. Large-scale evaluation could contribute to improving patient management and treatment planning. *Indicates equal authorship.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noab196.538
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
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  • 10
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    NIMG-28. PROSPECTIVE HISTOPATHOLOGY-VALIDATED MACHINE LEARNING FOR DISTINGUISHING TRUE PROGRESSION FROM TREATMENT-RELATED CHANGES IN GLIOBLASTOMA PATIENTS
    Oxford University Press (OUP) ; 2021
    In:  Neuro-Oncology Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi134-vi135
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi134-vi135
    Abstract: Decision making about the best course of treatment for glioblastoma patients becomes challenging when a new enhancing lesion appears in the vicinity of the surgical bed on follow-up MRI (after maximal safe tumor resection and chemoradiation), raising concerns for tumor progression (TP). Literature indicates 30-50% of these new lesions describe primarily treatment-related changes (TRC). We hypothesize that quantitative analysis of specific and sensitive features extracted from multi-parametric MRI (mpMRI) via machine learning (ML) techniques may yield non-invasive imaging signatures that distinguish TP from TRC and facilitate better treatment personalization. METHODS We have generated an ML model on a retrospective cohort of 58 subjects, and prospectively evaluated on an independent cohort of 58 previously unseen patients who underwent second resection for suspicious recurrence and had availability of advanced mpMRI (T1, T1-Gd, T2, T2-FLAIR, DTI, DSC). The features selected by our retrospective model, representing principal components analysis of intensity distributions, morphological, statistical, and texture descriptors, were extracted from the mpMRI of the prospective cohort. Integration of these features revealed signatures distinguishing between TP, mixed response, and TRC. Independently, a board-certified neuropathologist evaluated the resected tissue by blindly classifying it in the above three categories, based on mitotic figures, pseudopalisading necrosis, geographic necrosis, dystrophic calcification, vascular changes, and Ki67. RESULTS Tissues classified as TRC by the neuropathologist were associated with imaging phenotypes of lower angiogenesis (DSC-derived features), lower cellularity (DTI-derived features), and higher water concentration (T2, T2-FLAIR features). Our ML model characterized TP with 78% accuracy (sensitivity:86%, specificity:70%, AUC:0.80 (95%CI, 0.68-0.92)) and TRC with 81% accuracy (sensitivity:80%, specificity:81%, AUC:0.87 (95%CI, 0.72-1.00)). CONCLUSION Our proposed ML model reveals distinct non-invasive markers of TP and TRC, directly associated with histopathological changes in prospective glioblastoma patients. Reliable stratification of TP and TRC entities may help to noninvasively determine whether the course of treatment should change.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noab196.528
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
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