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Panigrahi S, Mohammed S, Rao A, Baladandayuthapani V. Integrative Bayesian models using Post-selective inference: A case study in radiogenomics. Biometrics 2023; 79:1801-1813. [PMID: 35973786 PMCID: PMC9931934 DOI: 10.1111/biom.13740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Accepted: 08/02/2022] [Indexed: 11/27/2022]
Abstract
Integrative analyses based on statistically relevant associations between genomics and a wealth of intermediary phenotypes (such as imaging) provide vital insights into their clinical relevance in terms of the disease mechanisms. Estimates for uncertainty in the resulting integrative models are however unreliable unless inference accounts for the selection of these associations with accuracy. In this paper, we develop selection-aware Bayesian methods, which (1) counteract the impact of model selection bias through a "selection-aware posterior" in a flexible class of integrative Bayesian models post a selection of promising variables via ℓ1 -regularized algorithms; (2) strike an inevitable trade-off between the quality of model selection and inferential power when the same data set is used for both selection and uncertainty estimation. Central to our methodological development, a carefully constructed conditional likelihood function deployed with a reparameterization mapping provides tractable updates when gradient-based Markov chain Monte Carlo (MCMC) sampling is used for estimating uncertainties from the selection-aware posterior. Applying our methods to a radiogenomic analysis, we successfully recover several important gene pathways and estimate uncertainties for their associations with patient survival times.
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Affiliation(s)
| | - Shariq Mohammed
- Department of Biostatistics, University of Michigan
- Department of Computational Medicine and Bioinformatics, University of Michigan
| | - Arvind Rao
- Department of Biostatistics, University of Michigan
- Department of Computational Medicine and Bioinformatics, University of Michigan
- Department of Biomedical Engineering, University of Michigan
- Department of Radiation Oncology, University of Michigan
| | - Veerabhadran Baladandayuthapani
- Department of Biostatistics, University of Michigan
- Department of Computational Medicine and Bioinformatics, University of Michigan
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Scola E, Del Vecchio G, Busto G, Bianchi A, Desideri I, Gadda D, Mancini S, Carlesi E, Moretti M, Desideri I, Muscas G, Della Puppa A, Fainardi E. Conventional and Advanced Magnetic Resonance Imaging Assessment of Non-Enhancing Peritumoral Area in Brain Tumor. Cancers (Basel) 2023; 15:cancers15112992. [PMID: 37296953 DOI: 10.3390/cancers15112992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 05/24/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
The non-enhancing peritumoral area (NEPA) is defined as the hyperintense region in T2-weighted and fluid-attenuated inversion recovery (FLAIR) images surrounding a brain tumor. The NEPA corresponds to different pathological processes, including vasogenic edema and infiltrative edema. The analysis of the NEPA with conventional and advanced magnetic resonance imaging (MRI) was proposed in the differential diagnosis of solid brain tumors, showing higher accuracy than MRI evaluation of the enhancing part of the tumor. In particular, MRI assessment of the NEPA was demonstrated to be a promising tool for distinguishing high-grade gliomas from primary lymphoma and brain metastases. Additionally, the MRI characteristics of the NEPA were found to correlate with prognosis and treatment response. The purpose of this narrative review was to describe MRI features of the NEPA obtained with conventional and advanced MRI techniques to better understand their potential in identifying the different characteristics of high-grade gliomas, primary lymphoma and brain metastases and in predicting clinical outcome and response to surgery and chemo-irradiation. Diffusion and perfusion techniques, such as diffusion tensor imaging (DTI), diffusional kurtosis imaging (DKI), dynamic susceptibility contrast-enhanced (DSC) perfusion imaging, dynamic contrast-enhanced (DCE) perfusion imaging, arterial spin labeling (ASL), spectroscopy and amide proton transfer (APT), were the advanced MRI procedures we reviewed.
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Affiliation(s)
- Elisa Scola
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, 50134 Florence, Italy
| | - Guido Del Vecchio
- Radiodiagnostic Unit N. 2, Department of Experimental and Clinical Biomedical Sciences, University of Florence, 50121 Florence, Italy
| | - Giorgio Busto
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, 50134 Florence, Italy
| | - Andrea Bianchi
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, 50134 Florence, Italy
| | - Ilaria Desideri
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, 50134 Florence, Italy
| | - Davide Gadda
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, 50134 Florence, Italy
| | - Sara Mancini
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, 50134 Florence, Italy
| | - Edoardo Carlesi
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, 50134 Florence, Italy
| | - Marco Moretti
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, 50134 Florence, Italy
| | - Isacco Desideri
- Radiation Oncology, Oncology Department, Careggi University Hospital, University of Florence, 50121 Florence, Italy
| | - Giovanni Muscas
- Neurosurgery Unit, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital, University of Florence, 50121 Florence, Italy
| | - Alessandro Della Puppa
- Neurosurgery Unit, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital, University of Florence, 50121 Florence, Italy
| | - Enrico Fainardi
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, 50134 Florence, Italy
- Neuroradiology Unit, Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, 50121 Florence, Italy
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Zhang M, Ye F, Su M, Cui M, Chen H, Ma X. The Prognostic Role of Peritumoral Edema in Patients with Newly Diagnosed Glioblastoma: A Retrospective Analysis. J Clin Neurosci 2021; 89:249-257. [PMID: 34119276 DOI: 10.1016/j.jocn.2021.04.042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 04/29/2021] [Accepted: 04/30/2021] [Indexed: 12/21/2022]
Abstract
OBJECTIVE Previous studies on glioblastomas (GBMs) have not reached a consensus on peritumoral edema (PTE)'s influence on survival. This study evaluated the PTE index's prognostic role in newly diagnosed GBMs using a well-designed method. METHODS Selected patients were reviewed after a rigorous screening process. Their general information was obtained from electronic medical records. The imaging metrics (MTD, TTM, TTE) representing tumor diameter, laterality, and PTE extent were obtained by manual measurement in Syngo FastView software. The PTE index was a ratio of TTE to MTD. Multiple variables were evaluated using analysis of variance and Cox regression model. RESULTS Of 143 patients, 62 were included in this study. MGMT promoter methylation and tumor laterality were both independent prognostic factors (p = 0.020, 0.042; HR = 0.272, 2.630). The lateral tumors' index was higher than that of the medial tumors (57.7% vs. 42.6%, p = 0.027). Low-index tumors were located in relatively medial positions compared with high-index tumors (TTM, 4.9 vs. 12.8, p = 0.032). This finding indicated that the PTE index tended to increase with tumor laterality. Moreover, the patients with low-index tumors had a significant survival disadvantage in the univariate analysis but not in the multivariate analysis (p = 0.023, 0.220). However, further analysis found that the combination of tumor laterality and PTE statistically stratified the survival outcome. The patients with lateral high-index tumors survived significantly longer (p = 0.022, HR = 1.927). CONCLUSIONS In contrast with the previous studies, this study recommends combining PTE and tumor laterality for survival stratification in newly diagnosed GBMs.
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Affiliation(s)
- Meng Zhang
- The Department of Neurosurgery, The First Medical Centre, Chinese PLA General Hospital, Fuxing Road 28, Haidian District, Beijing 100853, China; The Department of Neurosurgery, The Second Hospital of Southern District of Chinese Navy, Sanya Bay Road 82, Tianya District, Sanya 572000, China.
| | - Fuyue Ye
- The Department of Neurosurgery, The First Affiliated Hospital of Hainan Medical University, Longhua Road 31, Longhua District, Haikou 570102, China
| | - Meng Su
- The Department of Neurosurgery, The First Medical Centre, Chinese PLA General Hospital, Fuxing Road 28, Haidian District, Beijing 100853, China
| | - Meng Cui
- The Department of Neurosurgery, The First Medical Centre, Chinese PLA General Hospital, Fuxing Road 28, Haidian District, Beijing 100853, China
| | - Hongzun Chen
- The Department of Neurosurgery, The Second Hospital of Southern District of Chinese Navy, Sanya Bay Road 82, Tianya District, Sanya 572000, China
| | - Xiaodong Ma
- The Department of Neurosurgery, The First Medical Centre, Chinese PLA General Hospital, Fuxing Road 28, Haidian District, Beijing 100853, China.
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Liu X, Li Y, Qian Z, Sun Z, Xu K, Wang K, Liu S, Fan X, Li S, Zhang Z, Jiang T, Wang Y. A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas. NEUROIMAGE-CLINICAL 2018; 20:1070-1077. [PMID: 30366279 PMCID: PMC6202688 DOI: 10.1016/j.nicl.2018.10.014] [Citation(s) in RCA: 132] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2018] [Revised: 08/16/2018] [Accepted: 10/15/2018] [Indexed: 12/20/2022]
Abstract
Objective The aim of this study was to develop a radiomics signature for prediction of progression-free survival (PFS) in lower-grade gliomas and to investigate the genetic background behind the radiomics signature. Methods In this retrospective study, training (n = 216) and validation (n = 84) cohorts were collected from the Chinese Glioma Genome Atlas and the Cancer Genome Atlas, respectively. For each patient, a total of 431 radiomics features were extracted from preoperative T2-weighted magnetic resonance images. A radiomics signature was generated in the training cohort, and its prognostic value was evaluated in both the training and validation cohorts. The genetic characteristics of the group with high-risk scores were identified by radiogenomic analysis, and a nomogram was established for prediction of PFS. Results There was a significant association between the radiomics signature (including 9 screened radiomics features) and PFS, which was independent of other clinicopathologic factors in both the training (P < 0.001, multivariable Cox regression) and validation (P = 0.045, multivariable Cox regression) cohorts. Radiogenomic analysis revealed that the radiomics signature was associated with the immune response, programmed cell death, cell proliferation, and vasculature development. A nomogram established using the radiomics signature and clinicopathologic risk factors demonstrated high accuracy and good calibration for prediction of PFS in both the training (C-index, 0.684) and validation (C-index, 0.823) cohorts. Conclusions PFS can be predicted non-invasively in patients with LGGs by a group of radiomics features that could reflect the biological processes of these tumors. We developed a non-invasive model for the prediction of PFS in patients with lower-grade gliomas. We further revealed the biological processes underlying the radiomic signature by using comprehensive radiogenomic analysis. PFS of lower-grade gliomas could be predicted effectively based on the radiomics model.
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Affiliation(s)
- Xing Liu
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yiming Li
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Zenghui Qian
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Zhiyan Sun
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Kaibin Xu
- Chinese Academy of Sciences, Institute of Automation, Beijing, China
| | - Kai Wang
- Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shuai Liu
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xing Fan
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Shaowu Li
- Neurological Imaging Center, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Zhong Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tao Jiang
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Center of Brain Tumor, Beijing Institute for Brain Disorders, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China.
| | - Yinyan Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
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Treiber JM, Steed TC, Brandel MG, Patel KS, Dale AM, Carter BS, Chen CC. Molecular physiology of contrast enhancement in glioblastomas: An analysis of The Cancer Imaging Archive (TCIA). J Clin Neurosci 2018; 55:86-92. [PMID: 29934058 DOI: 10.1016/j.jocn.2018.06.018] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Revised: 03/06/2018] [Accepted: 06/04/2018] [Indexed: 12/23/2022]
Abstract
The physiologic processes underlying MRI contrast enhancement in glioblastoma patients remain poorly understood. MRIs of 148 glioblastoma subjects from The Cancer Imaging Archive were segmented using Iterative Probabilistic Voxel Labeling (IPVL). Three aspects of contrast enhancement (CE) were parametrized: the mean intensity of all CE voxels (CEi), the intensity heterogeneity in CE (CEh), and volumetric ratio of CE to necrosis (CEr). Associations between these parameters and patterns of gene expression were analyzed using DAVID functional enrichment analysis. Glioma CpG island methylator phenotype (G-CIMP) glioblastomas were poorly enhancing. Otherwise, no differences in CE parameters were found between proneural, neural, mesenchymal, and classical glioblastomas. High CEi was associated with expression of genes that mediate inflammatory responses. High CEh was associated with increased expression of genes that regulate remodeling of extracellular matrix (ECM) and endothelial permeability. High CEr was associated with increased expression of genes that mediate cellular response to stressful metabolic states, including hypoxia and starvation. Our results indicate that CE in glioblastoma is associated with distinct biological processes involved in inflammatory response and tissue hypoxia. Integrative analysis of these CE parameters may yield meaningful information pertaining to the biologic state of glioblastomas and guide future therapeutic paradigms.
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Affiliation(s)
- Jeffrey M Treiber
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA.
| | - Tyler C Steed
- Department of Neurosurgery, Emory University, Atlanta, GA, USA.
| | - Michael G Brandel
- Department of Neurosurgery, University of California, San Diego, La Jolla, CA, USA.
| | - Kunal S Patel
- Department of Neurosurgery, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA.
| | - Anders M Dale
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, USA; Department of Radiology, University of California San Diego, La Jolla, CA, USA.
| | - Bob S Carter
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA.
| | - Clark C Chen
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN, USA.
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Beig N, Patel J, Prasanna P, Hill V, Gupta A, Correa R, Bera K, Singh S, Partovi S, Varadan V, Ahluwalia M, Madabhushi A, Tiwari P. Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma. Sci Rep 2018; 8:7. [PMID: 29311558 PMCID: PMC5758516 DOI: 10.1038/s41598-017-18310-0] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 12/04/2017] [Indexed: 12/24/2022] Open
Abstract
Hypoxia, a characteristic trait of Glioblastoma (GBM), is known to cause resistance to chemo-radiation treatment and is linked with poor survival. There is hence an urgent need to non-invasively characterize tumor hypoxia to improve GBM management. We hypothesized that (a) radiomic texture descriptors can capture tumor heterogeneity manifested as a result of molecular variations in tumor hypoxia, on routine treatment naïve MRI, and (b) these imaging based texture surrogate markers of hypoxia can discriminate GBM patients as short-term (STS), mid-term (MTS), and long-term survivors (LTS). 115 studies (33 STS, 41 MTS, 41 LTS) with gadolinium-enhanced T1-weighted MRI (Gd-T1w) and T2-weighted (T2w) and FLAIR MRI protocols and the corresponding RNA sequences were obtained. After expert segmentation of necrotic, enhancing, and edematous/nonenhancing tumor regions for every study, 30 radiomic texture descriptors were extracted from every region across every MRI protocol. Using the expression profile of 21 hypoxia-associated genes, a hypoxia enrichment score (HES) was obtained for the training cohort of 85 cases. Mutual information score was used to identify a subset of radiomic features that were most informative of HES within 3-fold cross-validation to categorize studies as STS, MTS, and LTS. When validated on an additional cohort of 30 studies (11 STS, 9 MTS, 10 LTS), our results revealed that the most discriminative features of HES were also able to distinguish STS from LTS (p = 0.003).
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Affiliation(s)
- Niha Beig
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, 44106, USA.
| | - Jay Patel
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, 44106, USA
| | - Prateek Prasanna
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, 44106, USA
| | - Virginia Hill
- Department of Neuroradiology, Imaging Institute, Cleveland Clinic, Cleveland, 44106, USA
| | - Amit Gupta
- University Hospitals of Cleveland, Department of Radiology, Cleveland, 44106, USA
| | - Ramon Correa
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, 44106, USA
| | - Kaustav Bera
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, 44106, USA
| | - Salendra Singh
- Case Western Reserve University, School of Medicine, Cleveland, 44106, USA
| | - Sasan Partovi
- University Hospitals of Cleveland, Department of Radiology, Cleveland, 44106, USA
| | - Vinay Varadan
- Case Western Reserve University, School of Medicine, Cleveland, 44106, USA
| | - Manmeet Ahluwalia
- Brain Tumor and Neuro-Oncology Center, Cleveland Clinic, Cleveland, 44106, USA
| | - Anant Madabhushi
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, 44106, USA
| | - Pallavi Tiwari
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, 44106, USA.
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