1
|
Sanchez I, Rahman R. Radiogenomics as an Integrated Approach to Glioblastoma Precision Medicine. Curr Oncol Rep 2024:10.1007/s11912-024-01580-z. [PMID: 39009914 DOI: 10.1007/s11912-024-01580-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/05/2024] [Indexed: 07/17/2024]
Abstract
PURPOSE OF REVIEW Isocitrate dehydrogenase wild-type glioblastoma is the most aggressive primary brain tumour in adults. Its infiltrative nature and heterogeneity confer a dismal prognosis, despite multimodal treatment. Precision medicine is increasingly advocated to improve survival rates in glioblastoma management; however, conventional neuroimaging techniques are insufficient in providing the detail required for accurate diagnosis of this complex condition. RECENT FINDINGS Advanced magnetic resonance imaging allows more comprehensive understanding of the tumour microenvironment. Combining diffusion and perfusion magnetic resonance imaging to create a multiparametric scan enhances diagnostic power and can overcome the unreliability of tumour characterisation by standard imaging. Recent progress in deep learning algorithms establishes their remarkable ability in image-recognition tasks. Integrating these with multiparametric scans could transform the diagnosis and monitoring of patients by ensuring that the entire tumour is captured. As a corollary, radiomics has emerged as a powerful approach to offer insights into diagnosis, prognosis, treatment, and tumour response through extraction of information from radiological scans, and transformation of these tumour characteristics into quantitative data. Radiogenomics, which links imaging features with genomic profiles, has exhibited its ability in characterising glioblastoma, and determining therapeutic response, with the potential to revolutionise management of glioblastoma. The integration of deep learning algorithms into radiogenomic models has established an automated, highly reproducible means to predict glioblastoma molecular signatures, further aiding prognosis and targeted therapy. However, challenges including lack of large cohorts, absence of standardised guidelines and the 'black-box' nature of deep learning algorithms, must first be overcome before this workflow can be applied in clinical practice.
Collapse
Affiliation(s)
- Isabella Sanchez
- Biodiscovery Institute, School of Medicine, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Ruman Rahman
- Biodiscovery Institute, School of Medicine, University of Nottingham, Nottingham, NG7 2RD, UK.
| |
Collapse
|
2
|
Sun C, Wang S, Ma Z, Zhou J, Ding Z, Yuan G, Pan Y. Neutrophils in glioma microenvironment: from immune function to immunotherapy. Front Immunol 2024; 15:1393173. [PMID: 38779679 PMCID: PMC11109384 DOI: 10.3389/fimmu.2024.1393173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 04/25/2024] [Indexed: 05/25/2024] Open
Abstract
Glioma is a malignant tumor of the central nervous system (CNS). Currently, effective treatment options for gliomas are still lacking. Neutrophils, as an important member of the tumor microenvironment (TME), are widely distributed in circulation. Recently, the discovery of cranial-meningeal channels and intracranial lymphatic vessels has provided new insights into the origins of neutrophils in the CNS. Neutrophils in the brain may originate more from the skull and adjacent vertebral bone marrow. They cross the blood-brain barrier (BBB) under the action of chemokines and enter the brain parenchyma, subsequently migrating to the glioma TME and undergoing phenotypic changes upon contact with tumor cells. Under glycolytic metabolism model, neutrophils show complex and dual functions in different stages of cancer progression, including participation in the malignant progression, immune suppression, and anti-tumor effects of gliomas. Additionally, neutrophils in the TME interact with other immune cells, playing a crucial role in cancer immunotherapy. Targeting neutrophils may be a novel generation of immunotherapy and improve the efficacy of cancer treatments. This article reviews the molecular mechanisms of neutrophils infiltrating the central nervous system from the external environment, detailing the origin, functions, classifications, and targeted therapies of neutrophils in the context of glioma.
Collapse
Affiliation(s)
- Chao Sun
- The Second Clinical Medical School, Lanzhou University, Lanzhou, China
- Department of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, China
- Key Laboratory of Neurology of Gansu Province, Lanzhou University Second Hospital, Lanzhou, China
| | - Siwen Wang
- The Second Clinical Medical School, Lanzhou University, Lanzhou, China
| | - Zhen Ma
- The Second Clinical Medical School, Lanzhou University, Lanzhou, China
- Department of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, China
- Key Laboratory of Neurology of Gansu Province, Lanzhou University Second Hospital, Lanzhou, China
| | - Jinghuan Zhou
- The Second Clinical Medical School, Lanzhou University, Lanzhou, China
- Department of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, China
- Key Laboratory of Neurology of Gansu Province, Lanzhou University Second Hospital, Lanzhou, China
| | - Zilin Ding
- The Second Clinical Medical School, Lanzhou University, Lanzhou, China
- Department of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, China
- Key Laboratory of Neurology of Gansu Province, Lanzhou University Second Hospital, Lanzhou, China
| | - Guoqiang Yuan
- The Second Clinical Medical School, Lanzhou University, Lanzhou, China
- Department of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, China
- Key Laboratory of Neurology of Gansu Province, Lanzhou University Second Hospital, Lanzhou, China
| | - Yawen Pan
- The Second Clinical Medical School, Lanzhou University, Lanzhou, China
- Department of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, China
- Key Laboratory of Neurology of Gansu Province, Lanzhou University Second Hospital, Lanzhou, China
| |
Collapse
|
3
|
Guo J, Fathi Kazerooni A, Toorens E, Akbari H, Yu F, Sako C, Mamourian E, Shinohara RT, Koumenis C, Bagley SJ, Morrissette JJD, Binder ZA, Brem S, Mohan S, Lustig RA, O'Rourke DM, Ganguly T, Bakas S, Nasrallah MP, Davatzikos C. Integrating imaging and genomic data for the discovery of distinct glioblastoma subtypes: a joint learning approach. Sci Rep 2024; 14:4922. [PMID: 38418494 PMCID: PMC10902376 DOI: 10.1038/s41598-024-55072-y] [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: 04/26/2023] [Accepted: 02/19/2024] [Indexed: 03/01/2024] Open
Abstract
Glioblastoma is a highly heterogeneous disease, with variations observed at both phenotypical and molecular levels. Personalized therapies would be facilitated by non-invasive in vivo approaches for characterizing this heterogeneity. In this study, we developed unsupervised joint machine learning between radiomic and genomic data, thereby identifying distinct glioblastoma subtypes. A retrospective cohort of 571 IDH-wildtype glioblastoma patients were included in the study, and pre-operative multi-parametric MRI scans and targeted next-generation sequencing (NGS) data were collected. L21-norm minimization was used to select a subset of 12 radiomic features from the MRI scans, and 13 key driver genes from the five main signal pathways most affected in glioblastoma were selected from the genomic data. Subtypes were identified using a joint learning approach called Anchor-based Partial Multi-modal Clustering on both radiomic and genomic modalities. Kaplan-Meier analysis identified three distinct glioblastoma subtypes: high-risk, medium-risk, and low-risk, based on overall survival outcome (p < 0.05, log-rank test; Hazard Ratio = 1.64, 95% CI 1.17-2.31, Cox proportional hazard model on high-risk and low-risk subtypes). The three subtypes displayed different phenotypical and molecular characteristics in terms of imaging histogram, co-occurrence of genes, and correlation between the two modalities. Our findings demonstrate the synergistic value of integrated radiomic signatures and molecular characteristics for glioblastoma subtyping. Joint learning on both modalities can aid in better understanding the molecular basis of phenotypical signatures of glioblastoma, and provide insights into the biological underpinnings of tumor formation and progression.
Collapse
Affiliation(s)
- Jun Guo
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, 3700 Hamilton Walk, 7Th Floor, Philadelphia, PA, 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, 3700 Hamilton Walk, 7Th Floor, Philadelphia, PA, 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA
- Center for Data-Driven Discovery in Biomedicine (D3b), Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Erik Toorens
- Penn Genomic Analysis Core, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hamed Akbari
- Department of Bioengineering, School of Engineering, Santa Clara University, Santa Clara, CA, USA
| | - Fanyang Yu
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, 3700 Hamilton Walk, 7Th Floor, Philadelphia, PA, 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, 3700 Hamilton Walk, 7Th Floor, Philadelphia, PA, 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, 3700 Hamilton Walk, 7Th Floor, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, 3700 Hamilton Walk, 7Th Floor, Philadelphia, PA, 19104, USA
- Penn Statistics in Imaging and Visualization (PennSIVE) Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Constantinos Koumenis
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Stephen J Bagley
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Glioblastoma Translational Center of Excellence, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Jennifer J D Morrissette
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Zev A Binder
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Glioblastoma Translational Center of Excellence, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Steven Brem
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Glioblastoma Translational Center of Excellence, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, 3700 Hamilton Walk, 7Th Floor, Philadelphia, PA, 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert A Lustig
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Donald M O'Rourke
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Glioblastoma Translational Center of Excellence, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Tapan Ganguly
- Penn Genomic Analysis Core, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, 3700 Hamilton Walk, 7Th Floor, Philadelphia, PA, 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Division of Computational Pathology, Department of Pathology & Laboratory Medicine, School of Medicine, Indiana University, Indianapolis, IN, USA
| | - MacLean P Nasrallah
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, 3700 Hamilton Walk, 7Th Floor, Philadelphia, PA, 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, 3700 Hamilton Walk, 7Th Floor, Philadelphia, PA, 19104, USA.
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| |
Collapse
|
4
|
Patel KS, Yao J, Cho NS, Sanvito F, Tessema K, Alvarado A, Dudley L, Rodriguez F, Everson R, Cloughesy TF, Salamon N, Liau LM, Kornblum HI, Ellingson BM. pH-Weighted amine chemical exchange saturation transfer echo planar imaging visualizes infiltrating glioblastoma cells. Neuro Oncol 2024; 26:115-126. [PMID: 37591790 PMCID: PMC10768991 DOI: 10.1093/neuonc/noad150] [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: 05/22/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND Given the invasive nature of glioblastoma, tumor cells exist beyond the contrast-enhancing (CE) region targeted during treatment. However, areas of non-enhancing (NE) tumors are difficult to visualize and delineate from edematous tissue. Amine chemical exchange saturation transfer echo planar imaging (CEST-EPI) is a pH-sensitive molecular magnetic resonance imaging technique that was evaluated in its ability to identify infiltrating NE tumors and prognosticate survival. METHODS In this prospective study, CEST-EPI was obtained in 30 patients and areas with elevated CEST contrast ("CEST+" based on the asymmetry in magnetization transfer ratio: MTRasym at 3 ppm) within NE regions were quantitated. Median MTRasym at 3 ppm and volume of CEST + NE regions were correlated with progression-free survival (PFS). In 20 samples from 14 patients, image-guided biopsies of these areas were obtained to correlate MTRasym at 3 ppm to tumor and non-tumor cell burden using immunohistochemistry. RESULTS In 15 newly diagnosed and 15 recurrent glioblastoma, higher median MTRasym at 3ppm within CEST + NE regions (P = .007; P = .0326) and higher volumes of CEST + NE tumor (P = .020; P < .001) were associated with decreased PFS. CE recurrence occurred in areas of preoperative CEST + NE regions in 95.4% of patients. MTRasym at 3 ppm was correlated with presence of tumor, cell density, %Ki-67 positivity, and %CD31 positivity (P = .001; P < .001; P < .001; P = .001). CONCLUSIONS pH-weighted amine CEST-EPI allows for visualization of NE tumor, likely through surrounding acidification of the tumor microenvironment. The magnitude and volume of CEST + NE tumor correlates with tumor cell density, degree of proliferating or "active" tumor, and PFS.
Collapse
Affiliation(s)
- Kunal S Patel
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, California, USA
- The Intellectual and Developmental Disabilities Research Center and Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, California, USA
| | - Nicholas S Cho
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, California, USA
| | - Francesco Sanvito
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, California, USA
| | - Kaleab Tessema
- The Intellectual and Developmental Disabilities Research Center and Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Alvaro Alvarado
- The Intellectual and Developmental Disabilities Research Center and Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Lindsey Dudley
- The Intellectual and Developmental Disabilities Research Center and Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Fausto Rodriguez
- Department of Pathology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Richard Everson
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Timothy F Cloughesy
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, California, USA
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Harley I Kornblum
- The Intellectual and Developmental Disabilities Research Center and Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Benjamin M Ellingson
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, California, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, California, USA
| |
Collapse
|
5
|
Iyer K, Saini S, Bhadra S, Kulavi S, Bandyopadhyay J. Precision medicine advancements in glioblastoma: A systematic review. Biomedicine (Taipei) 2023; 13:1-13. [PMID: 37937301 PMCID: PMC10627207 DOI: 10.37796/2211-8039.1403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 02/04/2023] [Accepted: 02/10/2023] [Indexed: 11/09/2023] Open
Abstract
Background Glioblastoma multiforme, commonly known as GBM or glioblastoma is a grade IV astrocytoma. Brain tumors are difficult to treat and lead to poor prognosis and survival in patients. Gliomas are categorized into four different grades among which GBM is the worst grade primary brain tumor with a survival of less than a year. The genomic heterogeneity of the brain tumor results in different profiles for patients diagnosed with glioblastoma. Precision medicine focuses on this specific tumor type and suggests specialized treatment for better prognosis and overall survival (OS). Purpose With the recent advancements in Genome-Wide Studies (GWS) and various characterizations of brain tumors based on genetic, transcriptomic, proteomic, epigenetic, and metabolomics, this review discusses the advancements and opportunities of precision medicine therapeutics, drugs, and diagnosis methods based on the different profiles of glioblastoma. Methods This review has exhaustively surveyed several pieces of works from various literature databases. Conclusion It is evident that most primary brain tumors including glioblastoma require specific and precision therapeutics for better prognosis and OS. In present and future, molecular understanding and discovering specific therapies are essential for treatment in the field of neurooncology.
Collapse
Affiliation(s)
| | | | | | - Sohini Kulavi
- Department of Biotechnology, Maulana Abul Kalam Azad University of Technology, NH-12 (Old NH-34) Simhat, Haringhata, Nadia, 741249, West Bengal,
India
| | - Jaya Bandyopadhyay
- Department of Biotechnology, Maulana Abul Kalam Azad University of Technology, NH-12 (Old NH-34) Simhat, Haringhata, Nadia, 741249, West Bengal,
India
| |
Collapse
|
6
|
Long H, Zhang P, Bi Y, Yang C, Wu M, He D, Huang S, Yang K, Qi S, Wang J. MRI radiomic features of peritumoral edema may predict the recurrence sites of glioblastoma multiforme. Front Oncol 2023; 12:1042498. [PMID: 36686829 PMCID: PMC9845721 DOI: 10.3389/fonc.2022.1042498] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 12/02/2022] [Indexed: 01/05/2023] Open
Abstract
Background and purpose As one of the most aggressive malignant tumor in the central nervous system, the main cause of poor outcome of glioblastoma (GBM) is recurrence, a non-invasive method which can predict the area of recurrence pre-operation is necessary.To investigate whether there is radiological heterogeneity within peritumoral edema and identify the reproducible radiomic features predictive of the sites of recurrence of glioblastoma(GBM), which may be of value to optimize patients' management. Materials and methods The clinical information and MR images (contrast-enhanced T1 weighted and FLAIR sequences) of 22 patients who have been histologically proven glioblastoma, were retrospectively evaluated. Kaplan-Meier methods was used for survival analysis. Oedematous regions were manually segmented by an expert into recurrence region, non-recurrence region. A set of 94 radiomic features were obtained from each region using the function of analyzing MR image of 3D slicer. Paired t test was performed to identify the features existing significant difference. Subsequently, the data of two patients from TCGA database was used to evaluate whether these features have clinical value. Results Ten features with significant differences between the recurrence and non-recurrence subregions were identified and verified on two individual patients from the TCGA database with pathologically confirmed diagnosis of GBM. Conclusions Our results suggested that heterogeneity does exist in peritumoral edema, indicating that the radiomic features of peritumoral edema from routine MR images can be utilized to predict the sites of GBM recurrence. Our findings may further guide the surgical treatment strategy for GBM.
Collapse
Affiliation(s)
- Hao Long
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China,The First Clinical Medicine College, Southern Medical University, Guangzhou, China
| | - Ping Zhang
- Department of oncology, Guangdong 999 Brain Hospital, Guangzhou, China
| | - Yuewei Bi
- The First Clinical Medicine College, Southern Medical University, Guangzhou, China,Neural Networks Surgery Team, Southern Medical University, Guangzhou, China
| | - Chen Yang
- The First Clinical Medicine College, Southern Medical University, Guangzhou, China,Neural Networks Surgery Team, Southern Medical University, Guangzhou, China
| | - Manfeng Wu
- The First Clinical Medicine College, Southern Medical University, Guangzhou, China,Neural Networks Surgery Team, Southern Medical University, Guangzhou, China
| | - Dian He
- The First Clinical Medicine College, Southern Medical University, Guangzhou, China,Neural Networks Surgery Team, Southern Medical University, Guangzhou, China
| | - Shaozhuo Huang
- The First Clinical Medicine College, Southern Medical University, Guangzhou, China,Neural Networks Surgery Team, Southern Medical University, Guangzhou, China
| | - Kaijun Yang
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China,The First Clinical Medicine College, Southern Medical University, Guangzhou, China
| | - Songtao Qi
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China,The First Clinical Medicine College, Southern Medical University, Guangzhou, China
| | - Jun Wang
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China,The First Clinical Medicine College, Southern Medical University, Guangzhou, China,Neural Networks Surgery Team, Southern Medical University, Guangzhou, China,*Correspondence: Jun Wang,
| |
Collapse
|
7
|
Wu J, Mayer AT, Li R. Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy. Semin Cancer Biol 2022; 84:310-328. [PMID: 33290844 PMCID: PMC8319834 DOI: 10.1016/j.semcancer.2020.12.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 11/29/2020] [Accepted: 12/02/2020] [Indexed: 02/07/2023]
Abstract
Radiological imaging is an integral component of cancer care, including diagnosis, staging, and treatment response monitoring. It contains rich information about tumor phenotypes that are governed not only by cancer cellintrinsic biological processes but also by the tumor microenvironment, such as the composition and function of tumor-infiltrating immune cells. By analyzing the radiological scans using a quantitative radiomics approach, robust relations between specific imaging and molecular phenotypes can be established. Indeed, a number of studies have demonstrated the feasibility of radiogenomics for predicting intrinsic molecular subtypes and gene expression signatures in breast cancer based on MRI. In parallel, promising results have been shown for inferring the amount of tumor-infiltrating lymphocytes, a key factor for the efficacy of cancer immunotherapy, from standard-of-care radiological images. Compared with the biopsy-based approach, radiogenomics offers a unique avenue to profile the molecular makeup of the tumor and immune microenvironment as well as its evolution in a noninvasive and holistic manner through longitudinal imaging scans. Here, we provide a systematic review of the state of the art radiogenomics studies in the era of immunotherapy and discuss emerging paradigms and opportunities in AI and deep learning approaches. These technical advances are expected to transform the radiogenomics field, leading to the discovery of reliable imaging biomarkers. This will pave the way for their clinical translation to guide precision cancer therapy.
Collapse
Affiliation(s)
- Jia Wu
- Department of Imaging Physics, MD Anderson Cancer Center, Texas, 77030, USA; Department of Thoracic/Head & Neck Medical Oncology, MD Anderson Cancer Center, Texas, 77030, USA.
| | - Aaron T Mayer
- Department of Bioengineering, Stanford University, Stanford, California, 94305, USA; Department of Radiology, Stanford University, Stanford, California, 94305, USA; Molecular Imaging Program at Stanford, Stanford University, Stanford, California, 94305, USA; BioX Program at Stanford, Stanford University, Stanford, California, 94305, USA
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University, Stanford, California, 94305, USA
| |
Collapse
|
8
|
Corr F, Grimm D, Saß B, Pojskić M, Bartsch JW, Carl B, Nimsky C, Bopp MHA. Radiogenomic Predictors of Recurrence in Glioblastoma—A Systematic Review. J Pers Med 2022; 12:jpm12030402. [PMID: 35330402 PMCID: PMC8952807 DOI: 10.3390/jpm12030402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 02/23/2022] [Accepted: 03/01/2022] [Indexed: 12/10/2022] Open
Abstract
Glioblastoma, as the most aggressive brain tumor, is associated with a poor prognosis and outcome. To optimize prognosis and clinical therapy decisions, there is an urgent need to stratify patients with increased risk for recurrent tumors and low therapeutic success to optimize individual treatment. Radiogenomics establishes a link between radiological and pathological information. This review provides a state-of-the-art picture illustrating the latest developments in the use of radiogenomic markers regarding prognosis and their potential for monitoring recurrence. Databases PubMed, Google Scholar, and Cochrane Library were searched. Inclusion criteria were defined as diagnosis of glioblastoma with histopathological and radiological follow-up. Out of 321 reviewed articles, 43 articles met these inclusion criteria. Included studies were analyzed for the frequency of radiological and molecular tumor markers whereby radiogenomic associations were analyzed. Six main associations were described: radiogenomic prognosis, MGMT status, IDH, EGFR status, molecular subgroups, and tumor location. Prospective studies analyzing prognostic features of glioblastoma together with radiological features are lacking. By reviewing the progress in the development of radiogenomic markers, we provide insights into the potential efficacy of such an approach for clinical routine use eventually enabling early identification of glioblastoma recurrence and therefore supporting a further personalized monitoring and treatment strategy.
Collapse
Affiliation(s)
- Felix Corr
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- EDU Institute of Higher Education, Villa Bighi, Chaplain’s House, KKR 1320 Kalkara, Malta
- Correspondence:
| | - Dustin Grimm
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- EDU Institute of Higher Education, Villa Bighi, Chaplain’s House, KKR 1320 Kalkara, Malta
| | - Benjamin Saß
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
| | - Mirza Pojskić
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
| | - Jörg W. Bartsch
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- Center for Mind, Brain and Behavior (CMBB), 35043 Marburg, Germany
| | - Barbara Carl
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- Department of Neurosurgery, Helios Dr. Horst Schmidt Kliniken, Ludwig-Erhard-Strasse 100, 65199 Wiesbaden, Germany
| | - Christopher Nimsky
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- Center for Mind, Brain and Behavior (CMBB), 35043 Marburg, Germany
| | - Miriam H. A. Bopp
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- Center for Mind, Brain and Behavior (CMBB), 35043 Marburg, Germany
| |
Collapse
|
9
|
Aftab K, Aamir FB, Mallick S, Mubarak F, Pope WB, Mikkelsen T, Rock JP, Enam SA. Radiomics for precision medicine in glioblastoma. J Neurooncol 2022; 156:217-231. [PMID: 35020109 DOI: 10.1007/s11060-021-03933-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 12/20/2021] [Indexed: 11/26/2022]
Abstract
INTRODUCTION Being the most common primary brain tumor, glioblastoma presents as an extremely challenging malignancy to treat with dismal outcomes despite treatment. Varying molecular epidemiology of glioblastoma between patients and intra-tumoral heterogeneity explains the failure of current one-size-fits-all treatment modalities. Radiomics uses machine learning to identify salient features of the tumor on brain imaging and promises patient-specific management in glioblastoma patients. METHODS We performed a comprehensive review of the available literature on studies investigating the role of radiomics and radiogenomics models for the diagnosis, stratification, prognostication as well as treatment planning and monitoring of glioblastoma. RESULTS Classifiers based on a combination of various MRI sequences, genetic information and clinical data can predict non-invasive tumor diagnosis, overall survival and treatment response with reasonable accuracy. However, the use of radiomics for glioblastoma treatment remains in infancy as larger sample sizes, standardized image acquisition and data extraction techniques are needed to develop machine learning models that can be translated effectively into clinical practice. CONCLUSION Radiomics has the potential to transform the scope of glioblastoma management through personalized medicine.
Collapse
Affiliation(s)
- Kiran Aftab
- Section of Neurosurgery, Department of Surgery, Aga Khan University, Karachi, Pakistan
| | | | - Saad Mallick
- Medical College, Aga Khan University, Karachi, Pakistan
| | - Fatima Mubarak
- Department of Radiology, Aga Khan University, Karachi, Pakistan
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Tom Mikkelsen
- Departments of Neurology and Neurosurgery, Henry Ford Hospital, Detroit, MI, USA
| | - Jack P Rock
- Department of Neurosurgery, Henry Ford Health System, Detroit, MI, USA
| | - Syed Ather Enam
- Section of Neurosurgery, Department of Surgery, Aga Khan University, Karachi, Pakistan.
| |
Collapse
|
10
|
Chaddad A, Daniel P, Zhang M, Rathore S, Sargos P, Desrosiers C, Niazi T. Deep radiomic signature with immune cell markers predicts the survival of glioma patients. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2020.10.117] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
|
11
|
Yang F, Xie Y, Tang J, Liu B, Luo Y, He Q, Zhang L, Xin L, Wang J, Wang S, Zhang S, Cao Q, Wang L, He L, Zhang L. Uncovering a Distinct Gene Signature in Endothelial Cells Associated With Contrast Enhancement in Glioblastoma. Front Oncol 2021; 11:683367. [PMID: 34222002 PMCID: PMC8245778 DOI: 10.3389/fonc.2021.683367] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 05/27/2021] [Indexed: 01/23/2023] Open
Abstract
Purpose Glioblastoma (GBM) is the most aggressive and lethal type of brain tumors. Magnetic resonance imaging (MRI) has been commonly used for GBM diagnosis. Contrast enhancement (CE) on T1-weighted sequences are presented in nearly all GBM as a result of high vascular permeability in glioblastomas. Although several radiomics studies indicated that CE is associated with distinct molecular signatures in tumors, the effects of vascular endothelial cells, the key component of blood brain barrier (BBB) controlling vascular permeability, on CE have not been thoroughly analyzed. Methods Endothelial cell enriched genes have been identified using transcriptome data from 128 patients by a systematic method based on correlation analysis. Distinct endothelial cell enriched genes associated with CE were identified by analyzing difference of correlation score between CE-high and CE–low GBM cases. Immunohistochemical staining was performed on in-house patient cohort to validate the selected genes associated with CE. Moreover, a survival analysis was conducted to uncover the relation between CE and patient survival. Results We illustrated that CE is associated with distinct vascular molecular imprints characterized by up-regulation of pro-inflammatory genes and deregulation of BBB related genes. Among them, PLVAP is up-regulated, whereas TJP1 and ABCG2 are down-regulated in the vasculature of GBM with high CE. In addition, we found that the high CE is associated with poor prognosis and GBM mesenchymal subtype. Conclusion We provide an additional insight to reveal the molecular trait for CE in MRI images with special focus on vascular endothelial cells, linking CE with BBB disruption in the molecular level. This study provides a potential new direction that may be applied for the treatment optimization based on MRI features.
Collapse
Affiliation(s)
- Fan Yang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin Neurological Institute, Key Laboratory of Post-Neuro-injury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education and Tianjin City, Tianjin, China
| | - Yuan Xie
- Key Laboratory of Ministry of Education for Medicinal Plant Resource and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Developing of Endangered Chinese Crude Drugs in Northwest of China, College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Jiefu Tang
- Trauma Center, The First Affiliated Hospital of Hunan University of Medicine, Huaihua, China
| | - Boxuan Liu
- Precision Medicine Center, The Second People's Hospital of Huaihua, Huaihua, China
| | - Yuancheng Luo
- School of Life Science, University of Liverpool, Liverpool, United Kingdom
| | - Qiyuan He
- Key Laboratory of Ministry of Education for Medicinal Plant Resource and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Developing of Endangered Chinese Crude Drugs in Northwest of China, College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Lingxue Zhang
- Key Laboratory of Ministry of Education for Medicinal Plant Resource and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Developing of Endangered Chinese Crude Drugs in Northwest of China, College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Lele Xin
- Key Laboratory of Ministry of Education for Medicinal Plant Resource and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Developing of Endangered Chinese Crude Drugs in Northwest of China, College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Jianhao Wang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin Neurological Institute, Key Laboratory of Post-Neuro-injury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education and Tianjin City, Tianjin, China
| | - Sinan Wang
- Key Laboratory of Ministry of Education for Medicinal Plant Resource and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Developing of Endangered Chinese Crude Drugs in Northwest of China, College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Shuqiang Zhang
- Key Laboratory of Ministry of Education for Medicinal Plant Resource and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Developing of Endangered Chinese Crude Drugs in Northwest of China, College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Qingze Cao
- Key Laboratory of Ministry of Education for Medicinal Plant Resource and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Developing of Endangered Chinese Crude Drugs in Northwest of China, College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Liang Wang
- Department of Neurosurgery, Tangdu Hospital of the Fourth Military Medical University (Air Force Medical University of PLA), Xi'an, China
| | - Liqun He
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin Neurological Institute, Key Laboratory of Post-Neuro-injury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education and Tianjin City, Tianjin, China.,Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, Uppsala, Sweden
| | - Lei Zhang
- Key Laboratory of Ministry of Education for Medicinal Plant Resource and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Developing of Endangered Chinese Crude Drugs in Northwest of China, College of Life Sciences, Shaanxi Normal University, Xi'an, China.,Precision Medicine Center, The Second People's Hospital of Huaihua, Huaihua, China
| |
Collapse
|
12
|
Abstract
The 2016 World Health Organization brain tumor classification is based on genomic and molecular profile of tumor tissue. These characteristics have improved understanding of the brain tumor and played an important role in treatment planning and prognostication. There is an ongoing effort to develop noninvasive imaging techniques that provide insight into tissue characteristics at the cellular and molecular levels. This article focuses on the molecular characteristics of gliomas, transcriptomic subtypes, and radiogenomic studies using semantic and radiomic features. The limitations and future directions of radiogenomics as a standalone diagnostic tool also are discussed.
Collapse
Affiliation(s)
- Chaitra Badve
- Department of Radiology, Division of Neuroradiology, University Hospitals Cleveland Medical Center, BSH 5056, 11100 Euclid Avenue, Cleveland, OH 44106, USA.
| | - Sangam Kanekar
- Department of Radiology and Neurology, Division of Neuroradiology, Penn State College of Medicine, Penn State Milton Hershey Medical Center, Mail Code H066 500, University Drive, Hershey, PA 17033, USA
| |
Collapse
|
13
|
Le NQK, Hung TNK, Do DT, Lam LHT, Dang LH, Huynh TT. Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI. Comput Biol Med 2021; 132:104320. [PMID: 33735760 DOI: 10.1016/j.compbiomed.2021.104320] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 03/05/2021] [Accepted: 03/05/2021] [Indexed: 12/13/2022]
Abstract
BACKGROUND In the field of glioma, transcriptome subtypes have been considered as an important diagnostic and prognostic biomarker that may help improve the treatment efficacy. However, existing identification methods of transcriptome subtypes are limited due to the relatively long detection period, the unattainability of tumor specimens via biopsy or surgery, and the fleeting nature of intralesional heterogeneity. In search of a superior model over previous ones, this study evaluated the efficiency of eXtreme Gradient Boosting (XGBoost)-based radiomics model to classify transcriptome subtypes in glioblastoma patients. METHODS This retrospective study retrieved patients from TCGA-GBM and IvyGAP cohorts with pathologically diagnosed glioblastoma, and separated them into different transcriptome subtypes groups. GBM patients were then segmented into three different regions of MRI: enhancement of the tumor core (ET), non-enhancing portion of the tumor core (NET), and peritumoral edema (ED). We subsequently used handcrafted radiomics features (n = 704) from multimodality MRI and two-level feature selection techniques (Spearman correlation and F-score tests) in order to find the features that could be relevant. RESULTS After the feature selection approach, we identified 13 radiomics features that were the most meaningful ones that can be used to reach the optimal results. With these features, our XGBoost model reached the predictive accuracies of 70.9%, 73.3%, 88.4%, and 88.4% for classical, mesenchymal, neural, and proneural subtypes, respectively. Our model performance has been improved in comparison with the other models as well as previous works on the same dataset. CONCLUSION The use of XGBoost and two-level feature selection analysis (Spearman correlation and F-score) could be expected as a potential combination for classifying transcriptome subtypes with high performance and might raise public attention for further research on radiomics-based GBM models.
Collapse
Affiliation(s)
- Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, 106, Taiwan; Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, 106, Taiwan; Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, 110, Taiwan.
| | - Truong Nguyen Khanh Hung
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan; Orthopedic and Trauma Department, Cho Ray Hospital, Ho Chi Minh City, 70000, Viet Nam
| | - Duyen Thi Do
- Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, 106, Taiwan
| | - Luu Ho Thanh Lam
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan; Children's Hospital 2, Ho Chi Minh City, 70000, Viet Nam
| | - Luong Huu Dang
- Department of Otolaryngology, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, 70000, Viet Nam
| | - Tuan-Tu Huynh
- Department of Electrical Engineering, Yuan Ze University, No. 135, Yuandong Road, Zhongli, 320, Taoyuan, Taiwan; Department of Electrical Electronic and Mechanical Engineering, Lac Hong University, No. 10, Huynh Van Nghe Road, Bien Hoa, Dong Nai, 76120, Viet Nam
| |
Collapse
|
14
|
Goenka A, Tiek D, Song X, Huang T, Hu B, Cheng SY. The Many Facets of Therapy Resistance and Tumor Recurrence in Glioblastoma. Cells 2021; 10:cells10030484. [PMID: 33668200 PMCID: PMC7995978 DOI: 10.3390/cells10030484] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 02/19/2021] [Accepted: 02/19/2021] [Indexed: 12/14/2022] Open
Abstract
Glioblastoma (GBM) is the most lethal type of primary brain cancer. Standard care using chemo- and radio-therapy modestly increases the overall survival of patients; however, recurrence is inevitable, due to treatment resistance and lack of response to targeted therapies. GBM therapy resistance has been attributed to several extrinsic and intrinsic factors which affect the dynamics of tumor evolution and physiology thus creating clinical challenges. Tumor-intrinsic factors such as tumor heterogeneity, hypermutation, altered metabolomics and oncologically activated alternative splicing pathways change the tumor landscape to facilitate therapy failure and tumor progression. Moreover, tumor-extrinsic factors such as hypoxia and an immune-suppressive tumor microenvironment (TME) are the chief causes of immunotherapy failure in GBM. Amid the success of immunotherapy in other cancers, GBM has occurred as a model of resistance, thus focusing current efforts on not only alleviating the immunotolerance but also evading the escape mechanisms of tumor cells to therapy, caused by inter- and intra-tumoral heterogeneity. Here we review the various mechanisms of therapy resistance in GBM, caused by the continuously evolving tumor dynamics as well as the complex TME, which cumulatively contribute to GBM malignancy and therapy failure; in an attempt to understand and identify effective therapies for recurrent GBM.
Collapse
Affiliation(s)
| | | | | | | | | | - Shi-Yuan Cheng
- Correspondence: ; Tel.: +1-312-503-3043; Fax: +1-312-503-5603
| |
Collapse
|
15
|
Raghunand N, Gatenby RA. Bridging Spatial Scales From Radiographic Images to Cellular and Molecular Properties in Cancers. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00053-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
|
16
|
Wang X, Ning W, Qiu Z, Li S, Zhang H, Yu C. Tumor-associated macrophages based signaling pathway analysis and hub genes identification in glioma. Medicine (Baltimore) 2020; 99:e23840. [PMID: 33371165 PMCID: PMC7748342 DOI: 10.1097/md.0000000000023840] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 11/21/2020] [Indexed: 12/14/2022] Open
Abstract
Tumor-associated macrophages (TAMs) play a crucial role in the immune response to many malignancies, but the signaling pathways by which the glioma microenvironment cross-talk with TAMs are poorly understood. The aim of this study was to uncover the potential signaling pathways of the regulation of TAMs and identify candidate targets for therapeutic intervention of glioma through bioinformatics analysis.Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets were used to download RNA-Seq data and microarray data of human glioma specimen. Differentially expressed genes (DEGs) between CD68-high samples and CD68-low samples were sorted. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the DEGs was conducted. Protein-protein interaction (PPI) network were formed to identify the hub genes.The prognostic value of TAMs in glioma patients was confirmed. A total of 477 specific DEGs were sorted. The signaling pathway was identified in pathway enrichment and the DEGs showed prominent representations of immune response networks in glioma. The hub genes including C3, IL6, ITGB2, PTAFR, TIMP1 and VAMP8 were identified form the PPI network and they were all correlated positively with the expression of CD68 and showed the excellent prognostic value in glioma patients.TAMs can be used as a good prognostic indicator in glioma patients. By analyzing comprehensive bioinformatics data, we uncovered the underlying signaling pathway of the DEGs between glioma patients with high and low expression level of CD68. Furthermore, the 6 hub genes identified were closely associated with TAMs in glioma microenvironment and need further investigation.
Collapse
|
17
|
Smedley NF, El-Saden S, Hsu W. Discovering and interpreting transcriptomic drivers of imaging traits using neural networks. Bioinformatics 2020; 36:3537-3548. [PMID: 32101278 DOI: 10.1093/bioinformatics/btaa126] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 01/07/2020] [Accepted: 02/19/2020] [Indexed: 12/20/2022] Open
Abstract
MOTIVATION Cancer heterogeneity is observed at multiple biological levels. To improve our understanding of these differences and their relevance in medicine, approaches to link organ- and tissue-level information from diagnostic images and cellular-level information from genomics are needed. However, these 'radiogenomic' studies often use linear or shallow models, depend on feature selection, or consider one gene at a time to map images to genes. Moreover, no study has systematically attempted to understand the molecular basis of imaging traits based on the interpretation of what the neural network has learned. These studies are thus limited in their ability to understand the transcriptomic drivers of imaging traits, which could provide additional context for determining clinical outcomes. RESULTS We present a neural network-based approach that takes high-dimensional gene expression data as input and performs non-linear mapping to an imaging trait. To interpret the models, we propose gene masking and gene saliency to extract learned relationships from radiogenomic neural networks. In glioblastoma patients, our models outperformed comparable classifiers (>0.10 AUC) and our interpretation methods were validated using a similar model to identify known relationships between genes and molecular subtypes. We found that tumor imaging traits had specific transcription patterns, e.g. edema and genes related to cellular invasion, and 10 radiogenomic traits were significantly predictive of survival. We demonstrate that neural networks can model transcriptomic heterogeneity to reflect differences in imaging and can be used to derive radiogenomic traits with clinical value. AVAILABILITY AND IMPLEMENTATION https://github.com/novasmedley/deepRadiogenomics. CONTACT whsu@mednet.ucla.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Nova F Smedley
- Medical & Imaging Informatics.,Department of Radiological Sciences.,Department of Bioengineering
| | - Suzie El-Saden
- Medical & Imaging Informatics.,Department of Radiological Sciences
| | - William Hsu
- Medical & Imaging Informatics.,Department of Radiological Sciences.,Department of Bioengineering.,Bioinformatics IDP, University of California Los Angeles, Los Angeles, CA 90024, USA
| |
Collapse
|
18
|
Fu X, Wu C, Han N, Liu N, Han S, Liu X, Li S, Yan C. Depressive and anxiety disorders worsen the prognosis of glioblastoma. Aging (Albany NY) 2020; 12:20095-20110. [PMID: 33113511 PMCID: PMC7655183 DOI: 10.18632/aging.103593] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2020] [Accepted: 05/25/2020] [Indexed: 12/14/2022]
Abstract
Glioblastoma multiforme (GBM) is one of the most malignant tumors. Depressive and anxiety disorders may co-exist with GBM. We investigated whether depression and anxiety influenced the outcomes of GBM. The Patient Health Questionnaire 9-item (PHQ-9) and Generalized Anxiety Disorder 7-item (GAD-7) scales were used to investigate the mental condition of GBM patients in our department, and the overall survival times of these patients were monitored. The scores on both scales were higher in GBM patients than in healthy controls. For each scale, GBM patients were divided into high- and low-score groups based on the average score. The prognosis was poorer for GBM patients in the high-score groups than for those in the low-score groups. Moreover, magnetic resonance imaging revealed that tumor necrosis was more prevalent among high-scored GBM patients. Cellular experiments were performed on primary GBM cells from patients with either high or low scores on both scales. Sphere formation, EdU and wound healing assays revealed greater proliferation and invasion capacities in GBM cells from patients with high scores on both scales. Western blotting assay revealed significantly different expression of epithelial and mesenchymal markers between the two groups. Thus, our analysis revealed a clinically important correlation between depression/anxiety and GBM prognosis.
Collapse
Affiliation(s)
- Xiaojun Fu
- Sanbo Brain Hospital, Capital Medical University, Beijing, PR China.,Capital Medical University, Beijing, PR China
| | - Chenxing Wu
- Sanbo Brain Hospital, Capital Medical University, Beijing, PR China
| | - Ning Han
- Department of Neurosurgery, Chinese PLA Tianjin Rehabilitation and Recuperation Center of Joint Service Support Force, Tianjin, PR China
| | - Ning Liu
- Sanbo Brain Hospital, Capital Medical University, Beijing, PR China
| | - Song Han
- Sanbo Brain Hospital, Capital Medical University, Beijing, PR China
| | - Xuebin Liu
- Zhong Guang Tianyi Bio Technology Co., Ltd., Beijing, China
| | - Shouwei Li
- Sanbo Brain Hospital, Capital Medical University, Beijing, PR China
| | - Changxiang Yan
- Sanbo Brain Hospital, Capital Medical University, Beijing, PR China
| |
Collapse
|
19
|
Neutrophil-induced ferroptosis promotes tumor necrosis in glioblastoma progression. Nat Commun 2020; 11:5424. [PMID: 33110073 PMCID: PMC7591536 DOI: 10.1038/s41467-020-19193-y] [Citation(s) in RCA: 219] [Impact Index Per Article: 54.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 10/02/2020] [Indexed: 12/16/2022] Open
Abstract
Tumor necrosis commonly exists and predicts poor prognoses in many cancers. Although it is thought to result from chronic ischemia, the underlying nature and mechanisms driving the involved cell death remain obscure. Here, we show that necrosis in glioblastoma (GBM) involves neutrophil-triggered ferroptosis. In a hyperactivated transcriptional coactivator with PDZ-binding motif-driven GBM mouse model, neutrophils coincide with necrosis temporally and spatially. Neutrophil depletion dampens necrosis. Neutrophils isolated from mouse brain tumors kill cocultured tumor cells. Mechanistically, neutrophils induce iron-dependent accumulation of lipid peroxides within tumor cells by transferring myeloperoxidase-containing granules into tumor cells. Inhibition or depletion of myeloperoxidase suppresses neutrophil-induced tumor cell cytotoxicity. Intratumoral glutathione peroxidase 4 overexpression or acyl-CoA synthetase long chain family member 4 depletion diminishes necrosis and aggressiveness of tumors. Furthermore, analyses of human GBMs support that neutrophils and ferroptosis are associated with necrosis and predict poor survival. Thus, our study identifies ferroptosis as the underlying nature of necrosis in GBMs and reveals a pro-tumorigenic role of ferroptosis. Together, we propose that certain tumor damage(s) occurring during early tumor progression (i.e. ischemia) recruits neutrophils to the site of tissue damage and thereby results in a positive feedback loop, amplifying GBM necrosis development to its fullest extent. Tumour necrosis is associated with tumour aggressiveness and poor outcomes in patients with glioblastomas, but the underlying mechanisms remain poorly understood. Here, the authors show that in a xenograft mouse model of glioblastoma, tumour-infiltrating neutrophils amplify necrosis by promoting myeloperoxidase-induced tumour cell ferroptosis.
Collapse
|
20
|
Chelebian E, Fuster-Garcia E, Álvarez-Torres MDM, Juan-Albarracín J, García-Gómez JM. Higher vascularity at infiltrated peripheral edema differentiates proneural glioblastoma subtype. PLoS One 2020; 15:e0232500. [PMID: 33052913 PMCID: PMC7556526 DOI: 10.1371/journal.pone.0232500] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Accepted: 09/29/2020] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND AND PURPOSE Genetic classifications are crucial for understanding the heterogeneity of glioblastoma. Recently, perfusion MRI techniques have demonstrated associations molecular alterations. In this work, we investigated whether perfusion markers within infiltrated peripheral edema were associated with proneural, mesenchymal, classical and neural subtypes. MATERIALS AND METHODS ONCOhabitats open web services were used to obtain the cerebral blood volume at the infiltrated peripheral edema for MRI studies of 50 glioblastoma patients from The Cancer Imaging Archive: TCGA-GBM. ANOVA and Kruskal-Wallis tests were carried out in order to assess the association between vascular features and the Verhaak subtypes. For assessing specific differences, Mann-Whitney U-test was conducted. Finally, the association of overall survival with molecular and vascular features was assessed using univariate and multivariate Cox models. RESULTS ANOVA and Kruskal-Wallis tests for the maximum cerebral blood volume at the infiltrated peripheral edema between the four subclasses yielded false discovery rate corrected p-values of <0.001 and 0.02, respectively. This vascular feature was significantly higher (p = 0.0043) in proneural patients compared to the rest of the subtypes while conducting Mann-Whitney U-test. The multivariate Cox model pointed to redundant information provided by vascular features at the peripheral edema and proneural subtype when analyzing overall survival. CONCLUSIONS Higher relative cerebral blood volume at infiltrated peripheral edema is associated with proneural glioblastoma subtype suggesting underlying vascular behavior related to molecular composition in that area.
Collapse
Affiliation(s)
- Eduard Chelebian
- Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politècnica de València, València, Spain.,Department of Information Technology, Uppsala University, Uppsala, Sweden
| | | | - María Del Mar Álvarez-Torres
- Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politècnica de València, València, Spain
| | - Javier Juan-Albarracín
- Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politècnica de València, València, Spain
| | - Juan M García-Gómez
- Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politècnica de València, València, Spain
| |
Collapse
|
21
|
Witt JS, Rosenberg SA, Bassetti MF. MRI-guided adaptive radiotherapy for liver tumours: visualising the future. Lancet Oncol 2020; 21:e74-e82. [PMID: 32007208 DOI: 10.1016/s1470-2045(20)30034-6] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2019] [Revised: 12/01/2019] [Accepted: 12/02/2019] [Indexed: 12/12/2022]
Abstract
MRI-guided radiotherapy is a novel and rapidly evolving technology that might enhance the risk-benefit ratio. Through direct visualisation of the tumour and the nearby healthy tissues, the radiation oncologist can deliver highly accurate treatment even to mobile targets. Each individual treatment can be customised to changing anatomy, potentially reducing the risk of radiation-related toxicities while simultaneously increasing the dose delivered to the tumour. MRI-guided radiotherapy offers a new tool for the radiation oncologist, and creates an opportunity to achieve durable local control of liver tumours that might not otherwise be possible. Future work will allow us to expand the population eligible for curative-intent radiotherapy, optimise and customise radiation doses to specific tumours, and hopefully create opportunities for improving outcomes through machine learning and radiomics-based approaches. This Review outlines the current and future applications for MRI-guided radiotherapy with respect to metastatic and primary liver cancers.
Collapse
Affiliation(s)
- Jacob S Witt
- Department of Human Oncology, University of Wisconsin-Madison, Madison, WI, USA
| | - Stephen A Rosenberg
- Department of Radiation Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Michael F Bassetti
- Department of Human Oncology, University of Wisconsin-Madison, Madison, WI, USA.
| |
Collapse
|
22
|
Stringfield O, Arrington JA, Johnston SK, Rognin NG, Peeri NC, Balagurunathan Y, Jackson PR, Clark-Swanson KR, Swanson KR, Egan KM, Gatenby RA, Raghunand N. Multiparameter MRI Predictors of Long-Term Survival in Glioblastoma Multiforme. ACTA ACUST UNITED AC 2020; 5:135-144. [PMID: 30854451 PMCID: PMC6403044 DOI: 10.18383/j.tom.2018.00052] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Standard-of-care multiparameter magnetic resonance imaging (MRI) scans of the brain were used to objectively subdivide glioblastoma multiforme (GBM) tumors into regions that correspond to variations in blood flow, interstitial edema, and cellular density. We hypothesized that the distribution of these distinct tumor ecological "habitats" at the time of presentation will impact the course of the disease. We retrospectively analyzed initial MRI scans in 2 groups of patients diagnosed with GBM, a long-term survival group comprising subjects who survived >36 month postdiagnosis, and a short-term survival group comprising subjects who survived ≤19 month postdiagnosis. The single-institution discovery cohort contained 22 subjects in each group, while the multi-institution validation cohort contained 15 subjects per group. MRI voxel intensities were calibrated, and tumor voxels clustered on contrast-enhanced T1-weighted and fluid-attenuated inversion-recovery (FLAIR) images into 6 distinct "habitats" based on low- to medium- to high-contrast enhancement and low-high signal on FLAIR scans. Habitat 6 (high signal on calibrated contrast-enhanced T1-weighted and FLAIR sequences) comprised a significantly higher volume fraction of tumors in the long-term survival group (discovery cohort, 35% ± 6.5%; validation cohort, 34% ± 4.8%) compared with tumors in the short-term survival group (discovery cohort, 17% ± 4.5%, P < .03; validation cohort, 16 ± 4.0%, P < .007). Of the 6 distinct MRI-defined habitats, the fractional tumor volume of habitat 6 at diagnosis was significantly predictive of long- or short-term survival. We discuss a possible mechanistic basis for this association and implications for habitat-driven adaptive therapy of GBM.
Collapse
Affiliation(s)
| | - John A Arrington
- Departments of Diagnostic & Interventional Radiology.,Department of Oncologic Sciences, University of S Florida, Tampa, FL
| | - Sandra K Johnston
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ.,Department of Radiology, University of Washington, Seattle, WA; and
| | | | - Noah C Peeri
- Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL
| | | | - Pamela R Jackson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Kamala R Clark-Swanson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Kristin R Swanson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Kathleen M Egan
- Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL.,Department of Oncologic Sciences, University of S Florida, Tampa, FL
| | - Robert A Gatenby
- Departments of Diagnostic & Interventional Radiology.,Department of Oncologic Sciences, University of S Florida, Tampa, FL
| | - Natarajan Raghunand
- Cancer Physiology, and.,Department of Oncologic Sciences, University of S Florida, Tampa, FL
| |
Collapse
|
23
|
Lo Gullo R, Daimiel I, Morris EA, Pinker K. Combining molecular and imaging metrics in cancer: radiogenomics. Insights Imaging 2020; 11:1. [PMID: 31901171 PMCID: PMC6942081 DOI: 10.1186/s13244-019-0795-6] [Citation(s) in RCA: 106] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 09/25/2019] [Indexed: 02/07/2023] Open
Abstract
Background Radiogenomics is the extension of radiomics through the combination of genetic and radiomic data. Because genetic testing remains expensive, invasive, and time-consuming, and thus unavailable for all patients, radiogenomics may play an important role in providing accurate imaging surrogates which are correlated with genetic expression, thereby serving as a substitute for genetic testing. Main body In this article, we define the meaning of radiogenomics and the difference between radiomics and radiogenomics. We provide an up-to-date review of the radiomics and radiogenomics literature in oncology, focusing on breast, brain, gynecological, liver, kidney, prostate and lung malignancies. We also discuss the current challenges to radiogenomics analysis. Conclusion Radiomics and radiogenomics are promising to increase precision in diagnosis, assessment of prognosis, and prediction of treatment response, providing valuable information for patient care throughout the course of the disease, given that this information is easily obtainable with imaging. Larger prospective studies and standardization will be needed to define relevant imaging biomarkers before they can be implemented into the clinical workflow.
Collapse
Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, New York, NY, 10065, USA.
| | - Isaac Daimiel
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, New York, NY, 10065, USA
| | - Elizabeth A Morris
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, New York, NY, 10065, USA
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, New York, NY, 10065, USA.,Department of Biomedical Imaging and Image-guided Therapy, Molecular and Gender Imaging Service, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Wien, Austria
| |
Collapse
|
24
|
Wijethilake N, Islam M, Meedeniya D, Chitraranjan C, Perera I, Ren H. Radiogenomics of Glioblastoma: Identification of Radiomics Associated with Molecular Subtypes. MACHINE LEARNING IN CLINICAL NEUROIMAGING AND RADIOGENOMICS IN NEURO-ONCOLOGY 2020. [DOI: 10.1007/978-3-030-66843-3_22] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
|
25
|
Rathore S, Akbari H, Bakas S, Pisapia JM, Shukla G, Rudie JD, Da X, Davuluri RV, Dahmane N, O'Rourke DM, Davatzikos C. Multivariate Analysis of Preoperative Magnetic Resonance Imaging Reveals Transcriptomic Classification of de novo Glioblastoma Patients. Front Comput Neurosci 2019; 13:81. [PMID: 31920606 PMCID: PMC6923885 DOI: 10.3389/fncom.2019.00081] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Accepted: 11/12/2019] [Indexed: 12/30/2022] Open
Abstract
Glioblastoma, the most frequent primary malignant brain neoplasm, is genetically diverse and classified into four transcriptomic subtypes, i. e., classical, mesenchymal, proneural, and neural. Currently, detection of transcriptomic subtype is based on ex vivo analysis of tissue that does not capture the spatial tumor heterogeneity. In view of accumulative evidence of in vivo imaging signatures summarizing molecular features of cancer, this study seeks robust non-invasive radiographic markers of transcriptomic classification of glioblastoma, based solely on routine clinically-acquired imaging sequences. A pre-operative retrospective cohort of 112 pathology-proven de novo glioblastoma patients, having multi-parametric MRI (T1, T1-Gd, T2, T2-FLAIR), collected from the Hospital of the University of Pennsylvania were included. Following tumor segmentation into distinct radiographic sub-regions, diverse imaging features were extracted and support vector machines were employed to multivariately integrate these features and derive an imaging signature of transcriptomic subtype. Extracted features included intensity distributions, volume, morphology, statistics, tumors' anatomical location, and texture descriptors for each tumor sub-region. The derived signature was evaluated against the transcriptomic subtype of surgically-resected tissue specimens, using a 5-fold cross-validation method and a receiver-operating-characteristics analysis. The proposed model was 71% accurate in distinguishing among the four transcriptomic subtypes. The accuracy (sensitivity/specificity) for distinguishing each subtype (classical, mesenchymal, proneural, neural) from the rest was equal to 88.4% (71.4/92.3), 75.9% (83.9/72.8), 82.1% (73.1/84.9), and 75.9% (79.4/74.4), respectively. The findings were also replicated in The Cancer Genomic Atlas glioblastoma dataset. The obtained imaging signature for the classical subtype was dominated by associations with features related to edge sharpness, whereas for the mesenchymal subtype had more pronounced presence of higher T2 and T2-FLAIR signal in edema, and higher volume of enhancing tumor and edema. The proneural and neural subtypes were characterized by the lower T1-Gd signal in enhancing tumor and higher T2-FLAIR signal in edema, respectively. Our results indicate that quantitative multivariate analysis of features extracted from clinically-acquired MRI may provide a radiographic biomarker of the transcriptomic profile of glioblastoma. Importantly our findings can be influential in surgical decision-making, treatment planning, and assessment of inoperable tumors.
Collapse
Affiliation(s)
- Saima Rathore
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Hamed Akbari
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Jared M Pisapia
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.,Division of Neurosurgery, Children Hospital of Philadelphia, Philadelphia, PA, United States
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.,Christiana Care Health System, Philadelphia, PA, United States
| | - Jeffrey D Rudie
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Xiao Da
- Brigham and Women's Hospital, Boston, MA, United States
| | - Ramana V Davuluri
- Department of Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Nadia Dahmane
- Department of Neurological Surgery, Weill Cornell Medicine, New York, NY, United States
| | - Donald M O'Rourke
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| |
Collapse
|
26
|
Rebsamen M, Knecht U, Reyes M, Wiest R, Meier R, McKinley R. Divide and Conquer: Stratifying Training Data by Tumor Grade Improves Deep Learning-Based Brain Tumor Segmentation. Front Neurosci 2019; 13:1182. [PMID: 31749678 PMCID: PMC6848279 DOI: 10.3389/fnins.2019.01182] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 10/18/2019] [Indexed: 11/13/2022] Open
Abstract
It is a general assumption in deep learning that more training data leads to better performance, and that models will learn to generalize well across heterogeneous input data as long as that variety is represented in the training set. Segmentation of brain tumors is a well-investigated topic in medical image computing, owing primarily to the availability of a large publicly-available dataset arising from the long-running yearly Multimodal Brain Tumor Segmentation (BraTS) challenge. Research efforts and publications addressing this dataset focus predominantly on technical improvements of model architectures and less on properties of the underlying data. Using the dataset and the method ranked third in the BraTS 2018 challenge, we performed experiments to examine the impact of tumor type on segmentation performance. We propose to stratify the training dataset into high-grade glioma (HGG) and low-grade glioma (LGG) subjects and train two separate models. Although we observed only minor gains in overall mean dice scores by this stratification, examining case-wise rankings of individual subjects revealed statistically significant improvements. Compared to a baseline model trained on both HGG and LGG cases, two separately trained models led to better performance in 64.9% of cases (p < 0.0001) for the tumor core. An analysis of subjects which did not profit from stratified training revealed that cases were missegmented which had poor image quality, or which presented clinically particularly challenging cases (e.g., underrepresented subtypes such as IDH1-mutant tumors), underlining the importance of such latent variables in the context of tumor segmentation. In summary, we found that segmentation models trained on the BraTS 2018 dataset, stratified according to tumor type, lead to a significant increase in segmentation performance. Furthermore, we demonstrated that this gain in segmentation performance is evident in the case-wise ranking of individual subjects but not in summary statistics. We conclude that it may be useful to consider the segmentation of brain tumors of different types or grades as separate tasks, rather than developing one tool to segment them all. Consequently, making this information available for the test data should be considered, potentially leading to a more clinically relevant BraTS competition.
Collapse
Affiliation(s)
- Michael Rebsamen
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Urspeter Knecht
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
| | - Mauricio Reyes
- Healthcare Imaging A.I. Lab, Insel Data Science Center, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Raphael Meier
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Richard McKinley
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| |
Collapse
|
27
|
AlRayahi J, Zapotocky M, Ramaswamy V, Hanagandi P, Branson H, Mubarak W, Raybaud C, Laughlin S. Pediatric Brain Tumor Genetics: What Radiologists Need to Know. Radiographics 2019; 38:2102-2122. [PMID: 30422762 DOI: 10.1148/rg.2018180109] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Brain tumors are the most common solid tumors in the pediatric population. Pediatric neuro-oncology has changed tremendously during the past decade owing to ongoing genomic advances. The diagnosis, prognosis, and treatment of pediatric brain tumors are now highly reliant on the genetic profile and histopathologic features of the tumor rather than the histopathologic features alone, which previously were the reference standard. The clinical information expected to be gleaned from radiologic interpretations also has evolved. Imaging is now expected to not only lead to a relevant short differential diagnosis but in certain instances also aid in predicting the specific tumor and subtype and possibly the prognosis. These processes fall under the umbrella of radiogenomics. Therefore, to continue to actively participate in patient care and/or radiogenomic research, it is important that radiologists have a basic understanding of the molecular mechanisms of common pediatric central nervous system tumors. The genetic features of pediatric low-grade gliomas, high-grade gliomas, medulloblastomas, and ependymomas are reviewed; differences between pediatric and adult gliomas are highlighted; and the critical oncogenic pathways of each tumor group are described. The role of the mitogen-activated protein kinase pathway in pediatric low-grade gliomas and of histone mutations as epigenetic regulators in pediatric high-grade gliomas is emphasized. In addition, the oncogenic drivers responsible for medulloblastoma, the classification of ependymomas, and the associated imaging correlations and clinical implications are discussed. ©RSNA, 2018.
Collapse
Affiliation(s)
- Jehan AlRayahi
- From the Departments of Diagnostic Imaging (J.A., W.M.), Neurooncology (M.Z., V.R.), and Pediatric Neuroradiology (H.B., C.R., S.L.), The Hospital for Sick Children, University of Toronto, 555 University Ave, Toronto, ON, Canada M5G 1X8; and Departments of Diagnostic Imaging (J.A., P.H.) and Pediatric Interventional Radiology (W.M.), Sidra Medical and Research Center, Doha, Ad Dawhah, Qatar
| | - Michal Zapotocky
- From the Departments of Diagnostic Imaging (J.A., W.M.), Neurooncology (M.Z., V.R.), and Pediatric Neuroradiology (H.B., C.R., S.L.), The Hospital for Sick Children, University of Toronto, 555 University Ave, Toronto, ON, Canada M5G 1X8; and Departments of Diagnostic Imaging (J.A., P.H.) and Pediatric Interventional Radiology (W.M.), Sidra Medical and Research Center, Doha, Ad Dawhah, Qatar
| | - Vijay Ramaswamy
- From the Departments of Diagnostic Imaging (J.A., W.M.), Neurooncology (M.Z., V.R.), and Pediatric Neuroradiology (H.B., C.R., S.L.), The Hospital for Sick Children, University of Toronto, 555 University Ave, Toronto, ON, Canada M5G 1X8; and Departments of Diagnostic Imaging (J.A., P.H.) and Pediatric Interventional Radiology (W.M.), Sidra Medical and Research Center, Doha, Ad Dawhah, Qatar
| | - Prasad Hanagandi
- From the Departments of Diagnostic Imaging (J.A., W.M.), Neurooncology (M.Z., V.R.), and Pediatric Neuroradiology (H.B., C.R., S.L.), The Hospital for Sick Children, University of Toronto, 555 University Ave, Toronto, ON, Canada M5G 1X8; and Departments of Diagnostic Imaging (J.A., P.H.) and Pediatric Interventional Radiology (W.M.), Sidra Medical and Research Center, Doha, Ad Dawhah, Qatar
| | - Helen Branson
- From the Departments of Diagnostic Imaging (J.A., W.M.), Neurooncology (M.Z., V.R.), and Pediatric Neuroradiology (H.B., C.R., S.L.), The Hospital for Sick Children, University of Toronto, 555 University Ave, Toronto, ON, Canada M5G 1X8; and Departments of Diagnostic Imaging (J.A., P.H.) and Pediatric Interventional Radiology (W.M.), Sidra Medical and Research Center, Doha, Ad Dawhah, Qatar
| | - Walid Mubarak
- From the Departments of Diagnostic Imaging (J.A., W.M.), Neurooncology (M.Z., V.R.), and Pediatric Neuroradiology (H.B., C.R., S.L.), The Hospital for Sick Children, University of Toronto, 555 University Ave, Toronto, ON, Canada M5G 1X8; and Departments of Diagnostic Imaging (J.A., P.H.) and Pediatric Interventional Radiology (W.M.), Sidra Medical and Research Center, Doha, Ad Dawhah, Qatar
| | - Charles Raybaud
- From the Departments of Diagnostic Imaging (J.A., W.M.), Neurooncology (M.Z., V.R.), and Pediatric Neuroradiology (H.B., C.R., S.L.), The Hospital for Sick Children, University of Toronto, 555 University Ave, Toronto, ON, Canada M5G 1X8; and Departments of Diagnostic Imaging (J.A., P.H.) and Pediatric Interventional Radiology (W.M.), Sidra Medical and Research Center, Doha, Ad Dawhah, Qatar
| | - Suzanne Laughlin
- From the Departments of Diagnostic Imaging (J.A., W.M.), Neurooncology (M.Z., V.R.), and Pediatric Neuroradiology (H.B., C.R., S.L.), The Hospital for Sick Children, University of Toronto, 555 University Ave, Toronto, ON, Canada M5G 1X8; and Departments of Diagnostic Imaging (J.A., P.H.) and Pediatric Interventional Radiology (W.M.), Sidra Medical and Research Center, Doha, Ad Dawhah, Qatar
| |
Collapse
|
28
|
Jackson CM, Choi J, Lim M. Mechanisms of immunotherapy resistance: lessons from glioblastoma. Nat Immunol 2019; 20:1100-1109. [PMID: 31358997 DOI: 10.1038/s41590-019-0433-y] [Citation(s) in RCA: 407] [Impact Index Per Article: 81.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 05/22/2019] [Indexed: 01/25/2023]
Abstract
Glioblastoma (GBM) is the deadliest form of brain cancer, with a median survival of less than 2 years despite surgical resection, radiation, and chemotherapy. GBM's rapid progression, resistance to therapy, and inexorable recurrence have been attributed to several factors, including its rapid growth rate, its molecular heterogeneity, its propensity to infiltrate vital brain structures, the regenerative capacity of treatment-resistant cancer stem cells, and challenges in achieving high concentrations of chemotherapeutic agents in the central nervous system. Escape from immunosurveillance is increasingly recognized as a landmark event in cancer biology. Translation of this framework to clinical oncology has positioned immunotherapy as a pillar of cancer treatment. Amid the bourgeoning successes of cancer immunotherapy, GBM has emerged as a model of resistance to immunotherapy. Here we review the mechanisms of immunotherapy resistance in GBM and discuss how insights into GBM-immune system interactions might inform the next generation of immunotherapeutics for GBM and other resistant pathologies.
Collapse
Affiliation(s)
- Christopher M Jackson
- Department of Neurosurgery, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - John Choi
- Department of Neurosurgery, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael Lim
- Department of Neurosurgery, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| |
Collapse
|
29
|
Fathi Kazerooni A, Bakas S, Saligheh Rad H, Davatzikos C. Imaging signatures of glioblastoma molecular characteristics: A radiogenomics review. J Magn Reson Imaging 2019; 52:54-69. [PMID: 31456318 DOI: 10.1002/jmri.26907] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 08/09/2019] [Indexed: 02/06/2023] Open
Abstract
Over the past few decades, the advent and development of genomic assessment methods and computational approaches have raised the hopes for identifying therapeutic targets that may aid in the treatment of glioblastoma. However, the targeted therapies have barely been successful in their effort to cure glioblastoma patients, leaving them with a grim prognosis. Glioblastoma exhibits high heterogeneity, both spatially and temporally. The existence of different genetic subpopulations in glioblastoma allows this tumor to adapt itself to environmental forces. Therefore, patients with glioblastoma respond poorly to the prescribed therapies, as treatments are directed towards the whole tumor and not to the specific genetic subregions. Genomic alterations within the tumor develop distinct radiographic phenotypes. In this regard, MRI plays a key role in characterizing molecular signatures of glioblastoma, based on regional variations and phenotypic presentation of the tumor. Radiogenomics has emerged as a (relatively) new field of research to explore the connections between genetic alterations and imaging features. Radiogenomics offers numerous advantages, including noninvasive and global assessment of the tumor and its response to therapies. In this review, we summarize the potential role of radiogenomic techniques to stratify patients according to their specific tumor characteristics with the goal of designing patient-specific therapies. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:54-69.
Collapse
Affiliation(s)
- Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Hamidreza Saligheh Rad
- Quantitative MR Imaging and Spectroscopy Group (QMISG), Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| |
Collapse
|
30
|
Prediction of IDH1 Mutation Status in Glioblastoma Using Machine Learning Technique Based on Quantitative Radiomic Data. World Neurosurg 2019; 125:e688-e696. [DOI: 10.1016/j.wneu.2019.01.157] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 01/14/2019] [Accepted: 01/17/2019] [Indexed: 12/22/2022]
|
31
|
Ozturk K, Soylu E, Tolunay S, Narter S, Hakyemez B. Dynamic Contrast-Enhanced T1-Weighted Perfusion Magnetic Resonance Imaging Identifies Glioblastoma Immunohistochemical Biomarkers via Tumoral and Peritumoral Approach: A Pilot Study. World Neurosurg 2019; 128:e195-e208. [PMID: 31003026 DOI: 10.1016/j.wneu.2019.04.089] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 04/08/2019] [Accepted: 04/09/2019] [Indexed: 12/13/2022]
Abstract
OBJECTIVE We aimed to evaluate the usefulness of dynamic contrast-enhanced T1-weighted perfusion magnetic resonance imaging (DCE-pMRI) to predict certain immunohistochemical (IHC) biomarkers of glioblastoma (GB) in this pilot study. METHODS We retrospectively reviewed 36 patients (male/female, 25:11; mean age, 53 years; age range, 29-85 years) who had pretreatment DCE-pMRI with IHC analysis of their excised GBs. Regions of interest of the enhancing tumor (ER) and nonenhancing peritumoral region (NER) were used to calculate DCE-pMRI parameters of volume transfer constant, back flux constant, volume of the extravascular extracellular space, initial area under enhancement curve, and maximum slope. IHC biomarkers including Ki-67 labeling index, epidermal growth factor receptor (EGFR), oligodendrocyte transcription factor 2 (OLIG2), isocitrate dehydrogenase 1 (IDH1), and p53 mutation status were determined. The imaging metrics of GB with IHC markers were compared using the Kruskal-Wallis test and Spearman correlation analysis. RESULTS Among 30 patients with available IDH1 status, 14 patients (46.6%) had IDH1 mutation. EGFR amplification was present in 24/36 (66.6%) patients. Mean Ki-67 labeling index was 29% (range, 1.5%-80%). p53 mutation was present in 20/36 GBs (55%), whereas OLIG2 expression was positive in 29/36 GBs (80.5%). Various DCE-pMRI parameters gathered from the ER and NER were significantly correlated with IDH1 mutation, EGFR amplification, and OLIG2 expression (P < 0.05). Ki-67 labeling index showed a strong positive correlation with initial area under enhancement curve (r = 0.619; P < 0.001). CONCLUSIONS DCE-pMRI could determine surrogate IHC biomarkers in GB via tumoral and peritumoral approach, potential targets for individualized treatment protocols.
Collapse
Affiliation(s)
- Kerem Ozturk
- Department of Radiology, Uludag University Faculty of Medicine, Bursa, Turkey
| | - Esra Soylu
- Department of Radiology, Uludag University Faculty of Medicine, Bursa, Turkey
| | - Sahsine Tolunay
- Department of Pathology, Uludag University Faculty of Medicine, Bursa, Turkey
| | - Selin Narter
- Department of Pathology, Uludag University Faculty of Medicine, Bursa, Turkey
| | - Bahattin Hakyemez
- Department of Radiology, Uludag University Faculty of Medicine, Bursa, Turkey.
| |
Collapse
|
32
|
Krivoshapkin AL, Sergeev GS, Gaytan AS, Kalneus LE, Kurbatov VP, Abdullaev OA, Salim N, Bulanov DV, Simonovich AE. Automated Volumetric Analysis of Postoperative Magnetic Resonance Imaging Predicts Survival in Patients with Glioblastoma. World Neurosurg 2019; 126:e1510-e1517. [PMID: 30910753 DOI: 10.1016/j.wneu.2019.03.142] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2018] [Revised: 03/13/2019] [Accepted: 03/14/2019] [Indexed: 11/26/2022]
Abstract
BACKGROUND Glioblastomas (GBMs) are primary brain tumors that are very difficult to treat. Magnetic resonance imaging (MRI) is the reference tool for diagnosis, postoperative control, and follow-up of GBM. The MRI tumor contrast enhancement part serves as a target for surgery. However, there are controversial data about the influence of pre- and postoperative tumor volumetric MRI parameters on overall survival (OS). METHODS Data of 57 patients with GBM were analyzed retrospectively. All patients had maximum safe resection and standard adjuvant treatment. All patients underwent 1.5-T MRI with contrast in the first 24 hours postoperatively. The data of pre- and postoperative volumetric parameters were analyzed using the original software. RESULTS Correlation analysis between the postoperative volume of the tumor contrast enhancement part and the patient's OS revealed a significant level (on the Chaddock scale) of inverse correlation. Residual tumor volume associated with OS of >6 months was determined as <2.5 cm3. The mortality risk in the first 6 months after tumor resection is 3.4 times higher when the tumor remnant is >2.5 cm3 (risk ratio, 3.4; P = 0.0002). CONCLUSIONS The volume of MRI contrast-enhancing GBM remnants after surgery, automatically measured by the software, was a significant predictor for early postoperative progression and death.
Collapse
Affiliation(s)
- Alexey L Krivoshapkin
- Department of Neurosurgery, Novosibirsk State Medical University, Novosibirsk, Russia.
| | - Gleb S Sergeev
- Neurosurgical Department, European Medical Center, Moscow, Russia
| | - Alekey S Gaytan
- Neurosurgical Department, European Medical Center, Moscow, Russia
| | - Leonid E Kalneus
- Physics Department, Novosibirsk State University, Novosibirsk, Russia
| | | | - Orkhan A Abdullaev
- Department of Neurosurgery, Novosibirsk State Medical University, Novosibirsk, Russia
| | - Nidal Salim
- Radiotherapy Center, European Medical Center, Moscow, Russia
| | | | | |
Collapse
|
33
|
Tissue-type mapping of gliomas. NEUROIMAGE-CLINICAL 2018; 21:101648. [PMID: 30630760 PMCID: PMC6411966 DOI: 10.1016/j.nicl.2018.101648] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Revised: 11/05/2018] [Accepted: 12/22/2018] [Indexed: 11/24/2022]
Abstract
Purpose To develop a statistical method of combining multimodal MRI (mMRI) of adult glial brain tumours to generate tissue heterogeneity maps that indicate tumour grade and infiltration margins. Materials and methods We performed a retrospective analysis of mMRI from patients with histological diagnosis of glioma (n = 25). 1H Magnetic Resonance Spectroscopic Imaging (MRSI) was used to label regions of “pure” low- or high-grade tumour across image types. Normal brain and oedema characteristics were defined from healthy controls (n = 10) and brain metastasis patients (n = 10) respectively. Probability density distributions (PDD) for each tissue type were extracted from intensity normalised proton density and T2-weighted images, and p and q diffusion maps. Superpixel segmentation and Bayesian inference was used to produce whole-brain tissue-type maps. Results Total lesion volumes derived automatically from tissue-type maps correlated with those from manual delineation (p < 0.001, r = 0.87). Large high-grade volumes were determined in all grade III & IV (n = 16) tumours, in grade II gemistocytic rich astrocytomas (n = 3) and one astrocytoma with a histological diagnosis of grade II. For patients with known outcome (n = 20), patients with survival time < 2 years (3 grade II, 2 grade III and 10 grade IV) had a high-grade volume significantly greater than zero (Wilcoxon signed rank p < 0.0001) and also significantly greater high grade volume than the 5 grade II patients with survival >2 years (Mann Witney p = 0.0001). Regions classified from mMRI as oedema had non-tumour-like 1H MRS characteristics. Conclusions 1H MRSI can label tumour tissue types to enable development of a mMRI tissue type mapping algorithm, with potential to aid management of patients with glial tumours. Non-Gaussian multimodal MRI characteristics of high and low grade glioma tissue. Bayesian inference of multimodal MRI derives whole brain tumour tissue-type maps. Automated segmentation of normal and tumour tissue volumes. Visualisation of glioma heterogeneity, infiltration, necrosis and vasogenic oedema.
Collapse
|
34
|
Panayides AS, Pattichis MS, Leandrou S, Pitris C, Constantinidou A, Pattichis CS. Radiogenomics for Precision Medicine With a Big Data Analytics Perspective. IEEE J Biomed Health Inform 2018; 23:2063-2079. [PMID: 30596591 DOI: 10.1109/jbhi.2018.2879381] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Precision medicine promises better healthcare delivery by improving clinical practice. Using evidence-based substratification of patients, the objective is to achieve better prognosis, diagnosis, and treatment that will transform existing clinical pathways toward optimizing care for the specific needs of each patient. The wealth of today's healthcare data, often characterized as big data, provides invaluable resources toward new knowledge discovery that has the potential to advance precision medicine. The latter requires interdisciplinary efforts that will capitalize the information, know-how, and medical data of newly formed groups fusing different backgrounds and expertise. The objective of this paper is to provide insights with respect to the state-of-the-art research in precision medicine. More specifically, our goal is to highlight the fundamental challenges in emerging fields of radiomics and radiogenomics by reviewing the case studies of Cancer and Alzheimer's disease, describe the computational challenges from a big data analytics perspective, and discuss standardization and open data initiatives that will facilitate the adoption of precision medicine methods and practices.
Collapse
|
35
|
Garcia GCT, Dhermain FG. The subventricular zone concept: ready for therapeutic implications? Neuro Oncol 2018; 20:1423-1424. [PMID: 30239848 DOI: 10.1093/neuonc/noy147] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Gabriel C T Garcia
- Department of Radiology, Gustave Roussy University Hospital, Villejuif, France
| | - Frederic G Dhermain
- Department of Radiation Oncology, Gustave Roussy University Hospital, Villejuif, France
| |
Collapse
|
36
|
Dreher C, Oberhollenzer J, Meissner JE, Windschuh J, Schuenke P, Regnery S, Sahm F, Bickelhaupt S, Bendszus M, Wick W, Unterberg A, Zaiss M, Bachert P, Ladd ME, Schlemmer HP, Radbruch A, Paech D. Chemical exchange saturation transfer (CEST) signal intensity at 7T MRI of WHO IV° gliomas is dependent on the anatomic location. J Magn Reson Imaging 2018; 49:777-785. [PMID: 30133046 DOI: 10.1002/jmri.26215] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 05/23/2018] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Chemical exchange saturation transfer (CEST) is a novel MRI technique applied to brain tumor patients. PURPOSE To investigate the anatomic location dependence of CEST MRI obtained at 7T and histopathological/molecular parameters in WHO IV° glioma patients. STUDY TYPE Analytic prospective study. POPULATION Twenty-one patients with newly diagnosed WHO IV° gliomas were studied prior to surgery; 11 healthy volunteers were investigated. FIELD STRENGTH/SEQUENCE Conventional MRI (contrast-enhanced, T2 w and diffusion-weighted imaging) at 3T and T2 w and CEST MRI at 7T was performed for patients and both patients and volunteers. ASSESSMENT Mean CEST signal intensities (nuclear-Overhauser-enhancement [NOE], amide-proton-transfer [APT], downfield NOE-suppressed APT [dns-APT]), ADC values, and histopathological/molecular parameters were evaluated with regard to hemisphere location and contact with the subventricular zone. CEST signal intensities of cerebral tissue of healthy volunteers were evaluated with regard to hemisphere discrimination. STATISTICAL TESTS Spearman correlation, Mann-Whitney U-test, Wilcoxon signed-rank-test, Fisher's exact test, and area under the receiver operating curve. RESULTS Maximum APT and dns-APT signal intensities were significantly different in right vs. left hemisphere gliomas (P = 0.037 and P = 0.007), but not in right vs. left hemisphere cerebral tissue of healthy subjects (P = 0.062-0.859). Mean ADC values were significantly decreased in right vs. left hemisphere gliomas (P = 0.044). Mean NOE signal intensity did not differ significantly between gliomas of either hemisphere (P = 0.820), but in case of subventricular zone contact (P = 0.047). A significant correlation was observed between APT and dns-APT and ADC signal intensities (rs = -0.627, P = 0.004 and rs = -0.534, P = 0.019), but not between NOE and ADC (rs = -0.341, P = 0.154). Histopathological/molecular parameters were not significantly different concerning the tumor location (P = 0.104-1.000, P = 0.286-0.696). DATA CONCLUSION APT, dns-APT, and ADC were inversely correlated and depended on the gliomas' hemisphere location. NOE showed significant dependence on subventricular zone contact. Location dependency of APT- and NOE-mediated CEST effects should be considered in clinical investigations of CEST MRI. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:777-785.
Collapse
Affiliation(s)
- Constantin Dreher
- German Cancer Research Center (DKFZ), Division of Radiology, Heidelberg, Germany
| | | | - Jan-Eric Meissner
- German Cancer Research Center (DKFZ), Division of Medical Physics in Radiology, Heidelberg, Germany
| | - Johannes Windschuh
- German Cancer Research Center (DKFZ), Division of Medical Physics in Radiology, Heidelberg, Germany.,Department of Radiology, New York University Langone Medical Center, New York, New York, USA
| | - Patrick Schuenke
- German Cancer Research Center (DKFZ), Division of Medical Physics in Radiology, Heidelberg, Germany
| | - Sebastian Regnery
- Department of Radiooncology, University Hospital Heidelberg, Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.,CCU Neuropathology, German Consortium for Translational Cancer Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Martin Bendszus
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Wolfgang Wick
- Department of Neurology, University Hospital Heidelberg, Heidelberg, Germany
| | - Andreas Unterberg
- Department of Neurosurgery, University Hospital Heidelberg, Heidelberg, Germany
| | - Moritz Zaiss
- Max-Planck-Institute for biological cybernetics, Magnetic Resonance Center, Tuebingen, Germany
| | - Peter Bachert
- German Cancer Research Center (DKFZ), Division of Medical Physics in Radiology, Heidelberg, Germany
| | - Mark E Ladd
- German Cancer Research Center (DKFZ), Division of Medical Physics in Radiology, Heidelberg, Germany.,Faculty of Physics and Astronomy, University of Heidelberg, Germany.,Faculty of Medicine, University of Heidelberg, Germany
| | | | - Alexander Radbruch
- German Cancer Research Center (DKFZ), Division of Radiology, Heidelberg, Germany
| | - Daniel Paech
- German Cancer Research Center (DKFZ), Division of Radiology, Heidelberg, Germany
| |
Collapse
|
37
|
Liu X, Li Y, Sun Z, Li S, Wang K, Fan X, Liu Y, Wang L, Wang Y, Jiang T. Molecular profiles of tumor contrast enhancement: A radiogenomic analysis in anaplastic gliomas. Cancer Med 2018; 7:4273-4283. [PMID: 30117304 PMCID: PMC6144143 DOI: 10.1002/cam4.1672] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2017] [Revised: 06/16/2018] [Accepted: 06/19/2018] [Indexed: 12/15/2022] Open
Abstract
The presence of contrast enhancement (CE) on magnetic resonance (MR) imaging is conventionally regarded as an indicator for tumor malignancy. However, the biological behaviors and molecular mechanism of enhanced tumor are not well illustrated. The aim of this study was to investigate the molecular profiles associated with anaplastic gliomas (AGs) presenting CE on postcontrast T1‐weighted MR imaging. In this retrospective database study, RNA sequencing and MR imaging data of 91 AGs from the Cancer Genome Atlas (TCGA) and 64 from the Chinese Glioma Genome Atlas (CGGA) were collected. Gene set enrichment analysis (GSEA), significant analysis of microarray, generalized linear models, and Least absolute shrinkage and selection operator algorithm were used to explore radiogenomic and prognostic signatures of AG patients. GSEA indicated that angiogenesis and epithelial‐mesenchymal transition were significantly associated with post‐CE. Genes driving immune system response, cell proliferation, and focal adhesions were also significantly enriched. Gene ontology of 237 differential genes indicated consistent results. A 48‐gene signature for CE was identified in TCGA and validated in CGGA dataset (area under the curve = 0.9787). Furthermore, seven genes derived from the CE‐specific signature could stratify AG patients into two subgroups based on overall survival time according to corresponding risk score. Comprehensive analysis of post‐CE and genomic characteristics leads to a better understanding of radiology‐pathology correlations. Our gene signature helps interpret the occurrence of radiological traits and predict clinical outcomes. Additionally, we found nine prognostic quantitative radiomic features of CE and investigated the underlying biological processes of them.
Collapse
Affiliation(s)
- Xing Liu
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yiming Li
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Zhiyan Sun
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Shaowu Li
- Neurological Imaging Center, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Kai Wang
- Department of Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xing Fan
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yuqing Liu
- Molecular Pathology Center, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Lei Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yinyan Wang
- 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
| |
Collapse
|
38
|
Diamandis E, Gabriel CPS, Würtemberger U, Guggenberger K, Urbach H, Staszewski O, Lassmann S, Schnell O, Grauvogel J, Mader I, Heiland DH. MR-spectroscopic imaging of glial tumors in the spotlight of the 2016 WHO classification. J Neurooncol 2018; 139:431-440. [PMID: 29704080 DOI: 10.1007/s11060-018-2881-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Accepted: 03/25/2018] [Indexed: 01/29/2023]
Abstract
BACKGROUND The purpose of this study is to map spatial metabolite differences across three molecular subgroups of glial tumors, defined by the IDH1/2 mutation and 1p19q-co-deletion, using magnetic resonance spectroscopy. This work reports a new MR spectroscopy based classification algorithm by applying a radiomics analytics pipeline. MATERIALS 65 patients received anatomical and chemical shift imaging (5 × 5 × 20 mm voxel size). Tumor regions were segmented and registered to corresponding spectroscopic voxels. Spectroscopic features were computed (n = 860) in a radiomic approach and selected by a classification algorithm. Finally, a random forest machine-learning model was trained to predict the molecular subtypes. RESULTS A cluster analysis identified three robust spectroscopic clusters based on the mean silhouette widths. Molecular subgroups were significantly associated with the computed spectroscopic clusters (Fisher's Exact test p < 0.01). A machine-learning model was trained and validated by public available MRS data (n = 19). The analysis showed an accuracy rate in the Random Forest model by 93.8%. CONCLUSIONS MR spectroscopy is a robust tool for predicting the molecular subtype in gliomas and adds important diagnostic information to the preoperative diagnostic work-up of glial tumor patients. MR-spectroscopy could improve radiological diagnostics in the future and potentially influence clinical and surgical decisions to improve individual tumor treatment.
Collapse
Affiliation(s)
- Elie Diamandis
- Department of Neuroradiology, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Carl Phillip Simon Gabriel
- Department of Neuroradiology, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Urs Würtemberger
- Department of Neuroradiology, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Konstanze Guggenberger
- Department of Neuroradiology, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Horst Urbach
- Department of Neuroradiology, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ori Staszewski
- Institute of Neuropathology, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Silke Lassmann
- Institute for Pathology, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Oliver Schnell
- Department of Neurosurgery, Medical Center - University of Freiburg, Breisacher Straße 64, 79106, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jürgen Grauvogel
- Department of Neurosurgery, Medical Center - University of Freiburg, Breisacher Straße 64, 79106, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Irina Mader
- Department of Neuroradiology, Medical Center - University of Freiburg, Freiburg, Germany
- Clinic for Neuropediatrics and Neurorehabilitation, Epilepsy Center for Children and Adolescents, Schön Klinik, Vogtareuth, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Dieter Henrik Heiland
- Department of Neurosurgery, Medical Center - University of Freiburg, Breisacher Straße 64, 79106, Freiburg, Germany.
- Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| |
Collapse
|
39
|
Peeken JC, Bernhofer M, Wiestler B, Goldberg T, Cremers D, Rost B, Wilkens JJ, Combs SE, Nüsslin F. Radiomics in radiooncology - Challenging the medical physicist. Phys Med 2018; 48:27-36. [PMID: 29728226 DOI: 10.1016/j.ejmp.2018.03.012] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 03/07/2018] [Accepted: 03/20/2018] [Indexed: 02/06/2023] Open
Abstract
PURPOSE Noticing the fast growing translation of artificial intelligence (AI) technologies to medical image analysis this paper emphasizes the future role of the medical physicist in this evolving field. Specific challenges are addressed when implementing big data concepts with high-throughput image data processing like radiomics and machine learning in a radiooncology environment to support clinical decisions. METHODS Based on the experience of our interdisciplinary radiomics working group, techniques for processing minable data, extracting radiomics features and associating this information with clinical, physical and biological data for the development of prediction models are described. A special emphasis was placed on the potential clinical significance of such an approach. RESULTS Clinical studies demonstrate the role of radiomics analysis as an additional independent source of information with the potential to influence the radiooncology practice, i.e. to predict patient prognosis, treatment response and underlying genetic changes. Extending the radiomics approach to integrate imaging, clinical, genetic and dosimetric data ('panomics') challenges the medical physicist as member of the radiooncology team. CONCLUSIONS The new field of big data processing in radiooncology offers opportunities to support clinical decisions, to improve predicting treatment outcome and to stimulate fundamental research on radiation response both of tumor and normal tissue. The integration of physical data (e.g. treatment planning, dosimetric, image guidance data) demands an involvement of the medical physicist in the radiomics approach of radiooncology. To cope with this challenge national and international organizations for medical physics should organize more training opportunities in artificial intelligence technologies in radiooncology.
Collapse
Affiliation(s)
- Jan C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany
| | - Michael Bernhofer
- Department of Informatics, Technical University of Munich (TUM), Boltzmannstraße 3, 85748 Garching, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany
| | | | - Daniel Cremers
- Department of Informatics, Technical University of Munich (TUM), Boltzmannstraße 3, 85748 Garching, Germany
| | - Burkhard Rost
- Department of Informatics, Technical University of Munich (TUM), Boltzmannstraße 3, 85748 Garching, Germany
| | - Jan J Wilkens
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; Institute of Innovative Radiotherapy (iRT), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Ingolstaedter Landstrasse 1, 85764 Neuherberg, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany
| | - Fridtjof Nüsslin
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany.
| |
Collapse
|
40
|
Feng R, Loewenstern J, Aggarwal A, Pawha P, Gilani A, Iloreta AM, Bakst R, Miles B, Bederson J, Costa A, Gupta V, Shrivastava RK. Cerebral Radiation Necrosis: An Analysis of Clinical and Quantitative Imaging and Volumetric Features. World Neurosurg 2018; 111:e485-e494. [DOI: 10.1016/j.wneu.2017.12.104] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 12/14/2017] [Accepted: 12/15/2017] [Indexed: 10/18/2022]
|
41
|
Chiang GC, Kovanlikaya I, Choi C, Ramakrishna R, Magge R, Shungu DC. Magnetic Resonance Spectroscopy, Positron Emission Tomography and Radiogenomics-Relevance to Glioma. Front Neurol 2018; 9:33. [PMID: 29459844 PMCID: PMC5807339 DOI: 10.3389/fneur.2018.00033] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Accepted: 01/15/2018] [Indexed: 12/22/2022] Open
Abstract
Advances in metabolic imaging techniques have allowed for more precise characterization of gliomas, particularly as it relates to tumor recurrence or pseudoprogression. Furthermore, the emerging field of radiogenomics where radiographic features are systemically correlated with molecular markers has the potential to achieve the holy grail of neuro-oncologic neuro-radiology, namely molecular diagnosis without requiring tissue specimens. In this section, we will review the utility of metabolic imaging and discuss the current state of the art related to the radiogenomics of glioblastoma.
Collapse
Affiliation(s)
- Gloria C Chiang
- Department of Neuroradiology, Weill Cornell Medical College, New York, NY, United States
| | - Ilhami Kovanlikaya
- Department of Neuroradiology, Weill Cornell Medical College, New York, NY, United States
| | - Changho Choi
- Radiology, Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Rohan Ramakrishna
- Department of Neurological Surgery, Weill Cornell Medical College, New York, NY, United States
| | - Rajiv Magge
- Department of Neurology, Weill Cornell Medical College, New York, NY, United States
| | - Dikoma C Shungu
- Department of Neuroradiology, Weill Cornell Medical College, New York, NY, United States
| |
Collapse
|
42
|
Iqbal S, Khan MUG, Saba T, Rehman A. Computer-assisted brain tumor type discrimination using magnetic resonance imaging features. Biomed Eng Lett 2018; 8:5-28. [PMID: 30603187 PMCID: PMC6208555 DOI: 10.1007/s13534-017-0050-3] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Revised: 08/15/2017] [Accepted: 09/21/2017] [Indexed: 12/16/2022] Open
Abstract
Medical imaging plays an integral role in the identification, segmentation, and classification of brain tumors. The invention of MRI has opened new horizons for brain-related research. Recently, researchers have shifted their focus towards applying digital image processing techniques to extract, analyze and categorize brain tumors from MRI. Categorization of brain tumors is defined in a hierarchical way moving from major to minor ones. A plethora of work could be seen in literature related to the classification of brain tumors in categories such as benign and malignant. However, there are only a few works reported on the multiclass classification of brain images where each part of the image containing tumor is tagged with major and minor categories. The precise classification is difficult to achieve due to ambiguities in images and overlapping characteristics of different type of tumors. In the current study, a comprehensive review of recent research on brain tumors multiclass classification using MRI is provided. These multiclass classification studies are categorized into two major groups: XX and YY and each group are further divided into three sub-groups. A set of common parameters from the reviewed works is extracted and compared to highlight the merits and demerits of individual works. Based on our analysis, we provide a set of recommendations for researchers and professionals working in the area of brain tumors classification.
Collapse
Affiliation(s)
- Sajid Iqbal
- Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, Pakistan
| | - M. Usman Ghani Khan
- Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, Pakistan
| | - Tanzila Saba
- College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586 Saudi Arabia
| | - Amjad Rehman
- College of Computer and Information Systems, Al-Yamamah University, Riyadh, 11512 Saudi Arabia
| |
Collapse
|
43
|
Noninvasive Glioblastoma Testing: Multimodal Approach to Monitoring and Predicting Treatment Response. DISEASE MARKERS 2018; 2018:2908609. [PMID: 29581794 PMCID: PMC5822799 DOI: 10.1155/2018/2908609] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Accepted: 11/20/2017] [Indexed: 12/30/2022]
Abstract
Glioblastoma is the most aggressive adult primary brain tumor which is incurable despite intensive multimodal treatment. Inter- and intratumoral heterogeneity poses one of the biggest barriers in the diagnosis and treatment of glioblastoma, causing differences in treatment response and outcome. Noninvasive prognostic and predictive tests are highly needed to complement the current armamentarium. Noninvasive testing of glioblastoma uses multiple techniques that can capture the heterogeneity of glioblastoma. This set of diagnostic approaches comprises advanced MRI techniques, nuclear imaging, liquid biopsy, and new integrated approaches including radiogenomics and radiomics. New treatment options such as agents targeted at driver oncogenes and immunotherapy are currently being developed, but benefit for glioblastoma patients still has to be demonstrated. Understanding and unraveling tumor heterogeneity and microenvironment can help to create a treatment regime that is patient-tailored to these specific tumor characteristics. Improved noninvasive tests are crucial to this success. This review discusses multiple diagnostic approaches and their effect on predicting and monitoring treatment response in glioblastoma.
Collapse
|
44
|
Tang Q, Lian Y, Yu J, Wang Y, Shi Z, Chen L. Anatomic mapping of molecular subtypes in diffuse glioma. BMC Neurol 2017; 17:183. [PMID: 28915860 PMCID: PMC5602933 DOI: 10.1186/s12883-017-0961-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 09/04/2017] [Indexed: 01/01/2023] Open
Abstract
Background Tumor location served as an important prognostic factor in glioma patients was considered to postulate molecular features according to cell origin theory. However, anatomic distribution of unique molecular subtypes was not widely investigated. The relationship between molecular phenotype and histological subgroup were also vague based on tumor location. Our group focuses on the study of glioma anatomic location of distinctive molecular subgroups and histology subtypes, and explores the possibility of their consistency based on clinical background. Methods We retrospectively reviewed 143 cases with both molecular information (IDH1/TERT/1p19q) and MRI images diagnosed as cerebral diffuse gliomas. The anatomic distribution was analyzed between distinctive molecular subgroups and its relationship with histological subtypes. The influence of tumor location, molecular stratification and histology diagnosis on survival outcome was investigated as well. Results Anatomic locations of cerebral diffuse glioma indicate varied clinical outcome. Based on that, it can be stratified into five principal molecular subgroups according to IDH1/TERT/1p19q status. Triple-positive (IDH1 and TERT mutation with 1p19q codeletion) glioma tended to be oligodendroglioma present with much better clinical outcome compared to TERT mutation only group who is glioblastoma inclined (median overall survival 39 months VS 18 months). Five molecular subgroups were demonstrated with distinctive locational distribution. This kind of anatomic feature is consistent with its corresponding histological subtypes. Discussion Each molecular subgroup in glioma has unique anatomic location which indicates distinctive clinical outcome. Molecular diagnosis can be served as perfect complementary tool for the precise diagnosis. Integration of histomolecular diagnosis will be much more helpful in routine clinical practice in the future.
Collapse
Affiliation(s)
- Qisheng Tang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Yuxi Lian
- Department of Electronic Engineering, Fudan University, Shanghai, China
| | - Jinhua Yu
- Department of Electronic Engineering, Fudan University, Shanghai, China.
| | - Yuanyuan Wang
- Department of Electronic Engineering, Fudan University, Shanghai, China.
| | - Zhifeng Shi
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
| | - Liang Chen
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| |
Collapse
|
45
|
Tumor image-derived texture features are associated with CD3 T-cell infiltration status in glioblastoma. Oncotarget 2017; 8:101244-101254. [PMID: 29254160 PMCID: PMC5731870 DOI: 10.18632/oncotarget.20643] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Accepted: 08/07/2017] [Indexed: 01/22/2023] Open
Abstract
This study analyzed magnetic resonance imaging (MRI) scans of Glioblastoma (GB) patients to develop an imaging-derived predictive model for assessing the extent of intratumoral CD3 T-cell infiltration. Pre-surgical T1-weighted post-contrast and T2-weighted Fluid-Attenuated-Inversion-Recovery (FLAIR) MRI scans, with corresponding mRNA expression of CD3D/E/G were obtained through The Cancer Genome Atlas (TCGA) for 79 GB patients. The tumor region was contoured and 86 image-derived features were extracted across the T1-post contrast and FLAIR images. Six imaging features—kurtosis, contrast, small zone size emphasis, low gray level zone size emphasis, high gray level zone size emphasis, small zone high gray level emphasis—were found associated with CD3 activity and used to build a predictive model for CD3 infiltration in an independent data set of 69 GB patients (using a 50-50 split for training and testing). For the training set, the image-based prediction model for CD3 infiltration achieved accuracy of 97.1% and area under the curve (AUC) of 0.993. For the test set, the model achieved accuracy of 76.5% and AUC of 0.847. This suggests a relationship between image-derived textural features and CD3 T-cell infiltration enabling the non-invasive inference of intratumoral CD3 T-cell infiltration in GB patients, with potential value for the radiological assessment of response to immune therapeutics.
Collapse
|
46
|
Abstract
Radiogenomics is a relatively new and exciting field within radiology that links different imaging features with diverse genomic events. Genomics advances provided by the Cancer Genome Atlas and the Human Genome Project have enabled us to harness and integrate this information with noninvasive imaging phenotypes to create a better 3-dimensional understanding of tumor behavior and biology. Beyond imaging-histopathology, imaging genomic linkages provide an important layer of complexity that can help in evaluating and stratifying patients into clinical trials, monitoring treatment response, and enhancing patient outcomes. This article reviews some of the important radiogenomic literatures in brain tumors.
Collapse
|
47
|
Abstract
Primary brain tumors, most commonly gliomas, are histopathologically typed and graded as World Health Organization (WHO) grades I-IV according to increasing degrees of malignancy. These grades provide prognostic information and guidance on treatment such as radiation therapy and chemotherapy after surgery. Despite the confirmed value of the WHO grading system, results of a multitude of studies and prospective interventional trials now indicate that tumors with identical morphologic criteria can have highly different outcomes. Molecular markers can allow subtypes of tumors of the same morphologic type and WHO grade to be distinguished and are, therefore, of great interest in personalization of brain tumor treatment. Recent genomic-wide studies have resulted in a far more comprehensive understanding of the genomic alterations in gliomas and provide suggestions for a new molecularly based classification. Magnetic resonance (MR) imaging phenotypes can serve as noninvasive surrogates for tumor genotypes and can provide important information for diagnosis, prognosis, and, eventually, personalized treatment. The newly emerged field of radiogenomics allows specific MR imaging phenotypes to be linked with gene expression profiles. In this article, the authors review the conventional and advanced imaging features of three tumoral genotypes with prognostic and therapeutic consequences: (a) isocitrate dehydrogenase mutation; (b) the combined loss of the short arm of chromosome 1 and the long arm of chromosome 19, or 1p19q codeletion; and (c) methylguanine methyltransferase promoter methylation. © RSNA, 2017.
Collapse
Affiliation(s)
- Marion Smits
- From the Department of Radiology, Erasmus MC University Medical Center Rotterdam, PO Box 2040, 3000 CA Rotterdam, the Netherlands (M.S.); and Brain Tumor Center, Erasmus MC Cancer Center, Rotterdam, the Netherlands (M.J.v.d.B.)
| | - Martin J van den Bent
- From the Department of Radiology, Erasmus MC University Medical Center Rotterdam, PO Box 2040, 3000 CA Rotterdam, the Netherlands (M.S.); and Brain Tumor Center, Erasmus MC Cancer Center, Rotterdam, the Netherlands (M.J.v.d.B.)
| |
Collapse
|
48
|
Abstract
PURPOSE OF REVIEW Magnetic resonance imaging (MRI) is routinely employed in the diagnosis and clinical management of brain tumors. This review provides an overview of the advancements in the field of MRI, with a particular focus on the quantitative assessment by advanced physiological magnetic resonance techniques in light of the new molecular classification of brain tumor. RECENT FINDINGS Understanding how molecular phenotypes of brain tumors are reflected in noninvasive imaging is the goal of radiogenomics, which aims at determining the association between imaging features and molecular markers in neuro-oncology. Advanced MRI techniques such as diffusion magnetic resonance imaging and perfusion-weighted imaging add important structural, hemodynamic, and physiological information for tumor diagnosis and classification, as well as to stratify tumor response. Magnetic resonance spectroscopy is able to depict with unprecedented accuracy metabolic biomarkers, which are relevant for molecular subtyping. Ultra-high-field imaging enhances anatomical detail and enables to explore new horizon in tumor imaging. SUMMARY The noninvasive MRI-based assessment of tumor malignancy and molecular status may offer the opportunity to predict prognosis and to select patients who may be candidates for individualized targeted therapies, providing more sensitive tools for their follow-up.
Collapse
|
49
|
Taylor EN, Ding Y, Zhu S, Cheah E, Alexander P, Lin L, Aninwene GE, Hoffman MP, Mahajan A, Mohamed AS, McDannold N, Fuller CD, Chen CC, Gilbert RJ. Association between tumor architecture derived from generalized Q-space MRI and survival in glioblastoma. Oncotarget 2017; 8:41815-41826. [PMID: 28404971 PMCID: PMC5522030 DOI: 10.18632/oncotarget.16296] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 01/24/2017] [Indexed: 01/22/2023] Open
Abstract
While it is recognized that the overall resistance of glioblastoma to treatment may be related to intra-tumor patterns of structural heterogeneity, imaging methods to assess such patterns remain rudimentary. METHODS We utilized a generalized Q-space imaging (GQI) algorithm to analyze magnetic resonance imaging (MRI) derived from a rodent model of glioblastoma and 2 clinical datasets to correlate GQI, histology, and survival. RESULTS In a rodent glioblastoma model, GQI demonstrated a poorly coherent core region, consisting of diffusion tracts <5 mm, surrounded by a shell of highly coherent diffusion tracts, 6-25 mm. Histologically, the core region possessed a high degree of necrosis, whereas the shell consisted of organized sheets of anaplastic cells with elevated mitotic index. These attributes define tumor architecture as the macroscopic organization of variably aligned tumor cells. Applied to MRI data from The Cancer Imaging Atlas (TCGA), the core-shell diffusion tract-length ratio (c/s ratio) correlated linearly with necrosis, which, in turn, was inversely associated with survival (p = 0.00002). We confirmed in an independent cohort of patients (n = 62) that the c/s ratio correlated inversely with survival (p = 0.0004). CONCLUSIONS The analysis of MR images by GQI affords insight into tumor architectural patterns in glioblastoma that correlate with biological heterogeneity and clinical outcome.
Collapse
Affiliation(s)
- Erik N. Taylor
- Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
| | - Yao Ding
- Radiation Oncology, University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Shan Zhu
- Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
| | - Eric Cheah
- Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
| | - Phillip Alexander
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Leon Lin
- Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
| | - George E. Aninwene
- Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
| | - Matthew P. Hoffman
- Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
| | - Anita Mahajan
- Radiation Oncology, University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Abdallah S.R. Mohamed
- Radiation Oncology, University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Nathan McDannold
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Clifton D. Fuller
- Radiation Oncology, University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Clark C. Chen
- Center for Theoretical and Applied Neuro-Oncology and Department of Neurosurgery, University of California, San Diego, CA, USA
| | - Richard J. Gilbert
- Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
| |
Collapse
|
50
|
Incoronato M, Aiello M, Infante T, Cavaliere C, Grimaldi AM, Mirabelli P, Monti S, Salvatore M. Radiogenomic Analysis of Oncological Data: A Technical Survey. Int J Mol Sci 2017; 18:ijms18040805. [PMID: 28417933 PMCID: PMC5412389 DOI: 10.3390/ijms18040805] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2016] [Revised: 04/06/2017] [Accepted: 04/08/2017] [Indexed: 12/18/2022] Open
Abstract
In the last few years, biomedical research has been boosted by the technological development of analytical instrumentation generating a large volume of data. Such information has increased in complexity from basic (i.e., blood samples) to extensive sets encompassing many aspects of a subject phenotype, and now rapidly extending into genetic and, more recently, radiomic information. Radiogenomics integrates both aspects, investigating the relationship between imaging features and gene expression. From a methodological point of view, radiogenomics takes advantage of non-conventional data analysis techniques that reveal meaningful information for decision-support in cancer diagnosis and treatment. This survey is aimed to review the state-of-the-art techniques employed in radiomics and genomics with special focus on analysis methods based on molecular and multimodal probes. The impact of single and combined techniques will be discussed in light of their suitability in correlation and predictive studies of specific oncologic diseases.
Collapse
Affiliation(s)
| | - Marco Aiello
- IRCCS SDN, Via E. Gianturco, 113, 80143 Naples, Italy.
| | | | | | | | | | - Serena Monti
- IRCCS SDN, Via E. Gianturco, 113, 80143 Naples, Italy.
| | | |
Collapse
|