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Feng Z, Chen G, Huang Y, Zhang K, Wu G, Xing W, Wu Y, Zhou Y, Sun C. TAK-242 inhibits glioblastoma invasion, migration, and proneural-mesenchymal transition by inhibiting TLR4 signaling. Exp Cell Res 2024; 439:114091. [PMID: 38740168 DOI: 10.1016/j.yexcr.2024.114091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 04/02/2024] [Accepted: 05/11/2024] [Indexed: 05/16/2024]
Abstract
Resatorvid (TAK-242), a small-molecule inhibitor of Toll-like receptor 4 (TLR4), has the ability to cross the blood-brain barrier (BBB). In this study, we explored the role of TAK-242 on glioblastoma (GBM) invasion, migration, and proneural-mesenchymal transition (PMT). RNA sequencing (RNA-Seq) data and full clinical information of glioma patients were downloaded from the Chinese Glioma Genome Atlas (CGGA) and the Cancer Genome Atlas (TCGA) cohorts and then analyzed using R language; patients were grouped based on proneural (PN) and mesenchymal (MES) subtypes. Bioinformatics analysis was used to detect the difference in survival and TLR4-pathway expression between these groups. Cell viability assay, wound-healing test, and transwell assay, as well as an intracranial xenotransplantation mice model, were used to assess the functional role of TAK-242 in GBM in vitro and in vivo. RNA-Seq, Western blot, and immunofluorescence were employed to investigate the possible mechanism. TLR4 expression in GBM was significantly higher than in normal brain tissue and upregulated the expression of MES marker genes. Moreover, TAK-242 inhibited GBM progression in vitro and in vivo via linking with PMT, which could be a novel treatment strategy for inhibiting GBM recurrence.
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Affiliation(s)
- Zibin Feng
- Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Jiangsu, Suzhou 215006, China; Department of Neurosurgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an 710054, Shaanxi Province, China
| | - Guangliang Chen
- Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Jiangsu, Suzhou 215006, China
| | - Yunfan Huang
- Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Jiangsu, Suzhou 215006, China
| | - Kai Zhang
- Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Jiangsu, Suzhou 215006, China
| | - Guanzhang Wu
- Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Jiangsu, Suzhou 215006, China
| | - Weixin Xing
- Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Jiangsu, Suzhou 215006, China
| | - Yue Wu
- Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Jiangsu, Suzhou 215006, China
| | - Youxin Zhou
- Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Jiangsu, Suzhou 215006, China.
| | - Chunming Sun
- Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Jiangsu, Suzhou 215006, China.
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Hermoza R, Nascimento JC, Carneiro G. Weakly-supervised preclinical tumor localization associated with survival prediction from lung cancer screening Chest X-ray images. Comput Med Imaging Graph 2024; 115:102395. [PMID: 38729092 DOI: 10.1016/j.compmedimag.2024.102395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 04/13/2024] [Accepted: 04/30/2024] [Indexed: 05/12/2024]
Abstract
In this paper, we hypothesize that it is possible to localize image regions of preclinical tumors in a Chest X-ray (CXR) image by a weakly-supervised training of a survival prediction model using a dataset containing CXR images of healthy patients and their time-to-death label. These visual explanations can empower clinicians in early lung cancer detection and increase patient awareness of their susceptibility to the disease. To test this hypothesis, we train a censor-aware multi-class survival prediction deep learning classifier that is robust to imbalanced training, where classes represent quantized number of days for time-to-death prediction. Such multi-class model allows us to use post-hoc interpretability methods, such as Grad-CAM, to localize image regions of preclinical tumors. For the experiments, we propose a new benchmark based on the National Lung Cancer Screening Trial (NLST) dataset to test weakly-supervised preclinical tumor localization and survival prediction models, and results suggest that our proposed method shows state-of-the-art C-index survival prediction and weakly-supervised preclinical tumor localization results. To our knowledge, this constitutes a pioneer approach in the field that is able to produce visual explanations of preclinical events associated with survival prediction results.
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Affiliation(s)
- Renato Hermoza
- Australian Institute for Machine Learning, The University of Adelaide, Australia.
| | - Jacinto C Nascimento
- Institute for Systems and Robotics (ISR/IST), LARSyS, Instituto Superior Técnico, Universidade de Lisboa, Portugal.
| | - Gustavo Carneiro
- Centre for Vision, Speech and Signal Processing (CVSSP), The University of Surrey, UK.
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3
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Wu G, Shi Z, Li Z, Xie X, Tang Q, Zhu J, Yang Z, Wang Y, Wu J, Yu J. Study of radiochemotherapy decision-making for young high-risk low-grade glioma patients using a macroscopic and microscopic combined radiomics model. Eur Radiol 2024; 34:2861-2872. [PMID: 37889272 DOI: 10.1007/s00330-023-10378-9] [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: 06/28/2023] [Revised: 09/06/2023] [Accepted: 09/16/2023] [Indexed: 10/28/2023]
Abstract
OBJECTIVES As a few types of glioma, young high-risk low-grade gliomas (HRLGGs) have higher requirements for postoperative quality of life. Although adjuvant chemotherapy with delayed radiotherapy is the first treatment strategy for HRLGGs, not all HRLGGs benefit from it. Accurate assessment of chemosensitivity in HRLGGs is vital for making treatment choices. This study developed a multimodal fusion radiomics (MFR) model to support radiochemotherapy decision-making for HRLGGs. METHODS A MFR model combining macroscopic MRI and microscopic pathological images was proposed. Multiscale features including macroscopic tumor structure and microscopic histological layer and nuclear information were grabbed by unique paradigm, respectively. Then, these features were adaptively incorporated into the MFR model through attention mechanism to predict the chemosensitivity of temozolomide (TMZ) by means of objective response rate and progression free survival (PFS). RESULTS Macroscopic tumor texture complexity and microscopic nuclear size showed significant statistical differences (p < 0.001) between sensitivity and insensitivity groups. The MFR model achieved stable prediction results, with an area under the curve of 0.950 (95% CI: 0.942-0.958), sensitivity of 0.833 (95% CI: 0.780-0.848), specificity of 0.929 (95% CI: 0.914-0.936), positive predictive value of 0.833 (95% CI: 0.811-0.860), and negative predictive value of 0.929 (95% CI: 0.914-0.934). The predictive efficacy of MFR was significantly higher than that of the reported molecular markers (p < 0.001). MFR was also demonstrated to be a predictor of PFS. CONCLUSIONS A MFR model including radiomics and pathological features predicts accurately the response postoperative TMZ treatment. CLINICAL RELEVANCE STATEMENT Our MFR model could identify young high-risk low-grade glioma patients who can have the most benefit from postoperative upfront temozolomide (TMZ) treatment. KEY POINTS • Multimodal radiomics is proposed to support the radiochemotherapy of glioma. • Some macro and micro image markers related to tumor chemotherapy sensitivity are revealed. • The proposed model surpasses reported molecular markers, with a promising area under the curve (AUC) of 0.95.
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Affiliation(s)
- Guoqing Wu
- School of Information Science and Technology, Fudan University, Shanghai, China
- Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China
| | - Zhifeng Shi
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
| | - Zeyang Li
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
| | - Xuan Xie
- School of Information Science and Technology, Fudan University, Shanghai, China
- Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China
| | - Qisheng Tang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
| | - Jingjing Zhu
- Department of Pathology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhong Yang
- Department of Radiology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yuanyuan Wang
- School of Information Science and Technology, Fudan University, Shanghai, China
- Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China
| | - Jinsong Wu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
- National Center for Neurological Disorders, Shanghai, China.
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China.
- Neurosurgical Institute of Fudan University, Shanghai, China.
| | - Jinhua Yu
- School of Information Science and Technology, Fudan University, Shanghai, China.
- Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China.
- The AI Lab of Huashan Hospital, Fudan University, Shanghai, China.
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Ahmed HS, Devaraj T, Singhvi M, Dasan TA, Ranganath P. Radio-anatomical evaluation of clinical and radiomic profile of multi-parametric magnetic resonance imaging of de novo glioblastoma multiforme. J Egypt Natl Canc Inst 2024; 36:13. [PMID: 38644430 DOI: 10.1186/s43046-024-00217-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 03/06/2024] [Indexed: 04/23/2024] Open
Abstract
BACKGROUND Glioblastoma (GBM) is a fatal, fast-growing, and aggressive brain tumor arising from glial cells or their progenitors. It is a primary malignancy with a poor prognosis. The current study aims at evaluating the neuroradiological parameters of de novo GBM by analyzing the brain multi-parametric magnetic resonance imaging (mpMRI) scans acquired from a publicly available database analysis of the scans. METHODS The dataset used was the mpMRI scans for de novo glioblastoma (GBM) patients from the University of Pennsylvania Health System, called the UPENN-GBM dataset. This was a collection from The Cancer Imaging Archive (TCIA), a part of the National Cancer Institute. The MRIs were reviewed by a single diagnostic radiologist, and the tumor parameters were recorded, wherein all recorded data was corroborated with the clinical findings. RESULTS The study included a total of 58 subjects who were predominantly male (male:female ratio of 1.07:1). The mean age with SD was 58.49 (11.39) years. Mean survival days with SD were 347 (416.21) days. The left parietal lobe was the most commonly found tumor location with 11 (18.96%) patients. The mean intensity for T1, T2, and FLAIR with SD was 1.45E + 02 (20.42), 1.11E + 02 (17.61), and 141.64 (30.67), respectively (p = < 0.001). The tumor dimensions of anteroposterior, transverse, and craniocaudal gave a z-score (significance level = 0.05) of - 2.53 (p = 0.01), - 3.89 (p < 0.001), and 1.53 (p = 0.12), respectively. CONCLUSION The current study takes a third-party database and reduces physician bias from interfering with study findings. Further prospective and retrospective studies are needed to provide conclusive data.
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Affiliation(s)
- H Shafeeq Ahmed
- Department of Radio-Diagnosis, Bangalore Medical College and Research Institute, Bangalore, 560002, India.
- Department of Anatomy, Bangalore Medical College and Research Institute, Karnataka, Bangalore, 560002, India.
| | - Trupti Devaraj
- Department of Radio-Diagnosis, Bangalore Medical College and Research Institute, Bangalore, 560002, India
| | - Maanini Singhvi
- Department of Radio-Diagnosis, Bangalore Medical College and Research Institute, Bangalore, 560002, India
- Department of Anatomy, Bangalore Medical College and Research Institute, Karnataka, Bangalore, 560002, India
| | - T Arul Dasan
- Department of Radio-Diagnosis, Bangalore Medical College and Research Institute, Bangalore, 560002, India
| | - Priya Ranganath
- Department of Anatomy, Bangalore Medical College and Research Institute, Karnataka, Bangalore, 560002, India
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Fattahi M, Maghsudlu M, Razipour M, Movahedpour A, Ghadami M, Alizadeh M, Khatami SH, Taheri-Anganeh M, Ghasemi E, Ghasemi H, Aiiashi S, Ghadami E. MicroRNA biosensors for detection of glioblastoma. Clin Chim Acta 2024; 556:117829. [PMID: 38355000 DOI: 10.1016/j.cca.2024.117829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 02/10/2024] [Accepted: 02/11/2024] [Indexed: 02/16/2024]
Abstract
Glioblastoma (GBM) is the most common type of malignant brain tumor.The discovery of microRNAs and their unique properties have made them suitable tools as biomarkers for cancer diagnosis, prognosis, and evaluation of therapeutic response using different types of nanomaterials as sensitive and specific biosensors. In this review, we discuss microRNA-based electrochemical biosensing systems and the use of nanoparticles in the evolving development of microRNA-based biosensors in glioblastoma.
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Affiliation(s)
- Mehdi Fattahi
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam; School of Engineering & Technology, Duy Tan University, Da Nang, Vietnam
| | - Mohadese Maghsudlu
- Department of Medical Genetics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Masoumeh Razipour
- Department of Medical Genetics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Mohsen Ghadami
- Department of Medical Genetics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehdi Alizadeh
- Department of Clinical Biochemistry, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyyed Hossein Khatami
- Department of Clinical Biochemistry, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mortaza Taheri-Anganeh
- Cellular and Molecular Research Center, Cellular and Molecular Medicine Research Institute, Urmia University of Medical Sciences, Urmia, Iran
| | | | | | - Saleh Aiiashi
- Abadan University of Medical Sciences, Abadan, Iran.
| | - Elham Ghadami
- Department of Medical Genetics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
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6
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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.
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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.
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Rizvi SMD, Almazni IA, Moawadh MS, Alharbi ZM, Helmi N, Alqahtani LS, Hussain T, Alafnan A, Moin A, Elkhalifa AO, Awadelkareem AM, Khalid M, Tiwari RK. Targeting NF-κB signaling cascades of glioblastoma by a natural benzophenone, garcinol, via in vitro and molecular docking approaches. Front Chem 2024; 12:1352009. [PMID: 38435669 PMCID: PMC10904546 DOI: 10.3389/fchem.2024.1352009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 01/03/2024] [Indexed: 03/05/2024] Open
Abstract
Glioblastoma multiforme (GBM) is regarded as the most aggressive form of brain tumor delineated by high cellular heterogeneity; it is resistant to conventional therapeutic regimens. In this study, the anti-cancer potential of garcinol, a naturally derived benzophenone, was assessed against GBM. During the analysis, we observed a reduction in the viability of rat glioblastoma C6 cells at a concentration of 30 µM of the extract (p < 0.001). Exposure to garcinol also induced nuclear fragmentation and condensation, as evidenced by DAPI-stained photomicrographs of C6 cells. The dissipation of mitochondrial membrane potential in a dose-dependent fashion was linked to the activation of caspases. Furthermore, it was observed that garcinol mediated the inhibition of NF-κB (p < 0.001) and decreased the expression of genes associated with cell survival (Bcl-XL, Bcl-2, and survivin) and proliferation (cyclin D1). Moreover, garcinol showed interaction with NF-κB through some important amino acid residues, such as Pro275, Trp258, Glu225, and Gly259 during molecular docking analysis. Comparative analysis with positive control (temozolomide) was also performed. We found that garcinol induced apoptotic cell death via inhibiting NF-κB activity in C6 cells, thus implicating it as a plausible therapeutic agent for GBM.
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Affiliation(s)
- Syed Mohd Danish Rizvi
- Department of Pharmaceutics, College of Pharmacy, University of Ha’il, Ha’il, Saudi Arabia
| | - Ibrahim A. Almazni
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Najran University, Najran, Saudi Arabia
| | - Mamdoh S. Moawadh
- Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk, Saudi Arabia
| | - Zeyad M. Alharbi
- Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk, Saudi Arabia
| | - Nawal Helmi
- Department of Biochemistry, College of Science, University of Jeddah, Jeddah, Saudi Arabia
| | - Leena S. Alqahtani
- Department of Biochemistry, College of Science, University of Jeddah, Jeddah, Saudi Arabia
| | - Talib Hussain
- Department of Pharmacology and Toxicology, College of Pharmacy, University of Ha’il, Ha’il, Saudi Arabia
| | - Ahmed Alafnan
- Department of Pharmacology and Toxicology, College of Pharmacy, University of Ha’il, Ha’il, Saudi Arabia
| | - Afrasim Moin
- Department of Pharmaceutics, College of Pharmacy, University of Ha’il, Ha’il, Saudi Arabia
| | - AbdElmoneim O. Elkhalifa
- Department of Clinical Nutrition, College of Applied Medical Sciences, University of Hail, Ha’il, Saudi Arabia
| | - Amir Mahgoub Awadelkareem
- Department of Clinical Nutrition, College of Applied Medical Sciences, University of Hail, Ha’il, Saudi Arabia
| | - Mohammad Khalid
- Department of Pharmacognosy, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Rohit Kumar Tiwari
- Department of Clinical Research, Sharda School of Allied Health Sciences, Sharda University, Gautam Budh Nagar, India
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8
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Jovanovich N, Habib A, Chilukuri A, Hameed NUF, Deng H, Shanahan R, Head JR, Zinn PO. Sex-specific molecular differences in glioblastoma: assessing the clinical significance of genetic variants. Front Oncol 2024; 13:1340386. [PMID: 38322284 PMCID: PMC10844554 DOI: 10.3389/fonc.2023.1340386] [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: 11/17/2023] [Accepted: 12/28/2023] [Indexed: 02/08/2024] Open
Abstract
Introduction Glioblastoma multiforme (GBM) is one of the most aggressive types of brain cancer, and despite rigorous research, patient prognosis remains poor. The characterization of sex-specific differences in incidence and overall survival (OS) of these patients has led to an investigation of the molecular mechanisms that may underlie this dimorphism. Methods We reviewed the published literature describing the gender specific differences in GBM Biology reported in the last ten years and summarized the available information that may point towards a patient-tailored GBM therapy. Results Radiomics analyses have revealed that imaging parameters predict OS and treatment response of GBM patients in a sex-specific manner. Moreover, gender-based analysis of the transcriptome GBM tumors has found differential expression of various genes, potentially impacting the OS survival of patients in a sex-dependent manner. In addition to gene expression differences, the timing (subclonal or clonal) of the acquisition of common GBM-driver mutations, metabolism requirements, and immune landscape of these tumors has also been shown to be sex-specific, leading to a differential therapeutic response by sex. In male patients, transformed astrocytes are more sensitive to glutaminase 1 (GLS1) inhibition due to increased requirements for glutamine uptake. In female patients, GBM is more sensitive to anti-IL1β due to an increased population of circulating granulocytic myeloid-derived suppressor cells (gMDSC). Conclusion Moving forward, continued elucidation of GBM sexual dimorphism will be critical in improving the OS of GBM patients by ensuring that treatment plans are structured to exploit these sex-specific, molecular vulnerabilities in GBM tumors.
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Affiliation(s)
- Nicolina Jovanovich
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Ahmed Habib
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Akanksha Chilukuri
- Rangos Research Center, Children’s Hospital, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - N. U. Farrukh Hameed
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Hansen Deng
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Regan Shanahan
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Jeffrey R. Head
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Pascal O. Zinn
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
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Abdel-Rahman SA, Gabr M. Small Molecule Immunomodulators as Next-Generation Therapeutics for Glioblastoma. Cancers (Basel) 2024; 16:435. [PMID: 38275876 PMCID: PMC10814352 DOI: 10.3390/cancers16020435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/14/2024] [Accepted: 01/18/2024] [Indexed: 01/27/2024] Open
Abstract
Glioblastoma (GBM), the most aggressive astrocytic glioma, remains a therapeutic challenge despite multimodal approaches. Immunotherapy holds promise, but its efficacy is hindered by the highly immunosuppressive GBM microenvironment. This review underscores the urgent need to comprehend the intricate interactions between glioma and immune cells, shaping the immunosuppressive tumor microenvironment (TME) in GBM. Immunotherapeutic advancements have shown limited success, prompting exploration of immunomodulatory approaches targeting tumor-associated macrophages (TAMs) and microglia, constituting a substantial portion of the GBM TME. Converting protumor M2-like TAMs to antitumor M1-like phenotypes emerges as a potential therapeutic strategy for GBM. The blood-brain barrier (BBB) poses an additional challenge to successful immunotherapy, restricting drug delivery to GBM TME. Research efforts to enhance BBB permeability have mainly focused on small molecules, which can traverse the BBB more effectively than biologics. Despite over 200 clinical trials for GBM, studies on small molecule immunomodulators within the GBM TME are scarce. Developing small molecules with optimal brain penetration and selectivity against immunomodulatory pathways presents a promising avenue for combination therapies in GBM. This comprehensive review discusses various immunomodulatory pathways in GBM progression with a focus on immune checkpoints and TAM-related targets. The exploration of such molecules, with the capacity to selectively target key immunomodulatory pathways and penetrate the BBB, holds the key to unlocking new combination therapy approaches for GBM.
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Affiliation(s)
- Somaya A. Abdel-Rahman
- Department of Radiology, Molecular Imaging Innovations Institute (MI3), Weill Cornell Medicine, New York, NY 10065, USA
- Department of Medicinal Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt
| | - Moustafa Gabr
- Department of Radiology, Molecular Imaging Innovations Institute (MI3), Weill Cornell Medicine, New York, NY 10065, USA
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10
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Wang ZH, Li J, Liu Q, Qian JC, Li QQ, Wang QY, Zeng LT, Li SJ, Gao X, Pan JX, Gao XF, Wu K, Hu GX, Iwakuma T, Cai JP. A modified nucleoside O6-methyl-2'-deoxyguanosine-5'-triphosphate exhibits anti-glioblastoma activity in a caspase-independent manner. Pharmacol Res 2024; 199:106990. [PMID: 37984506 DOI: 10.1016/j.phrs.2023.106990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 11/07/2023] [Accepted: 11/13/2023] [Indexed: 11/22/2023]
Abstract
Resistance to temozolomide (TMZ), the frontline chemotherapeutic agent for glioblastoma (GBM), has emerged as a formidable obstacle, underscoring the imperative to identify alternative therapeutic strategies to improve patient outcomes. In this study, we comprehensively evaluated a novel agent, O6-methyl-2'-deoxyguanosine-5'-triphosphate (O6-methyl-dGTP) for its anti-GBM activity both in vitro and in vivo. Notably, O6-methyl-dGTP exhibited pronounced cytotoxicity against GBM cells, including those resistant to TMZ and overexpressing O6-methylguanine-DNA methyltransferase (MGMT). Mechanistic investigations revealed that O6-methyl-dGTP could be incorporated into genomic DNA, disrupting nucleotide pools balance, and inducing replication stress, resulting in S-phase arrest and DNA damage. The compound exerted its anti-tumor properties through the activation of AIF-mediated apoptosis and the parthanatos pathway. In vivo studies using U251 and Ln229 cell xenografts supported the robust tumor-inhibitory capacity of O6-methyl-dGTP. In an orthotopic transplantation model with U87MG cells, O6-methyl-dGTP showcased marginally superior tumor-suppressive activity compared to TMZ. In summary, our research, for the first time, underscores the potential of O6-methyl-dGTP as an effective candidate against GBM, laying a robust scientific groundwork for its potential clinical adoption in GBM treatment regimens.
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Affiliation(s)
- Zi-Hui Wang
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, China; Graduate School of Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Jin Li
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, China
| | - Qian Liu
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, China; Graduate School of Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Jian-Chang Qian
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Qing-Qing Li
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Qing-Yu Wang
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, China
| | - Lv-Tao Zeng
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, China
| | - Si-Jia Li
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, China
| | - Xin Gao
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, China
| | - Jia-Xin Pan
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, China
| | - Xu-Fan Gao
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China; Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Kun Wu
- Wu Xi AppTec (Tianjin) Co., Ltd, China
| | - Guo-Xin Hu
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Tomoo Iwakuma
- Children's Mercy Research Institute, Kansas City, MO 64108, USA
| | - Jian-Ping Cai
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, China; Graduate School of Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
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11
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Hajianfar G, Haddadi Avval A, Hosseini SA, Nazari M, Oveisi M, Shiri I, Zaidi H. Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics. LA RADIOLOGIA MEDICA 2023; 128:1521-1534. [PMID: 37751102 PMCID: PMC10700216 DOI: 10.1007/s11547-023-01725-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 09/05/2023] [Indexed: 09/27/2023]
Abstract
PURPOSE Glioblastoma Multiforme (GBM) represents the predominant aggressive primary tumor of the brain with short overall survival (OS) time. We aim to assess the potential of radiomic features in predicting the time-to-event OS of patients with GBM using machine learning (ML) algorithms. MATERIALS AND METHODS One hundred nineteen patients with GBM, who had T1-weighted contrast-enhanced and T2-FLAIR MRI sequences, along with clinical data and survival time, were enrolled. Image preprocessing methods included 64 bin discretization, Laplacian of Gaussian (LOG) filters with three Sigma values and eight variations of Wavelet Transform. Images were then segmented, followed by the extraction of 1212 radiomic features. Seven feature selection (FS) methods and six time-to-event ML algorithms were utilized. The combination of preprocessing, FS, and ML algorithms (12 × 7 × 6 = 504 models) was evaluated by multivariate analysis. RESULTS Our multivariate analysis showed that the best prognostic FS/ML combinations are the Mutual Information (MI)/Cox Boost, MI/Generalized Linear Model Boosting (GLMB) and MI/Generalized Linear Model Network (GLMN), all of which were done via the LOG (Sigma = 1 mm) preprocessing method (C-index = 0.77). The LOG filter with Sigma = 1 mm preprocessing method, MI, GLMB and GLMN achieved significantly higher C-indices than other preprocessing, FS, and ML methods (all p values < 0.05, mean C-indices of 0.65, 0.70, and 0.64, respectively). CONCLUSION ML algorithms are capable of predicting the time-to-event OS of patients using MRI-based radiomic and clinical features. MRI-based radiomics analysis in combination with clinical variables might appear promising in assisting clinicians in the survival prediction of patients with GBM. Further research is needed to establish the applicability of radiomics in the management of GBM in the clinic.
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Affiliation(s)
- Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | | | - Seyyed Ali Hosseini
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC, Canada
| | - Mostafa Nazari
- Department of Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehrdad Oveisi
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland.
- Geneva University Neurocenter, Geneva University, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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12
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Arriaga MA, Amieva JA, Quintanilla J, Jimenez A, Ledezma J, Lopez S, Martirosyan KS, Chew SA. The application of electrosprayed minocycline-loaded PLGA microparticles for the treatment of glioblastoma. Biotechnol Bioeng 2023; 120:3409-3422. [PMID: 37605630 PMCID: PMC10592149 DOI: 10.1002/bit.28527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 05/09/2023] [Accepted: 07/17/2023] [Indexed: 08/23/2023]
Abstract
The survival of patients with glioblastoma multiforme (GBM), the most common and invasive form of malignant brain tumors, remains poor despite advances in current treatment methods including surgery, radiotherapy, and chemotherapy. Minocycline is a semi-synthetic tetracycline derivative that has been widely used as an antibiotic and more recently, it has been utilized as an antiangiogenic factor to inhibit tumorigenesis. The objective of this study was to investigate the utilization of electrospraying process to fabricate minocycline-loaded poly(lactic-co-glycolic acid) (PLGA) microparticles with high drug loading and loading efficiency and to evaluate their ability to induce cell toxicity in human glioblastoma (i.e., U87-MG) cells. The results from this study demonstrated that solvent mixture of dicholoromethane (DCM) and methanol is the optimal solvent combination for minocycline and larger amount of methanol (i.e., 70:30) resulted in a higher drug loading. All three solvent ratios of DCM:methanol tested produced microparticles that were both spherical and smooth, all in the micron size range. The electrosprayed microparticles were able to elicit a cytotoxic response in U87-MG glioblastoma cells at a lower concentration of drug compared to the free drug. This work provides proof of concept to the hypothesis that electrosprayed minocycline-loaded PLGA microparticles can be a promising agent for the treatment of GBM and could have potential application for cancer therapies.
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Affiliation(s)
- Marco A. Arriaga
- Department of Health and Biomedical Sciences, University of Texas Rio Grande Valley, One West University Blvd., Brownsville, TX 78520
| | - Juan A. Amieva
- Department of Health and Biomedical Sciences, University of Texas Rio Grande Valley, One West University Blvd., Brownsville, TX 78520
| | - Jaqueline Quintanilla
- Department of Health and Biomedical Sciences, University of Texas Rio Grande Valley, One West University Blvd., Brownsville, TX 78520
| | - Angela Jimenez
- Department of Health and Biomedical Sciences, University of Texas Rio Grande Valley, One West University Blvd., Brownsville, TX 78520
| | - Julio Ledezma
- Department of Health and Biomedical Sciences, University of Texas Rio Grande Valley, One West University Blvd., Brownsville, TX 78520
| | - Silverio Lopez
- Department of Physics and Astronomy, University of Texas Rio Grande Valley, One West University Blvd., Brownsville, TX 78520
| | - Karen S. Martirosyan
- Department of Physics and Astronomy, University of Texas Rio Grande Valley, One West University Blvd., Brownsville, TX 78520
| | - Sue Anne Chew
- Department of Health and Biomedical Sciences, University of Texas Rio Grande Valley, One West University Blvd., Brownsville, TX 78520
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13
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Nguyen-Thi PT, Nguyen TT, Phan HL, Ho TT, Vo TV, Vo GV. Cell membrane-based nanomaterials for therapeutics of neurodegenerative diseases. Neurochem Int 2023; 170:105612. [PMID: 37714337 DOI: 10.1016/j.neuint.2023.105612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 04/20/2023] [Accepted: 09/10/2023] [Indexed: 09/17/2023]
Abstract
Central nervous system (CNS) diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), glioblastoma (GBM), and peripheral nerve injury have been documented as incurable diseases, which lead to serious impacts on human health especially prevalent in the aging population worldwide. Most of the treatment strategies fail due to low efficacy, toxicity, and poor brain penetration. Recently, advancements in nanotechnology have helped alleviate the challenges associated with the application of cell membrane-based nanomaterials against CNS diseases. In the following review, the existing types of cell membrane-based nanomaterials systems which have improved therapeutic efficacy for CNS diseases would be described. A summary of recent progress in the incorporation of nanomaterials in cell membrane-based production, separation, and analysis will be provided. Addition to, challenges relate to large-scale manufacturing of cell membrane-based nanomaterials and future clinical trial of such platforms will be discussed.
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Affiliation(s)
| | - Thuy Trang Nguyen
- Faculty of Chemical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City, 71420, Viet Nam.
| | - Hoang Long Phan
- Faculty of Pharmacy, Van Lang University, Ho Chi Minh City, 700000, Viet Nam
| | - Thanh-Tam Ho
- Institute for Global Health Innovations, Duy Tan University, Da Nang, 550000, Viet Nam; Faculty of Pharmacy, Duy Tan University, Da Nang, 550000, Viet Nam.
| | - Toi Van Vo
- Tissue Engineering and Regenerative Medicine Department, School of Biomedical Engineering, International University, Ho Chi Minh City, Viet Nam; Vietnam National University - Ho Chi Minh City (VNU-HCM), Ho Chi Minh City, 700000, Viet Nam
| | - Giau Van Vo
- Department of Biomedical Engineering, School of Medicine, Vietnam National University -Ho Chi Minh City (VNU-HCM), Ho Chi Minh City, 700000, Viet Nam; Research Center for Genetics and Reproductive Health (CGRH), School of Medicine, Vietnam National University, Ho Chi Minh City (VNU-HCM), Ho Chi Minh City, 70000, Viet Nam; Vietnam National University - Ho Chi Minh City (VNU-HCM), Ho Chi Minh City, 700000, Viet Nam
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14
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Premachandran S, Haldavnekar R, Ganesh S, Das S, Venkatakrishnan K, Tan B. Self-Functionalized Superlattice Nanosensor Enables Glioblastoma Diagnosis Using Liquid Biopsy. ACS NANO 2023; 17:19832-19852. [PMID: 37824714 DOI: 10.1021/acsnano.3c04118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
Glioblastoma (GBM), the most aggressive and lethal brain cancer, is detected only in the advanced stage, resulting in a median survival rate of 15 months. Therefore, there is an urgent need to establish GBM diagnosis tools to identify the tumor accurately. The clinical relevance of the current liquid biopsy techniques for GBM diagnosis remains mostly undetermined, owing to the challenges posed by the blood-brain barrier (BBB) that restricts biomarkers entering the circulation, resulting in the unavailability of clinically validated circulating GBM markers. GBM-specific liquid biopsy for diagnosis and prognosis of GBM has not yet been developed. Here, we introduce extracellular vesicles of GBM cancer stem cells (GBM CSC-EVs) as a previously unattempted, stand-alone GBM diagnosis modality. As GBM CSCs are fundamental building blocks of tumor initiation and recurrence, it is desirable to investigate these reliable signals of malignancy in circulation for accurate GBM diagnosis. So far, there are no clinically validated circulating biomarkers available for GBM. Therefore, a marker-free approach was essential since conventional liquid biopsy relying on isolation methodology was not viable. Additionally, a mechanism capable of trace-level detection was crucial to detecting the rare GBM CSC-EVs from the complex environment in circulation. To break these barriers, we applied an ultrasensitive superlattice sensor, self-functionalized for surface-enhanced Raman scattering (SERS), to obtain holistic molecular profiling of GBM CSC-EVs with a marker-free approach. The superlattice sensor exhibited substantial SERS enhancement and ultralow limit of detection (LOD of attomolar 10-18 M concentration) essential for trace-level detection of invisible GBM CSC-EVs directly from patient serum (without isolation). We detected as low as 5 EVs in 5 μL of solution, achieving the lowest LOD compared to existing SERS-based studies. We have experimentally demonstrated the crucial role of the signals of GBM CSC-EVs in the precise detection of glioblastoma. This was evident from the unique molecular profiles of GBM CSC-EVs demonstrating significant variation compared to noncancer EVs and EVs of GBM cancer cells, thus adding more clarity to the current understanding of GBM CSC-EVs. Preliminary validation of our approach was undertaken with a small amount of peripheral blood (5 μL) derived from GBM patients with 100% sensitivity and 97% specificity. Identification of the signals of GBM CSC-EV in clinical sera specimens demonstrated that our technology could be used for accurate GBM detection. Our technology has the potential to improve GBM liquid biopsy, including real-time surveillance of GBM evolution in patients upon clinical validation. This demonstration of liquid biopsy with GBM CSC-EV provides an opportunity to introduce a paradigm potentially impacting the current landscape of GBM diagnosis.
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Affiliation(s)
- Srilakshmi Premachandran
- Institute for Biomedical Engineering, Science and Technology (I BEST), Partnership between Toronto Metropolitan University (formerly Ryerson University) and St. Michael's Hospital, Toronto, Ontario M5B 1W8, Canada
- Ultrashort Laser Nanomanufacturing Research Facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
- Nano Characterization Laboratory, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
- Nano-Bio Interface facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
| | - Rupa Haldavnekar
- Institute for Biomedical Engineering, Science and Technology (I BEST), Partnership between Toronto Metropolitan University (formerly Ryerson University) and St. Michael's Hospital, Toronto, Ontario M5B 1W8, Canada
- Ultrashort Laser Nanomanufacturing Research Facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
- Nano Characterization Laboratory, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
- Nano-Bio Interface facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
| | - Swarna Ganesh
- Institute for Biomedical Engineering, Science and Technology (I BEST), Partnership between Toronto Metropolitan University (formerly Ryerson University) and St. Michael's Hospital, Toronto, Ontario M5B 1W8, Canada
- Ultrashort Laser Nanomanufacturing Research Facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
- Nano Characterization Laboratory, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
- Nano-Bio Interface facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
| | - Sunit Das
- Scientist, St. Michael's Hospital, Toronto, Ontario M5B 1W8, Canada
- Institute of Medical Sciences, Neurosurgery, University of Toronto, Toronto, Ontario M5T 1P5, Canada
| | - Krishnan Venkatakrishnan
- Keenan Research Center for Biomedical Science, Unity Health Toronto, Toronto, Ontario M5B 1W8, Canada
- Institute for Biomedical Engineering, Science and Technology (I BEST), Partnership between Toronto Metropolitan University (formerly Ryerson University) and St. Michael's Hospital, Toronto, Ontario M5B 1W8, Canada
- Ultrashort Laser Nanomanufacturing Research Facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
- Nano-Bio Interface facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
| | - Bo Tan
- Keenan Research Center for Biomedical Science, Unity Health Toronto, Toronto, Ontario M5B 1W8, Canada
- Institute for Biomedical Engineering, Science and Technology (I BEST), Partnership between Toronto Metropolitan University (formerly Ryerson University) and St. Michael's Hospital, Toronto, Ontario M5B 1W8, Canada
- Nano Characterization Laboratory, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
- Nano-Bio Interface facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
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15
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Bond KM, Curtin L, Ranjbar S, Afshari AE, Hu LS, Rubin JB, Swanson KR. An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients. Front Oncol 2023; 13:1185738. [PMID: 37849813 PMCID: PMC10578440 DOI: 10.3389/fonc.2023.1185738] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 08/21/2023] [Indexed: 10/19/2023] Open
Abstract
Imaging is central to the clinical surveillance of brain tumors yet it provides limited insight into a tumor's underlying biology. Machine learning and other mathematical modeling approaches can leverage paired magnetic resonance images and image-localized tissue samples to predict almost any characteristic of a tumor. Image-based modeling takes advantage of the spatial resolution of routine clinical scans and can be applied to measure biological differences within a tumor, changes over time, as well as the variance between patients. This approach is non-invasive and circumvents the intrinsic challenges of inter- and intratumoral heterogeneity that have historically hindered the complete assessment of tumor biology and treatment responsiveness. It can also reveal tumor characteristics that may guide both surgical and medical decision-making in real-time. Here we describe a general framework for the acquisition of image-localized biopsies and the construction of spatiotemporal radiomics models, as well as case examples of how this approach may be used to address clinically relevant questions.
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Affiliation(s)
- Kamila M. Bond
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States
- Hospital of University of Pennsylvania, Department of Neurosurgery, Philadelphia, PA, United States
| | - Lee Curtin
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States
| | - Sara Ranjbar
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States
| | - Ariana E. Afshari
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States
| | - Leland S. Hu
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States
- Department of Radiology, Mayo Clinic, Phoenix, AZ, United States
| | - Joshua B. Rubin
- Departments of Neuroscience and Pediatrics, Washington University School of Medicine, St. Louis, MO, United States
| | - Kristin R. Swanson
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States
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16
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Mahajan A, Burrewar M, Agarwal U, Kss B, Mlv A, Guha A, Sahu A, Choudhari A, Pawar V, Punia V, Epari S, Sahay A, Gupta T, Chinnaswamy G, Shetty P, Moiyadi A. Deep learning based clinico-radiological model for paediatric brain tumor detection and subtype prediction. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:669-684. [PMID: 37720352 PMCID: PMC10501890 DOI: 10.37349/etat.2023.00159] [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: 02/03/2023] [Accepted: 04/13/2023] [Indexed: 09/19/2023] Open
Abstract
Aim Early diagnosis of paediatric brain tumors significantly improves the outcome. The aim is to study magnetic resonance imaging (MRI) features of paediatric brain tumors and to develop an automated segmentation (AS) tool which could segment and classify tumors using deep learning methods and compare with radiologist assessment. Methods This study included 94 cases, of which 75 were diagnosed cases of ependymoma, medulloblastoma, brainstem glioma, and pilocytic astrocytoma and 19 were normal MRI brain cases. The data was randomized into training data, 64 cases; test data, 21 cases and validation data, 9 cases to devise a deep learning algorithm to segment the paediatric brain tumor. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the deep learning model were compared with radiologist's findings. Performance evaluation of AS was done based on Dice score and Hausdorff95 distance. Results Analysis of MRI semantic features was done with necrosis and haemorrhage as predicting features for ependymoma, diffusion restriction and cystic changes were predictors for medulloblastoma. The accuracy of detecting abnormalities was 90%, with a specificity of 100%. Further segmentation of the tumor into enhancing and non-enhancing components was done. The segmentation results for whole tumor (WT), enhancing tumor (ET), and non-enhancing tumor (NET) have been analyzed by Dice score and Hausdorff95 distance. The accuracy of prediction of all MRI features was compared with experienced radiologist's findings. Substantial agreement observed between the classification by model and the radiologist's given classification [K-0.695 (K is Cohen's kappa score for interrater reliability)]. Conclusions The deep learning model had very high accuracy and specificity for predicting the magnetic resonance (MR) characteristics and close to 80% accuracy in predicting tumor type. This model can serve as a potential tool to make a timely and accurate diagnosis for radiologists not trained in neuroradiology.
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Affiliation(s)
- Abhishek Mahajan
- Clatterbridge Centre for Oncology NHS Foundation Trust, L7 8YA, Liverpool, UK
| | - Mayur Burrewar
- Department of Radiodiagnosis, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Ujjwal Agarwal
- Department of Radiodiagnosis, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | | | - Apparao Mlv
- Endimension Technology Pvt Ltd, Maharashtra, India
| | - Amrita Guha
- Department of Radiodiagnosis, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Arpita Sahu
- Department of Radiodiagnosis, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Amit Choudhari
- Department of Radiodiagnosis, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Vivek Pawar
- Endimension Technology Pvt Ltd, Maharashtra, India
| | - Vivek Punia
- Endimension Technology Pvt Ltd, Maharashtra, India
| | - Sridhar Epari
- Department of Pathology, Tata Memorial Hospital, Parel, Mumbai 400012, India
| | - Ayushi Sahay
- Department of Pathology, Tata Memorial Hospital, Parel, Mumbai 400012, India
| | - Tejpal Gupta
- Department of Radiodiagnosis, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Girish Chinnaswamy
- Department of Paediatric Oncology, Tata Memorial Hospital, Parel, Mumbai 400012, India
| | - Prakash Shetty
- Department of Surgical Oncology, Tata Memorial Hospital, Parel, Mumbai 400012, India
| | - Aliasgar Moiyadi
- Department of Surgical Oncology, Tata Memorial Hospital, Parel, Mumbai 400012, India
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Mahajan A, B G, Wadhwa S, Agarwal U, Baid U, Talbar S, Janu AK, Patil V, Noronha V, Mummudi N, Tibdewal A, Agarwal JP, Yadav S, Kumar Kaushal R, Puranik A, Purandare N, Prabhash K. Deep learning based automated epidermal growth factor receptor and anaplastic lymphoma kinase status prediction of brain metastasis in non-small cell lung cancer. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:657-668. [PMID: 37745691 PMCID: PMC10511818 DOI: 10.37349/etat.2023.00158] [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: 12/19/2022] [Accepted: 04/13/2023] [Indexed: 09/26/2023] Open
Abstract
Aim The aim of this study was to investigate the feasibility of developing a deep learning (DL) algorithm for classifying brain metastases from non-small cell lung cancer (NSCLC) into epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement groups and to compare the accuracy with classification based on semantic features on imaging. Methods Data set of 117 patients was analysed from 2014 to 2018 out of which 33 patients were EGFR positive, 43 patients were ALK positive and 41 patients were negative for either mutation. Convolutional neural network (CNN) architecture efficient net was used to study the accuracy of classification using T1 weighted (T1W) magnetic resonance imaging (MRI) sequence, T2 weighted (T2W) MRI sequence, T1W post contrast (T1post) MRI sequence, fluid attenuated inversion recovery (FLAIR) MRI sequences. The dataset was divided into 80% training and 20% testing. The associations between mutation status and semantic features, specifically sex, smoking history, EGFR mutation and ALK rearrangement status, extracranial metastasis, performance status and imaging variables of brain metastasis were analysed using descriptive analysis [chi-square test (χ2)], univariate and multivariate logistic regression analysis assuming 95% confidence interval (CI). Results In this study of 117 patients, the analysis by semantic method showed 79.2% of the patients belonged to ALK positive were non-smokers as compared to double negative groups (P = 0.03). There was a 10-fold increase in ALK positivity as compared to EGFR positivity in ring enhancing lesions patients (P = 0.015) and there was also a 6.4-fold increase in ALK positivity as compared to double negative groups in meningeal involvement patients (P = 0.004). Using CNN Efficient Net DL model, the study achieved 76% accuracy in classifying ALK rearrangement and EGFR mutations without manual segmentation of metastatic lesions. Analysis of the manually segmented dataset resulted in improved accuracy of 89% through this model. Conclusions Both semantic features and DL model showed comparable accuracy in classifying EGFR mutation and ALK rearrangement. Both methods can be clinically used to predict mutation status while biopsy or genetic testing is undertaken.
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Affiliation(s)
- Abhishek Mahajan
- Clatterbridge Centre for Oncology NHS Foundation Trust, L7 8YA Liverpool, UK
- Department of Radiodiagnosis, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Gurukrishna B
- Department of Radiodiagnosis, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Shweta Wadhwa
- Department of Radiodiagnosis, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Ujjwal Agarwal
- Department of Radiodiagnosis, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Ujjwal Baid
- Department of Electronics and Telecommunication Engineering, SGGS Institute of Engineering and Technology, Nanded 431606, Maharashtra, India
| | - Sanjay Talbar
- Department of Electronics and Telecommunication Engineering, SGGS Institute of Engineering and Technology, Nanded 431606, Maharashtra, India
| | - Amit Kumar Janu
- Department of Radiodiagnosis, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Vijay Patil
- Department of Medical Oncology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Vanita Noronha
- Department of Medical Oncology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Naveen Mummudi
- Department of Radiation Oncology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Anil Tibdewal
- Department of Radiation Oncology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - JP Agarwal
- Department of Radiation Oncology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Subash Yadav
- Department of Pathology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Rajiv Kumar Kaushal
- Department of Pathology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Ameya Puranik
- Department of Nuclear Medicine, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Nilendu Purandare
- Department of Nuclear Medicine, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
| | - Kumar Prabhash
- Department of Medical Oncology, Tata Memorial Hospital, Parel, Mumbai 400012, Maharashtra, India
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Ismail M, Craig S, Ahmed R, de Blank P, Tiwari P. Opportunities and Advances in Radiomics and Radiogenomics for Pediatric Medulloblastoma Tumors. Diagnostics (Basel) 2023; 13:2727. [PMID: 37685265 PMCID: PMC10487205 DOI: 10.3390/diagnostics13172727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 08/19/2023] [Accepted: 08/21/2023] [Indexed: 09/10/2023] Open
Abstract
Recent advances in artificial intelligence have greatly impacted the field of medical imaging and vastly improved the development of computational algorithms for data analysis. In the field of pediatric neuro-oncology, radiomics, the process of obtaining high-dimensional data from radiographic images, has been recently utilized in applications including survival prognostication, molecular classification, and tumor type classification. Similarly, radiogenomics, or the integration of radiomic and genomic data, has allowed for building comprehensive computational models to better understand disease etiology. While there exist excellent review articles on radiomics and radiogenomic pipelines and their applications in adult solid tumors, in this review article, we specifically review these computational approaches in the context of pediatric medulloblastoma tumors. Based on our systematic literature research via PubMed and Google Scholar, we provide a detailed summary of a total of 15 articles that have utilized radiomic and radiogenomic analysis for survival prognostication, tumor segmentation, and molecular subgroup classification in the context of pediatric medulloblastoma. Lastly, we shed light on the current challenges with the existing approaches as well as future directions and opportunities with using these computational radiomic and radiogenomic approaches for pediatric medulloblastoma tumors.
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Affiliation(s)
- Marwa Ismail
- Department of Radiology, University of Wisconsin-Madison, Madison, WI 53706, USA; (S.C.); (P.T.)
| | - Stephen Craig
- Department of Radiology, University of Wisconsin-Madison, Madison, WI 53706, USA; (S.C.); (P.T.)
| | - Raheel Ahmed
- Department of Neurosurgery, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53706, USA;
| | - Peter de Blank
- Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA;
| | - Pallavi Tiwari
- Department of Radiology, University of Wisconsin-Madison, Madison, WI 53706, USA; (S.C.); (P.T.)
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
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19
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de Ruiter Swain J, Michalopoulou E, Noch EK, Lukey MJ, Van Aelst L. Metabolic partitioning in the brain and its hijacking by glioblastoma. Genes Dev 2023; 37:681-702. [PMID: 37648371 PMCID: PMC10546978 DOI: 10.1101/gad.350693.123] [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] [Indexed: 09/01/2023]
Abstract
The different cell types in the brain have highly specialized roles with unique metabolic requirements. Normal brain function requires the coordinated partitioning of metabolic pathways between these cells, such as in the neuron-astrocyte glutamate-glutamine cycle. An emerging theme in glioblastoma (GBM) biology is that malignant cells integrate into or "hijack" brain metabolism, co-opting neurons and glia for the supply of nutrients and recycling of waste products. Moreover, GBM cells communicate via signaling metabolites in the tumor microenvironment to promote tumor growth and induce immune suppression. Recent findings in this field point toward new therapeutic strategies to target the metabolic exchange processes that fuel tumorigenesis and suppress the anticancer immune response in GBM. Here, we provide an overview of the intercellular division of metabolic labor that occurs in both the normal brain and the GBM tumor microenvironment and then discuss the implications of these interactions for GBM therapy.
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Affiliation(s)
- Jed de Ruiter Swain
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
- Cold Spring Harbor Laboratory School of Biological Sciences, Cold Spring Harbor, New York 11724, USA
| | | | - Evan K Noch
- Department of Neurology, Division of Neuro-oncology, Weill Cornell Medicine, New York, New York 10021, USA
| | - Michael J Lukey
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA;
| | - Linda Van Aelst
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA;
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Tasci E, Jagasia S, Zhuge Y, Sproull M, Cooley Zgela T, Mackey M, Camphausen K, Krauze AV. RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma. Cancers (Basel) 2023; 15:2672. [PMID: 37345009 DOI: 10.3390/cancers15102672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/03/2023] [Accepted: 05/06/2023] [Indexed: 06/23/2023] Open
Abstract
Glioblastomas (GBM) are rapidly growing, aggressive, nearly uniformly fatal, and the most common primary type of brain cancer. They exhibit significant heterogeneity and resistance to treatment, limiting the ability to analyze dynamic biological behavior that drives response and resistance, which are central to advancing outcomes in glioblastoma. Analysis of the proteome aimed at signal change over time provides a potential opportunity for non-invasive classification and examination of the response to treatment by identifying protein biomarkers associated with interventions. However, data acquired using large proteomic panels must be more intuitively interpretable, requiring computational analysis to identify trends. Machine learning is increasingly employed, however, it requires feature selection which has a critical and considerable effect on machine learning problems when applied to large-scale data to reduce the number of parameters, improve generalization, and find essential predictors. In this study, using 7k proteomic data generated from the analysis of serum obtained from 82 patients with GBM pre- and post-completion of concurrent chemoirradiation (CRT), we aimed to select the most discriminative proteomic features that define proteomic alteration that is the result of administering CRT. Thus, we present a novel rank-based feature weighting method (RadWise) to identify relevant proteomic parameters using two popular feature selection methods, least absolute shrinkage and selection operator (LASSO) and the minimum redundancy maximum relevance (mRMR). The computational results show that the proposed method yields outstanding results with very few selected proteomic features, with higher accuracy rate performance than methods that do not employ a feature selection process. While the computational method identified several proteomic signals identical to the clinical intuitive (heuristic approach), several heuristically identified proteomic signals were not selected while other novel proteomic biomarkers not selected with the heuristic approach that carry biological prognostic relevance in GBM only emerged with the novel method. The computational results show that the proposed method yields promising results, reducing 7k proteomic data to 7 selected proteomic features with a performance value of 93.921%, comparing favorably with techniques that do not employ feature selection.
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Affiliation(s)
- Erdal Tasci
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA
| | - Sarisha Jagasia
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA
| | - Ying Zhuge
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA
| | - Mary Sproull
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA
| | - Theresa Cooley Zgela
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA
| | - Megan Mackey
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA
| | - Kevin Camphausen
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA
| | - Andra Valentina Krauze
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA
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Malhotra R, Singh Saini B, Gupta S. An interpretable feature-learned model for overall survival classification of High-Grade Gliomas. Phys Med 2023; 110:102591. [PMID: 37126962 DOI: 10.1016/j.ejmp.2023.102591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 02/23/2023] [Accepted: 04/13/2023] [Indexed: 05/03/2023] Open
Abstract
PURPOSE An accurate and well-defined survival prediction of High Grade Gliomas (HGGs) is indispensable because of its high incidence and aggressiveness. Therefore, this paper presents a unified framework for fully automatic overall survival classification and its interpretation. METHODS AND MATERIALS Initially, a glioma detection model is utilized to detect the tumorous images. A pre-processing module is designed for extracting 2D slices and creating a survival data array for the classification network. Then, the classification pipeline is integrated with two separate pathways: a modality-specific and a modality-concatenated pathway. The modality-specific pathway runs three separate CNNs for extracting rich predictive features from three sub-regions of HGGs (peritumoral edema, enhancing tumor and necrosis) by using three neuro-imaging modalities. In these pathways, the image vectors of the different modalities are also concatenated to the final fusion layer to overcome the loss of lower-level tumor features. Furthermore, to exploit the intra-modality correlations, a modality-concatenated pathway is also added to the classification pipeline. The experiments are conducted on BraTS 2018 and BraTS 2019 benchmarks, demonstrating that the proposed approach performs competitively in classifying HGG patients into three survival groups, namely, short, mid, and long survivors. RESULTS The proposed approach achieves an overall classification accuracy, sensitivity, and specificity of about 0.998, 0.997, and 0.999, respectively, for the BraTS 2018 dataset, and for BraTS 2019, these values correspond to 1.000, 0.999, and 0.999. CONCLUSIONS The results indicate that the proposed model achieves the highest values of the evaluation metrics for the overall survival classification of HGG.
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Affiliation(s)
- Radhika Malhotra
- Department of Electronics and Communication, Dr B R Ambedkar National Institute of Technology, Jalandhar, Punjab 144011, India.
| | - Barjinder Singh Saini
- Department of Electronics and Communication, Dr B R Ambedkar National Institute of Technology, Jalandhar, Punjab 144011, India
| | - Savita Gupta
- Department of Computer Science and Engg., UIET, Sector 25, Panjab University, Chandigarh 160023, India
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22
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Ocaña-Tienda B, Pérez-Beteta J, Villanueva-García JD, Romero-Rosales JA, Molina-García D, Suter Y, Asenjo B, Albillo D, Ortiz de Mendivil A, Pérez-Romasanta LA, González-Del Portillo E, Llorente M, Carballo N, Nagib-Raya F, Vidal-Denis M, Luque B, Reyes M, Arana E, Pérez-García VM. A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data. Sci Data 2023; 10:208. [PMID: 37059722 PMCID: PMC10104872 DOI: 10.1038/s41597-023-02123-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 03/30/2023] [Indexed: 04/16/2023] Open
Abstract
Brain metastasis (BM) is one of the main complications of many cancers, and the most frequent malignancy of the central nervous system. Imaging studies of BMs are routinely used for diagnosis of disease, treatment planning and follow-up. Artificial Intelligence (AI) has great potential to provide automated tools to assist in the management of disease. However, AI methods require large datasets for training and validation, and to date there have been just one publicly available imaging dataset of 156 BMs. This paper publishes 637 high-resolution imaging studies of 75 patients harboring 260 BM lesions, and their respective clinical data. It also includes semi-automatic segmentations of 593 BMs, including pre- and post-treatment T1-weighted cases, and a set of morphological and radiomic features for the cases segmented. This data-sharing initiative is expected to enable research into and performance evaluation of automatic BM detection, lesion segmentation, disease status evaluation and treatment planning methods for BMs, as well as the development and validation of predictive and prognostic tools with clinical applicability.
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Affiliation(s)
- Beatriz Ocaña-Tienda
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain.
| | - Julián Pérez-Beteta
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain
| | | | - José A Romero-Rosales
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain
| | - David Molina-García
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain
| | - Yannick Suter
- Medical Image Analysis Group, ARTORG Research Center, Bern, Switzerland
| | - Beatriz Asenjo
- Radiology Department, Hospital Regional Universitario de Málaga, Málaga, Spain
| | - David Albillo
- Radiology Department, MD Anderson Cancer Center, Madrid, Spain
| | | | | | | | - Manuel Llorente
- Radiology Department, MD Anderson Cancer Center, Madrid, Spain
| | | | - Fátima Nagib-Raya
- Radiology Department, Hospital Regional Universitario de Málaga, Málaga, Spain
| | - Maria Vidal-Denis
- Radiology Department, Hospital Regional Universitario de Málaga, Málaga, Spain
| | - Belén Luque
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain
| | - Mauricio Reyes
- Medical Image Analysis Group, ARTORG Research Center, Bern, Switzerland
| | - Estanislao Arana
- Radiology Department, Fundación Instituto Valenciano de Oncología, Valencia, Spain.
| | - Víctor M Pérez-García
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain
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Glory Precious J, Keren Evangeline I, Kirubha SPA. Brain tumour segmentation and survival prognostication using 3D radiomics features and machine learning algorithms. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2023. [DOI: 10.1080/21681163.2023.2189487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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24
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Yang J, Luly KM, Green JJ. Nonviral nanoparticle gene delivery into the CNS for neurological disorders and brain cancer applications. WILEY INTERDISCIPLINARY REVIEWS. NANOMEDICINE AND NANOBIOTECHNOLOGY 2023; 15:e1853. [PMID: 36193561 PMCID: PMC10023321 DOI: 10.1002/wnan.1853] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/24/2022] [Accepted: 08/11/2022] [Indexed: 03/15/2023]
Abstract
Nonviral nanoparticles have emerged as an attractive alternative to viral vectors for gene therapy applications, utilizing a range of lipid-based, polymeric, and inorganic materials. These materials can either encapsulate or be functionalized to bind nucleic acids and protect them from degradation. To effectively elicit changes to gene expression, the nanoparticle carrier needs to undergo a series of steps intracellularly, from interacting with the cellular membrane to facilitate cellular uptake to endosomal escape and nucleic acid release. Adjusting physiochemical properties of the nanoparticles, such as size, charge, and targeting ligands, can improve cellular uptake and ultimately gene delivery. Applications in the central nervous system (CNS; i.e., neurological diseases, brain cancers) face further extracellular barriers for a gene-carrying nanoparticle to surpass, with the most significant being the blood-brain barrier (BBB). Approaches to overcome these extracellular challenges to deliver nanoparticles into the CNS include systemic, intracerebroventricular, intrathecal, and intranasal administration. This review describes and compares different biomaterials for nonviral nanoparticle-mediated gene therapy to the CNS and explores challenges and recent preclinical and clinical developments in overcoming barriers to nanoparticle-mediated delivery to the brain. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease Therapeutic Approaches and Drug Discovery > Emerging Technologies Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.
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Affiliation(s)
- Joanna Yang
- Departments of Biomedical Engineering, Ophthalmology, Oncology, Neurosurgery, Materials Science & Engineering, and Chemical & Biomolecular Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kathryn M Luly
- Departments of Biomedical Engineering, Ophthalmology, Oncology, Neurosurgery, Materials Science & Engineering, and Chemical & Biomolecular Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jordan J Green
- Departments of Biomedical Engineering, Ophthalmology, Oncology, Neurosurgery, Materials Science & Engineering, and Chemical & Biomolecular Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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Salome P, Sforazzini F, Grugnara G, Kudak A, Dostal M, Herold-Mende C, Heiland S, Debus J, Abdollahi A, Knoll M. MR Intensity Normalization Methods Impact Sequence Specific Radiomics Prognostic Model Performance in Primary and Recurrent High-Grade Glioma. Cancers (Basel) 2023; 15:cancers15030965. [PMID: 36765922 PMCID: PMC9913466 DOI: 10.3390/cancers15030965] [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: 01/07/2023] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
PURPOSE This study investigates the impact of different intensity normalization (IN) methods on the overall survival (OS) radiomics models' performance of MR sequences in primary (pHGG) and recurrent high-grade glioma (rHGG). METHODS MR scans acquired before radiotherapy were retrieved from two independent cohorts (rHGG C1: 197, pHGG C2: 141) from multiple scanners (15, 14). The sequences are T1 weighted (w), contrast-enhanced T1w (T1wce), T2w, and T2w-FLAIR. Sequence-specific significant features (SF) associated with OS, extracted from the tumour volume, were derived after applying 15 different IN methods. Survival analyses were conducted using Cox proportional hazard (CPH) and Poisson regression (POI) models. A ranking score was assigned based on the 10-fold cross-validated (CV) concordance index (C-I), mean square error (MSE), and the Akaike information criterion (AICs), to evaluate the methods' performance. RESULTS Scatter plots of the 10-CV C-I and MSE against the AIC showed an impact on the survival predictions between the IN methods and MR sequences (C1/C2 C-I range: 0.62-0.71/0.61-0.72, MSE range: 0.20-0.42/0.13-0.22). White stripe showed stable results for T1wce (C1/C2 C-I: 0.71/0.65, MSE: 0.21/0.14). Combat (0.68/0.62, 0.22/0.15) and histogram matching (HM, 0.67/0.64, 0.22/0.15) showed consistent prediction results for T2w models. They were also the top-performing methods for T1w in C2 (Combat: 0.67, 0.13; HM: 0.67, 0.13); however, only HM achieved high predictions in C1 (0.66, 0.22). After eliminating IN impacted SF using Spearman's rank-order correlation coefficient, a mean decrease in the C-I and MSE of 0.05 and 0.03 was observed in all four sequences. CONCLUSION The IN method impacted the predictive power of survival models; thus, performance is sequence-dependent.
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Affiliation(s)
- Patrick Salome
- Clinical Cooperation Unit (CCU) Radiation Oncology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
- Heidelberg Medical Faculty, Heidelberg University, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Correspondence: (P.S.); (M.K.)
| | - Francesco Sforazzini
- Clinical Cooperation Unit (CCU) Radiation Oncology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
- Heidelberg Medical Faculty, Heidelberg University, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
| | - Gianluca Grugnara
- Department of Neuroradiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Andreas Kudak
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
- CCU Radiation Therapy, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
| | - Matthias Dostal
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
- CCU Radiation Therapy, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
| | - Christel Herold-Mende
- Brain Tumour Group, European Organization for Research and Treatment of Cancer, 1200 Brussels, Belgium
- Division of Neurosurgical Research, Department of Neurosurgery, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Jürgen Debus
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
| | - Amir Abdollahi
- Clinical Cooperation Unit (CCU) Radiation Oncology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
| | - Maximilian Knoll
- Clinical Cooperation Unit (CCU) Radiation Oncology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
- Correspondence: (P.S.); (M.K.)
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Glioma radiogenomics and artificial intelligence: road to precision cancer medicine. Clin Radiol 2023; 78:137-149. [PMID: 36241568 DOI: 10.1016/j.crad.2022.08.138] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 08/19/2022] [Indexed: 01/18/2023]
Abstract
Radiogenomics refers to the study of the relationship between imaging phenotypes and gene expression patterns/molecular characteristics, which might allow improved diagnosis, decision-making, and predicting patient outcomes in the context of multiple diseases. Central nervous system (CNS) tumours contribute to significant cancer-related mortality in the present age. Although historically CNS neoplasms were classified and graded based on microscopic appearance, there was discordance between two histologically similar tumours that showed varying prognosis and behaviour, attributable to their molecular signatures. These led to the incorporation of molecular markers in the classification of CNS neoplasms. Meanwhile, advancements in imaging technology such as diffusion-based imaging (including tractography), perfusion, and spectroscopy in addition to the conventional imaging of glial neoplasms, have opened an avenue for radiogenomics. This review touches upon the schema of the current classification of gliomas, concepts behind molecular markers, and parameters that are used in radiogenomics to characterise gliomas and the role of artificial intelligence for the same. Further, the role of radiomics in the grading of brain tumours, prediction of treatment response and prognosis has been discussed. Use of automated and semi-automated tumour segmentation for radiotherapy planning and follow-up has also been discussed briefly.
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Mahajan A, Chakrabarty N, Majithia J, Ahuja A, Agarwal U, Suryavanshi S, Biradar M, Sharma P, Raghavan B, Arafath R, Shukla S. Multisystem Imaging Recommendations/Guidelines: In the Pursuit of Precision Oncology. Indian J Med Paediatr Oncol 2023. [DOI: 10.1055/s-0043-1761266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023] Open
Abstract
AbstractWith an increasing rate of cancers in almost all age groups and advanced screening techniques leading to an early diagnosis and longer longevity of patients with cancers, it is of utmost importance that radiologists assigned with cancer imaging should be prepared to deal with specific expected and unexpected circumstances that may arise during the lifetime of these patients. Tailored integration of preventive and curative interventions with current health plans and global escalation of efforts for timely diagnosis of cancers will pave the path for a cancer-free world. The commonly encountered circumstances in the current era, complicating cancer imaging, include coronavirus disease 2019 infection, pregnancy and lactation, immunocompromised states, bone marrow transplant, and screening of cancers in the relevant population. In this article, we discuss the imaging recommendations pertaining to cancer screening and diagnosis in the aforementioned clinical circumstances.
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Affiliation(s)
- Abhishek Mahajan
- Department of Radiology, The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Nivedita Chakrabarty
- Radiodiagnosis, Tata Memorial Hospital, Mumbai, India
- Homi Bhabha National Institute, Mumbai, India
| | - Jinita Majithia
- Department of Radiodiagnosis, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | | | - Ujjwal Agarwal
- Radiodiagnosis, Tata Memorial Hospital, Mumbai, India
- Homi Bhabha National Institute, Mumbai, India
| | - Shubham Suryavanshi
- Radiodiagnosis, Tata Memorial Hospital, Mumbai, India
- Homi Bhabha National Institute, Mumbai, India
| | - Mahesh Biradar
- Radiodiagnosis, Tata Memorial Hospital, Mumbai, India
- Homi Bhabha National Institute, Mumbai, India
| | - Prerit Sharma
- Radiodiagnosis, Sharma Diagnostic Centre, Wardha, India
| | | | | | - Shreya Shukla
- Radiodiagnosis, Tata Memorial Hospital, Mumbai, India
- Homi Bhabha National Institute, Mumbai, India
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Multiview Deep Forest for Overall Survival Prediction in Cancer. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2023; 2023:7931321. [PMID: 36714327 PMCID: PMC9876666 DOI: 10.1155/2023/7931321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 12/16/2022] [Accepted: 01/03/2023] [Indexed: 01/19/2023]
Abstract
Overall survival (OS) in cancer is crucial for cancer treatment. Many machine learning methods have been applied to predict OS, but there are still the challenges of dealing with multiview data and overfitting. To overcome these problems, we propose a multiview deep forest (MVDF) in this paper. MVDF can learn the features of each view and fuse them with integrated learning and multiple kernel learning. Then, a gradient boost forest based on the information bottleneck theory is proposed to reduce redundant information and avoid overfitting. In addition, a pruning strategy for a cascaded forest is used to limit the impact of outlier data. Comprehensive experiments have been carried out on a data set from West China Hospital of Sichuan University and two public data sets. Results have demonstrated that our method outperforms the compared methods in predicting overall survival.
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You W, Mao Y, Jiao X, Wang D, Liu J, Lei P, Liao W. The combination of radiomics features and VASARI standard to predict glioma grade. Front Oncol 2023; 13:1083216. [PMID: 37035137 PMCID: PMC10073533 DOI: 10.3389/fonc.2023.1083216] [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: 10/28/2022] [Accepted: 03/07/2023] [Indexed: 04/11/2023] Open
Abstract
Background and Purpose Radiomics features and The Visually AcceSAble Rembrandt Images (VASARI) standard appear to be quantitative and qualitative evaluations utilized to determine glioma grade. This study developed a preoperative model to predict glioma grade and improve the efficacy of clinical strategies by combining these two assessment methods. Materials and Methods Patients diagnosed with glioma between March 2017 and September 2018 who underwent surgery and histopathology were enrolled in this study. A total of 3840 radiomic features were calculated; however, using the least absolute shrinkage and selection operator (LASSO) method, only 16 features were chosen to generate a radiomic signature. Three predictive models were developed using radiomic features and VASARI standard. The performance and validity of models were evaluated using decision curve analysis and 10-fold nested cross-validation. Results Our study included 102 patients: 35 with low-grade glioma (LGG) and 67 with high-grade glioma (HGG). Model 1 utilized both radiomics and the VASARI standard, which included radiomic signatures, proportion of edema, and deep white matter invasion. Models 2 and 3 were constructed with radiomics or VASARI, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.937 and 0.831, respectively, which was less than that of Model 1, with an AUC of 0.966. Conclusion The combination of radiomics features and the VASARI standard is a robust model for predicting glioma grades.
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Affiliation(s)
- Wei You
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Yitao Mao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Xiao Jiao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Dongcui Wang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Jianling Liu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Peng Lei
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Weihua Liao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Molecular Imaging Research Center, Central South University, Changsha, China
- *Correspondence: Weihua Liao,
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Mahajan A, Chakrabarty N. Editorial: The use of deep learning in mapping and diagnosis of cancers. Front Oncol 2022; 12:1077341. [PMID: 36582789 PMCID: PMC9793849 DOI: 10.3389/fonc.2022.1077341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 11/22/2022] [Indexed: 12/15/2022] Open
Affiliation(s)
- Abhishek Mahajan
- Department of Radiology, The Clatterbridge Cancer Liverpool, Liverpool, United Kingdom,*Correspondence: Abhishek Mahajan,
| | - Nivedita Chakrabarty
- Department of Radiodiagnosis, Tata Memorial Hospital, Homi Bhabha National Institute (HBNI), Mumbai, India
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García-García S, García-Galindo M, Arrese I, Sarabia R, Cepeda S. Current Evidence, Limitations and Future Challenges of Survival Prediction for Glioblastoma Based on Advanced Noninvasive Methods: A Narrative Review. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58121746. [PMID: 36556948 PMCID: PMC9786785 DOI: 10.3390/medicina58121746] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/16/2022] [Accepted: 11/28/2022] [Indexed: 12/03/2022]
Abstract
Background and Objectives: Survival estimation for patients diagnosed with Glioblastoma (GBM) is an important information to consider in patient management and communication. Despite some known risk factors, survival estimation remains a major challenge. Novel non-invasive technologies such as radiomics and artificial intelligence (AI) have been implemented to increase the accuracy of these predictions. In this article, we reviewed and discussed the most significant available research on survival estimation for GBM through advanced non-invasive methods. Materials and Methods: PubMed database was queried for articles reporting on survival prognosis for GBM through advanced image and data management methods. Articles including in their title or abstract the following terms were initially screened: ((glioma) AND (survival)) AND ((artificial intelligence) OR (radiomics)). Exclusively English full-text articles, reporting on humans, published as of 1 September 2022 were considered. Articles not reporting on overall survival, evaluating the effects of new therapies or including other tumors were excluded. Research with a radiomics-based methodology were evaluated using the radiomics quality score (RQS). Results: 382 articles were identified. After applying the inclusion criteria, 46 articles remained for further analysis. These articles were thoroughly assessed, summarized and discussed. The results of the RQS revealed some of the limitations of current radiomics investigation on this field. Limitations of analyzed studies included data availability, patient selection and heterogeneity of methodologies. Future challenges on this field are increasing data availability, improving the general understanding of how AI handles data and establishing solid correlations between image features and tumor's biology. Conclusions: Radiomics and AI methods of data processing offer a new paradigm of possibilities to tackle the question of survival prognosis in GBM.
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Affiliation(s)
- Sergio García-García
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
- Correspondence:
| | - Manuel García-Galindo
- Faculty of Medicine, University of Valladolid, Avenida Ramón y Cajal 7, 47003 Valladolid, Spain
| | - Ignacio Arrese
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
| | - Rosario Sarabia
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
| | - Santiago Cepeda
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
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Pálsson S, Cerri S, Poulsen HS, Urup T, Law I, Van Leemput K. Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images. Sci Rep 2022; 12:19744. [PMID: 36396681 PMCID: PMC9671967 DOI: 10.1038/s41598-022-19223-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 08/25/2022] [Indexed: 11/18/2022] Open
Abstract
Survival prediction models can potentially be used to guide treatment of glioblastoma patients. However, currently available MR imaging biomarkers holding prognostic information are often challenging to interpret, have difficulties generalizing across data acquisitions, or are only applicable to pre-operative MR data. In this paper we aim to address these issues by introducing novel imaging features that can be automatically computed from MR images and fed into machine learning models to predict patient survival. The features we propose have a direct anatomical-functional interpretation: They measure the deformation caused by the tumor on the surrounding brain structures, comparing the shape of various structures in the patient's brain to their expected shape in healthy individuals. To obtain the required segmentations, we use an automatic method that is contrast-adaptive and robust to missing modalities, making the features generalizable across scanners and imaging protocols. Since the features we propose do not depend on characteristics of the tumor region itself, they are also applicable to post-operative images, which have been much less studied in the context of survival prediction. Using experiments involving both pre- and post-operative data, we show that the proposed features carry prognostic value in terms of overall- and progression-free survival, over and above that of conventional non-imaging features.
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Affiliation(s)
- Sveinn Pálsson
- grid.5170.30000 0001 2181 8870Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Stefano Cerri
- grid.5170.30000 0001 2181 8870Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Hans Skovgaard Poulsen
- grid.475435.4Department of Oncology, The Finsen Center, Rigshospitalet, Copenhagen, Denmark
| | - Thomas Urup
- grid.475435.4Department of Oncology, The Finsen Center, Rigshospitalet, Copenhagen, Denmark
| | - Ian Law
- grid.475435.4Department of Clinical Physiology, Nuclear Medicine and PET, Center of Diagnostic Investigation, Rigshospitalet, Copenhagen, Denmark
| | - Koen Van Leemput
- grid.5170.30000 0001 2181 8870Department of Health Technology, Technical University of Denmark, Lyngby, Denmark ,grid.32224.350000 0004 0386 9924Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA
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Hsu EJ, Thomas J, Timmerman RD, Wardak Z, Dan TD, Patel TR, Sanford NN, Vo DT. Socioeconomic and demographic determinants of radiation treatment and outcomes in glioblastoma patients. Front Neurol 2022; 13:1024138. [DOI: 10.3389/fneur.2022.1024138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 10/31/2022] [Indexed: 11/12/2022] Open
Abstract
IntroductionPoor outcomes in glioblastoma patients, despite advancing treatment paradigms, indicate a need to determine non-physiologic prognostic indicators of patient outcome. The impact of specific socioeconomic and demographic patient factors on outcomes is unclear. We sought to identify socioeconomic and demographic patient characteristics associated with patient survival and tumor progression, and to characterize treatment options and healthcare utilization.MethodsA cohort of 169 patients with pathologically confirmed glioblastomas treated at our institution was retrospectively reviewed. Multivariable cox proportional hazards analysis for overall survival (OS) and cumulative incidence of progression was performed. Differences in treatment regimen, patient characteristics, and neuro-oncology office use between different age and depressive disorder history patient subgroups were calculated two-sample t-tests, Fisher's exact tests, or linear regression analysis.ResultsThe median age of all patients at the time of initiation of radiation therapy was 60.5 years. The median OS of the cohort was 13.1 months. Multivariable analysis identified age (Hazard Ratio 1.02, 95% CI 1.00–1.04) and total resection (Hazard Ratio 0.52, 95% CI 0.33–0.82) as significant predictors of OS. Increased number of radiation fractions (Hazard Ratio 0.90, 95% CI 0.82–0.98), depressive disorder history (Hazard Ratio 0.59, 95% CI 0.37–0.95), and total resection (Hazard Ratio 0.52, 95% CI 0.31–0.88) were associated with decreased incidence of progression. Notably, patients with depressive disorder history were observed to have more neuro-oncology physician office visits over time (median 12 vs. 16 visits, p = 0.0121). Patients older than 60 years and those with Medicare (vs. private) insurance were less likely to receive as many radiation fractions (p = 0.0014) or receive temozolomide concurrently with radiation (Odds Ratio 0.46, p = 0.0139).ConclusionOlder glioblastoma patients were less likely to receive as diverse of a treatment regimen as their younger counterparts, which may be partially driven by insurance type. Patients with depressive disorder history exhibited reduced incidence of progression, which may be due to more frequent health care contact during neuro-oncology physician office visits.
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Ul Haq N, Tahir B, Firdous S, Amir Mehmood M. Towards survival prediction of cancer patients using medical images. PeerJ Comput Sci 2022; 8:e1090. [PMID: 36426251 PMCID: PMC9680890 DOI: 10.7717/peerj-cs.1090] [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: 01/21/2022] [Accepted: 08/17/2022] [Indexed: 06/16/2023]
Abstract
Survival prediction of a patient is a critical task in clinical medicine for physicians and patients to make an informed decision. Several survival and risk scoring methods have been developed to estimate the survival score of patients using clinical information. For instance, the Global Registry of Acute Coronary Events (GRACE) and Thrombolysis in Myocardial Infarction (TIMI) risk scores are developed for the survival prediction of heart patients. Recently, state-of-the-art medical imaging and analysis techniques have paved the way for survival prediction of cancer patients by understanding key features extracted from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scanned images with the help of image processing and machine learning techniques. However, survival prediction is a challenging task due to the complexity in benchmarking of image features, feature selection methods, and machine learning models. In this article, we evaluate the performance of 156 visual features from radiomic and hand-crafted feature classes, six feature selection methods, and 10 machine learning models to benchmark their performance. In addition, MRI scanned Brain Tumor Segmentation (BraTS) and CT scanned non-small cell lung cancer (NSCLC) datasets are used to train classification and regression models. Our results highlight that logistic regression outperforms for the classification with 66 and 54% accuracy for BraTS and NSCLC datasets, respectively. Moreover, our analysis of best-performing features shows that age is a common and significant feature for survival prediction. Also, gray level and shape-based features play a vital role in regression. We believe that the study can be helpful for oncologists, radiologists, and medical imaging researchers to understand and automate the procedure of decision-making and prognosis of cancer patients.
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Affiliation(s)
- Nazeef Ul Haq
- Al-Khawarizmi Institute of Computer Science (KICS), University of Engineering and Technology (UET), Lahore, Pakistan
| | - Bilal Tahir
- Al-Khawarizmi Institute of Computer Science (KICS), University of Engineering and Technology (UET), Lahore, Pakistan
| | - Samar Firdous
- King Edward Medical University (KEMU), Lahore, Pakistan
| | - Muhammad Amir Mehmood
- Al-Khawarizmi Institute of Computer Science (KICS), University of Engineering and Technology (UET), Lahore, Pakistan
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Hsu EJ, Thomas J, Maher EA, Youssef M, Timmerman RD, Wardak Z, Lee M, Dan TD, Patel TR, Vo DT. Neutrophilia and post-radiation thrombocytopenia predict for poor prognosis in radiation-treated glioma patients. Front Oncol 2022; 12:1000280. [PMID: 36158642 PMCID: PMC9501690 DOI: 10.3389/fonc.2022.1000280] [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: 07/22/2022] [Accepted: 08/11/2022] [Indexed: 11/16/2022] Open
Abstract
Introduction Poor outcomes in glioma patients indicate a need to determine prognostic indicators of survival to better guide patient specific treatment options. While preoperative neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR) have been suggested as prognostic systemic inflammation markers, the impact of post-radiation changes in these cell types is unclear. We sought to identify which hematologic cell measurements before, during, or after radiation predicted for patient survival. Methods A cohort of 182 patients with pathologically confirmed gliomas treated at our institution was retrospectively reviewed. Patient blood samples were collected within one month before, during, or within 3 months after radiation for quantification of hematologic cell counts, for which failure patterns were evaluated. Multivariable cox proportional hazards analysis for overall survival (OS) and progression-free survival (PFS) was performed to control for patient variables. Results Multivariable analysis identified pre-radiation NLR > 4.0 (Hazard ratio = 1.847, p = 0.0039) and neutrophilia prior to (Hazard ratio = 1.706, p = 0.0185), during (Hazard ratio = 1.641, p = 0.0277), or after (Hazard ratio = 1.517, p = 0.0879) radiation as significant predictors of worse OS, with similar results for PFS. Post-radiation PLR > 200 (Hazard ratio = 0.587, p = 0.0062) and a percent increase in platelets after radiation (Hazard ratio = 0.387, p = 0.0077) were also associated with improved OS. Patients receiving more than 15 fractions of radiation exhibited greater post-radiation decreases in neutrophil and platelet counts than those receiving fewer. Patients receiving dexamethasone during radiation exhibited greater increases in neutrophil counts than those not receiving steroids. Lymphopenia, changes in lymphocyte counts, monocytosis, MLR, and changes in monocyte counts did not impact patient survival. Conclusion Neutrophilia at any time interval surrounding radiotherapy, pre-radiation NLR, and post-radiation thrombocytopenia, but not lymphocytes or monocytes, are predictors of poor patient survival in glioma patients.
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Affiliation(s)
- Eric J. Hsu
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, United States
- *Correspondence: Eric J. Hsu,
| | - Jamie Thomas
- Department of Neurological Surgery, UT Southwestern Medical Center, Dallas, TX, United States
| | - Elizabeth A. Maher
- Department of Internal Medicine, Division of Hematology and Oncology, UT Southwestern Medical Center, Dallas, TX, United States
- Department of Neurology, UT Southwestern Medical Center, Dallas, TX, United States
| | - Michael Youssef
- Department of Neurology, UT Southwestern Medical Center, Dallas, TX, United States
| | - Robert D. Timmerman
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, United States
| | - Zabi Wardak
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, United States
| | - Minjae Lee
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, United States
| | - Tu D. Dan
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, United States
| | - Toral R. Patel
- Department of Neurological Surgery, UT Southwestern Medical Center, Dallas, TX, United States
| | - Dat T. Vo
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, United States
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di Noia C, Grist JT, Riemer F, Lyasheva M, Fabozzi M, Castelli M, Lodi R, Tonon C, Rundo L, Zaccagna F. Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI. Diagnostics (Basel) 2022; 12:diagnostics12092125. [PMID: 36140526 PMCID: PMC9497964 DOI: 10.3390/diagnostics12092125] [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: 07/05/2022] [Revised: 08/05/2022] [Accepted: 08/17/2022] [Indexed: 11/24/2022] Open
Abstract
Given growing clinical needs, in recent years Artificial Intelligence (AI) techniques have increasingly been used to define the best approaches for survival assessment and prediction in patients with brain tumors. Advances in computational resources, and the collection of (mainly) public databases, have promoted this rapid development. This narrative review of the current state-of-the-art aimed to survey current applications of AI in predicting survival in patients with brain tumors, with a focus on Magnetic Resonance Imaging (MRI). An extensive search was performed on PubMed and Google Scholar using a Boolean research query based on MeSH terms and restricting the search to the period between 2012 and 2022. Fifty studies were selected, mainly based on Machine Learning (ML), Deep Learning (DL), radiomics-based methods, and methods that exploit traditional imaging techniques for survival assessment. In addition, we focused on two distinct tasks related to survival assessment: the first on the classification of subjects into survival classes (short and long-term or eventually short, mid and long-term) to stratify patients in distinct groups. The second focused on quantification, in days or months, of the individual survival interval. Our survey showed excellent state-of-the-art methods for the first, with accuracy up to ∼98%. The latter task appears to be the most challenging, but state-of-the-art techniques showed promising results, albeit with limitations, with C-Index up to ∼0.91. In conclusion, according to the specific task, the available computational methods perform differently, and the choice of the best one to use is non-univocal and dependent on many aspects. Unequivocally, the use of features derived from quantitative imaging has been shown to be advantageous for AI applications, including survival prediction. This evidence from the literature motivates further research in the field of AI-powered methods for survival prediction in patients with brain tumors, in particular, using the wealth of information provided by quantitative MRI techniques.
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Affiliation(s)
- Christian di Noia
- Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum—University of Bologna, 40125 Bologna, Italy
| | - James T. Grist
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford OX1 3PT, UK
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
- Oxford Centre for Clinical Magnetic Research Imaging, University of Oxford, Oxford OX3 9DU, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2SY, UK
| | - Frank Riemer
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, N-5021 Bergen, Norway
| | - Maria Lyasheva
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Miriana Fabozzi
- Centro Medico Polispecialistico (CMO), 80058 Torre Annunziata, Italy
| | - Mauro Castelli
- NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal
| | - Raffaele Lodi
- Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum—University of Bologna, 40125 Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, 40139 Bologna, Italy
| | - Caterina Tonon
- Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum—University of Bologna, 40125 Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, 40139 Bologna, Italy
| | - Leonardo Rundo
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy
| | - Fulvio Zaccagna
- Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum—University of Bologna, 40125 Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, 40139 Bologna, Italy
- Correspondence: ; Tel.: +39-0514969951
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Batool SM, Muralidharan K, Hsia T, Falotico S, Gamblin AS, Rosenfeld YB, Khanna SK, Balaj L, Carter BS. Highly sensitive EGFRvIII detection in circulating extracellular vesicle RNA of glioma patients. Clin Cancer Res 2022; 28:4070-4082. [PMID: 35849415 DOI: 10.1158/1078-0432.ccr-22-0444] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 05/01/2022] [Accepted: 07/14/2022] [Indexed: 11/16/2022]
Abstract
PURPOSE Liquid biopsy offers an attractive platform for non-invasive tumor diagnosis, prognostication and prediction of glioblastoma clinical outcomes. Prior studies report that 30-50% of GBM lesions characterized by EGFR amplification also harbor the EGFRvIII mutation. EXPERIMENTAL DESIGN A novel digital droplet PCR (ddPCR) assay for high GC content amplicons was developed and optimized for sensitive detection of EGFRvIII in tumor tissue and circulating extracellular vesicle RNA (EV RNA) isolated from the plasma of glioma patients. RESULTS Our optimized qPCR assay detected EGFRvIII mRNA in 81% (95% CI, 68% - 94%) of EGFR amplified glioma tumor tissue, indicating a higher than previously reported prevalence of EGFRvIII in glioma. Using the optimized ddPCR assay in discovery and blinded validation cohorts, we detected EGFRvIII mutation in 73% (95% CI, 64% - 82%) of patients with a specificity of 98% (95% CI, 87% - 100%), compared with qPCR tumor tissue analysis. Additionally, upon longitudinal monitoring in 4 patients, we report detection of EGFRvIII in the plasma of patients with different clinical outcomes, rising with tumor progression, and decreasing in response to treatment. CONCLUSION This study demonstrates the feasibility of detecting EGFRvIII mutation in plasma using a highly sensitive and specific ddPCR assay. We also show a higher than previously reported EGFRvIII prevalence in glioma tumor tissue. Several features of the assay are favorable for clinical implementation for detection and monitoring of EGFRvIII positive tumors.
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Affiliation(s)
| | | | - Tiffaney Hsia
- Massachusetts General Hospital, Boston, MA, United States
| | | | | | | | | | - Leonora Balaj
- Massachusetts General Hospital, Boston, United States
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Wu KC, Liao KS, Yeh LR, Wang YK. Drug Repurposing: The Mechanisms and Signaling Pathways of Anti-Cancer Effects of Anesthetics. Biomedicines 2022; 10:biomedicines10071589. [PMID: 35884894 PMCID: PMC9312706 DOI: 10.3390/biomedicines10071589] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/20/2022] [Accepted: 07/01/2022] [Indexed: 12/14/2022] Open
Abstract
Cancer is one of the leading causes of death worldwide. There are only limited treatment strategies that can be applied to treat cancer, including surgical resection, chemotherapy, and radiotherapy, but these have only limited effectiveness. Developing a new drug for cancer therapy is protracted, costly, and inefficient. Recently, drug repurposing has become a rising research field to provide new meaning for an old drug. By searching a drug repurposing database ReDO_DB, a brief list of anesthetic/sedative drugs, such as haloperidol, ketamine, lidocaine, midazolam, propofol, and valproic acid, are shown to possess anti-cancer properties. Therefore, in the current review, we will provide a general overview of the anti-cancer mechanisms of these anesthetic/sedative drugs and explore the potential underlying signaling pathways and clinical application of these drugs applied individually or in combination with other anti-cancer agents.
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Affiliation(s)
- King-Chuen Wu
- Department of Anesthesiology, Chang Gung Memorial Hospital, Chiayi 61363, Taiwan;
- Department of Nursing, Chang Gung University of Science and Technology, Chiayi 61363, Taiwan
| | - Kai-Sheng Liao
- Department of Pathology, Ditmanson Medical Foundation, Chia-Yi Christian Hospital, Chiayi 60002, Taiwan;
| | - Li-Ren Yeh
- Department of Anesthesiology, E-Da Cancer Hospital, Kaohsiung 82445, Taiwan
- Department of Medical Imaging and Radiology, Shu-Zen College of Medicine and Management, Kaohsiung 82144, Taiwan
- Correspondence: (L.-R.Y.); (Y.-K.W.); Tel.: +886-7-6150-022 (L.-R.Y.); +886-6-2353-535 (ext. 5333) (Y.-K.W.)
| | - Yang-Kao Wang
- Department of Cell Biology and Anatomy, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan
- Correspondence: (L.-R.Y.); (Y.-K.W.); Tel.: +886-7-6150-022 (L.-R.Y.); +886-6-2353-535 (ext. 5333) (Y.-K.W.)
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39
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The Efficacy of Shape Radiomics and Deep Features for Glioblastoma Survival Prediction by Deep Learning. ELECTRONICS 2022. [DOI: 10.3390/electronics11071038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Glioblastoma (known as glioblastoma multiforme) is one of the most aggressive brain malignancies, accounting for 48% of all primary brain tumors. For that reason, overall survival prediction plays a vital role in diagnosis and treatment planning for glioblastoma patients. The main target of our research is to demonstrate the effectiveness of features extracted from the combination of the whole tumor and enhancing tumor to the overall survival prediction. By the proposed method, there are two kinds of features, including shape radiomics and deep features, which is utilized for this task. Firstly, optimal shape radiomics features, consisting of sphericity, maximum 3D diameter, and surface area, are selected using the Cox proportional hazard model. Secondly, deep features are extracted by ResNet18 directly from magnetic resonance images. Finally, the combination of selected shape features, deep features, and clinical information fits the regression model for overall survival prediction. The proposed method achieves promising results, which obtained 57.1% and 97,531.8 for accuracy and mean squared error metrics, respectively. Furthermore, using selected features, the result on the mean squared error metric is slightly better than the competing methods. The experiments are conducted on the Brain Tumor Segmentation Challenge (BraTS) 2018 validation dataset.
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40
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Radiomic Features Associated with Extent of Resection in Glioma Surgery. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:341-347. [PMID: 34862558 DOI: 10.1007/978-3-030-85292-4_38] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Radiomics defines a set of techniques for extraction and quantification of digital medical data in an automated and reproducible way. Its goal is to detect features potentially related to a clinical task, like classification, diagnosis, prognosis, and response to treatment, going beyond the intrinsic limits of operator-dependency and qualitative description of conventional radiological evaluation on a mesoscopic scale. In the field of neuro-oncology, researchers have tried to create prognostic models for a better tumor diagnosis, histological and biomolecular classification, prediction of response to treatment, and identification of disease relapse. Concerning glioma surgery, the most significant aid that radiomics can give to surgery is to improve tumor extension detection and identify areas that are more prone to recurrence to increase the extent of tumor resection, thereby ameliorating the patients' prognosis. This chapter aims to review the fundamentals of radiomics models' creation, the latest advance of radiomics in neuro-oncology, and possible radiomic features associated with the extent of resection in the brain gliomas.
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Ammari S, Sallé de Chou R, Balleyguier C, Chouzenoux E, Touat M, Quillent A, Dumont S, Bockel S, Garcia GCTE, Elhaik M, Francois B, Borget V, Lassau N, Khettab M, Assi T. A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI. Diagnostics (Basel) 2021; 11:diagnostics11112043. [PMID: 34829395 PMCID: PMC8624566 DOI: 10.3390/diagnostics11112043] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 10/20/2021] [Accepted: 10/21/2021] [Indexed: 01/01/2023] Open
Abstract
Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adult patients with a median survival of around one year. Prediction of survival outcomes in GBM patients could represent a huge step in treatment personalization. The objective of this study was to develop machine learning (ML) algorithms for survival prediction of GBM patient. We identified a radiomic signature on a training-set composed of data from the 2019 BraTS challenge (210 patients) from MRI retrieved at diagnosis. Then, using this signature along with the age of the patients for training classification models, we obtained on test-sets AUCs of 0.85, 0.74 and 0.58 (0.92, 0.88 and 0.75 on the training-sets) for survival at 9-, 12- and 15-months, respectively. This signature was then validated on an independent cohort of 116 GBM patients with confirmed disease relapse for the prediction of patients surviving less or more than the median OS of 22 months. Our model insured an AUC of 0.71 (0.65 on train). The Kaplan–Meier method showed significant OS difference between groups (log-rank p = 0.05). These results suggest that radiomic signatures may improve survival outcome predictions in GBM thus creating a solid clinical tool for tailoring therapy in this population.
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Affiliation(s)
- Samy Ammari
- Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, 94805 Villejuif, France; (S.A.); (C.B.); (M.E.); (B.F.); (V.B.); (N.L.)
- Department of Imaging, Gustave Roussy, Université Paris Saclay, 94805 Villejuif, France;
| | - Raoul Sallé de Chou
- Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, 94805 Villejuif, France; (S.A.); (C.B.); (M.E.); (B.F.); (V.B.); (N.L.)
- Correspondence:
| | - Corinne Balleyguier
- Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, 94805 Villejuif, France; (S.A.); (C.B.); (M.E.); (B.F.); (V.B.); (N.L.)
- Department of Imaging, Gustave Roussy, Université Paris Saclay, 94805 Villejuif, France;
| | - Emilie Chouzenoux
- Centre de Vision Numérique, OPIS, CentraleSupélec, Inria, Université Paris-Saclay, 91190 Gif-sur-Yvette, France; (E.C.); (A.Q.)
| | - Mehdi Touat
- Service de Neurologie 2-Mazarin, AP-HP Hôpitaux Universitaires La Pitié Salpêtrière-Charles Foix, 75013 Paris, France;
- Institut du Cerveau et de la Moelle Epinière, CNRS, UMR S 1127, Inserm, Sorbonne Université, 75013 Paris, France
| | - Arnaud Quillent
- Centre de Vision Numérique, OPIS, CentraleSupélec, Inria, Université Paris-Saclay, 91190 Gif-sur-Yvette, France; (E.C.); (A.Q.)
| | - Sarah Dumont
- Department of oncology, Gustave Roussy, Université Paris Saclay, 94805 Villejuif, France; (S.D.); (T.A.)
| | - Sophie Bockel
- Department of Radiation Oncology, Gustave Roussy Cancer Campus, 94800 Villejuif, France;
| | | | - Mickael Elhaik
- Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, 94805 Villejuif, France; (S.A.); (C.B.); (M.E.); (B.F.); (V.B.); (N.L.)
| | - Bidault Francois
- Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, 94805 Villejuif, France; (S.A.); (C.B.); (M.E.); (B.F.); (V.B.); (N.L.)
- Department of Imaging, Gustave Roussy, Université Paris Saclay, 94805 Villejuif, France;
| | - Valentin Borget
- Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, 94805 Villejuif, France; (S.A.); (C.B.); (M.E.); (B.F.); (V.B.); (N.L.)
| | - Nathalie Lassau
- Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, 94805 Villejuif, France; (S.A.); (C.B.); (M.E.); (B.F.); (V.B.); (N.L.)
- Department of Imaging, Gustave Roussy, Université Paris Saclay, 94805 Villejuif, France;
| | - Mohamed Khettab
- Medical Oncology Unit, CHU de La Réunion, Université de La Réunion, 97410 Saint Pierre, France;
| | - Tarek Assi
- Department of oncology, Gustave Roussy, Université Paris Saclay, 94805 Villejuif, France; (S.D.); (T.A.)
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Predicting Short-Term Survival after Gross Total or Near Total Resection in Glioblastomas by Machine Learning-Based Radiomic Analysis of Preoperative MRI. Cancers (Basel) 2021; 13:cancers13205047. [PMID: 34680199 PMCID: PMC8533879 DOI: 10.3390/cancers13205047] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/28/2021] [Accepted: 10/06/2021] [Indexed: 01/06/2023] Open
Abstract
Radiomics, in combination with artificial intelligence, has emerged as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection? A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were pre-processed, and a total of 15,720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality (<6 months). Additionally, a survival analysis was performed using the random survival forest (RSF) algorithm. A total of 203 patients were enrolled in this study. In the classification task, the naive Bayes classifier obtained the best results in the test data set, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. The RSF model allowed the stratification of patients into low- and high-risk groups. In the test data set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761. In this study, we developed a reliable predictive model of short-term survival in GBM by applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering individual patient characteristics.
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Zolotovskaia M, Tkachev V, Sorokin M, Garazha A, Kim E, Kantelhardt SR, Bikar SE, Zottel A, Šamec N, Kuzmin D, Sprang B, Moisseev A, Giese A, Efimov V, Jovčevska I, Buzdin A. Algorithmically Deduced FREM2 Molecular Pathway Is a Potent Grade and Survival Biomarker of Human Gliomas. Cancers (Basel) 2021; 13:4117. [PMID: 34439271 PMCID: PMC8394245 DOI: 10.3390/cancers13164117] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 08/11/2021] [Accepted: 08/13/2021] [Indexed: 01/17/2023] Open
Abstract
Gliomas are the most common malignant brain tumors with high mortality rates. Recently we showed that the FREM2 gene has a role in glioblastoma progression. Here we reconstructed the FREM2 molecular pathway using the human interactome model. We assessed the biomarker capacity of FREM2 expression and its pathway as the overall survival (OS) and progression-free survival (PFS) biomarkers. To this end, we used three literature and one experimental RNA sequencing datasets collectively covering 566 glioblastomas (GBM) and 1097 low-grade gliomas (LGG). The activation level of deduced FREM2 pathway showed strong biomarker characteristics and significantly outperformed the FREM2 expression level itself. For all relevant datasets, it could robustly discriminate GBM and LGG (p < 1.63 × 10-13, AUC > 0.74). High FREM2 pathway activation level was associated with poor OS in LGG (p < 0.001), and low PFS in LGG (p < 0.001) and GBM (p < 0.05). FREM2 pathway activation level was poor prognosis biomarker for OS (p < 0.05) and PFS (p < 0.05) in LGG with IDH mutation, for PFS in LGG with wild type IDH (p < 0.001) and mutant IDH with 1p/19q codeletion(p < 0.05), in GBM with unmethylated MGMT (p < 0.05), and in GBM with wild type IDH (p < 0.05). Thus, we conclude that the activation level of the FREM2 pathway is a potent new-generation diagnostic and prognostic biomarker for multiple molecular subtypes of GBM and LGG.
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Affiliation(s)
- Marianna Zolotovskaia
- Omicsway Corp., Walnut, CA 91789, USA; (M.S.); (A.G.); (A.M.)
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
- Department of Oncology, Hematology and Radiotherapy, Pirogov Russian National Research Medical University, Moscow 117997, Russia
| | - Victor Tkachev
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
| | - Maxim Sorokin
- Omicsway Corp., Walnut, CA 91789, USA; (M.S.); (A.G.); (A.M.)
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
- Laboratory of Clinical Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
| | - Andrew Garazha
- Omicsway Corp., Walnut, CA 91789, USA; (M.S.); (A.G.); (A.M.)
| | - Ella Kim
- Clinic for Neurosurgery, Laboratory of Experimental Neurooncology, Johannes Gutenberg University Medical Centre, Langenbeckstrasse 1, 55124 Mainz, Germany; (E.K.); (S.R.K.); (B.S.)
| | - Sven Rainer Kantelhardt
- Clinic for Neurosurgery, Laboratory of Experimental Neurooncology, Johannes Gutenberg University Medical Centre, Langenbeckstrasse 1, 55124 Mainz, Germany; (E.K.); (S.R.K.); (B.S.)
| | - Sven-Ernö Bikar
- StarSEQ GmbH, Joh.-Joachim-Becher-Weg 30a, 55128 Mainz, Germany;
| | - Alja Zottel
- Medical Center for Molecular Biology, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov Trg 2, 1000 Ljubljana, Slovenia; (A.Z.); (N.Š.); (I.J.)
| | - Neja Šamec
- Medical Center for Molecular Biology, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov Trg 2, 1000 Ljubljana, Slovenia; (A.Z.); (N.Š.); (I.J.)
| | - Denis Kuzmin
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
| | - Bettina Sprang
- Clinic for Neurosurgery, Laboratory of Experimental Neurooncology, Johannes Gutenberg University Medical Centre, Langenbeckstrasse 1, 55124 Mainz, Germany; (E.K.); (S.R.K.); (B.S.)
| | - Alexey Moisseev
- Omicsway Corp., Walnut, CA 91789, USA; (M.S.); (A.G.); (A.M.)
| | - Alf Giese
- Orthocentrum Hamburg, Hansastrasse 1, 20149 Hamburg, Germany;
| | - Victor Efimov
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
| | - Ivana Jovčevska
- Medical Center for Molecular Biology, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov Trg 2, 1000 Ljubljana, Slovenia; (A.Z.); (N.Š.); (I.J.)
| | - Anton Buzdin
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
- Laboratory of Clinical Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia
- European Organization for Research and Treatment of Cancer (EORTC), Biostatistics and Bioinformatics Subgroup, 1200 Brussels, Belgium
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Zhao D, Kim DY, Chen P, Yu P, Ho S, Cheng SW, Zhao C, Guo JA, Li YR. Pan-Cancer Survival Classification With Clinicopathological and Targeted Gene Expression Features. Cancer Inform 2021; 20:11769351211035137. [PMID: 34376966 PMCID: PMC8330450 DOI: 10.1177/11769351211035137] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 07/05/2021] [Indexed: 11/15/2022] Open
Abstract
Prognostication for patients with cancer is important for clinical planning and management, but remains challenging given the large number of factors that can influence outcomes. As such, there is a need to identify features that can robustly predict patient outcomes. We evaluated 8608 patient tumor samples across 16 cancer types from The Cancer Genome Atlas and generated distinct survival classifiers for each using clinical and histopathological data accessible to standard oncology workflows. For cancers that had poor model performance, we deployed a random-forest-embedded sequential forward selection approach that began with an initial subset of the 15 most predictive clinicopathological features before sequentially appending the next most informative gene as an additional feature. With classifiers derived from clinical and histopathological features alone, we observed cancer-type-dependent model performance and an area under the receiver operating curve (AUROC) range of 0.65 to 0.91 across all 16 cancer types for 1- and 3-year survival prediction, with some classifiers consistently outperforming those for others. As such, for cancers that had poor model performance, we posited that the addition of more complex biomolecular features could enhance our ability to prognose patients where clinicopathological features were insufficient. With the inclusion of gene expression data, model performance for 3 select cancers (glioblastoma, stomach/gastric adenocarcinoma, ovarian serous carcinoma) markedly increased from initial AUROC scores of 0.66, 0.69, and 0.67 to 0.76, 0.77, and 0.77, respectively. As a whole, this study provides a thorough examination of the relative contributions of clinical, pathological, and gene expression data in predicting overall survival and reveals cancer types for which clinical features are already strong predictors and those where additional biomolecular information is needed.
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Affiliation(s)
- Daniel Zhao
- School of Medicine, New York Medical College, Valhalla, NY, USA
| | - Daniel Y Kim
- Molecular Pathology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Peter Chen
- Raytheon Technologies, Brooklyn, NY, USA
| | - Patrick Yu
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,College of Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Sophia Ho
- Northeastern University, Boston, MA, USA
| | | | - Cindy Zhao
- Northeastern University, Boston, MA, USA
| | - Jimmy A Guo
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA.,School of Medicine, UCSF, San Francisco, CA, USA
| | - Yun R Li
- Department of Radiation Oncology, UCSF, San Francisco, CA, USA
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45
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Soltani M, Bonakdar A, Shakourifar N, Babaei R, Raahemifar K. Efficacy of Location-Based Features for Survival Prediction of Patients With Glioblastoma Depending on Resection Status. Front Oncol 2021; 11:661123. [PMID: 34295809 PMCID: PMC8290179 DOI: 10.3389/fonc.2021.661123] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 06/14/2021] [Indexed: 12/24/2022] Open
Abstract
Cancer stands out as one of the fatal diseases people are facing all the time. Each year, a countless number of people die because of the late diagnosis of cancer or wrong treatments. Glioma, one of the most common primary brain tumors, has different aggressiveness and sub-regions, which can affect the risk of disease. Although prediction of overall survival based on multimodal magnetic resonance imaging (MRI) is challenging, in this study, we assess if and how location-based features of tumors can affect overall survival prediction. This approach is evaluated independently and in combination with radiomic features. The process is carried out on a data set entailing MRI images of patients with glioblastoma. To assess the impact of resection status, the data set is divided into two groups, patients were reported as gross total resection and unknown resection status. Then, different machine learning algorithms were used to evaluate how location features are linked with overall survival. Results from regression models indicate that location-based features have considerable effects on the patients' overall survival independently. Additionally, classifier models show an improvement in prediction accuracy by the addition of location-based features to radiomic features.
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Affiliation(s)
- Madjid Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
- Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
- Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada
- Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran
| | - Armin Bonakdar
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Nastaran Shakourifar
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Reza Babaei
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Kaamran Raahemifar
- College of Information Sciences and Technology (IST), Data Science and Artificial Intelligence Program, Penn State University, State College, Pennsylvania, PA, United States
- Chemical Engineering Department, University of Waterloo, Waterloo, ON, Canada
- Optometry & Vision Science Department, University of Waterloo, Waterloo, ON, Canada
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46
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Sulman EP, Eisenstat DD. World Cancer Day 2021 - Perspectives in Pediatric and Adult Neuro-Oncology. Front Oncol 2021; 11:659800. [PMID: 34041027 PMCID: PMC8142853 DOI: 10.3389/fonc.2021.659800] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 04/07/2021] [Indexed: 12/13/2022] Open
Abstract
Significant advances in our understanding of the molecular genetics of pediatric and adult brain tumors and the resulting rapid expansion of clinical molecular neuropathology have led to improvements in diagnostic accuracy and identified new targets for therapy. Moreover, there have been major improvements in all facets of clinical care, including imaging, surgery, radiation and supportive care. In selected cohorts of patients, targeted and immunotherapies have resulted in improved patient outcomes. Furthermore, adaptations to clinical trial design have facilitated our study of new agents and other therapeutic innovations. However, considerable work remains to be done towards extending survival for all patients with primary brain tumors, especially children and adults with diffuse midline gliomas harboring Histone H3 K27 mutations and adults with isocitrate dehydrogenase (IDH) wild-type, O6 guanine DNA-methyltransferase gene (MGMT) promoter unmethylated high grade gliomas. In addition to improvements in therapy and care, access to the advances in technology, such as particle radiation or biologic therapy, neuroimaging and molecular diagnostics in both developing and developed countries is needed to improve the outcome of patients with brain tumors.
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Affiliation(s)
- Erik P. Sulman
- Section of Neuro-oncology & Neurosurgical Oncology, Frontiers in Oncology and Frontiers in Neurology, Lausanne, Switzerland
- Department of Radiation Oncology, NYU Grossman School of Medicine, New York, NY, United States
- Brain and Spine Tumor Center, Laura and Isaac Perlmutter Cancer Center, New York, NY, United States
- NYU Langone Health, New York, NY, United States
| | - David D. Eisenstat
- Section of Neuro-oncology & Neurosurgical Oncology, Frontiers in Oncology and Frontiers in Neurology, Lausanne, Switzerland
- Children’s Cancer Centre, Royal Children’s Hospital, Parkville, VIC, Australia
- Murdoch Children’s Research Institute, Parkville, VIC, Australia
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
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47
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Cherian Kurian N, Sethi A, Reddy Konduru A, Mahajan A, Rane SU. A 2021 update on cancer image analytics with deep learning. WIRES DATA MINING AND KNOWLEDGE DISCOVERY 2021. [DOI: 10.1002/widm.1410] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Nikhil Cherian Kurian
- Department of Electrical Engineering Indian Institute of Technology, Bombay Mumbai India
| | - Amit Sethi
- Department of Electrical Engineering Indian Institute of Technology, Bombay Mumbai India
| | - Anil Reddy Konduru
- Department of Pathology Tata Memorial Center‐ACTREC, HBNI Navi Mumbai India
| | - Abhishek Mahajan
- Department of Radiology Tata Memorial Hospital, HBNI Mumbai India
| | - Swapnil Ulhas Rane
- Department of Pathology Tata Memorial Center‐ACTREC, HBNI Navi Mumbai India
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