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Prinzi F, Orlando A, Gaglio S, Vitabile S. Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1038-1053. [PMID: 38351223 PMCID: PMC11169144 DOI: 10.1007/s10278-024-01012-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 11/20/2023] [Accepted: 12/05/2023] [Indexed: 06/13/2024]
Abstract
Breast microcalcifications are observed in 80% of mammograms, and a notable proportion can lead to invasive tumors. However, diagnosing microcalcifications is a highly complicated and error-prone process due to their diverse sizes, shapes, and subtle variations. In this study, we propose a radiomic signature that effectively differentiates between healthy tissue, benign microcalcifications, and malignant microcalcifications. Radiomic features were extracted from a proprietary dataset, composed of 380 healthy tissue, 136 benign, and 242 malignant microcalcifications ROIs. Subsequently, two distinct signatures were selected to differentiate between healthy tissue and microcalcifications (detection task) and between benign and malignant microcalcifications (classification task). Machine learning models, namely Support Vector Machine, Random Forest, and XGBoost, were employed as classifiers. The shared signature selected for both tasks was then used to train a multi-class model capable of simultaneously classifying healthy, benign, and malignant ROIs. A significant overlap was discovered between the detection and classification signatures. The performance of the models was highly promising, with XGBoost exhibiting an AUC-ROC of 0.830, 0.856, and 0.876 for healthy, benign, and malignant microcalcifications classification, respectively. The intrinsic interpretability of radiomic features, and the use of the Mean Score Decrease method for model introspection, enabled models' clinical validation. In fact, the most important features, namely GLCM Contrast, FO Minimum and FO Entropy, were compared and found important in other studies on breast cancer.
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Affiliation(s)
- Francesco Prinzi
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy.
- Department of Computer Science and Technology, University of Cambridge, CB2 1TN, Cambridge, United Kingdom.
| | - Alessia Orlando
- Section of Radiology - Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University Hospital "Paolo Giaccone", Palermo, Italy
| | - Salvatore Gaglio
- Department of Engineering, University of Palermo, Palermo, Italy
- Institute for High-Performance Computing and Networking, National Research Council (ICAR-CNR), Palermo, Italy
| | - Salvatore Vitabile
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy
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2
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Boelders SM, Gehring K, Postma EO, Rutten GJM, Ong LLS. Cognitive functioning in untreated glioma patients: The limited predictive value of clinical variables. Neuro Oncol 2024; 26:670-683. [PMID: 38039386 PMCID: PMC10995520 DOI: 10.1093/neuonc/noad221] [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: 07/13/2023] [Indexed: 12/03/2023] Open
Abstract
BACKGROUND Previous research identified many clinical variables that are significantly related to cognitive functioning before surgery. It is not clear whether such variables enable accurate prediction for individual patients' cognitive functioning because statistical significance does not guarantee predictive value. Previous studies did not test how well cognitive functioning can be predicted for (yet) untested patients. Furthermore, previous research is limited in that only linear or rank-based methods with small numbers of variables were used. METHODS We used various machine learning models to predict preoperative cognitive functioning for 340 patients with glioma across 18 outcome measures. Predictions were made using a comprehensive set of clinical variables as identified from the literature. Model performances and optimized hyperparameters were interpreted. Moreover, Shapley additive explanations were calculated to determine variable importance and explore interaction effects. RESULTS Best-performing models generally demonstrated above-random performance. Performance, however, was unreliable for 14 out of 18 outcome measures with predictions worse than baseline models for a substantial number of train-test splits. Best-performing models were relatively simple and used most variables for prediction while not relying strongly on any variable. CONCLUSIONS Preoperative cognitive functioning could not be reliably predicted across cognitive tests using the comprehensive set of clinical variables included in the current study. Our results show that a holistic view of an individual patient likely is necessary to explain differences in cognitive functioning. Moreover, they emphasize the need to collect larger cross-center and multimodal data sets.
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Affiliation(s)
- Sander M Boelders
- Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands
- Department of Cognitive Sciences and AI, Tilburg University, Tilburg, The Netherlands
| | - Karin Gehring
- Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands
- Department of Cognitive Neuropsychology, Tilburg University, Tilburg, The Netherlands
| | - Eric O Postma
- Department of Cognitive Sciences and AI, Tilburg University, Tilburg, The Netherlands
| | - Geert-Jan M Rutten
- Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands
| | - Lee-Ling S Ong
- Department of Cognitive Sciences and AI, Tilburg University, Tilburg, The Netherlands
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3
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Fan H, Luo Y, Gu F, Tian B, Xiong Y, Wu G, Nie X, Yu J, Tong J, Liao X. Artificial intelligence-based MRI radiomics and radiogenomics in glioma. Cancer Imaging 2024; 24:36. [PMID: 38486342 PMCID: PMC10938723 DOI: 10.1186/s40644-024-00682-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 03/03/2024] [Indexed: 03/18/2024] Open
Abstract
The specific genetic subtypes that gliomas exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of gliomas pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). Radiomics and radiogenomics present a potential to precisely diagnose and predict survival and treatment responses, via morphological, textural, and functional features derived from MRI data, as well as genomic data. In spite of their advantages, it is still lacking standardized processes of feature extraction and analysis methodology among different research groups, which have made external validations infeasible. Radiomics and radiogenomics can be used to better understand the genomic basis of gliomas, such as tumor spatial heterogeneity, treatment response, molecular classifications and tumor microenvironment immune infiltration. These novel techniques have also been used to predict histological features, grade or even overall survival in gliomas. In this review, workflows of radiomics and radiogenomics are elucidated, with recent research on machine learning or artificial intelligence in glioma.
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Affiliation(s)
- Haiqing Fan
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Yilin Luo
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Fang Gu
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Bin Tian
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Yongqin Xiong
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Guipeng Wu
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Xin Nie
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Jing Yu
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Juan Tong
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Xin Liao
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China.
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Mahmoudi K, Kim DH, Tavakkol E, Kihira S, Bauer A, Tsankova N, Khan F, Hormigo A, Yedavalli V, Nael K. Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma. Cancers (Basel) 2024; 16:589. [PMID: 38339340 PMCID: PMC10854536 DOI: 10.3390/cancers16030589] [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: 01/08/2024] [Revised: 01/20/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM. METHODS In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (<18 months vs. ≥18 months) using multivariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regularization to reduce the risk of overfitting. This radiomic model in combination with clinical and histopathological data was inserted into a backward stepwise logistic regression model to assess survival. The diagnostic performance of this model was reported for the training and external validation sets. RESULTS A total of 116 patients were included for model development and 40 patients for external testing validation. The diagnostic performance (AUC/sensitivity/specificity) of the radiomic model generated from seven texture features in determination of ≥18 months survival was 0.71/69.0/70.3. Three variables remained as independent predictors of survival, including radiomics (p = 0.004), age (p = 0.039), and MGMT status (p = 0.025). This model yielded diagnostic performance (AUC/sensitivity/specificity) of 0.77/81.0/66.0 (training) and 0.89/100/78.6 (testing) in determination of survival ≥ 18 months. CONCLUSIONS Results show that our radiogenomic model generated from radiomic features at baseline MRI, age, and MGMT status can predict survival ≥ 18 months in patients with GBM.
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Affiliation(s)
- Keon Mahmoudi
- Department of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USA
| | - Daniel H. Kim
- Department of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USA
| | - Elham Tavakkol
- Department of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USA
| | - Shingo Kihira
- Department of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USA
| | - Adam Bauer
- Department of Radiology, Kaiser Permanente Fontana Medical Center, Fontana, CA 92335, USA
| | - Nadejda Tsankova
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Fahad Khan
- Department of Pathology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA
| | - Adilia Hormigo
- Department of Oncology, Montefiore Einstein Cancer Center, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Vivek Yedavalli
- Department of Radiology and Radiological Science, Johns Hopkins Bayview Medical Center, Baltimore, MD 21224, USA
| | - Kambiz Nael
- Department of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USA
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Tillmanns N, Lost J, Tabor J, Vasandani S, Vetsa S, Marianayagam N, Yalcin K, Erson-Omay EZ, von Reppert M, Jekel L, Merkaj S, Ramakrishnan D, Avesta A, de Oliveira Santo ID, Jin L, Huttner A, Bousabarah K, Ikuta I, Lin M, Aneja S, Turowski B, Aboian M, Moliterno J. Application of novel PACS-based informatics platform to identify imaging based predictors of CDKN2A allelic status in glioblastomas. Sci Rep 2023; 13:22942. [PMID: 38135704 PMCID: PMC10746716 DOI: 10.1038/s41598-023-48918-4] [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: 03/08/2023] [Accepted: 12/01/2023] [Indexed: 12/24/2023] Open
Abstract
Gliomas with CDKN2A mutations are known to have worse prognosis but imaging features of these gliomas are unknown. Our goal is to identify CDKN2A specific qualitative imaging biomarkers in glioblastomas using a new informatics workflow that enables rapid analysis of qualitative imaging features with Visually AcceSAble Rembrandtr Images (VASARI) for large datasets in PACS. Sixty nine patients undergoing GBM resection with CDKN2A status determined by whole-exome sequencing were included. GBMs on magnetic resonance images were automatically 3D segmented using deep learning algorithms incorporated within PACS. VASARI features were assessed using FHIR forms integrated within PACS. GBMs without CDKN2A alterations were significantly larger (64 vs. 30%, p = 0.007) compared to tumors with homozygous deletion (HOMDEL) and heterozygous loss (HETLOSS). Lesions larger than 8 cm were four times more likely to have no CDKN2A alteration (OR: 4.3; 95% CI 1.5-12.1; p < 0.001). We developed a novel integrated PACS informatics platform for the assessment of GBM molecular subtypes and show that tumors with HOMDEL are more likely to have radiographic evidence of pial invasion and less likely to have deep white matter invasion or subependymal invasion. These imaging features may allow noninvasive identification of CDKN2A allele status.
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Affiliation(s)
- Niklas Tillmanns
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225, Dusseldorf, Germany
| | - Jan Lost
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
| | - Joanna Tabor
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Sagar Vasandani
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Shaurey Vetsa
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | | | - Kanat Yalcin
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | | | - Marc von Reppert
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
| | - Leon Jekel
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
| | - Sara Merkaj
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
| | - Divya Ramakrishnan
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
| | - Arman Avesta
- Department of Radiation Oncology, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
| | - Irene Dixe de Oliveira Santo
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
| | - Lan Jin
- R&D, Sema4, 333 Ludlow Street, North Tower, 8th Floor, Stamford, CT, 06902, USA
| | - Anita Huttner
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | | | - Ichiro Ikuta
- Department of Radiology, Mayo Clinic Arizona, 5711 E Mayo Blvd, Phoenix, AZ, 85054, USA
| | - MingDe Lin
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA
- Visage Imaging, Inc., 12625 High Bluff Dr, Suite 205, San Diego, CA, 92130, USA
| | - Sanjay Aneja
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Bernd Turowski
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225, Dusseldorf, Germany
| | - Mariam Aboian
- Brain Tumor Research Group, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, PO Box 208042, New Haven, CT, 06520, USA.
- , New Haven, USA.
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Nakhate V, Gonzalez Castro LN. Artificial intelligence in neuro-oncology. Front Neurosci 2023; 17:1217629. [PMID: 38161802 PMCID: PMC10755952 DOI: 10.3389/fnins.2023.1217629] [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: 05/05/2023] [Accepted: 11/14/2023] [Indexed: 01/03/2024] Open
Abstract
Artificial intelligence (AI) describes the application of computer algorithms to the solution of problems that have traditionally required human intelligence. Although formal work in AI has been slowly advancing for almost 70 years, developments in the last decade, and particularly in the last year, have led to an explosion of AI applications in multiple fields. Neuro-oncology has not escaped this trend. Given the expected integration of AI-based methods to neuro-oncology practice over the coming years, we set to provide an overview of existing technologies as they are applied to the neuropathology and neuroradiology of brain tumors. We highlight current benefits and limitations of these technologies and offer recommendations on how to appraise novel AI-tools as they undergo consideration for integration into clinical workflows.
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Affiliation(s)
- Vihang Nakhate
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - L. Nicolas Gonzalez Castro
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- The Center for Neuro-Oncology, Dana–Farber Cancer Institute, Boston, MA, United States
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7
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Chen C, Du X, Yang L, Liu H, Li Z, Gou Z, Qi J. Research on application of radiomics in glioma: a bibliometric and visual analysis. Front Oncol 2023; 13:1083080. [PMID: 37771434 PMCID: PMC10523166 DOI: 10.3389/fonc.2023.1083080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 08/16/2023] [Indexed: 09/30/2023] Open
Abstract
Background With the continuous development of medical imaging informatics technology, radiomics has become a new and evolving field in medical applications. Radiomics aims to be an aid to support clinical decision making by extracting quantitative features from medical images and has a very wide range of applications. The purpose of this study was to perform a bibliometric and visual analysis of scientific results and research trends in the research application of radiomics in glioma. Methods We searched the Web of Science Core Collection (WOScc) for publications related to glioma radiomics. A bibliometric and visual analysis of online publications in this field related to countries/regions, authors, journals, references and keywords was performed using CiteSpace and R software. Results A total of 587 relevant literature published from 2012 to September 2022 were retrieved in WOScc, and finally a total of 484 publications were obtained according to the filtering criteria, including 393 (81.20%) articles and 91 (18.80%) reviews. The number of relevant publications increases year by year. The highest number of publications was from the USA (171 articles, 35.33%) and China (170 articles, 35.12%). The research institution with the highest number of publications was Chinese Acad Sci (24), followed by Univ Penn (22) and Fudan Univ (21). WANG Y (27) had the most publications, followed by LI Y (22), and WANG J (20). Among the 555 co-cited authors, LOUIS DN (207) and KICKINGEREDER P (207) were the most cited authors. FRONTIERS IN ONCOLOGY (42) was the most published journal and NEURO-ONCOLOGY (412) was the most co-cited journal. The most frequent keywords in all publications included glioblastoma (187), survival (136), classification (131), magnetic resonance imaging (113), machine learning (100), tumor (82), and feature (79), central nervous system (66), IDH (57), and radiomics (55). Cluster analysis was performed on the basis of keyword co-occurrence, and a total of 16 clusters were formed, indicating that these directions are the current hotspots of radiomics research applications in glioma and may be the future directions of continuous development. Conclusion In the past decade, radiomics has received much attention in the medical field and has been widely used in clinical research applications. Cooperation and communication between countries/regions need to be enhanced in future research to promote the development of radiomics in the field of medicine. In addition, the application of radiomics has improved the accuracy of pre-treatment diagnosis, efficacy prediction and prognosis assessment of glioma and helped to promote the development into precision medicine, the future still faces many challenges.
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Affiliation(s)
- Chunbao Chen
- Department of Neurosurgery, Afiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xue Du
- Department of Oncology, The People's Hospital of Hechuan, Chongqing, China
- Department of Oncology, North Sichuan Medical College, Nanchong, China
| | - Lu Yang
- Department of Oncology, Suining Central Hospital, Suining, China
| | - Hongjun Liu
- Department of Neurosurgery, Afiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Zhou Li
- Department of Neurosurgery, Nanchong Central Hospital, The Afiliated Nanchong Central Hospital of North Sichuan Medical College, Nanchong, China
| | - Zhangyang Gou
- Department of Neurosurgery, Afiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Jian Qi
- Department of Neurosurgery, Afiliated Hospital of North Sichuan Medical College, Nanchong, China
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Nag S, Bhattacharya B, Dutta S, Mandal D, Mukherjee S, Anand K, Eswaramoorthy R, Thorat N, Jha SK, Gorai S. Clinical Theranostics Trademark of Exosome in Glioblastoma Metastasis. ACS Biomater Sci Eng 2023; 9:5205-5221. [PMID: 37578350 DOI: 10.1021/acsbiomaterials.3c00212] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Glioblastoma (GBM) is an aggressive type of cancer that has led to the death of a large population. The traditional approach fails to develop a solution for GBM's suffering life. Extensive research into tumor microenvironments (TME) indicates that TME extracellular vesicles (EVs) play a vital role in cancer development and progression. EVs are classified into microvacuoles, apoptotic bodies, and exosomes. Exosomes are the most highlighted domains in cancer research. GBM cell-derived exosomes participate in multiple cancer progression events such as immune suppression, angiogenesis, premetastatic niche formation (PMN), ECM (extracellular matrix), EMT (epithelial-to-mesenchymal transition), metastasis, cancer stem cell development and therapeutic and drug resistance. GBM exosomes also carry the signature of a glioblastoma-related status. The exosome-based GBM examination is part of the new generation of liquid biopsy. It also solved early diagnostic limitations in GBM. Traditional therapeutic approaches do not cross the blood-brain barrier (BBB). Exosomes are a game changer in GBM treatment and it is emerging as a potential platform for effective, efficient, and specific therapeutic development. In this review, we have explored the exosome-GBM interlink, the clinical impact of exosomes on GBM biomarkers, the therapeutics signature of exosomes in GBM, exosome-based research challenges, and future directions in GBM. Therefore, the GBM-derived exosomes offer unique therapeutic opportunities, which are currently under preclinical and clinical testing.
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Affiliation(s)
- Sagnik Nag
- Department of Biosciences, School of Biosciences & Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu 632014, India
| | - Bikramjit Bhattacharya
- Department of Applied Microbiology, School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu 632014, India
| | - Swagata Dutta
- Department of Agricultural and food Engineering, IIT Kharagpur, Kharagpur, West Bengal 721302, India
| | - Debashmita Mandal
- Department of Biotechnology, Maulana Abul Kalam Azad University of Technology (MAKAUT), Haringhata, Nadia, West Bengal 741249, India
| | - Sayantanee Mukherjee
- Centre for Nanosciences and Molecular Medicine, Amrita Vishwa Vidyapeetham, Kochi, Kerala 682041, India
| | - Krishnan Anand
- Department of Chemical Pathology, School of Pathology, Faculty of Health Sciences, University of the Free State, Bloemfontein, 9300, South Africa
| | - Rajalakshmanan Eswaramoorthy
- Department of Biomaterials, Centre of Molecular Medicine and Diagnostics (COMManD), Saveetha Dental College and Hospitals, Saveetha institute of Medical and Technical sciences (SIMATS) Chennai 600077, India
| | - Nanasaheb Thorat
- Limerick Digital Cancer Research Centre and Department of Physics, Bernal Institute, University of Limerick, Castletroy, Co. Limerick, Limerick V94T9PX, Ireland
| | - Saurabh Kumar Jha
- Department of Biotechnology, School of Engineering & Technology (SET), Sharda University, Knowledge Park-III, Institutional Area, Greater Noida 201310, India
- Department of Biotechnology Engineering and Food Technology, Chandigarh University, Mohali 140413, India
- Department of Biotechnology, School of Applied and Life Sciences (SALS), Uttaranchal University, Dehradun 248007, India
| | - Sukhamoy Gorai
- Rush University Medical Center, 1620 W Harrison Street, Chicago, Illinois 60612, United States
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Alizadeh M, Broomand Lomer N, Azami M, Khalafi M, Shobeiri P, Arab Bafrani M, Sotoudeh H. Radiomics: The New Promise for Differentiating Progression, Recurrence, Pseudoprogression, and Radionecrosis in Glioma and Glioblastoma Multiforme. Cancers (Basel) 2023; 15:4429. [PMID: 37760399 PMCID: PMC10526457 DOI: 10.3390/cancers15184429] [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: 07/11/2023] [Revised: 08/29/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Glioma and glioblastoma multiform (GBM) remain among the most debilitating and life-threatening brain tumors. Despite advances in diagnosing approaches, patient follow-up after treatment (surgery and chemoradiation) is still challenging for differentiation between tumor progression/recurrence, pseudoprogression, and radionecrosis. Radiomics emerges as a promising tool in initial diagnosis, grading, and survival prediction in patients with glioma and can help differentiate these post-treatment scenarios. Preliminary published studies are promising about the role of radiomics in post-treatment glioma/GBM. However, this field faces significant challenges, including a lack of evidence-based solid data, scattering publication, heterogeneity of studies, and small sample sizes. The present review explores radiomics's capabilities in following patients with glioma/GBM status post-treatment and to differentiate tumor progression, recurrence, pseudoprogression, and radionecrosis.
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Affiliation(s)
- Mohammadreza Alizadeh
- Physiology Research Center, Iran University of Medical Sciences, Tehran 14496-14535, Iran;
| | - Nima Broomand Lomer
- Faculty of Medicine, Guilan University of Medical Sciences, Rasht 41937-13111, Iran;
| | - Mobin Azami
- Student Research Committee, Kurdistan University of Medical Sciences, Sanandaj 66186-34683, Iran;
| | - Mohammad Khalafi
- Radiology Department, Tabriz University of Medical Sciences, Tabriz 51656-65931, Iran;
| | - Parnian Shobeiri
- School of Medicine, Tehran University of Medical Sciences, Tehran 14167-53955, Iran; (P.S.); (M.A.B.)
| | - Melika Arab Bafrani
- School of Medicine, Tehran University of Medical Sciences, Tehran 14167-53955, Iran; (P.S.); (M.A.B.)
| | - Houman Sotoudeh
- Department of Radiology and Neurology, Heersink School of Medicine, University of Alabama at Birmingham (UAB), Birmingham, AL 35294, USA
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10
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microRNAs (miRNAs) in Glioblastoma Multiforme (GBM)-Recent Literature Review. Int J Mol Sci 2023; 24:ijms24043521. [PMID: 36834933 PMCID: PMC9965735 DOI: 10.3390/ijms24043521] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 01/25/2023] [Accepted: 02/08/2023] [Indexed: 02/12/2023] Open
Abstract
Glioblastoma multiforme (GBM) is the most common, malignant, poorly promising primary brain tumor. GBM is characterized by an infiltrating growth nature, abundant vascularization, and a rapid and aggressive clinical course. For many years, the standard treatment of gliomas has invariably been surgical treatment supported by radio- and chemotherapy. Due to the location and significant resistance of gliomas to conventional therapies, the prognosis of glioblastoma patients is very poor and the cure rate is low. The search for new therapy targets and effective therapeutic tools for cancer treatment is a current challenge for medicine and science. microRNAs (miRNAs) play a key role in many cellular processes, such as growth, differentiation, cell division, apoptosis, and cell signaling. Their discovery was a breakthrough in the diagnosis and prognosis of many diseases. Understanding the structure of miRNAs may contribute to the understanding of the mechanisms of cellular regulation dependent on miRNA and the pathogenesis of diseases underlying these short non-coding RNAs, including glial brain tumors. This paper provides a detailed review of the latest reports on the relationship between changes in the expression of individual microRNAs and the formation and development of gliomas. The use of miRNAs in the treatment of this cancer is also discussed.
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Hosseini SA, Hosseini E, Hajianfar G, Shiri I, Servaes S, Rosa-Neto P, Godoy L, Nasrallah MP, O’Rourke DM, Mohan S, Chawla S. MRI-Based Radiomics Combined with Deep Learning for Distinguishing IDH-Mutant WHO Grade 4 Astrocytomas from IDH-Wild-Type Glioblastomas. Cancers (Basel) 2023; 15:cancers15030951. [PMID: 36765908 PMCID: PMC9913426 DOI: 10.3390/cancers15030951] [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: 01/01/2023] [Revised: 01/30/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
This study aimed to investigate the potential of quantitative radiomic data extracted from conventional MR images in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type glioblastomas (GBMs). A cohort of 57 treatment-naïve patients with IDH-mutant grade 4 astrocytomas (n = 23) and IDH-wild-type GBMs (n = 34) underwent anatomical imaging on a 3T MR system with standard parameters. Post-contrast T1-weighted and T2-FLAIR images were co-registered. A semi-automatic segmentation approach was used to generate regions of interest (ROIs) from different tissue components of neoplasms. A total of 1050 radiomic features were extracted from each image. The data were split randomly into training and testing sets. A deep learning-based data augmentation method (CTGAN) was implemented to synthesize 200 datasets from the training sets. A total of 18 classifiers were used to distinguish two genotypes of grade 4 astrocytomas. From generated data using 80% training set, the best discriminatory power was obtained from core tumor regions overlaid on post-contrast T1 using the K-best feature selection algorithm and a Gaussian naïve Bayes classifier (AUC = 0.93, accuracy = 0.92, sensitivity = 1, specificity = 0.86, PR_AUC = 0.92). Similarly, high diagnostic performances were obtained from original and generated data using 50% and 30% training sets. Our findings suggest that conventional MR imaging-based radiomic features combined with machine/deep learning methods may be valuable in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type GBMs.
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Affiliation(s)
- Seyyed Ali Hosseini
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC H4H 1R3, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, QC H3A 2B4, Canada
- Correspondence: (S.A.H.); (S.C.); Tel.: +1-438-929-6575 (S.A.H.); +1-215-615-1662 (S.C.)
| | - Elahe Hosseini
- Department of Electrical and Computer Engineering, Kharazmi University, Tehran 15719-14911, Iran
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran 19956-14331, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland
| | - Stijn Servaes
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC H4H 1R3, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, QC H3A 2B4, Canada
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC H4H 1R3, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, QC H3A 2B4, Canada
| | - Laiz Godoy
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - MacLean P. Nasrallah
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Donald M. O’Rourke
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sanjeev Chawla
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
- Correspondence: (S.A.H.); (S.C.); Tel.: +1-438-929-6575 (S.A.H.); +1-215-615-1662 (S.C.)
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An integrative non-invasive malignant brain tumors classification and Ki-67 labeling index prediction pipeline with radiomics approach. Eur J Radiol 2023; 158:110639. [PMID: 36463703 DOI: 10.1016/j.ejrad.2022.110639] [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: 06/10/2022] [Revised: 10/05/2022] [Accepted: 11/25/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND The histological sub-classes of brain tumors and the Ki-67 labeling index (LI) of tumor cells are major factors in the diagnosis, prognosis, and treatment management of patients. Many existing studies primarily focused on the classification of two classes of brain tumors and the Ki-67LI of gliomas. This study aimed to develop a preoperative non-invasive radiomics pipeline based on multiparametric-MRI to classify-three types of brain tumors, glioblastoma (GBM), metastasis (MET) and primary central nervous system lymphoma (PCNSL), and to predict their corresponding Ki-67LI. METHODS In this retrospective study, 153 patients with malignant brain tumors were involved. The radiomics features were extracted from three types of MRI (T1-weighted imaging (T1WI), fluid-attenuated inversion recovery (FLAIR), and contrast-enhanced T1-weighted imaging (CE-T1WI)) with three masks (tumor core, edema, and whole tumor masks) and selected by a combination of Pearson correlation coefficient (CORR), LASSO, and Max-Relevance and Min-Redundancy (mRMR) filters. The performance of six classifiers was compared and the top three performing classifiers were used to construct the ensemble learning model (ELM). The proposed ELM was evaluated in the training dataset (108 patients) by 5-fold cross-validation and in the test dataset (45 patients) by hold-out. The accuracy (ACC), sensitivity (SEN), specificity (SPE), F1-Score, and the area under the receiver operating characteristic curve (AUC) indicators evaluated the performance of the models. RESULTS The best feature sets and ELM with the optimal performance were selected to construct the tri-categorized brain tumor aided diagnosis model (training dataset AUC: 0.96 (95% CI: 0.93, 0.99); test dataset AUC: 0.93) and Ki-67LI prediction model (training dataset AUC: 0.96 (95% CI: 0.94, 0.98); test dataset AUC: 0.91). The CE-T1WI was the best single modality for all classifiers. Meanwhile, the whole tumor was the most vital mask for the tumor classification and the tumor core was the most vital mask for the Ki-67LI prediction. CONCLUSION The developed radiomics models led to the precise preoperative classification of GBM, MET, and PCNSL and the prediction of Ki-67LI, which could be utilized in clinical practice for the treatment planning for brain tumors.
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Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology. Biomedicines 2022; 10:biomedicines10123205. [PMID: 36551961 PMCID: PMC9775324 DOI: 10.3390/biomedicines10123205] [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: 11/01/2022] [Revised: 12/02/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022] Open
Abstract
Glioblastoma (GBM) is a malignant brain tumor exhibiting rapid and infiltrative growth, with less than 10% of patients surviving over 5 years, despite aggressive and multimodal treatments. The poor prognosis and the lack of effective pharmacological treatments are imputable to a remarkable histological and molecular heterogeneity of GBM, which has led, to date, to the failure of precision oncology and targeted therapies. Identification of molecular biomarkers is a paradigm for comprehensive and tailored treatments; nevertheless, biopsy sampling has proved to be invasive and limited. Radiogenomics is an emerging translational field of research aiming to study the correlation between radiographic signature and underlying gene expression. Although a research field still under development, not yet incorporated into routine clinical practice, it promises to be a useful non-invasive tool for future personalized/adaptive neuro-oncology. This review provides an up-to-date summary of the recent advancements in the use of magnetic resonance imaging (MRI) radiogenomics for the assessment of molecular markers of interest in GBM regarding prognosis and response to treatments, for monitoring recurrence, also providing insights into the potential efficacy of such an approach for survival prognostication. Despite a high sensitivity and specificity in almost all studies, accuracy, reproducibility and clinical value of radiomic features are the Achilles heel of this newborn tool. Looking into the future, investigators' efforts should be directed towards standardization and a disciplined approach to data collection, algorithms, and statistical analysis.
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Brancato V, Cavaliere C, Garbino N, Isgrò F, Salvatore M, Aiello M. The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study. Front Oncol 2022; 12:1005805. [PMID: 36276163 PMCID: PMC9582951 DOI: 10.3389/fonc.2022.1005805] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 09/21/2022] [Indexed: 12/01/2022] Open
Abstract
Glioblastoma multiforme (GBM) typically exhibits substantial intratumoral heterogeneity at both microscopic and radiological resolution scales. Diffusion Weighted Imaging (DWI) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) are two functional MRI techniques that are commonly employed in clinic for the assessment of GBM tumor characteristics. This work presents initial results aiming at determining if radiomics features extracted from preoperative ADC maps and post-contrast T1 (T1C) images are associated with pathomic features arising from H&E digitized pathology images. 48 patients from the public available CPTAC-GBM database, for which both radiology and pathology images were available, were involved in the study. 91 radiomics features were extracted from ADC maps and post-contrast T1 images using PyRadiomics. 65 pathomic features were extracted from cell detection measurements from H&E images. Moreover, 91 features were extracted from cell density maps of H&E images at four different resolutions. Radiopathomic associations were evaluated by means of Spearman's correlation (ρ) and factor analysis. p values were adjusted for multiple correlations by using a false discovery rate adjustment. Significant cross-scale associations were identified between pathomics and ADC, both considering features (n = 186, 0.45 < ρ < 0.74 in absolute value) and factors (n = 5, 0.48 < ρ < 0.54 in absolute value). Significant but fewer ρ values were found concerning the association between pathomics and radiomics features (n = 53, 0.5 < ρ < 0.65 in absolute value) and factors (n = 2, ρ = 0.63 and ρ = 0.53 in absolute value). The results of this study suggest that cross-scale associations may exist between digital pathology and ADC and T1C imaging. This can be useful not only to improve the knowledge concerning GBM intratumoral heterogeneity, but also to strengthen the role of radiomics approach and its validation in clinical practice as "virtual biopsy", introducing new insights for omics integration toward a personalized medicine approach.
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Affiliation(s)
| | | | | | - Francesco Isgrò
- Department of Electrical Engineering and Information Technologies, University of Napoli Federico II, Napoli, Italy
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Zhang D, Zhu W, Guo J, Chen W, Gu X. Application of artificial intelligence in glioma researches: A bibliometric analysis. Front Oncol 2022; 12:978427. [PMID: 36033537 PMCID: PMC9403784 DOI: 10.3389/fonc.2022.978427] [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: 06/26/2022] [Accepted: 07/25/2022] [Indexed: 12/04/2022] Open
Abstract
Background There have been no researches assessing the research trends of the application of artificial intelligence in glioma researches with bibliometric methods. Purpose The aim of the study is to assess the research trends of the application of artificial intelligence in glioma researches with bibliometric analysis. Methods Documents were retrieved from web of science between 1996 and 2022. The bibliometrix package from Rstudio was applied for data analysis and plotting. Results A total of 1081 documents were retrieved from web of science between 1996 and 2022. The annual growth rate was 30.47%. The top 5 most productive countries were the USA, China, Germany, France, and UK. The USA and China have the strongest international cooperative link. Machine learning, deep learning, radiomics, and radiogenomics have been the key words and trend topics. “Neuro-Oncology”, “Frontiers in Oncology”, and “Cancers” have been the top 3 most relevant journals. The top 3 most relevant institutions were University of Pennsylvania, Capital Medical University, and Fudan University. Conclusions With the growth of publications concerning the application of artificial intelligence in glioma researches, bibliometric analysis help researchers to get access to the international academic collaborations and trend topics in the research field.
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Wu WF, Shen CW, Lai KM, Chen YJ, Lin EC, Chen CC. The Application of DTCWT on MRI-Derived Radiomics for Differentiation of Glioblastoma and Solitary Brain Metastases. J Pers Med 2022; 12:jpm12081276. [PMID: 36013225 PMCID: PMC9409920 DOI: 10.3390/jpm12081276] [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: 06/09/2022] [Revised: 07/17/2022] [Accepted: 07/28/2022] [Indexed: 11/16/2022] Open
Abstract
Background: While magnetic resonance imaging (MRI) is the imaging modality of choice for the evaluation of patients with brain tumors, it may still be challenging to differentiate glioblastoma multiforme (GBM) from solitary brain metastasis (SBM) due to their similar imaging features. This study aimed to evaluate the features extracted of dual-tree complex wavelet transform (DTCWT) from routine MRI protocol for preoperative differentiation of glioblastoma (GBM) and solitary brain metastasis (SBM). Methods: A total of 51 patients were recruited, including 27 GBM and 24 SBM patients. Their contrast-enhanced T1-weighted images (CET1WIs), T2 fluid-attenuated inversion recovery (T2FLAIR) images, diffusion-weighted images (DWIs), and apparent diffusion coefficient (ADC) images were employed in this study. The statistical features of the pre-transformed images and the decomposed images of the wavelet transform and DTCWT were utilized to distinguish between GBM and SBM. Results: The support vector machine (SVM) showed that DTCWT images have a better accuracy (82.35%), sensitivity (77.78%), specificity (87.50%), and the area under the curve of the receiver operating characteristic curve (AUC) (89.20%) than the pre-transformed and conventional wavelet transform images. By incorporating DTCWT and pre-transformed images, the accuracy (86.27%), sensitivity (81.48%), specificity (91.67%), and AUC (93.06%) were further improved. Conclusions: Our studies suggest that the features extracted from the DTCWT images can potentially improve the differentiation between GBM and SBM.
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Affiliation(s)
- Wen-Feng Wu
- Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 600, Taiwan; (W.-F.W.); (K.-M.L.)
| | - Chia-Wei Shen
- Department of Chemistry and Biochemistry, National Chung Cheng University, Chiayi 621, Taiwan; (C.-W.S.); (Y.-J.C.)
| | - Kuan-Ming Lai
- Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 600, Taiwan; (W.-F.W.); (K.-M.L.)
- Department of Medical Imaging and Radiological Sciences, Central Taiwan University of Science and Technology, Taichung 406, Taiwan
| | - Yi-Jen Chen
- Department of Chemistry and Biochemistry, National Chung Cheng University, Chiayi 621, Taiwan; (C.-W.S.); (Y.-J.C.)
| | - Eugene C. Lin
- Department of Chemistry and Biochemistry, National Chung Cheng University, Chiayi 621, Taiwan; (C.-W.S.); (Y.-J.C.)
- Correspondence: (E.C.L.); (C.-C.C.); Tel.: +886-52-720-411 (ext. 66418) (E.C.L.); +886-52-765-041 (ext. 7521) (C.-C.C.)
| | - Chien-Chin Chen
- Department of Pathology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 600, Taiwan
- Department of Cosmetic Science, Chia Nan University of Pharmacy and Science, Tainan 717, Taiwan
- Department of Biotechnology and Bioindustry Sciences, College of Bioscience and Biotechnology, National Cheng Kung University, Tainan 701, Taiwan
- Correspondence: (E.C.L.); (C.-C.C.); Tel.: +886-52-720-411 (ext. 66418) (E.C.L.); +886-52-765-041 (ext. 7521) (C.-C.C.)
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Li AY, Iv M. Conventional and Advanced Imaging Techniques in Post-treatment Glioma Imaging. FRONTIERS IN RADIOLOGY 2022; 2:883293. [PMID: 37492665 PMCID: PMC10365131 DOI: 10.3389/fradi.2022.883293] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 06/06/2022] [Indexed: 07/27/2023]
Abstract
Despite decades of advancement in the diagnosis and therapy of gliomas, the most malignant primary brain tumors, the overall survival rate is still dismal, and their post-treatment imaging appearance remains very challenging to interpret. Since the limitations of conventional magnetic resonance imaging (MRI) in the distinction between recurrence and treatment effect have been recognized, a variety of advanced MR and functional imaging techniques including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), MR spectroscopy (MRS), as well as a variety of radiotracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) have been investigated for this indication along with voxel-based and more quantitative analytical methods in recent years. Machine learning and radiomics approaches in recent years have shown promise in distinguishing between recurrence and treatment effect as well as improving prognostication in a malignancy with a very short life expectancy. This review provides a comprehensive overview of the conventional and advanced imaging techniques with the potential to differentiate recurrence from treatment effect and includes updates in the state-of-the-art in advanced imaging with a brief overview of emerging experimental techniques. A series of representative cases are provided to illustrate the synthesis of conventional and advanced imaging with the clinical context which informs the radiologic evaluation of gliomas in the post-treatment setting.
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Affiliation(s)
- Anna Y. Li
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States
| | - Michael Iv
- Division of Neuroimaging and Neurointervention, Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States
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Corr F, Grimm D, Saß B, Pojskić M, Bartsch JW, Carl B, Nimsky C, Bopp MHA. Radiogenomic Predictors of Recurrence in Glioblastoma—A Systematic Review. J Pers Med 2022; 12:jpm12030402. [PMID: 35330402 PMCID: PMC8952807 DOI: 10.3390/jpm12030402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 02/23/2022] [Accepted: 03/01/2022] [Indexed: 12/10/2022] Open
Abstract
Glioblastoma, as the most aggressive brain tumor, is associated with a poor prognosis and outcome. To optimize prognosis and clinical therapy decisions, there is an urgent need to stratify patients with increased risk for recurrent tumors and low therapeutic success to optimize individual treatment. Radiogenomics establishes a link between radiological and pathological information. This review provides a state-of-the-art picture illustrating the latest developments in the use of radiogenomic markers regarding prognosis and their potential for monitoring recurrence. Databases PubMed, Google Scholar, and Cochrane Library were searched. Inclusion criteria were defined as diagnosis of glioblastoma with histopathological and radiological follow-up. Out of 321 reviewed articles, 43 articles met these inclusion criteria. Included studies were analyzed for the frequency of radiological and molecular tumor markers whereby radiogenomic associations were analyzed. Six main associations were described: radiogenomic prognosis, MGMT status, IDH, EGFR status, molecular subgroups, and tumor location. Prospective studies analyzing prognostic features of glioblastoma together with radiological features are lacking. By reviewing the progress in the development of radiogenomic markers, we provide insights into the potential efficacy of such an approach for clinical routine use eventually enabling early identification of glioblastoma recurrence and therefore supporting a further personalized monitoring and treatment strategy.
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Affiliation(s)
- Felix Corr
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- EDU Institute of Higher Education, Villa Bighi, Chaplain’s House, KKR 1320 Kalkara, Malta
- Correspondence:
| | - Dustin Grimm
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- EDU Institute of Higher Education, Villa Bighi, Chaplain’s House, KKR 1320 Kalkara, Malta
| | - Benjamin Saß
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
| | - Mirza Pojskić
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
| | - Jörg W. Bartsch
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- Center for Mind, Brain and Behavior (CMBB), 35043 Marburg, Germany
| | - Barbara Carl
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- Department of Neurosurgery, Helios Dr. Horst Schmidt Kliniken, Ludwig-Erhard-Strasse 100, 65199 Wiesbaden, Germany
| | - Christopher Nimsky
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- Center for Mind, Brain and Behavior (CMBB), 35043 Marburg, Germany
| | - Miriam H. A. Bopp
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- Center for Mind, Brain and Behavior (CMBB), 35043 Marburg, Germany
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