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Valenzuela-Fuenzalida JJ, Moyano-Valarezo L, Silva-Bravo V, Milos-Brandenberg D, Orellana-Donoso M, Nova-Baeza P, Suazo-Santibáñez A, Rodríguez-Luengo M, Oyanedel-Amaro G, Sanchis-Gimeno J, Gutiérrez Espinoza H. Association between the Anatomical Location of Glioblastoma and Its Evaluation with Clinical Considerations: A Systematic Review and Meta-Analysis. J Clin Med 2024; 13:3460. [PMID: 38929990 DOI: 10.3390/jcm13123460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 06/04/2024] [Accepted: 06/07/2024] [Indexed: 06/28/2024] Open
Abstract
Background: Glioblastoma is a primary malignant brain tumor; it is aggressive with a high degree of malignancy and unfavorable prognosis and is the most common type of malignant brain tumor. Glioblastomas can be located in the brain, cerebellum, brainstem, and spinal cord, originating from glial cells, particularly astrocytes. Methods: The databases MEDLINE, Scopus, Web of Science, Google Scholar, and CINAHL were researched up to January 2024. Two authors independently performed the search, study selection, and data extraction. Methodological quality was evaluated with an assurance tool for anatomical studies (AQUA). The statistical mean, standard deviation, and difference of means calculated with the Student's t-test for presence between hemispheres and presence in the frontal and temporal lobes were analyzed. Results: A total of 123 studies met the established selection criteria, with a total of 6224 patients. In relation to the mean, GBM between hemispheres had a mean of 33.36 (SD 58.00) in the right hemisphere and a mean of 34.70 (SD 65.07) in the left hemisphere, due to the difference in averages between hemispheres. There were no statistically significant differences, p = 0.35. For the comparison between the presence of GBM in the frontal lobe and the temporal lobe, there was a mean in the frontal lobe of 23.23 (SD 40.03), while in the temporal lobe, the mean was 22.05 (SD 43.50), and for the difference in means between the frontal lobe and the temporal lobe, there was no statistically significant difference for the presence of GBM, p = 0.178. Conclusions: We believe that before a treatment, it will always be correct to know where the GBM is located and how it behaves clinically, in order to generate correct conservative or surgical treatment guidelines for each patient. We believe that more detailed studies are also needed to show why GBM is associated more with some regions than others, despite the brain structure being homologous to other regions in which GMB occurs less frequently, which is why knowing its predominant presence in brain regions is very important.
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Affiliation(s)
- Juan Jose Valenzuela-Fuenzalida
- Departamento de Ciencias Química y Biológicas, Facultad de Ciencias de la Salud, Universidad Bernardo O'Higgins, Santiago 8320000, Chile
- Departament de Morfología, Facultad de Medicina, Universidad Andrés Bello, Santiago 8370146, Chile
| | - Laura Moyano-Valarezo
- Departament de Morfología, Facultad de Medicina, Universidad Andrés Bello, Santiago 8370146, Chile
| | - Vicente Silva-Bravo
- Departament de Morfología, Facultad de Medicina, Universidad Andrés Bello, Santiago 8370146, Chile
| | - Daniel Milos-Brandenberg
- Departament de Morfología, Facultad de Medicina, Universidad Andrés Bello, Santiago 8370146, Chile
- Escuela de Medicina, Facultad Ciencias de la Salud, Universidad del Alba, Santiago 8320000, Chile
| | - Mathias Orellana-Donoso
- Escuela de Medicina, Universidad Finis Terrae, Santiago 7501015, Chile
- Department of Morphological Sciences, Faculty of Medicine and Science, Universidad San Sebastián, Santiago 8420524, Chile
| | - Pablo Nova-Baeza
- Departament de Morfología, Facultad de Medicina, Universidad Andrés Bello, Santiago 8370146, Chile
| | | | | | - Gustavo Oyanedel-Amaro
- Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Santiago 8910060, Chile
| | - Juan Sanchis-Gimeno
- GIAVAL Research Group, Department of Anatomy and Human Embryology, Faculty of Medicine, University of Valencia, 46001 Valencia, Spain
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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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Wei Y, Chen X, Zhu L, Zhang L, Schonlieb CB, Price S, Li C. Multi-Modal Learning for Predicting the Genotype of Glioma. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3167-3178. [PMID: 37022918 DOI: 10.1109/tmi.2023.3244038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
The isocitrate dehydrogenase (IDH) gene mutation is an essential biomarker for the diagnosis and prognosis of glioma. It is promising to better predict glioma genotype by integrating focal tumor image and geometric features with brain network features derived from MRI. Convolutional neural networks show reasonable performance in predicting IDH mutation, which, however, cannot learn from non-Euclidean data, e.g., geometric and network data. In this study, we propose a multi-modal learning framework using three separate encoders to extract features of focal tumor image, tumor geometrics and global brain networks. To mitigate the limited availability of diffusion MRI, we develop a self-supervised approach to generate brain networks from anatomical multi-sequence MRI. Moreover, to extract tumor-related features from the brain network, we design a hierarchical attention module for the brain network encoder. Further, we design a bi-level multi-modal contrastive loss to align the multi-modal features and tackle the domain gap at the focal tumor and global brain. Finally, we propose a weighted population graph to integrate the multi-modal features for genotype prediction. Experimental results on the testing set show that the proposed model outperforms the baseline deep learning models. The ablation experiments validate the performance of different components of the framework. The visualized interpretation corresponds to clinical knowledge with further validation. In conclusion, the proposed learning framework provides a novel approach for predicting the genotype of glioma.
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Choi Y, Jang J, Kim BS, Ahn KJ. Pretreatment MR-based radiomics in patients with glioblastoma: A systematic review and meta-analysis of prognostic endpoints. Eur J Radiol 2023; 168:111130. [PMID: 37827087 DOI: 10.1016/j.ejrad.2023.111130] [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: 06/27/2023] [Revised: 09/23/2023] [Accepted: 09/30/2023] [Indexed: 10/14/2023]
Abstract
PURPOSE Recent studies have shown promise of MR-based radiomics in predicting the survival of patients with untreated glioblastoma. This study aimed to comprehensively collate evidence to assess the prognostic value of radiomics in glioblastoma. METHODS PubMed-MEDLINE, Embase, and Web of Science were searched to find original articles investigating the prognostic value of MR-based radiomics in glioblastoma published up to July 14, 2023. Concordance indexes (C-indexes) and Cox proportional hazards ratios (HRs) of overall survival (OS) and progression-free survival (PFS) were pooled via random-effects modeling. For studies aimed at classifying long-term and short-term PFS, a hierarchical regression model was used to calculate pooled sensitivity and specificity. Between-study heterogeneity was assessed using the Higgin inconsistency index (I2). Subgroup regression analysis was performed to find potential factors contributing to heterogeneity. Publication bias was assessed via funnel plots and the Egger test. RESULTS Among 1371 abstracts, 18 and 17 studies were included for qualitative and quantitative data synthesis, respectively. Respective pooled C-indexes and HRs for OS were 0.65 (95 % confidence interval [CI], 0.58-0.72) and 2.88 (95 % CI, 2.28-3.64), whereas those for PFS were 0.61 (95 % CI, 0.55-0.66) and 2.78 (95 % CI, 1.91-4.03). Among 4 studies that predicted short-term PFS, the pooled sensitivity and specificity were 0.77 (95 % CI, 0.58-0.89) and 0.60 (95 % CI, 0.45-0.73), respectively. There was a substantial between-study heterogeneity among studies with the survival endpoint of OS C-index (n = 9, I2 = 83.8 %). Publication bias was not observed overall. CONCLUSION Pretreatment MR-based radiomics provided modest prognostic value in both OS and PFS in patients with glioblastoma.
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Affiliation(s)
- Yangsean Choi
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Republic of Korea; Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea
| | - Jinhee Jang
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Republic of Korea
| | - Bum-Soo Kim
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Republic of Korea
| | - Kook-Jin Ahn
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Republic of Korea.
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Abstract
ABSTRACT The mechanical traits of cancer include abnormally high solid stress as well as drastic and spatially heterogeneous changes in intrinsic mechanical tissue properties. Whereas solid stress elicits mechanosensory signals promoting tumor progression, mechanical heterogeneity is conducive to cell unjamming and metastatic spread. This reductionist view of tumorigenesis and malignant transformation provides a generalized framework for understanding the physical principles of tumor aggressiveness and harnessing them as novel in vivo imaging markers. Magnetic resonance elastography is an emerging imaging technology for depicting the viscoelastic properties of biological soft tissues and clinically characterizing tumors in terms of their biomechanical properties. This review article presents recent technical developments, basic results, and clinical applications of magnetic resonance elastography in patients with malignant tumors.
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Affiliation(s)
- Jing Guo
- From the Department of Radiology
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Perillo T, de Giorgi M, Papace UM, Serino A, Cuocolo R, Manto A. Current role of machine learning and radiogenomics in precision neuro-oncology. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:545-555. [PMID: 37720347 PMCID: PMC10501892 DOI: 10.37349/etat.2023.00151] [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: 12/20/2022] [Accepted: 04/20/2023] [Indexed: 09/19/2023] Open
Abstract
In the past few years, artificial intelligence (AI) has been increasingly used to create tools that can enhance workflow in medicine. In particular, neuro-oncology has benefited from the use of AI and especially machine learning (ML) and radiogenomics, which are subfields of AI. ML can be used to develop algorithms that dynamically learn from available medical data in order to automatically do specific tasks. On the other hand, radiogenomics can identify relationships between tumor genetics and imaging features, thus possibly giving new insights into the pathophysiology of tumors. Therefore, ML and radiogenomics could help treatment tailoring, which is crucial in personalized neuro-oncology. The aim of this review is to illustrate current and possible future applications of ML and radiomics in neuro-oncology.
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Affiliation(s)
- Teresa Perillo
- Department of Neuroradiology, “Umberto I” Hospital, 84014 Norcera Inferiore, Italy
| | - Marco de Giorgi
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80138 Naples, Italy
| | - Umberto Maria Papace
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80138 Naples, Italy
| | - Antonietta Serino
- Department of Neuroradiology, “Umberto I” Hospital, 84014 Norcera Inferiore, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, 84084 Fisciano, Italy
| | - Andrea Manto
- Department of Neuroradiology, “Umberto I” Hospital, 84014 Norcera Inferiore, Italy
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Subramanian S, Ghafouri A, Scheufele KM, Himthani N, Davatzikos C, Biros G. Ensemble Inversion for Brain Tumor Growth Models With Mass Effect. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:982-995. [PMID: 36378796 PMCID: PMC10201550 DOI: 10.1109/tmi.2022.3221913] [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] [Indexed: 05/24/2023]
Abstract
We propose a method for extracting physics-based biomarkers from a single multiparametric Magnetic Resonance Imaging (mpMRI) scan bearing a glioma tumor. We account for mass effect, the deformation of brain parenchyma due to the growing tumor, which on its own is an important radiographic feature but its automatic quantification remains an open problem. In particular, we calibrate a partial differential equation (PDE) tumor growth model that captures mass effect, parameterized by a single scalar parameter, tumor proliferation, migration, while localizing the tumor initiation site. The single-scan calibration problem is severely ill-posed because the precancerous, healthy, brain anatomy is unknown. To address the ill-posedness, we introduce an ensemble inversion scheme that uses a number of normal subject brain templates as proxies for the healthy precancer subject anatomy. We verify our solver on a synthetic dataset and perform a retrospective analysis on a clinical dataset of 216 glioblastoma (GBM) patients. We analyze the reconstructions using our calibrated biophysical model and demonstrate that our solver provides both global and local quantitative measures of tumor biophysics and mass effect. We further highlight the improved performance in model calibration through the inclusion of mass effect in tumor growth models-including mass effect in the model leads to 10% increase in average dice coefficients for patients with significant mass effect. We further evaluate our model by introducing novel biophysics-based features and using them for survival analysis. Our preliminary analysis suggests that including such features can improve patient stratification and survival prediction.
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Ismail M, Prasanna P, Bera K, Statsevych V, Hill V, Singh G, Partovi S, Beig N, McGarry S, Laviolette P, Ahluwalia M, Madabhushi A, Tiwari P. Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:1764-1777. [PMID: 35108202 PMCID: PMC9575333 DOI: 10.1109/tmi.2022.3148780] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The concept of tumor field effect implies that cancer is a systemic disease with its impact way beyond the visible tumor confines. For instance, in Glioblastoma (GBM), an aggressive brain tumor, the increase in intracranial pressure due to tumor burden often leads to brain herniation and poor outcomes. Our work is based on the rationale that highly aggressive tumors tend to grow uncontrollably, leading to pronounced biomechanical tissue deformations in the normal parenchyma, which when combined with local morphological differences in the tumor confines on MRI scans, will comprehensively capture tumor field effect. Specifically, we present an integrated MRI-based descriptor, radiomic-Deformation and Textural Heterogeneity (r-DepTH). This descriptor comprises measurements of the subtle perturbations in tissue deformations throughout the surrounding normal parenchyma due to mass effect. This involves non-rigidly aligning the patients' MRI scans to a healthy atlas via diffeomorphic registration. The resulting inverse mapping is used to obtain the deformation field magnitudes in the normal parenchyma. These measurements are then combined with a 3D texture descriptor, Co-occurrence of Local Anisotropic Gradient Orientations (COLLAGE), which captures the morphological heterogeneity and infiltration within the tumor confines, on MRI scans. In this work, we extensively evaluated r-DepTH for survival risk-stratification on a total of 207 GBM cases from 3 different cohorts (Cohort 1 ( n1 = 53 ), Cohort 2 ( n2 = 75 ), and Cohort 3 ( n3 = 79 )), where each of these three cohorts was used as a training set for our model separately, and the other two cohorts were used for testing, independently, for each training experiment. When employing Cohort 1 for training, r-DepTH yielded Concordance indices (C-indices) of 0.7 and 0.65, hazard ratios (HR) and Confidence Intervals (CI) of 10 (6 - 19) and 5 (3 - 8) on Cohorts 2 and 3, respectively. Similarly, training on Cohort 2 yielded C-indices of 0.6 and 0.7, HR and CI of 1 (0.7 - 2) and 3 (2 - 5) on Cohorts 1 and 3, respectively. Finally, training on Cohort 3 yielded C-indices of 0.75 and 0.63, HR and CI of 24 (10 - 57) and 12 (6 - 21) on Cohorts 1 and 2, respectively. Our results show that r-DepTH descriptor may serve as a comprehensive and a robust MRI-based prognostic marker of disease aggressiveness and survival in solid tumors.
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Fuster-Garcia E, Thokle Hovden I, Fløgstad Svensson S, Larsson C, Vardal J, Bjørnerud A, Emblem KE. Quantification of Tissue Compression Identifies High-Grade Glioma Patients with Reduced Survival. Cancers (Basel) 2022; 14:cancers14071725. [PMID: 35406497 PMCID: PMC8997138 DOI: 10.3390/cancers14071725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/22/2022] [Accepted: 03/25/2022] [Indexed: 02/01/2023] Open
Abstract
The compression of peritumoral healthy tissue in brain tumor patients is considered a major cause of the life-threatening neurologic symptoms. Although significant deformations caused by the tumor growth can be observed radiologically, the quantification of minor tissue deformations have not been widely investigated. In this study, we propose a method to quantify subtle peritumoral deformations. A total of 127 MRI longitudinal studies from 23 patients with high-grade glioma were included. We estimate longitudinal displacement fields based on a symmetric normalization algorithm and we propose four biomarkers. We assess the interpatient and intrapatient association between proposed biomarkers and the survival based on Cox analyses, and the potential of the biomarkers to stratify patients according to their survival based on Kaplan−Meier analysis. Biomarkers show a significant intrapatient association with survival (p < 0.05); however, only compression biomarkers show the ability to stratify patients between those with higher and lower overall survival (AUC = 0.83, HR = 6.30, p < 0.05 for CompCH). The compression biomarkers present three times higher Hazard Ratios than those representing only displacement. Our study provides a robust and automated method for quantifying and delineating compression in the peritumoral area. Based on the proposed methodology, we found an association between lower compression in the peritumoral area and good prognosis in high-grade glial tumors.
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Affiliation(s)
- Elies Fuster-Garcia
- Biomedical Data Science Laboratory, Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politècnica de València, 46022 Valencia, Spain
- Correspondence:
| | - Ivar Thokle Hovden
- Department of Diagnostic Physics, Oslo University Hospital, 0372 Oslo, Norway; (I.T.H.); (S.F.S.); (K.E.E.)
- Department of Physics, University of Oslo, 0316 Oslo, Norway;
| | - Siri Fløgstad Svensson
- Department of Diagnostic Physics, Oslo University Hospital, 0372 Oslo, Norway; (I.T.H.); (S.F.S.); (K.E.E.)
- Department of Physics, University of Oslo, 0316 Oslo, Norway;
| | - Christopher Larsson
- Department of Neurosurgery, Oslo University Hospital, 0372 Oslo, Norway;
- Unit for Computational Radiology and Artificial Intelligence, Oslo University Hospital, 0372 Oslo, Norway;
| | - Jonas Vardal
- Unit for Computational Radiology and Artificial Intelligence, Oslo University Hospital, 0372 Oslo, Norway;
- Department of Radiology, Drammen Hospital, Vestre Viken Hospital Trust, 3004 Drammen, Norway
| | - Atle Bjørnerud
- Department of Physics, University of Oslo, 0316 Oslo, Norway;
- Unit for Computational Radiology and Artificial Intelligence, Oslo University Hospital, 0372 Oslo, Norway;
- Department of Psychology, Faculty for Social Sciences, University of Oslo, 0851 Oslo, Norway
| | - Kyrre E. Emblem
- Department of Diagnostic Physics, Oslo University Hospital, 0372 Oslo, Norway; (I.T.H.); (S.F.S.); (K.E.E.)
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Iyer S, Ismail M, Tamrazi B, Salloum R, de Blank P, Margol A, Correa R, Chen J, Bera K, Statsevych V, Ho ML, Vaidya P, Verma R, Hawes D, Judkins A, Fu P, Madabhushi A, Tiwari P. Novel MRI deformation-heterogeneity radiomic features are associated with molecular subgroups and overall survival in pediatric medulloblastoma: Preliminary findings from a multi-institutional study. Front Oncol 2022; 12:915143. [PMID: 36620600 PMCID: PMC9811390 DOI: 10.3389/fonc.2022.915143] [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: 04/07/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
Introduction Medulloblastoma (MB) is a malignant, heterogenous brain tumor. Advances in molecular profiling have led to identifying four molecular subgroups of MB (WNT, SHH, Group 3, Group 4), each with distinct clinical behaviors. We hypothesize that (1) aggressive MB tumors, growing heterogeneously, induce pronounced local structural deformations in the surrounding parenchyma, and (b) these local deformations as captured on Gadolinium (Gd)-enhanced-T1w MRI are independently associated with molecular subgroups, as well as overall survival in MB patients. Methods In this work, a total of 88 MB studies from 2 institutions were analyzed. Following tumor delineation, Gd-T1w scan for every patient was registered to a normal age-specific T1w-MRI template via deformable registration. Following patient-atlas registration, local structural deformations in the brain parenchyma were obtained for every patient by computing statistics from deformation magnitudes obtained from every 5mm annular region, 0 < d < 60 mm, where d is the distance from the tumor infiltrating edge. Results Multi-class comparison via ANOVA yielded significant differences between deformation magnitudes obtained for Group 3, Group 4, and SHH molecular subgroups, observed up to 60-mm outside the tumor edge. Additionally, Kaplan-Meier survival analysis showed that the local deformation statistics, combined with the current clinical risk-stratification approaches (molecular subgroup information and Chang's classification), could identify significant differences between high-risk and low-risk survival groups, achieving better performance results than using any of these approaches individually. Discussion These preliminary findings suggest there exists significant association of our tumor-induced deformation descriptor with overall survival in MB, and that there could be an added value in using the proposed radiomic descriptor along with the current risk classification approaches, towards more reliable risk assessment in pediatric MB.
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Affiliation(s)
- Sukanya Iyer
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Marwa Ismail
- Department of Radiology and Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Benita Tamrazi
- Department of Pathology, Children's Hospital Los Angeles, Los Angeles, CA, United States
| | - Ralph Salloum
- Division of Hematology, Oncology & Bone Marrow Transplant, Nationwide Children's Hospital, Columbus, OH, United States
| | - Peter de Blank
- Division of Oncology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Ashley Margol
- Department of Pathology, Children's Hospital Los Angeles, Los Angeles, CA, United States
| | - Ramon Correa
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Jonathan Chen
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Volodymyr Statsevych
- Department of Neuroradiology, Imaging Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Mai-Lan Ho
- Department of Radiology, Nationwide Children's Hospital, Columbus, OH, United States
| | - Pranjal Vaidya
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Ruchika Verma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Debra Hawes
- Department of Pathology, Children's Hospital Los Angeles, Los Angeles, CA, United States
| | - Alexander Judkins
- Department of Pathology, Children's Hospital Los Angeles, Los Angeles, CA, United States
| | - Pingfu Fu
- Radiology and Imaging Sciences, Biomedical Informatics (BMI) and Pathology, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Anant Madabhushi
- Radiology and Imaging Sciences, Biomedical Informatics (BMI) and Pathology, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Pallavi Tiwari
- Department of Radiology and Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States
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Radiomics and radiogenomics in gliomas: a contemporary update. Br J Cancer 2021; 125:641-657. [PMID: 33958734 PMCID: PMC8405677 DOI: 10.1038/s41416-021-01387-w] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 03/10/2021] [Accepted: 03/31/2021] [Indexed: 02/03/2023] Open
Abstract
The natural history and treatment landscape of primary brain tumours are complicated by the varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low-grade lesions), as well as the dilemmas with identification of radiation necrosis, tumour progression, and pseudoprogression on MRI. Radiomics and radiogenomics promise to offer precise diagnosis, predict prognosis, and assess tumour response to modern chemotherapy/immunotherapy and radiation therapy. This is achieved by a triumvirate of morphological, textural, and functional signatures, derived from a high-throughput extraction of quantitative voxel-level MR image metrics. However, the lack of standardisation of acquisition parameters and inconsistent methodology between working groups have made validations unreliable, hence multi-centre studies involving heterogenous study populations are warranted. We elucidate novel radiomic and radiogenomic workflow concepts and state-of-the-art descriptors in sub-visual MR image processing, with relevant literature on applications of such machine learning techniques in glioma management.
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Beig N, Bera K, Tiwari P. Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges. Neurooncol Adv 2020; 2:iv3-iv14. [PMID: 33521636 PMCID: PMC7829475 DOI: 10.1093/noajnl/vdaa148] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Neuro-oncology largely consists of malignancies of the brain and central nervous system including both primary as well as metastatic tumors. Currently, a significant clinical challenge in neuro-oncology is to tailor therapies for patients based on a priori knowledge of their survival outcome or treatment response to conventional or experimental therapies. Radiomics or the quantitative extraction of subvisual data from conventional radiographic imaging has recently emerged as a powerful data-driven approach to offer insights into clinically relevant questions related to diagnosis, prediction, prognosis, as well as assessing treatment response. Furthermore, radiogenomic approaches provide a mechanism to establish statistical correlations of radiomic features with point mutations and next-generation sequencing data to further leverage the potential of routine MRI scans to serve as "virtual biopsy" maps. In this review, we provide an introduction to radiomic and radiogenomic approaches in neuro-oncology, including a brief description of the workflow involving preprocessing, tumor segmentation, and extraction of "hand-crafted" features from the segmented region of interest, as well as identifying radiogenomic associations that could ultimately lead to the development of reliable prognostic and predictive models in neuro-oncology applications. Lastly, we discuss the promise of radiomics and radiogenomic approaches in personalizing treatment decisions in neuro-oncology, as well as the challenges with clinical adoption, which will rely heavily on their demonstrated resilience to nonstandardization in imaging protocols across sites and scanners, as well as in their ability to demonstrate reproducibility across large multi-institutional cohorts.
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Affiliation(s)
- Niha Beig
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Pallavi Tiwari
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
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Wach J, Hamed M, Schuss P, Güresir E, Herrlinger U, Vatter H, Schneider M. Impact of initial midline shift in glioblastoma on survival. Neurosurg Rev 2020; 44:1401-1409. [PMID: 32494988 DOI: 10.1007/s10143-020-01328-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 04/16/2020] [Accepted: 05/28/2020] [Indexed: 12/21/2022]
Abstract
The impact of midline shift (MLS) on long-term survival and progression in glioblastoma (GBM) is unknown. The objective of this study was to analyze the influence of mass effect on survival and progression with consideration of the patient demographics, tumor morphology, operative techniques, molecular pathology, and postoperative treatment. One hundred ninety-eight patients with GBM were analyzed retrospectively. Both MLS groups (< or ≥ 10 mm) were compared with regard to survival, progression-free survival (PFS), and postoperative course of Karnofsky Performance Status (KPS). A two-sided Fisher exact test showed no statistically significant differences in the confounders between the low- and high-MLS groups. The median survival was 18.0 months (95% confidence interval (CI) = 15.3-20.7) in the low-MLS group (n = 173) and 9.0 months (95% CI = 4.8-13.2) in the high-MLS group (n = 25) (p = 0.045). In the high-MLS group, 59.1% (13/22) with an initially high MLS had a KPS of less than 70% after 3 months, whereas 20.5% of the low-MLS group had a KPS of less than 70% (p < 0.001). Binary logistic regression analysis including the O-6-methylguanine-DNA methyltransferase (MGMT) status, extent of resection, baseline KPS, and MIB-I index showed low MLS as the only predictor for survival at 12 months (p = 0.046, odds ratio (OR) = 2.70, 95% CI = 1.0-7.2). Median PFS was 6.0 months in the high-MLS group and 9.0 months in the low-MLS group (log-rank test; p = 0.08). An initial midline shift of 10 mm or greater seems to be an imaging characteristic that independently predicts the survival in glioblastoma.
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Affiliation(s)
- Johannes Wach
- Department of Neurosurgery, University Hospital Bonn, Venusberg Campus 1, 53127, Bonn, Germany.
| | - Motaz Hamed
- Department of Neurosurgery, University Hospital Bonn, Venusberg Campus 1, 53127, Bonn, Germany
| | - Patrick Schuss
- Department of Neurosurgery, University Hospital Bonn, Venusberg Campus 1, 53127, Bonn, Germany
| | - Erdem Güresir
- Department of Neurosurgery, University Hospital Bonn, Venusberg Campus 1, 53127, Bonn, Germany
| | - Ulrich Herrlinger
- Division of Clinical Neurooncology, Department of Neurology and Centre of Integrated Oncology, University Hospital Bonn, Bonn, Germany
| | - Hartmut Vatter
- Department of Neurosurgery, University Hospital Bonn, Venusberg Campus 1, 53127, Bonn, Germany
| | - Matthias Schneider
- Department of Neurosurgery, University Hospital Bonn, Venusberg Campus 1, 53127, Bonn, Germany
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