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Ren P, Bao H, Wang S, Wang Y, Bai Y, Lai J, Yi L, Liu Q, Li W, Zhang X, Sun L, Liu Q, Cui X, Zhang X, Liang P, Liang X. Multi-scale brain attributes contribute to the distribution of diffuse glioma subtypes. Int J Cancer 2024. [PMID: 38949756 DOI: 10.1002/ijc.35068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 04/11/2024] [Accepted: 06/06/2024] [Indexed: 07/02/2024]
Abstract
Gliomas are primary brain tumors and are among the most malignant types. Adult-type diffuse gliomas can be classified based on their histological and molecular signatures as IDH-wildtype glioblastoma, IDH-mutant astrocytoma, and IDH-mutant and 1p/19q-codeleted oligodendroglioma. Recent studies have shown that each subtype of glioma has its own specific distribution pattern. However, the mechanisms underlying the specific distributions of glioma subtypes are not entirely clear despite partial explanations such as cell origin. To investigate the impact of multi-scale brain attributes on glioma distribution, we constructed cumulative frequency maps for diffuse glioma subtypes based on T1w structural images and evaluated the spatial correlation between tumor frequency and diverse brain attributes, including postmortem gene expression, functional connectivity metrics, cerebral perfusion, glucose metabolism, and neurotransmitter signaling. Regression models were constructed to evaluate the contribution of these factors to the anatomic distribution of different glioma subtypes. Our findings revealed that the three different subtypes of gliomas had distinct distribution patterns, showing spatial preferences toward different brain environmental attributes. Glioblastomas were especially likely to occur in regions enriched with synapse-related pathways and diverse neurotransmitter receptors. Astrocytomas and oligodendrogliomas preferentially occurred in areas enriched with genes associated with neutrophil-mediated immune responses. The functional network characteristics and neurotransmitter distribution also contributed to oligodendroglioma distribution. Our results suggest that different brain transcriptomic, neurotransmitter, and connectomic attributes are the factors that determine the specific distributions of glioma subtypes. These findings highlight the importance of bridging diverse scales of biological organization when studying neurological dysfunction.
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Affiliation(s)
- Peng Ren
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Hongbo Bao
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shuai Wang
- Medical Imaging Department, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yinyan Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yan Bai
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Jiacheng Lai
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Liye Yi
- Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qing Liu
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Wenting Li
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xinyu Zhang
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lili Sun
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Qiuyi Liu
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xuehua Cui
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xiushi Zhang
- Medical Imaging Department, Harbin Medical University Cancer Hospital, Harbin, China
| | - Peng Liang
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xia Liang
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China
- Frontiers Science Center for Matter Behave in Space Environment, Harbin Institute of Technology, Harbin, China
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2
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Kossmann MRP, Ehret F, Roohani S, Winter SF, Ghadjar P, Acker G, Senger C, Schmid S, Zips D, Kaul D. Histopathologically confirmed radiation-induced damage of the brain - an in-depth analysis of radiation parameters and spatio-temporal occurrence. Radiat Oncol 2023; 18:198. [PMID: 38087368 PMCID: PMC10717523 DOI: 10.1186/s13014-023-02385-3] [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: 09/23/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Radiation-induced damage (RID) after radiotherapy (RT) of primary brain tumors and metastases can be challenging to clinico-radiographically distinguish from tumor progression. RID includes pseudoprogression and radiation necrosis; the latter being irreversible and often associated with severe symptoms. While histopathology constitutes the diagnostic gold standard, biopsy-controlled clinical studies investigating RID remain limited. Whether certain brain areas are potentially more vulnerable to RID remains an area of active investigation. Here, we analyze histopathologically confirmed cases of RID in relation to the temporal and spatial dose distribution. METHODS Histopathologically confirmed cases of RID after photon-based RT for primary or secondary central nervous system malignancies were included. Demographic, clinical, and dosimetric data were collected from patient records and treatment planning systems. We calculated the equivalent dose in 2 Gy fractions (EQD22) and the biologically effective dose (BED2) for normal brain tissue (α/β ratio of 2 Gy) and analyzed the spatial and temporal distribution using frequency maps. RESULTS Thirty-three patients were identified. High-grade glioma patients (n = 18) mostly received one normofractionated RT series (median cumulative EQD22 60 Gy) to a large planning target volume (PTV) (median 203.9 ccm) before diagnosis of RID. Despite the low EQD22 and BED2, three patients with an accelerated hyperfractionated RT developed RID. In contrast, brain metastases patients (n = 15; 16 RID lesions) were often treated with two or more RT courses and with radiosurgery or fractionated stereotactic RT, resulting in a higher cumulative EQD22 (median 162.4 Gy), to a small PTV (median 6.7 ccm). All (n = 34) RID lesions occurred within the PTV of at least one of the preceding RT courses. RID in the high-grade glioma group showed a frontotemporal distribution pattern, whereas, in metastatic patients, RID was observed throughout the brain with highest density in the parietal lobe. The cumulative EQD22 was significantly lower in RID lesions that involved the subventricular zone (SVZ) than in lesions without SVZ involvement (median 60 Gy vs. 141 Gy, p = 0.01). CONCLUSIONS Accelerated hyperfractionated RT can lead to RID despite computationally low EQD22 and BED2 in high-grade glioma patients. The anatomical location of RID corresponded to the general tumor distribution of gliomas and metastases. The SVZ might be a particularly vulnerable area.
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Affiliation(s)
- Mario R P Kossmann
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany
- Department of Radiotherapy and Radiation Oncology, Pius-Hospital Oldenburg, Georgstr. 12, 26121, Oldenburg, Germany
| | - Felix Ehret
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Berlin, Germany; German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Siyer Roohani
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Berlin, Germany; German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Sebastian F Winter
- Division of Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Pirus Ghadjar
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Güliz Acker
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurosurgery, Charitéplatz 1, 10117, Berlin, Germany
| | - Carolin Senger
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Simone Schmid
- Charité - Universitätsmedizin Berlin, Berlin, Germany; German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neuropathology, Charitéplatz 1, 10117, Berlin, Germany
| | - Daniel Zips
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Berlin, Germany; German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - David Kaul
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany.
- Charité - Universitätsmedizin Berlin, Berlin, Germany; German Cancer Consortium (DKTK), partner site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany.
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3
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Bao H, Wang H, Sun Q, Wang Y, Liu H, Liang P, Lv Z. The involvement of brain regions associated with lower KPS and shorter survival time predicts a poor prognosis in glioma. Front Neurol 2023; 14:1264322. [PMID: 38111796 PMCID: PMC10725945 DOI: 10.3389/fneur.2023.1264322] [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: 07/20/2023] [Accepted: 11/14/2023] [Indexed: 12/20/2023] Open
Abstract
Background Isocitrate dehydrogenase-wildtype glioblastoma (IDH-wildtype GBM) and IDH-mutant astrocytoma have distinct biological behaviors and clinical outcomes. The location of brain tumors is closely associated not only with clinical symptoms and prognosis but also with key molecular alterations such as IDH. Therefore, we hypothesize that the key brain regions influencing the prognosis of glioblastoma and astrocytoma are likely to differ. This study aims to (1) identify specific regions that are associated with the Karnofsky Performance Scale (KPS) or overall survival (OS) in IDH-wildtype GBM and IDH-mutant astrocytoma and (2) test whether the involvement of these regions could act as a prognostic indicator. Methods A total of 111 patients with IDH-wildtype GBM and 78 patients with IDH-mutant astrocytoma from the Cancer Imaging Archive database were included in the study. Voxel-based lesion-symptom mapping (VLSM) was used to identify key brain areas for lower KPS and shorter OS. Next, we analyzed the structural and cognitive dysfunction associated with these regions. The survival analysis was carried out using Kaplan-Meier survival curves. Another 72 GBM patients and 48 astrocytoma patients from Harbin Medical University Cancer Hospital were used as a validation cohort. Results Tumors located in the insular cortex, parahippocampal gyrus, and middle and superior temporal gyrus of the left hemisphere tended to lead to lower KPS and shorter OS in IDH-wildtype GBM. The regions that were significantly correlated with lower KPS in IDH-mutant astrocytoma included the subcallosal cortex and cingulate gyrus. These regions were associated with diverse structural and cognitive impairments. The involvement of these regions was an independent predictor for shorter survival in both GBM and astrocytoma. Conclusion This study identified the specific regions that were significantly associated with OS or KPS in glioma. The results may help neurosurgeons evaluate patient survival before surgery and understand the pathogenic mechanisms of glioma in depth.
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Affiliation(s)
- Hongbo Bao
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Huan Wang
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
| | - Qian Sun
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
| | - Yujie Wang
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
| | - Hui Liu
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
| | - Peng Liang
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
| | - Zhonghua Lv
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, China
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Zhang L, Bordey A. Advances in glioma models using in vivo electroporation to highjack neurodevelopmental processes. Biochim Biophys Acta Rev Cancer 2023; 1878:188951. [PMID: 37433417 DOI: 10.1016/j.bbcan.2023.188951] [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: 01/29/2023] [Revised: 07/04/2023] [Accepted: 07/05/2023] [Indexed: 07/13/2023]
Abstract
Glioma is the most prevalent type of neurological malignancies. Despite decades of efforts in neurosurgery, chemotherapy and radiation therapy, glioma remains one of the most treatment-resistant brain tumors with unfavorable outcomes. Recent progresses in genomic and epigenetic profiling have revealed new concepts of genetic events involved in the etiology of gliomas in humans, meanwhile, revolutionary technologies in gene editing and delivery allows to code these genetic "events" in animals to genetically engineer glioma models. This approach models the initiation and progression of gliomas in a natural microenvironment with an intact immune system and facilitates probing therapeutic strategies. In this review, we focus on recent advances in in vivo electroporation-based glioma modeling and outline the established genetically engineered glioma models (GEGMs).
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Affiliation(s)
- Longbo Zhang
- Departments of Neurosurgery, Changde hospital, Xiangya School of Medicine, Central South University, 818 Renmin Street, Wuling District, Changde, Hunan 415003, China; Departments of Neurosurgery, and National Clinical Research Center of Geriatric Disorders, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, China; Departments of Neurosurgery, and Cellular & Molecular Physiology, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06520-8082, USA.
| | - Angelique Bordey
- Departments of Neurosurgery, and Cellular & Molecular Physiology, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06520-8082, USA
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Das A, Ding S, Liu R, Huang C. Quantifying the Growth of Glioblastoma Tumors Using Multimodal MRI Brain Images. Cancers (Basel) 2023; 15:3614. [PMID: 37509277 PMCID: PMC10377296 DOI: 10.3390/cancers15143614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 07/11/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023] Open
Abstract
Predicting the eventual volume of tumor cells, that might proliferate from a given tumor, can help in cancer early detection and medical procedure planning to prevent their migration to other organs. In this work, a new statistical framework is proposed using Bayesian techniques for detecting the eventual volume of cells expected to proliferate from a glioblastoma (GBM) tumor. Specifically, the tumor region was first extracted using a parallel image segmentation algorithm. Once the tumor region was determined, we were interested in the number of cells that could proliferate from this tumor until its survival time. For this, we constructed the posterior distribution of the tumor cell numbers based on the proposed likelihood function and a certain prior volume. Furthermore, we extended the detection model and conducted a Bayesian regression analysis by incorporating radiomic features to discover those non-tumor cells that remained undetected. The main focus of the study was to develop a time-independent prediction model that could reliably predict the ultimate volume a malignant tumor of the fourth-grade severity could attain and which could also determine if the incorporation of the radiomic properties of the tumor enhanced the chances of no malignant cells remaining undetected.
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Affiliation(s)
- Anisha Das
- Department of Statistics, Florida State University, Tallahassee, FL 32306, USA
| | - Shengxian Ding
- Department of Statistics, Florida State University, Tallahassee, FL 32306, USA
| | - Rongjie Liu
- Department of Statistics, Florida State University, Tallahassee, FL 32306, USA
| | - Chao Huang
- Department of Statistics, Florida State University, Tallahassee, FL 32306, USA
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6
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Sansone G, Pini L, Salvalaggio A, Gaiola M, Volpin F, Baro V, Padovan M, Anglani M, Facchini S, Chioffi F, Zagonel V, D’Avella D, Denaro L, Lombardi G, Corbetta M. Patterns of gray and white matter functional networks involvement in glioblastoma patients: indirect mapping from clinical MRI scans. Front Neurol 2023; 14:1175576. [PMID: 37409023 PMCID: PMC10318144 DOI: 10.3389/fneur.2023.1175576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 05/22/2023] [Indexed: 07/07/2023] Open
Abstract
Background Resting-state functional-MRI studies identified several cortical gray matter functional networks (GMNs) and white matter functional networks (WMNs) with precise anatomical localization. Here, we aimed at describing the relationships between brain's functional topological organization and glioblastoma (GBM) location. Furthermore, we assessed whether GBM distribution across these networks was associated with overall survival (OS). Materials and methods We included patients with histopathological diagnosis of IDH-wildtype GBM, presurgical MRI and survival data. For each patient, we recorded clinical-prognostic variables. GBM core and edema were segmented and normalized to a standard space. Pre-existing functional connectivity-based atlases were used to define network parcellations: 17 GMNs and 12 WMNs were considered in particular. We computed the percentage of lesion overlap with GMNs and WMNs, both for core and edema. Differences between overlap percentages were assessed through descriptive statistics, ANOVA, post-hoc tests, Pearson's correlation tests and canonical correlations. Multiple linear and non-linear regression tests were employed to explore relationships with OS. Results 99 patients were included (70 males, mean age 62 years). The most involved GMNs included ventral somatomotor, salient ventral attention and default-mode networks; the most involved WMNs were ventral frontoparietal tracts, deep frontal white matter, and superior longitudinal fasciculus system. Superior longitudinal fasciculus system and dorsal frontoparietal tracts were significantly more included in the edema (p < 0.001). 5 main patterns of GBM core distribution across functional networks were found, while edema localization was less classifiable. ANOVA showed significant differences between mean overlap percentages, separately for GMNs and WMNs (p-values<0.0001). Core-N12 overlap predicts higher OS, although its inclusion does not increase the explained OS variance. Discussion and conclusion Both GBM core and edema preferentially overlap with specific GMNs and WMNs, especially associative networks, and GBM core follows five main distribution patterns. Some inter-related GMNs and WMNs were co-lesioned by GBM, suggesting that GBM distribution is not independent of the brain's structural and functional organization. Although the involvement of ventral frontoparietal tracts (N12) seems to have some role in predicting survival, network-topology information is overall scarcely informative about OS. fMRI-based approaches may more effectively demonstrate the effects of GBM on brain networks and survival.
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Affiliation(s)
- Giulio Sansone
- Department of Neuroscience, University of Padova, Padova, Italy
| | - Lorenzo Pini
- Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
| | - Alessandro Salvalaggio
- Department of Neuroscience, University of Padova, Padova, Italy
- Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
| | - Matteo Gaiola
- Department of Neuroscience, University of Padova, Padova, Italy
| | - Francesco Volpin
- Division of Neurosurgery, Azienda Ospedaliera Università di Padova, Padova, Italy
| | - Valentina Baro
- Academic Neurosurgery, Department of Neurosciences, University of Padova, Padova, Italy
| | - Marta Padovan
- Department of Oncology, Oncology 1, Veneto Institute of Oncology IOV-IRCCS, Padova, Italy
| | | | - Silvia Facchini
- Department of Neuroscience, University of Padova, Padova, Italy
| | - Franco Chioffi
- Division of Neurosurgery, Azienda Ospedaliera Università di Padova, Padova, Italy
| | - Vittorina Zagonel
- Department of Oncology, Oncology 1, Veneto Institute of Oncology IOV-IRCCS, Padova, Italy
| | - Domenico D’Avella
- Academic Neurosurgery, Department of Neurosciences, University of Padova, Padova, Italy
| | - Luca Denaro
- Academic Neurosurgery, Department of Neurosciences, University of Padova, Padova, Italy
| | - Giuseppe Lombardi
- Department of Oncology, Oncology 1, Veneto Institute of Oncology IOV-IRCCS, Padova, Italy
| | - Maurizio Corbetta
- Department of Neuroscience, University of Padova, Padova, Italy
- Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
- Venetian Institute of Molecular Medicine (VIMM), Fondazione Biomedica, Padova, Italy
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7
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Bao H, Ren P, Yi L, Lv Z, Ding W, Li C, Li S, Li Z, Yang X, Liang X, Liang P. New insights into glioma frequency maps: From genetic and transcriptomic correlate to survival prediction. Int J Cancer 2023; 152:998-1012. [PMID: 36305649 PMCID: PMC10100131 DOI: 10.1002/ijc.34336] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 09/18/2022] [Accepted: 10/17/2022] [Indexed: 01/06/2023]
Abstract
Increasing evidence indicates that glioma topographic location is linked to the cellular origin, molecular alterations and genetic profile. This research aims to (a) reveal the underlying mechanisms of tumor location predilection in glioblastoma multiforme (GBM) and lower-grade glioma (LGG) and (b) leverage glioma location features to predict prognosis. MRI images from 396 GBM and 190 LGG (115 astrocytoma and 75 oligodendroglioma) patients were standardized to construct frequency maps and analyzed by voxel-based lesion-symptom mapping. We then investigated the spatial correlation between glioma distribution with gene expression in healthy brains. We also evaluated transcriptomic differences in tumor tissue from predilection and nonpredilection sites. Furthermore, we quantitively characterized tumor anatomical localization and explored whether it was significantly related to overall survival. Finally, we employed a support vector machine to build a survival prediction model for GBM patients. GBMs exhibited a distinct location predilection from LGGs. GBMs were nearer to the subventricular zone and more likely to be localized to regions enriched with synaptic signaling, whereas astrocytoma and oligodendroglioma tended to occur in areas associated with the immune response. Synapse, neurotransmitters and calcium ion channel-related genes were all activated in GBM tissues coming from predilection regions. Furthermore, we characterized tumor location features in terms of a series of tumor-to-predilection distance metrics, which were able to predict GBM 1-year survival status with an accuracy of 0.71. These findings provide new perspectives on our understanding of tumor anatomic localization. The spatial features of glioma are of great value in individual therapy and prognosis prediction.
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Affiliation(s)
- Hongbo Bao
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China.,Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China.,School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Peng Ren
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China.,School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Liye Yi
- Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Zhonghua Lv
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Wencai Ding
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Chenlong Li
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Siyang Li
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China.,School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Zhipeng Li
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China.,School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xue Yang
- Department of Information, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xia Liang
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China
| | - Peng Liang
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China
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8
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Colopi A, Fuda S, Santi S, Onorato A, Cesarini V, Salvati M, Balistreri CR, Dolci S, Guida E. Impact of age and gender on glioblastoma onset, progression, and management. Mech Ageing Dev 2023; 211:111801. [PMID: 36996926 DOI: 10.1016/j.mad.2023.111801] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 03/06/2023] [Accepted: 03/20/2023] [Indexed: 03/30/2023]
Abstract
Glioblastoma (GBM) is the most common primary malignant brain tumor in adults, while its frequency in pediatric patients is 10-15%. For this reason, age is considered one of the major risk factors for the development of GBM, as it correlates with cellular aging phenomena involving glial cells and favoring the process of tumor transformation. Gender differences have been also identified, as the incidence of GBM is higher in males than in females, coupled with a worse outcome. In this review, we analyze age- and gender- dependent differences in GBM onset, mutational landscape, clinical manifestations, and survival, according to the literature of the last 20 years, focusing on the major risk factors involved in tumor development and on the mutations and gene alterations most frequently found in adults vs young patients and in males vs females. We then highlight the impact of age and gender on clinical manifestations and tumor localization and their involvement in the time of diagnosis and in determining the tumor prognostic value.
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Affiliation(s)
- Ambra Colopi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | - Serena Fuda
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | - Samuele Santi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | - Angelo Onorato
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | - Valeriana Cesarini
- Department of Biomedicine, Institute of Translational Pharmacology-CNR, Rome, Italy
| | - Maurizio Salvati
- Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Carmela Rita Balistreri
- Cellular and Molecular Laboratory, Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, Corso Tukory 211, 90134 Palermo, Italy
| | - Susanna Dolci
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy.
| | - Eugenia Guida
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy.
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9
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Iglesias JE, Billot B, Balbastre Y, Magdamo C, Arnold SE, Das S, Edlow BL, Alexander DC, Golland P, Fischl B. SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry. SCIENCE ADVANCES 2023; 9:eadd3607. [PMID: 36724222 PMCID: PMC9891693 DOI: 10.1126/sciadv.add3607] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 01/04/2023] [Indexed: 05/10/2023]
Abstract
Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in hospitals across the world. These have the potential to revolutionize our understanding of many neurological diseases, but their morphometric analysis has not yet been possible due to their anisotropic resolution. We present an artificial intelligence technique, "SynthSR," that takes clinical brain MRI scans with any MR contrast (T1, T2, etc.), orientation (axial/coronal/sagittal), and resolution and turns them into high-resolution T1 scans that are usable by virtually all existing human neuroimaging tools. We present results on segmentation, registration, and atlasing of >10,000 scans of controls and patients with brain tumors, strokes, and Alzheimer's disease. SynthSR yields morphometric results that are very highly correlated with what one would have obtained with high-resolution T1 scans. SynthSR allows sample sizes that have the potential to overcome the power limitations of prospective research studies and shed new light on the healthy and diseased human brain.
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Affiliation(s)
- Juan E. Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Benjamin Billot
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Yaël Balbastre
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Steven E. Arnold
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Brian L. Edlow
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA
| | - Daniel C. Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA
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Lam LHT, Do DT, Diep DTN, Nguyet DLN, Truong QD, Tri TT, Thanh HN, Le NQK. Molecular subtype classification of low-grade gliomas using magnetic resonance imaging-based radiomics and machine learning. NMR IN BIOMEDICINE 2022; 35:e4792. [PMID: 35767281 DOI: 10.1002/nbm.4792] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 06/24/2022] [Accepted: 06/27/2022] [Indexed: 05/22/2023]
Abstract
In 2016, the World Health Organization (WHO) updated the glioma classification by incorporating molecular biology parameters, including low-grade glioma (LGG). In the new scheme, LGGs have three molecular subtypes: isocitrate dehydrogenase (IDH)-mutated 1p/19q-codeleted, IDH-mutated 1p/19q-noncodeleted, and IDH-wild type 1p/19q-noncodeleted entities. This work proposes a model prediction of LGG molecular subtypes using magnetic resonance imaging (MRI). MR images were segmented and converted into radiomics features, thereby providing predictive information about the brain tumor classification. With 726 raw features obtained from the feature extraction procedure, we developed a hybrid machine learning-based radiomics by incorporating a genetic algorithm and eXtreme Gradient Boosting (XGBoost) classifier, to ascertain 12 optimal features for tumor classification. To resolve imbalanced data, the synthetic minority oversampling technique (SMOTE) was applied in our study. The XGBoost algorithm outperformed the other algorithms on the training dataset by an accuracy value of 0.885. We continued evaluating the XGBoost model, then achieved an overall accuracy of 0.6905 for the three-subtype classification of LGGs on an external validation dataset. Our model is among just a few to have resolved the three-subtype LGG classification challenge with high accuracy compared with previous studies performing similar work.
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Affiliation(s)
- Luu Ho Thanh Lam
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Children's Hospital 2, Ho Chi Minh City, Vietnam
| | - Duyen Thi Do
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Doan Thi Ngoc Diep
- Department of Pediatrics, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | | | | | | | - Huynh Ngoc Thanh
- Department of Pediatrics, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan
- Neuroscience Research Center, Taipei Medical University, Taipei, Taiwan
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11
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Lam LHT, Chu NT, Tran TO, Do DT, Le NQK. A Radiomics-Based Machine Learning Model for Prediction of Tumor Mutational Burden in Lower-Grade Gliomas. Cancers (Basel) 2022; 14:cancers14143492. [PMID: 35884551 PMCID: PMC9324877 DOI: 10.3390/cancers14143492] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 07/03/2022] [Accepted: 07/12/2022] [Indexed: 02/07/2023] Open
Abstract
Simple Summary Lower-grade glioma (LGG) is a kind of center nervous system neoplasm that arises from the glial cells. Lower-grade glioma patients have a median survival time in the range of 1.5–8 years based on the tumor genotypes. In term of epidemiology, most of the lower-grade glioma patients are diagnosed at young adult of age, which led to an early age of death. For exact diagnosis and effective treatment, a pathological result from biopsy sample is required. However, it is long turnaround time. In this study, using pre-operative magnetic resonance images, we developed a non-invasive model to classify tumor mutational burden (TMB), a prognostic factor of treatment response in lower-grade glioma patients, with an accuracy of 0.7936. To our knowledge, our study represents the best model for classification of TMB in LGG patients at present. Abstract Glioma is a Center Nervous System (CNS) neoplasm that arises from the glial cells. In a new scheme category of the World Health Organization 2016, lower-grade gliomas (LGGs) are grade II and III gliomas. Following the discovery of suppression of negative immune regulation, immunotherapy is a promising effective treatment method for lower-grade glioma patients. However, the therapy is not effective for all types of LGGs, and tumor mutational burden (TMB) has been shown to be a potential biomarker for the susceptibility and prognosis of immunotherapy in lower-grade glioma patients. Hence, predicting TMB benefits brain cancer patients. In this study, we investigated the correlation between MRI (magnetic resonance imaging)-based radiomic features and TMB in LGG by applying machine learning methods. Six machine learning classifiers were examined on the features extracted from the genetic algorithm. Subsequently, a light gradient boosting machine (LightGBM) succeeded in selecting 11 radiomics signatures for TMB classification. Our LightGBM model resulted in high accuracy of 0.7936, and reached a balance between sensitivity and specificity, achieving 0.76 and 0.8107, respectively. To our knowledge, our study represents the best model for classification of TMB in LGG patients at present.
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Affiliation(s)
- Luu Ho Thanh Lam
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan;
- Children’s Hospital 2, Ho Chi Minh City 70000, Vietnam
| | - Ngan Thy Chu
- City Children’s Hospital, Ho Chi Minh City 70000, Vietnam;
| | - Thi-Oanh Tran
- International Ph.D. Program for Cell Therapy and Regeneration Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan;
- Hematology and Blood Transfusion Center, Bach Mai Hospital, Hanoi 115-19, Vietnam
| | - Duyen Thi Do
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 106, Taiwan;
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 106, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 106, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan
- Neuroscience Research Center, Taipei Medical University, Taipei 110, Taiwan
- Correspondence: ; Tel.: +886-2-66382736 (ext. 1992)
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12
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Untapped Neuroimaging Tools for Neuro-Oncology: Connectomics and Spatial Transcriptomics. Cancers (Basel) 2022; 14:cancers14030464. [PMID: 35158732 PMCID: PMC8833690 DOI: 10.3390/cancers14030464] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 01/13/2022] [Accepted: 01/15/2022] [Indexed: 01/27/2023] Open
Abstract
Simple Summary Brain imaging, specifically magnetic resonance imaging (MRI), plays a key role in the clinical and research aspects of neuro-oncology. Novel neuroimaging techniques enable the transformation of a brain MRI into a so-called average brain. This allows projects using already acquired brain MRIs to perform group analyses and draw conclusions. Once the data are in this average brain, several types of analyses can be performed. For example, determining the most vulnerable locations for certain tumor types or perhaps even the underlying circuitry and gene expression that might cause predisposition to tumor growth. This information may further our understanding of tumor behavior, leading to better patient counseling, surgery timing, and treatment monitoring. Abstract Neuro-oncology research is broad and includes several branches, one of which is neuroimaging. Magnetic resonance imaging (MRI) is instrumental for the diagnosis and treatment monitoring of patients with brain tumors. Most commonly, structural and perfusion MRI sequences are acquired to characterize tumors and understand their behaviors. Thanks to technological advances, structural brain MRI can now be transformed into a so-called average brain accounting for individual morphological differences, which enables retrospective group analysis. These normative analyses are uncommonly used in neuro-oncology research. Once the data have been normalized, voxel-wise analyses and spatial mapping can be performed. Additionally, investigations of underlying connectomics can be performed using functional and structural templates. Additionally, a recently available template of spatial transcriptomics has enabled the assessment of associated gene expression. The few published normative analyses have shown relationships between tumor characteristics and spatial localization, as well as insights into the circuitry associated with epileptogenic tumors and depression after cingulate tumor resection. The wide breadth of possibilities with normative analyses remain largely unexplored, specifically in terms of connectomics and imaging transcriptomics. We provide a framework for performing normative analyses in oncology while also highlighting their limitations. Normative analyses are an opportunity to address neuro-oncology questions from a different perspective.
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Mandal AS, Romero-Garcia R, Seidlitz J, Hart MG, Alexander-Bloch AF, Suckling J. Lesion covariance networks reveal proposed origins and pathways of diffuse gliomas. Brain Commun 2021; 3:fcab289. [PMID: 34917940 PMCID: PMC8669792 DOI: 10.1093/braincomms/fcab289] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 10/24/2021] [Accepted: 10/25/2021] [Indexed: 12/13/2022] Open
Abstract
Diffuse gliomas have been hypothesized to originate from neural stem cells in the subventricular zone and develop along previously healthy brain networks. Here, we evaluated these hypotheses by mapping independent sources of glioma localization and determining their relationships with neurogenic niches, genetic markers and large-scale connectivity networks. By applying independent component analysis to lesion data from 242 adult patients with high- and low-grade glioma, we identified three lesion covariance networks, which reflect clusters of frequent glioma localization. Replicability of the lesion covariance networks was assessed in an independent sample of 168 glioma patients. We related the lesion covariance networks to important clinical variables, including tumour grade and patient survival, as well as genomic information such as molecular genetic subtype and bulk transcriptomic profiles. Finally, we systematically cross-correlated the lesion covariance networks with structural and functional connectivity networks derived from neuroimaging data of over 4000 healthy UK BioBank participants to uncover intrinsic brain networks that may that underlie tumour development. The three lesion covariance networks overlapped with the anterior, posterior and inferior horns of the lateral ventricles respectively, extending into the frontal, parietal and temporal cortices. These locations were independently replicated. The first lesion covariance network, which overlapped with the anterior horn, was associated with low-grade, isocitrate dehydrogenase -mutated/1p19q-codeleted tumours, as well as a neural transcriptomic signature and improved overall survival. Each lesion covariance network significantly coincided with multiple structural and functional connectivity networks, with the first bearing an especially strong relationship with brain connectivity, consistent with its neural transcriptomic profile. Finally, we identified subcortical, periventricular structures with functional connectivity patterns to the cortex that significantly matched each lesion covariance network. In conclusion, we demonstrated replicable patterns of glioma localization with clinical relevance and spatial correspondence with large-scale functional and structural connectivity networks. These results are consistent with prior reports of glioma growth along white matter pathways, as well as evidence for the coordination of glioma stem cell proliferation by neuronal activity. Our findings describe how the locations of gliomas relate to their proposed subventricular origins, suggesting a model wherein periventricular brain connectivity guides tumour development.
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Affiliation(s)
- Ayan S Mandal
- Department of Psychiatry, Brain Mapping Unit, University of Cambridge, Cambridge, CB2 0SZ, UK
- Department of Psychiatry, Brain-Gene Development Lab, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Rafael Romero-Garcia
- Department of Psychiatry, Brain Mapping Unit, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Jakob Seidlitz
- Department of Psychiatry, Brain-Gene Development Lab, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Michael G Hart
- Department of Psychiatry, Brain Mapping Unit, University of Cambridge, Cambridge, CB2 0SZ, UK
- Academic Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, Brain-Gene Development Lab, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - John Suckling
- Department of Psychiatry, Brain Mapping Unit, University of Cambridge, Cambridge, CB2 0SZ, UK
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Fathi Kazerooni A, Bagley SJ, Akbari H, Saxena S, Bagheri S, Guo J, Chawla S, Nabavizadeh A, Mohan S, Bakas S, Davatzikos C, Nasrallah MP. Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine. Cancers (Basel) 2021; 13:cancers13235921. [PMID: 34885031 PMCID: PMC8656630 DOI: 10.3390/cancers13235921] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 12/22/2022] Open
Abstract
Simple Summary Radiomics and radiogenomics offer new insight into high-grade glioma biology, as well as into glioma behavior in response to standard therapies. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the role of radiomics in providing more accurate diagnoses, prognostication, and surveillance of patients with high-grade glioma, and on the potential application of radiomics in clinical practice, with the overarching goal of advancing precision medicine for optimal patient care. Abstract Machine learning (ML) integrated with medical imaging has introduced new perspectives in precision diagnostics of high-grade gliomas, through radiomics and radiogenomics. This has raised hopes for characterizing noninvasive and in vivo biomarkers for prediction of patient survival, tumor recurrence, and genomics and therefore encouraging treatments tailored to individualized needs. Characterization of tumor infiltration based on pre-operative multi-parametric magnetic resonance imaging (MP-MRI) scans may allow prediction of the loci of future tumor recurrence and thereby aid in planning the course of treatment for the patients, such as optimizing the extent of resection and the dose and target area of radiation. Imaging signatures of tumor genomics can help in identifying the patients who benefit from certain targeted therapies. Specifying molecular properties of gliomas and prediction of their changes over time and with treatment would allow optimization of treatment. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the promise of radiomics and radiogenomics for allowing personalized treatments of patients with gliomas and discuss the challenges and limitations of these methods in multi-institutional clinical trials and suggestions to mitigate the issues and the future directions.
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Affiliation(s)
- Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Stephen J. Bagley
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
- Glioblastoma Translational Center of Excellence, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hamed Akbari
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Sanjay Saxena
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Sina Bagheri
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Jun Guo
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Sanjeev Chawla
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Ali Nabavizadeh
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
- Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - MacLean P. Nasrallah
- Glioblastoma Translational Center of Excellence, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Correspondence:
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15
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Carrano A, Juarez JJ, Incontri D, Ibarra A, Cazares HG. Sex-Specific Differences in Glioblastoma. Cells 2021; 10:cells10071783. [PMID: 34359952 PMCID: PMC8303471 DOI: 10.3390/cells10071783] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 07/08/2021] [Accepted: 07/09/2021] [Indexed: 12/13/2022] Open
Abstract
Sex differences have been well identified in many brain tumors. Even though glioblastoma (GBM) is the most common primary malignant brain tumor in adults and has the worst outcome, well-established differences between men and women are limited to incidence and outcome. Little is known about sex differences in GBM at the disease phenotype and genetical/molecular level. This review focuses on a deep understanding of the pathophysiology of GBM, including hormones, metabolic pathways, the immune system, and molecular changes, along with differences between men and women and how these dimorphisms affect disease outcome. The information analyzed in this review shows a greater incidence and worse outcome in male patients with GBM compared with female patients. We highlight the protective role of estrogen and the upregulation of androgen receptors and testosterone having detrimental effects on GBM. Moreover, hormones and the immune system work in synergy to directly affect the GBM microenvironment. Genetic and molecular differences have also recently been identified. Specific genes and molecular pathways, either upregulated or downregulated depending on sex, could potentially directly dictate GBM outcome differences. It appears that sexual dimorphism in GBM affects patient outcome and requires an individualized approach to management considering the sex of the patient, especially in relation to differences at the molecular level.
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Affiliation(s)
- Anna Carrano
- Department of Neurologic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Juan Jose Juarez
- Centro de Investigación en Ciencias de la Salud (CICSA), FCS, Universidad Anáhuac México Campus Norte, Huixquilucan 52786, Edo. de México, Mexico; (J.J.J.); (D.I.); (A.I.)
| | - Diego Incontri
- Centro de Investigación en Ciencias de la Salud (CICSA), FCS, Universidad Anáhuac México Campus Norte, Huixquilucan 52786, Edo. de México, Mexico; (J.J.J.); (D.I.); (A.I.)
| | - Antonio Ibarra
- Centro de Investigación en Ciencias de la Salud (CICSA), FCS, Universidad Anáhuac México Campus Norte, Huixquilucan 52786, Edo. de México, Mexico; (J.J.J.); (D.I.); (A.I.)
| | - Hugo Guerrero Cazares
- Department of Neurologic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA;
- Correspondence:
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Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression. BRAINLESION : GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES. BRAINLES (WORKSHOP) 2021; 12658:157-167. [PMID: 34514469 DOI: 10.1007/978-3-030-72084-1_15] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Glioblastoma ( GBM ) is arguably the most aggressive, infiltrative, and heterogeneous type of adult brain tumor. Biophysical modeling of GBM growth has contributed to more informed clinical decision-making. However, deploying a biophysical model to a clinical environment is challenging since underlying computations are quite expensive and can take several hours using existing technologies. Here we present a scheme to accelerate the computation. In particular, we present a deep learning ( DL )-based logistic regression model to estimate the GBM's biophysical growth in seconds. This growth is defined by three tumor-specific parameters: 1) a diffusion coefficient in white matter ( Dw ), which prescribes the rate of infiltration of tumor cells in white matter, 2) a mass-effect parameter ( Mp ), which defines the average tumor expansion, and 3) the estimated time ( T ) in number of days that the tumor has been growing. Preoperative structural multi-parametric MRI ( mpMRI ) scans from n = 135 subjects of the TCGA-GBM imaging collection are used to quantitatively evaluate our approach. We consider the mpMRI intensities within the region defined by the abnormal FLAIR signal envelope for training one DL model for each of the tumor-specific growth parameters. We train and validate the DL-based predictions against parameters derived from biophysical inversion models. The average Pearson correlation coefficients between our DL-based estimations and the biophysical parameters are 0.85 for Dw, 0.90 for Mp, and 0.94 for T, respectively. This study unlocks the power of tumor-specific parameters from biophysical tumor growth estimation. It paves the way towards their clinical translation and opens the door for leveraging advanced radiomic descriptors in future studies by means of a significantly faster parameter reconstruction compared to biophysical growth modeling approaches.
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Tatekawa H, Uetani H, Hagiwara A, Yao J, Oughourlian TC, Ueda I, Raymond C, Lai A, Cloughesy TF, Nghiemphu PL, Liau LM, Bahri S, Pope WB, Salamon N, Ellingson BM. Preferential tumor localization in relation to 18F-FDOPA uptake for lower-grade gliomas. J Neurooncol 2021; 152:573-582. [PMID: 33704629 DOI: 10.1007/s11060-021-03730-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 03/01/2021] [Indexed: 11/28/2022]
Abstract
PURPOSE Although tumor localization and 3,4-dihydroxy-6-18F-fluoro-L-phenylalanine (FDOPA) uptake may have an association, preferential tumor localization in relation to FDOPA uptake is yet to be investigated in lower-grade gliomas (LGGs). This study aimed to identify differences in the frequency of tumor localization between FDOPA hypometabolic and hypermetabolic LGGs using a probabilistic radiographic atlas. METHODS Fifty-one patients with newly diagnosed LGG (WHO grade II, 29; III, 22; isocitrate dehydrogenase wild-type, 21; mutant 1p19q non-codeleted,16; mutant codeleted, 14) who underwent FDOPA positron emission tomography (PET) were retrospectively selected. Semiautomated tumor segmentation on FLAIR was performed. Patients with LGGs were separated into two groups (FDOPA hypometabolic and hypermetabolic LGGs) according to the normalized maximum standardized uptake value of FDOPA PET (a threshold of the uptake in the striatum) within the segmented regions. Spatial normalization procedures to build a 3D MRI-based atlas using each segmented region were validated by an analysis of differential involvement statistical mapping. RESULTS Superimposition of regions of interest showed a high number of hypometabolic LGGs localized in the frontal lobe, while a high number of hypermetabolic LGGs was localized in the insula, putamen, and temporal lobe. The statistical mapping revealed that hypometabolic LGGs occurred more frequently in the superior frontal gyrus (close to the supplementary motor area), while hypermetabolic LGGs occurred more frequently in the insula. CONCLUSION Radiographic atlases revealed preferential frontal lobe localization for FDOPA hypometabolic LGGs, which may be associated with relatively early detection.
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Affiliation(s)
- Hiroyuki Tatekawa
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.,Department of Radiological Science, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Diagnostic and Interventional Radiology, Osaka City University Graduate School of Medicine, Osaka, Japan
| | - Hiroyuki Uetani
- Department of Radiological Science, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.,Department of Radiological Science, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.,Department of Radiological Science, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Bioengineering, Henry Samueli School of Engineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Talia C Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.,Department of Radiological Science, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Neuroscience Interdepartmental Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Issei Ueda
- Department of Radiological Science, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.,Department of Radiological Science, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Albert Lai
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Phioanh L Nghiemphu
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Linda M Liau
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Shadfar Bahri
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Whitney B Pope
- Department of Radiological Science, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Noriko Salamon
- Department of Radiological Science, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA. .,Department of Radiological Science, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. .,Department of Bioengineering, Henry Samueli School of Engineering, University of California Los Angeles, Los Angeles, CA, USA. .,Neuroscience Interdepartmental Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. .,UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
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18
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Rathore S, Mohan S, Bakas S, Sako C, Badve C, Pati S, Singh A, Bounias D, Ngo P, Akbari H, Gastounioti A, Bergman M, Bilello M, Shinohara RT, Yushkevich P, O'Rourke DM, Sloan AE, Kontos D, Nasrallah MP, Barnholtz-Sloan JS, Davatzikos C. Multi-institutional noninvasive in vivo characterization of IDH, 1p/19q, and EGFRvIII in glioma using neuro-Cancer Imaging Phenomics Toolkit (neuro-CaPTk). Neurooncol Adv 2021; 2:iv22-iv34. [PMID: 33521638 PMCID: PMC7829474 DOI: 10.1093/noajnl/vdaa128] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Background Gliomas represent a biologically heterogeneous group of primary brain tumors with uncontrolled cellular proliferation and diffuse infiltration that renders them almost incurable, thereby leading to a grim prognosis. Recent comprehensive genomic profiling has greatly elucidated the molecular hallmarks of gliomas, including the mutations in isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2), loss of chromosomes 1p and 19q (1p/19q), and epidermal growth factor receptor variant III (EGFRvIII). Detection of these molecular alterations is based on ex vivo analysis of surgically resected tissue specimen that sometimes is not adequate for testing and/or does not capture the spatial tumor heterogeneity of the neoplasm. Methods We developed a method for noninvasive detection of radiogenomic markers of IDH both in lower-grade gliomas (WHO grade II and III tumors) and glioblastoma (WHO grade IV), 1p/19q in IDH-mutant lower-grade gliomas, and EGFRvIII in glioblastoma. Preoperative MRIs of 473 glioma patients from 3 of the studies participating in the ReSPOND consortium (collection I: Hospital of the University of Pennsylvania [HUP: n = 248], collection II: The Cancer Imaging Archive [TCIA; n = 192], and collection III: Ohio Brain Tumor Study [OBTS, n = 33]) were collected. Neuro-Cancer Imaging Phenomics Toolkit (neuro-CaPTk), a modular platform available for cancer imaging analytics and machine learning, was leveraged to extract histogram, shape, anatomical, and texture features from delineated tumor subregions and to integrate these features using support vector machine to generate models predictive of IDH, 1p/19q, and EGFRvIII. The models were validated using 3 configurations: (1) 70-30% training-testing splits or 10-fold cross-validation within individual collections, (2) 70-30% training-testing splits within merged collections, and (3) training on one collection and testing on another. Results These models achieved a classification accuracy of 86.74% (HUP), 85.45% (TCIA), and 75.15% (TCIA) in identifying EGFRvIII, IDH, and 1p/19q, respectively, in configuration I. The model, when applied on combined data in configuration II, yielded a classification success rate of 82.50% in predicting IDH mutation (HUP + TCIA + OBTS). The model when trained on TCIA dataset yielded classification accuracy of 84.88% in predicting IDH in HUP dataset. Conclusions Using machine learning algorithms, high accuracy was achieved in the prediction of IDH, 1p/19q, and EGFRvIII mutation. Neuro-CaPTk encompasses all the pipelines required to replicate these analyses in multi-institutional settings and could also be used for other radio(geno)mic analyses.
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Affiliation(s)
- Saima Rathore
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals Cleveland, Cleveland, Ohio, USA
| | - Sarthak Pati
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ashish Singh
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Dimitrios Bounias
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Phuc Ngo
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Hamed Akbari
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Aimilia Gastounioti
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Mark Bergman
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Paul Yushkevich
- Penn Image Computing and Science Lab (PICSL), University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Donald M O'Rourke
- Glioblastoma Translational Center of Excellence, Abramson Cancer Center, Philadelphia, Pennsylvania, USA
| | - Andrew E Sloan
- Case Comprehensive Cancer Center, Cleveland, Ohio, USA.,Department of Neurological Surgery, University Hospitals Seidman Cancer Center, Cleveland, Ohio, USA.,Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Despina Kontos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - MacLean P Nasrallah
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jill S Barnholtz-Sloan
- Case Comprehensive Cancer Center, Cleveland, Ohio, USA.,Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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19
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Ismail M, Hill V, Statsevych V, Mason E, Correa R, Prasanna P, Singh G, Bera K, Thawani R, Ahluwalia M, Madabhushi A, Tiwari P. Can Tumor Location on Pre-treatment MRI Predict Likelihood of Pseudo-Progression vs. Tumor Recurrence in Glioblastoma?-A Feasibility Study. Front Comput Neurosci 2021; 14:563439. [PMID: 33381018 PMCID: PMC7767991 DOI: 10.3389/fncom.2020.563439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 11/19/2020] [Indexed: 11/14/2022] Open
Abstract
A significant challenge in Glioblastoma (GBM) management is identifying pseudo-progression (PsP), a benign radiation-induced effect, from tumor recurrence, on routine imaging following conventional treatment. Previous studies have linked tumor lobar presence and laterality to GBM outcomes, suggesting that disease etiology and progression in GBM may be impacted by tumor location. Hence, in this feasibility study, we seek to investigate the following question: Can tumor location on treatment-naïve MRI provide early cues regarding likelihood of a patient developing pseudo-progression vs. tumor recurrence? In this study, 74 pre-treatment Glioblastoma MRI scans with PsP (33) and tumor recurrence (41) were analyzed. First, enhancing lesion on Gd-T1w MRI and peri-lesional hyperintensities on T2w/FLAIR were segmented by experts and then registered to a brain atlas. Using patients from the two phenotypes, we construct two atlases by quantifying frequency of occurrence of enhancing lesion and peri-lesion hyperintensities, by averaging voxel intensities across the population. Analysis of differential involvement was then performed to compute voxel-wise significant differences (p-value < 0.05) across the atlases. Statistically significant clusters were finally mapped to a structural atlas to provide anatomic localization of their location. Our results demonstrate that patients with tumor recurrence showed prominence of their initial tumor in the parietal lobe, while patients with PsP showed a multi-focal distribution of the initial tumor in the frontal and temporal lobes, insula, and putamen. These preliminary results suggest that lateralization of pre-treatment lesions toward certain anatomical areas of the brain may allow to provide early cues regarding assessing likelihood of occurrence of pseudo-progression from tumor recurrence on MRI scans.
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Affiliation(s)
- Marwa Ismail
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Virginia Hill
- Department of Neuroradiology, Imaging Institute, Cleveland Clinic, Cleveland, OH, United States.,Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Volodymyr Statsevych
- Department of Neuroradiology, Imaging Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Evan Mason
- Department of Neuroradiology, Imaging Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Ramon Correa
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
| | - Gagandeep Singh
- 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.,Maimonides Medical Center, New York, NY, United States
| | - Rajat Thawani
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Manmeet Ahluwalia
- Brain Tumor and Neuro-Oncology Center, Cleveland Clinic, Cleveland, OH, United States
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States.,Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, United States
| | - Pallavi Tiwari
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
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20
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Scheufele K, Subramanian S, Biros G. Fully Automatic Calibration of Tumor-Growth Models Using a Single mpMRI Scan. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:193-204. [PMID: 32931431 PMCID: PMC8565678 DOI: 10.1109/tmi.2020.3024264] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Our objective is the calibration of mathematical tumor growth models from a single multiparametric scan. The target problem is the analysis of preoperative Glioblastoma (GBM) scans. To this end, we present a fully automatic tumor-growth calibration methodology that integrates a single-species reaction-diffusion partial differential equation (PDE) model for tumor progression with multiparametric Magnetic Resonance Imaging (mpMRI) scans to robustly extract patient specific biomarkers i.e., estimates for (i) the tumor cell proliferation rate, (ii) the tumor cell migration rate, and (iii) the original, localized site(s) of tumor initiation. Our method is based on a sparse reconstruction algorithm for the tumor initial location (TIL). This problem is particularly challenging due to nonlinearity, ill-posedeness, and ill conditioning. We propose a coarse-to-fine multi-resolution continuation scheme with parameter decomposition to stabilize the inversion. We demonstrate robustness and practicality of our method by applying the proposed method to clinical data of 206 GBM patients. We analyze the extracted biomarkers and relate tumor origin with patient overall survival by mapping the former into a common atlas space. We present preliminary results that suggest improved accuracy for prediction of patient overall survival when a set of imaging features is augmented with estimated biophysical parameters. All extracted features, tumor initial positions, and biophysical growth parameters are made publicly available for further analysis. To our knowledge, this is the first fully automatic scheme that can handle multifocal tumors and can localize the TIL to a few millimeters.
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21
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Wang D, Liu J, Chen Q, Yang R, Jiang Q. Upregulation of glutaminase 2 and neutrophil cytosolic factor 2 is associated with the poor prognosis of glioblastoma. Biomark Med 2020; 14:1585-1597. [PMID: 33179520 DOI: 10.2217/bmm-2020-0341] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Background: This study aimed to identify glioblastoma prognosis-associated genes with potential diagnosis or prognosis values using integrated bioinformatics analysis. Results: In total, 1831 differentially expressed genes (DEGs) between the glioblastoma and control samples were identified and were clustered into seven weighed gene co-expression network analysis (WGCNA) modules. These DEGs were associated with different functional categories and pathways. Nine prognosis-associated DEGs (including glutaminase 2 [GLS2] and neutrophil cytosolic factor 2 [NCF2]) were identified, and the higher expression levels of GLS2 and NCF2 genes were associated with the poor prognosis of glioblastoma in 'The Cancer Genome Atlas' cohort and a clinical cohort. Conclusion: These results showed that the two genes play novel roles in the etiological and development of glioblastoma.
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Affiliation(s)
- Dong Wang
- Department of Neurosurgery, Ganzhou People's Hospital, Ganzhou 341000, China
| | - Jun Liu
- Department of Neurosurgery, Ganzhou People's Hospital, Ganzhou 341000, China
| | - Qiang Chen
- Department of Neurosurgery, Ganzhou People's Hospital, Ganzhou 341000, China
| | - RuiJin Yang
- Department of Neurosurgery, Ganzhou People's Hospital, Ganzhou 341000, China
| | - Qiuhua Jiang
- Department of Neurosurgery, Ganzhou People's Hospital, Ganzhou 341000, China
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22
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Sex differences in health and disease: A review of biological sex differences relevant to cancer with a spotlight on glioma. Cancer Lett 2020; 498:178-187. [PMID: 33130315 DOI: 10.1016/j.canlet.2020.07.030] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 07/16/2020] [Accepted: 07/24/2020] [Indexed: 12/12/2022]
Abstract
The influence of biological sex differences on human health and disease, while being increasingly recognized, has long been underappreciated and underexplored. While humans of all sexes are more alike than different, there is evidence for sex differences in the most basic aspects of human biology and these differences have consequences for the etiology and pathophysiology of many diseases. In a disease like cancer, these consequences manifest in the sex biases in incidence and outcome of many cancer types. The ability to deliver precise, targeted therapies to complex cancer cases is limited by our current understanding of the underlying sex differences. Gaining a better understanding of the implications and interplay of sex differences in diseases like cancer will thus be informative for clinical practice and biological research. Here we review the evidence for a broad array of biological sex differences in humans and discuss how these differences may relate to observed sex differences in various diseases, including many cancers and specifically glioblastoma. We focus on areas of human biology that play vital roles in healthy and disease states, including metabolism, development, hormones, and the immune system, and emphasize that the intersection of sex differences in these areas should not go overlooked. We further propose that mathematical approaches can be useful for exploring the extent to which sex differences affect disease outcomes and accounting for those in the development of therapeutic strategies.
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23
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Dubost F, Bruijne MD, Nardin M, Dalca AV, Donahue KL, Giese AK, Etherton MR, Wu O, Groot MD, Niessen W, Vernooij M, Rost NS, Schirmer MD. Multi-atlas image registration of clinical data with automated quality assessment using ventricle segmentation. Med Image Anal 2020; 63:101698. [PMID: 32339896 PMCID: PMC7275913 DOI: 10.1016/j.media.2020.101698] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 12/03/2019] [Accepted: 04/06/2020] [Indexed: 02/08/2023]
Abstract
Registration is a core component of many imaging pipelines. In case of clinical scans, with lower resolution and sometimes substantial motion artifacts, registration can produce poor results. Visual assessment of registration quality in large clinical datasets is inefficient. In this work, we propose to automatically assess the quality of registration to an atlas in clinical FLAIR MRI scans of the brain. The method consists of automatically segmenting the ventricles of a given scan using a neural network, and comparing the segmentation to the atlas ventricles propagated to image space. We used the proposed method to improve clinical image registration to a general atlas by computing multiple registrations - one directly to the general atlas and others via different age-specific atlases - and then selecting the registration that yielded the highest ventricle overlap. Finally, as an example application of the complete pipeline, a voxelwise map of white matter hyperintensity burden was computed using only the scans with registration quality above a predefined threshold. Methods were evaluated in a single-site dataset of more than 1000 scans, as well as a multi-center dataset comprising 142 clinical scans from 12 sites. The automated ventricle segmentation reached a Dice coefficient with manual annotations of 0.89 in the single-site dataset, and 0.83 in the multi-center dataset. Registration via age-specific atlases could improve ventricle overlap compared to a direct registration to the general atlas (Dice similarity coefficient increase up to 0.15). Experiments also showed that selecting scans with the registration quality assessment method could improve the quality of average maps of white matter hyperintensity burden, instead of using all scans for the computation of the white matter hyperintensity map. In this work, we demonstrated the utility of an automated tool for assessing image registration quality in clinical scans. This image quality assessment step could ultimately assist in the translation of automated neuroimaging pipelines to the clinic.
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Affiliation(s)
- Florian Dubost
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA; Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands.
| | - Marleen de Bruijne
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Marco Nardin
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Adrian V Dalca
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA
| | - Kathleen L Donahue
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Anne-Katrin Giese
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Mark R Etherton
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Marius de Groot
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC - University Medical Center Rotterdam, the Netherlands
| | - Wiro Niessen
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands; Department of Imaging Physics, Faculty of Applied Science, TU Delft, Delft, The Netherlands
| | - Meike Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, the Netherlands; Department of Epidemiology, Erasmus MC - University Medical Center Rotterdam, the Netherlands
| | - Natalia S Rost
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Markus D Schirmer
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA; Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA; Department of Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Germany.
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24
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Bakas S, Shukla G, Akbari H, Erus G, Sotiras A, Rathore S, Sako C, Min Ha S, Rozycki M, Shinohara RT, Bilello M, Davatzikos C. Overall survival prediction in glioblastoma patients using structural magnetic resonance imaging (MRI): advanced radiomic features may compensate for lack of advanced MRI modalities. J Med Imaging (Bellingham) 2020; 7:031505. [PMID: 32566694 DOI: 10.1117/1.jmi.7.3.031505] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 05/20/2020] [Indexed: 12/22/2022] Open
Abstract
Purpose: Glioblastoma, the most common and aggressive adult brain tumor, is considered noncurative at diagnosis, with 14 to 16 months median survival following treatment. There is increasing evidence that noninvasive integrative analysis of radiomic features can predict overall and progression-free survival, using advanced multiparametric magnetic resonance imaging (Adv-mpMRI). If successfully applicable, such noninvasive markers can considerably influence patient management. However, most patients prior to initiation of therapy typically undergo only basic structural mpMRI (Bas-mpMRI, i.e., T1, T1-Gd, T2, and T2-fluid-attenuated inversion recovery) preoperatively, rather than Adv-mpMRI that provides additional vascularization (dynamic susceptibility contrast-MRI) and cell-density (diffusion tensor imaging) related information. Approach: We assess a retrospective cohort of 101 glioblastoma patients with available Adv-mpMRI from a previous study, which has shown that an initial feature panel (IFP, i.e., intensity, volume, location, and growth model parameters) extracted from Adv-mpMRI can yield accurate overall survival stratification. We focus on demonstrating that equally accurate prediction models can be constructed using augmented radiomic feature panels (ARFPs, i.e., integrating morphology and textural descriptors) extracted solely from widely available Bas-mpMRI, obviating the need for using Adv-mpMRI. We extracted 1612 radiomic features from distinct tumor subregions to build multivariate models that stratified patients as long-, intermediate-, or short-survivors. Results: The classification accuracy of the model utilizing Adv-mpMRI protocols and the IFP was 72.77% and degraded to 60.89% when using only Bas-mpMRI. However, utilizing the ARFP on Bas-mpMRI improved the accuracy to 74.26%. Furthermore, Kaplan-Meier analysis demonstrated superior classification of subjects into short-, intermediate-, and long-survivor classes when using ARFP extracted from Bas-mpMRI. Conclusions: This quantitative evaluation indicates that accurate survival prediction in glioblastoma patients is feasible using solely Bas-mpMRI and integrative advanced radiomic features, which can compensate for the lack of Adv-mpMRI. Our finding holds promise for generalization across multiple institutions that may not have access to Adv-mpMRI and to better inform clinical decision-making about aggressive interventions and clinical trials.
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Affiliation(s)
- Spyridon Bakas
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Radiology, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Pathology and Laboratory Medicine, Philadelphia, PA, United States
| | - Gaurav Shukla
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,Thomas Jefferson University, Sidney Kimmel Cancer Center, Department of Radiation Oncology, Philadelphia, PA, United States
| | - Hamed Akbari
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Radiology, Philadelphia, PA, United States
| | - Guray Erus
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Radiology, Philadelphia, PA, United States
| | - Aristeidis Sotiras
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Radiology, Philadelphia, PA, United States.,Washington University in St. Louis, School of Medicine, Institute for Informatics, Saint Louis, MO, United States.,Washington University in St. Louis, Department of Radiology, Saint Louis, MO, United States
| | - Saima Rathore
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Radiology, Philadelphia, PA, United States
| | - Chiharu Sako
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Radiology, Philadelphia, PA, United States
| | - Sung Min Ha
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Radiology, Philadelphia, PA, United States
| | - Martin Rozycki
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Radiology, Philadelphia, PA, United States
| | - Russell T Shinohara
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Philadelphia, PA, United States
| | - Michel Bilello
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Radiology, Philadelphia, PA, United States
| | - Christos Davatzikos
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Radiology, Philadelphia, PA, United States
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25
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Mang A, Bakas S, Subramanian S, Davatzikos C, Biros G. Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology. Annu Rev Biomed Eng 2020; 22:309-341. [PMID: 32501772 PMCID: PMC7520881 DOI: 10.1146/annurev-bioeng-062117-121105] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Central nervous system (CNS) tumors come with vastly heterogeneous histologic, molecular, and radiographic landscapes, rendering their precise characterization challenging. The rapidly growing fields of biophysical modeling and radiomics have shown promise in better characterizing the molecular, spatial, and temporal heterogeneity of tumors. Integrative analysis of CNS tumors, including clinically acquired multi-parametric magnetic resonance imaging (mpMRI) and the inverse problem of calibrating biophysical models to mpMRI data, assists in identifying macroscopic quantifiable tumor patterns of invasion and proliferation, potentially leading to improved (a) detection/segmentation of tumor subregions and (b) computer-aided diagnostic/prognostic/predictive modeling. This article presents a summary of (a) biophysical growth modeling and simulation,(b) inverse problems for model calibration, (c) these models' integration with imaging workflows, and (d) their application to clinically relevant studies. We anticipate that such quantitative integrative analysis may even be beneficial in a future revision of the World Health Organization (WHO) classification for CNS tumors, ultimately improving patient survival prospects.
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Affiliation(s)
- Andreas Mang
- Department of Mathematics, University of Houston, Houston, Texas 77204, USA;
| | - Spyridon Bakas
- Department of Mathematics, University of Houston, Houston, Texas 77204, USA;
| | - Shashank Subramanian
- Oden Institute of Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA; ,
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA); Department of Radiology; and Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA; ,
| | - George Biros
- Oden Institute of Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA; ,
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26
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Grenko CM, Viaene AN, Nasrallah MP, Feldman MD, Akbari H, Bakas S. Towards Population-Based Histologic Stain Normalization of Glioblastoma. ACTA ACUST UNITED AC 2020; 11992:44-56. [PMID: 32743562 DOI: 10.1007/978-3-030-46640-4_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Glioblastoma ( 'GBM' ) is the most aggressive type of primary malignant adult brain tumor, with very heterogeneous radio-graphic, histologic, and molecular profiles. A growing body of advanced computational analyses are conducted towards further understanding the biology and variation in glioblastoma. To address the intrinsic heterogeneity among different computational studies, reference standards have been established to facilitate both radiographic and molecular analyses, e.g., anatomical atlas for image registration and housekeeping genes, respectively. However, there is an apparent lack of reference standards in the domain of digital pathology, where each independent study uses an arbitrarily chosen slide from their evaluation dataset for normalization purposes. In this study, we introduce a novel stain normalization approach based on a composite reference slide comprised of information from a large population of anatomically annotated hematoxylin and eosin ( 'H&E' ) whole-slide images from the Ivy Glioblastoma Atlas Project ( 'IvyGAP' ). Two board-certified neuropathologists manually reviewed and selected annotations in 509 slides, according to the World Health Organization definitions. We computed summary statistics from each of these approved annotations and weighted them based on their percent contribution to overall slide ( 'PCOS' ), to form a global histogram and stain vectors. Quantitative evaluation of pre- and post-normalization stain density statistics for each annotated region with PCOS > 0.05% yielded a significant (largest p = 0.001, two-sided Wilcoxon rank sum test) reduction of its intensity variation for both 'H' & 'E' . Subject to further large-scale evaluation, our findings support the proposed approach as a potentially robust population-based reference for stain normalization.
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Affiliation(s)
- Caleb M Grenko
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.,Center for Interdisciplinary Studies, Davidson College, Davidson, NC, USA
| | - Angela N Viaene
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - MacLean P Nasrallah
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael D Feldman
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hamed Akbari
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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27
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Pati S, Singh A, Rathore S, Gastounioti A, Bergman M, Ngo P, Ha SM, Bounias D, Minock J, Murphy G, Li H, Bhattarai A, Wolf A, Sridaran P, Kalarot R, Akbari H, Sotiras A, Thakur SP, Verma R, Shinohara RT, Yushkevich P, Fan Y, Kontos D, Davatzikos C, Bakas S. The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview. BRAINLESION : GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES. BRAINLES (WORKSHOP) 2020; 11993:380-394. [PMID: 32754723 PMCID: PMC7402244 DOI: 10.1007/978-3-030-46643-5_38] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The purpose of this manuscript is to provide an overview of the technical specifications and architecture of the Cancer imaging Phenomics Toolkit (CaPTk www.cbica.upenn.edu/captk), a cross-platform, open-source, easy-to-use, and extensible software platform for analyzing 2D and 3D images, currently focusing on radiographic scans of brain, breast, and lung cancer. The primary aim of this platform is to enable swift and efficient translation of cutting-edge academic research into clinically useful tools relating to clinical quantification, analysis, predictive modeling, decision-making, and reporting workflow. CaPTk builds upon established open-source software toolkits, such as the Insight Toolkit (ITK) and OpenCV, to bring together advanced computational functionality. This functionality describes specialized, as well as general-purpose, image analysis algorithms developed during active multi-disciplinary collaborative research studies to address real clinical requirements. The target audience of CaPTk consists of both computational scientists and clinical experts. For the former it provides i) an efficient image viewer offering the ability of integrating new algorithms, and ii) a library of readily-available clinically-relevant algorithms, allowing batch-processing of multiple subjects. For the latter it facilitates the use of complex algorithms for clinically-relevant studies through a user-friendly interface, eliminating the prerequisite of a substantial computational background. CaPTk's long-term goal is to provide widely-used technology to make use of advanced quantitative imaging analytics in cancer prediction, diagnosis and prognosis, leading toward a better understanding of the biological mechanisms of cancer development.
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Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Ashish Singh
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Saima Rathore
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Aimilia Gastounioti
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mark Bergman
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Phuc Ngo
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sung Min Ha
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dimitrios Bounias
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - James Minock
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Grayson Murphy
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Hongming Li
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amit Bhattarai
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Adam Wolf
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Patmaa Sridaran
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Ratheesh Kalarot
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Hamed Akbari
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Aristeidis Sotiras
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology and Institute for Informatics, School of Medicine, Washington University in St. Louis, Saint Louis, MO, USA
| | - Siddhesh P Thakur
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Ragini Verma
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), University of Pennsylvania, Philadelphia, PA, USA
| | - Paul Yushkevich
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Image Computing and Science Lab., University of Pennsylvania (PICSL), Philadelphia, PA, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Despina Kontos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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28
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Bakas S, Shukla G, Akbari H, Erus G, Sotiras A, Rathore S, Sako C, Min Ha S, Rozycki M, Singh A, Shinohara R, Bilello M, Davatzikos C. Integrative radiomic analysis for pre-surgical prognostic stratification of glioblastoma patients: from advanced to basic MRI protocols. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2020; 11315. [PMID: 33746333 DOI: 10.1117/12.2566505] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Glioblastoma, the most common and aggressive adult brain tumor, is considered non-curative at diagnosis. Current literature shows promise on imaging-based overall survival prediction for patients with glioblastoma while integrating advanced (structural, perfusion, and diffusion) multipara metric magnetic resonance imaging (Adv-mpMRI). However, most patients prior to initiation of therapy typically undergo only basic structural mpMRI (Bas-mpMRI, i.e., T1,T1-Gd,T2,T2-FLAIR) pre-operatively, rather than Adv-mpMRI. Here we assess a retrospective cohort of 101 glioblastoma patients with available Adv-mpMRI from a previous study, which has shown that an initial feature panel (IFP) extracted from Adv-mpMRI can yield accurate overall survival stratification. We further focus on demonstrating that equally accurate prediction models can be constructed using augmented feature panels (AFP) extracted solely from Bas-mpMRI, obviating the need for using Adv-mpMRI. The classification accuracy of the model utilizing Adv-mpMRI protocols and the IFP was 72.77%, and improved to 74.26% when utilizing the AFP on Bas-mpMRI. Furthermore, Kaplan-Meier analysis demonstrated superior classification of subjects into short-, intermediate-, and long-survivor classes when using AFPon Basic-mpMRI. This quantitative evaluation indicates that accurate survival prediction in glioblastoma patients is feasible by using solely Bas-mpMRI and integrative radiomic analysis can compensate for the lack of Adv-mpMRI. Our finding holds promise for predicting overall survival based on commonly-acquired Bas-mpMRI, and hence for potential generalization across multiple institutions that may not have access to Adv-mpMRI, facilitating better patient selection.
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Affiliation(s)
- Spyridon Bakas
- University of Pennsylvania, Department of Radiology, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, USA
| | - Gaurav Shukla
- University of Pennsylvania, Department of Radiology, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, USA
| | - Hamed Akbari
- University of Pennsylvania, Department of Radiology, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, USA
| | - Guray Erus
- University of Pennsylvania, Department of Radiology, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, USA
| | | | - Saima Rathore
- University of Pennsylvania, Department of Radiology, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, USA
| | - Chiharu Sako
- University of Pennsylvania, Department of Radiology, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, USA
| | - Sung Min Ha
- Washington University, Department of Radiology, St. Louis, MO, USA
| | | | - Ashish Singh
- University of Pennsylvania, Department of Radiology, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, USA
| | - Russell Shinohara
- University of Pennsylvania, Department of Biostatistics, University of Pennsylvania, USA
| | - Michel Bilello
- University of Pennsylvania, Department of Radiology, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, USA
| | - Christos Davatzikos
- University of Pennsylvania, Department of Radiology, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, USA
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29
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EGFR amplification is a real independent prognostic impact factor between young adults and adults over 45yo with wild-type glioblastoma? J Neurooncol 2019; 146:275-284. [PMID: 31889239 DOI: 10.1007/s11060-019-03364-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 12/09/2019] [Indexed: 12/19/2022]
Abstract
BACKGROUND In 2019 a group of University of Pennsylvania (Hoffman et al., J Neurooncol 145: 321-328, 2019) aimed to explore the prognostic impact of expression of epidermal growth factor receptor (EGFR), one of the most common genetic alterations in WT-GBM, in young adults with IDH-WT GBM, suggesting an inferior outcomes in young adults (< 45yo) with newly diagnosed, IDH-WT GBM. At the same time, our group were considering the dimension of this subpopulation treated in our centre, and we performed the same analysis, comparing datas with affected elderly adults. METHODS We explore the prognostic impact of EGFR expression status in young adults with IDH-WT GBM, and compare this impact with the affected elderly adults. We therefore analyzed clinical characteristics, tumor genetics, and clinical outcomes in a cohort of adults aged 18-45 years with newly diagnosed WT GBM. We selected a total of 146 patients affected by newly diagnosed IDH-WT GBM who underwent surgery, radiation, and chemotherapy in our Institution in the period ranging between January 2014 and December 2016. We focused primarily on the prognostic impact of EGFR expression. RESULTS We confirmed through a Bivariate Analysis that the Age of the Patients, the Volume of the lesions, were statistically strongly associated with the survival parameters; The general OS of the cohort presented a breakthrough point between the patients who were respectively younger and older than 45 years, EGFR mutation was per se not associated to a survival reduction in all the cohort patients. When analyzing exclusively the Survival parameters of the patients whose age was under 40, it was possible to outline a non statistically significant trend towards a lesser OS in younger patients harboring an EGFR expression. CONCLUSIONS Once again the main difference in terms of OS in GBM is shown in a EOR and in Age. To our knowledge, ours is the second study (Hoffman et al., J Neurooncol 145: 321-328, 2019) to evaluate the prognostic impact of EGFR CN gain specifically in young adults with IDH-WT GBM and in the era of modern radiation and Temozolomide, but is the first one to compares this impact with a population of adults over 45, and correlates this date with clinical onset, dimension and localization of disease between this groups. We suggest other centers to evaluate this important finding with a larger number of patients and we are inclined to accept collaborations to increase the power of this study.
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30
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Huang RY. Using 3D MRI Anatomic Maps to Determine Prognosis for Glioblastomas. Radiology 2019; 293:644-645. [PMID: 31596185 DOI: 10.1148/radiol.2019192088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Raymond Y Huang
- From the Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA, 02115
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31
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Roux A, Roca P, Edjlali M, Sato K, Zanello M, Dezamis E, Gori P, Lion S, Fleury A, Dhermain F, Meder JF, Chrétien F, Lechapt E, Varlet P, Oppenheim C, Pallud J. MRI Atlas of IDH Wild-Type Supratentorial Glioblastoma: Probabilistic Maps of Phenotype, Management, and Outcomes. Radiology 2019; 293:633-643. [PMID: 31592732 DOI: 10.1148/radiol.2019190491] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background Tumor location is a main prognostic parameter in patients with glioblastoma. Probabilistic MRI-based brain atlases specifying the probability of tumor location associated with important demographic, clinical, histomolecular, and management data are lacking for isocitrate dehydrogenase (IDH) wild-type glioblastomas. Purpose To correlate glioblastoma location with clinical phenotype, surgical management, and outcomes by using a probabilistic analysis in a three-dimensional (3D) MRI-based atlas. Materials and Methods This retrospective study included all adults surgically treated for newly diagnosed IDH wild-type supratentorial glioblastoma in a tertiary adult surgical neuro-oncology center (2006-2016). Semiautomated tumor segmentation and spatial normalization procedures to build a 3D MRI-based atlas were validated. The authors performed probabilistic analyses by using voxel-based lesion symptom mapping technology. The Liebermeister test was used for binary data, and the generalized linear model was used for continuous data. Results A total of 392 patients (mean age, 61 years ± 13; 233 men) were evaluated. The authors identified the preferential location of glioblastomas according to subventricular zone, age, sex, clinical presentation, revised Radiation Therapy Oncology Group-Recursive Partitioning Analysis class, Karnofsky performance status, O6-methylguanine DNA methyltransferase promoter methylation status, surgical management, and survival. The superficial location distant from the eloquent area was more likely associated with a preserved functional status at diagnosis (348 of 392 patients [89%], P < .05), a large surgical resection (173 of 392 patients [44%], P < .05), and prolonged overall survival (163 of 334 patients [49%], P < .05). In contrast, deep location and location within eloquent brain areas were more likely associated with an impaired functional status at diagnosis (44 of 392 patients [11%], P < .05), a neurologic deficit (282 of 392 patients [72%], P < .05), treatment with biopsy only (183 of 392 patients [47%], P < .05), and shortened overall survival (171 of 334 patients [51%], P < .05). Conclusion The authors identified the preferential location of isocitrate dehydrogenase wild-type glioblastomas according to parameters of interest and provided an image-based integration of multimodal information impacting survival results. This suggests the role of glioblastoma location as a surrogate and multimodal parameter integrating several known prognostic factors. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Huang in this issue.
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Affiliation(s)
- Alexandre Roux
- From the Department of Neurosurgery, Sainte-Anne Hospital, Paris, France (A.R., M.Z., E.D., J.P.); Paris Descartes University, Sorbonne Paris Cité, Paris, France (A.R., P.R., M.E., M.Z., J.F.M., F.C., E.L., P.V., C.O., J.P.); UMR 1266 INSERM, IMA-BRAIN, Institute of Psychiatry and Neurosciences of Paris, Paris, France (A.R., P.R., M.E., K.S., M.Z., P.G., S.L., A.F., J.F.M., P.V., C.O., J.P.); Department of Neuroradiology, Sainte-Anne Hospital, Paris, France (M.E., J.F.M., C.O.); Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan (K.S.); LTCI, Telecom ParisTech, Paris, France (P.G.); Department of Radiotherapy, Gustave Roussy University Hospital, Villejuif, France (F.D.); and Department of Neuropathology, Sainte-Anne Hospital, Paris, France (F.C., E.L., P.V.)
| | - Pauline Roca
- From the Department of Neurosurgery, Sainte-Anne Hospital, Paris, France (A.R., M.Z., E.D., J.P.); Paris Descartes University, Sorbonne Paris Cité, Paris, France (A.R., P.R., M.E., M.Z., J.F.M., F.C., E.L., P.V., C.O., J.P.); UMR 1266 INSERM, IMA-BRAIN, Institute of Psychiatry and Neurosciences of Paris, Paris, France (A.R., P.R., M.E., K.S., M.Z., P.G., S.L., A.F., J.F.M., P.V., C.O., J.P.); Department of Neuroradiology, Sainte-Anne Hospital, Paris, France (M.E., J.F.M., C.O.); Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan (K.S.); LTCI, Telecom ParisTech, Paris, France (P.G.); Department of Radiotherapy, Gustave Roussy University Hospital, Villejuif, France (F.D.); and Department of Neuropathology, Sainte-Anne Hospital, Paris, France (F.C., E.L., P.V.)
| | - Myriam Edjlali
- From the Department of Neurosurgery, Sainte-Anne Hospital, Paris, France (A.R., M.Z., E.D., J.P.); Paris Descartes University, Sorbonne Paris Cité, Paris, France (A.R., P.R., M.E., M.Z., J.F.M., F.C., E.L., P.V., C.O., J.P.); UMR 1266 INSERM, IMA-BRAIN, Institute of Psychiatry and Neurosciences of Paris, Paris, France (A.R., P.R., M.E., K.S., M.Z., P.G., S.L., A.F., J.F.M., P.V., C.O., J.P.); Department of Neuroradiology, Sainte-Anne Hospital, Paris, France (M.E., J.F.M., C.O.); Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan (K.S.); LTCI, Telecom ParisTech, Paris, France (P.G.); Department of Radiotherapy, Gustave Roussy University Hospital, Villejuif, France (F.D.); and Department of Neuropathology, Sainte-Anne Hospital, Paris, France (F.C., E.L., P.V.)
| | - Kanako Sato
- From the Department of Neurosurgery, Sainte-Anne Hospital, Paris, France (A.R., M.Z., E.D., J.P.); Paris Descartes University, Sorbonne Paris Cité, Paris, France (A.R., P.R., M.E., M.Z., J.F.M., F.C., E.L., P.V., C.O., J.P.); UMR 1266 INSERM, IMA-BRAIN, Institute of Psychiatry and Neurosciences of Paris, Paris, France (A.R., P.R., M.E., K.S., M.Z., P.G., S.L., A.F., J.F.M., P.V., C.O., J.P.); Department of Neuroradiology, Sainte-Anne Hospital, Paris, France (M.E., J.F.M., C.O.); Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan (K.S.); LTCI, Telecom ParisTech, Paris, France (P.G.); Department of Radiotherapy, Gustave Roussy University Hospital, Villejuif, France (F.D.); and Department of Neuropathology, Sainte-Anne Hospital, Paris, France (F.C., E.L., P.V.)
| | - Marc Zanello
- From the Department of Neurosurgery, Sainte-Anne Hospital, Paris, France (A.R., M.Z., E.D., J.P.); Paris Descartes University, Sorbonne Paris Cité, Paris, France (A.R., P.R., M.E., M.Z., J.F.M., F.C., E.L., P.V., C.O., J.P.); UMR 1266 INSERM, IMA-BRAIN, Institute of Psychiatry and Neurosciences of Paris, Paris, France (A.R., P.R., M.E., K.S., M.Z., P.G., S.L., A.F., J.F.M., P.V., C.O., J.P.); Department of Neuroradiology, Sainte-Anne Hospital, Paris, France (M.E., J.F.M., C.O.); Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan (K.S.); LTCI, Telecom ParisTech, Paris, France (P.G.); Department of Radiotherapy, Gustave Roussy University Hospital, Villejuif, France (F.D.); and Department of Neuropathology, Sainte-Anne Hospital, Paris, France (F.C., E.L., P.V.)
| | - Edouard Dezamis
- From the Department of Neurosurgery, Sainte-Anne Hospital, Paris, France (A.R., M.Z., E.D., J.P.); Paris Descartes University, Sorbonne Paris Cité, Paris, France (A.R., P.R., M.E., M.Z., J.F.M., F.C., E.L., P.V., C.O., J.P.); UMR 1266 INSERM, IMA-BRAIN, Institute of Psychiatry and Neurosciences of Paris, Paris, France (A.R., P.R., M.E., K.S., M.Z., P.G., S.L., A.F., J.F.M., P.V., C.O., J.P.); Department of Neuroradiology, Sainte-Anne Hospital, Paris, France (M.E., J.F.M., C.O.); Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan (K.S.); LTCI, Telecom ParisTech, Paris, France (P.G.); Department of Radiotherapy, Gustave Roussy University Hospital, Villejuif, France (F.D.); and Department of Neuropathology, Sainte-Anne Hospital, Paris, France (F.C., E.L., P.V.)
| | - Pietro Gori
- From the Department of Neurosurgery, Sainte-Anne Hospital, Paris, France (A.R., M.Z., E.D., J.P.); Paris Descartes University, Sorbonne Paris Cité, Paris, France (A.R., P.R., M.E., M.Z., J.F.M., F.C., E.L., P.V., C.O., J.P.); UMR 1266 INSERM, IMA-BRAIN, Institute of Psychiatry and Neurosciences of Paris, Paris, France (A.R., P.R., M.E., K.S., M.Z., P.G., S.L., A.F., J.F.M., P.V., C.O., J.P.); Department of Neuroradiology, Sainte-Anne Hospital, Paris, France (M.E., J.F.M., C.O.); Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan (K.S.); LTCI, Telecom ParisTech, Paris, France (P.G.); Department of Radiotherapy, Gustave Roussy University Hospital, Villejuif, France (F.D.); and Department of Neuropathology, Sainte-Anne Hospital, Paris, France (F.C., E.L., P.V.)
| | - Stéphanie Lion
- From the Department of Neurosurgery, Sainte-Anne Hospital, Paris, France (A.R., M.Z., E.D., J.P.); Paris Descartes University, Sorbonne Paris Cité, Paris, France (A.R., P.R., M.E., M.Z., J.F.M., F.C., E.L., P.V., C.O., J.P.); UMR 1266 INSERM, IMA-BRAIN, Institute of Psychiatry and Neurosciences of Paris, Paris, France (A.R., P.R., M.E., K.S., M.Z., P.G., S.L., A.F., J.F.M., P.V., C.O., J.P.); Department of Neuroradiology, Sainte-Anne Hospital, Paris, France (M.E., J.F.M., C.O.); Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan (K.S.); LTCI, Telecom ParisTech, Paris, France (P.G.); Department of Radiotherapy, Gustave Roussy University Hospital, Villejuif, France (F.D.); and Department of Neuropathology, Sainte-Anne Hospital, Paris, France (F.C., E.L., P.V.)
| | - Ariane Fleury
- From the Department of Neurosurgery, Sainte-Anne Hospital, Paris, France (A.R., M.Z., E.D., J.P.); Paris Descartes University, Sorbonne Paris Cité, Paris, France (A.R., P.R., M.E., M.Z., J.F.M., F.C., E.L., P.V., C.O., J.P.); UMR 1266 INSERM, IMA-BRAIN, Institute of Psychiatry and Neurosciences of Paris, Paris, France (A.R., P.R., M.E., K.S., M.Z., P.G., S.L., A.F., J.F.M., P.V., C.O., J.P.); Department of Neuroradiology, Sainte-Anne Hospital, Paris, France (M.E., J.F.M., C.O.); Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan (K.S.); LTCI, Telecom ParisTech, Paris, France (P.G.); Department of Radiotherapy, Gustave Roussy University Hospital, Villejuif, France (F.D.); and Department of Neuropathology, Sainte-Anne Hospital, Paris, France (F.C., E.L., P.V.)
| | - Frédéric Dhermain
- From the Department of Neurosurgery, Sainte-Anne Hospital, Paris, France (A.R., M.Z., E.D., J.P.); Paris Descartes University, Sorbonne Paris Cité, Paris, France (A.R., P.R., M.E., M.Z., J.F.M., F.C., E.L., P.V., C.O., J.P.); UMR 1266 INSERM, IMA-BRAIN, Institute of Psychiatry and Neurosciences of Paris, Paris, France (A.R., P.R., M.E., K.S., M.Z., P.G., S.L., A.F., J.F.M., P.V., C.O., J.P.); Department of Neuroradiology, Sainte-Anne Hospital, Paris, France (M.E., J.F.M., C.O.); Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan (K.S.); LTCI, Telecom ParisTech, Paris, France (P.G.); Department of Radiotherapy, Gustave Roussy University Hospital, Villejuif, France (F.D.); and Department of Neuropathology, Sainte-Anne Hospital, Paris, France (F.C., E.L., P.V.)
| | - Jean-François Meder
- From the Department of Neurosurgery, Sainte-Anne Hospital, Paris, France (A.R., M.Z., E.D., J.P.); Paris Descartes University, Sorbonne Paris Cité, Paris, France (A.R., P.R., M.E., M.Z., J.F.M., F.C., E.L., P.V., C.O., J.P.); UMR 1266 INSERM, IMA-BRAIN, Institute of Psychiatry and Neurosciences of Paris, Paris, France (A.R., P.R., M.E., K.S., M.Z., P.G., S.L., A.F., J.F.M., P.V., C.O., J.P.); Department of Neuroradiology, Sainte-Anne Hospital, Paris, France (M.E., J.F.M., C.O.); Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan (K.S.); LTCI, Telecom ParisTech, Paris, France (P.G.); Department of Radiotherapy, Gustave Roussy University Hospital, Villejuif, France (F.D.); and Department of Neuropathology, Sainte-Anne Hospital, Paris, France (F.C., E.L., P.V.)
| | - Fabrice Chrétien
- From the Department of Neurosurgery, Sainte-Anne Hospital, Paris, France (A.R., M.Z., E.D., J.P.); Paris Descartes University, Sorbonne Paris Cité, Paris, France (A.R., P.R., M.E., M.Z., J.F.M., F.C., E.L., P.V., C.O., J.P.); UMR 1266 INSERM, IMA-BRAIN, Institute of Psychiatry and Neurosciences of Paris, Paris, France (A.R., P.R., M.E., K.S., M.Z., P.G., S.L., A.F., J.F.M., P.V., C.O., J.P.); Department of Neuroradiology, Sainte-Anne Hospital, Paris, France (M.E., J.F.M., C.O.); Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan (K.S.); LTCI, Telecom ParisTech, Paris, France (P.G.); Department of Radiotherapy, Gustave Roussy University Hospital, Villejuif, France (F.D.); and Department of Neuropathology, Sainte-Anne Hospital, Paris, France (F.C., E.L., P.V.)
| | - Emmanuèle Lechapt
- From the Department of Neurosurgery, Sainte-Anne Hospital, Paris, France (A.R., M.Z., E.D., J.P.); Paris Descartes University, Sorbonne Paris Cité, Paris, France (A.R., P.R., M.E., M.Z., J.F.M., F.C., E.L., P.V., C.O., J.P.); UMR 1266 INSERM, IMA-BRAIN, Institute of Psychiatry and Neurosciences of Paris, Paris, France (A.R., P.R., M.E., K.S., M.Z., P.G., S.L., A.F., J.F.M., P.V., C.O., J.P.); Department of Neuroradiology, Sainte-Anne Hospital, Paris, France (M.E., J.F.M., C.O.); Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan (K.S.); LTCI, Telecom ParisTech, Paris, France (P.G.); Department of Radiotherapy, Gustave Roussy University Hospital, Villejuif, France (F.D.); and Department of Neuropathology, Sainte-Anne Hospital, Paris, France (F.C., E.L., P.V.)
| | - Pascale Varlet
- From the Department of Neurosurgery, Sainte-Anne Hospital, Paris, France (A.R., M.Z., E.D., J.P.); Paris Descartes University, Sorbonne Paris Cité, Paris, France (A.R., P.R., M.E., M.Z., J.F.M., F.C., E.L., P.V., C.O., J.P.); UMR 1266 INSERM, IMA-BRAIN, Institute of Psychiatry and Neurosciences of Paris, Paris, France (A.R., P.R., M.E., K.S., M.Z., P.G., S.L., A.F., J.F.M., P.V., C.O., J.P.); Department of Neuroradiology, Sainte-Anne Hospital, Paris, France (M.E., J.F.M., C.O.); Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan (K.S.); LTCI, Telecom ParisTech, Paris, France (P.G.); Department of Radiotherapy, Gustave Roussy University Hospital, Villejuif, France (F.D.); and Department of Neuropathology, Sainte-Anne Hospital, Paris, France (F.C., E.L., P.V.)
| | - Catherine Oppenheim
- From the Department of Neurosurgery, Sainte-Anne Hospital, Paris, France (A.R., M.Z., E.D., J.P.); Paris Descartes University, Sorbonne Paris Cité, Paris, France (A.R., P.R., M.E., M.Z., J.F.M., F.C., E.L., P.V., C.O., J.P.); UMR 1266 INSERM, IMA-BRAIN, Institute of Psychiatry and Neurosciences of Paris, Paris, France (A.R., P.R., M.E., K.S., M.Z., P.G., S.L., A.F., J.F.M., P.V., C.O., J.P.); Department of Neuroradiology, Sainte-Anne Hospital, Paris, France (M.E., J.F.M., C.O.); Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan (K.S.); LTCI, Telecom ParisTech, Paris, France (P.G.); Department of Radiotherapy, Gustave Roussy University Hospital, Villejuif, France (F.D.); and Department of Neuropathology, Sainte-Anne Hospital, Paris, France (F.C., E.L., P.V.)
| | - Johan Pallud
- From the Department of Neurosurgery, Sainte-Anne Hospital, Paris, France (A.R., M.Z., E.D., J.P.); Paris Descartes University, Sorbonne Paris Cité, Paris, France (A.R., P.R., M.E., M.Z., J.F.M., F.C., E.L., P.V., C.O., J.P.); UMR 1266 INSERM, IMA-BRAIN, Institute of Psychiatry and Neurosciences of Paris, Paris, France (A.R., P.R., M.E., K.S., M.Z., P.G., S.L., A.F., J.F.M., P.V., C.O., J.P.); Department of Neuroradiology, Sainte-Anne Hospital, Paris, France (M.E., J.F.M., C.O.); Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan (K.S.); LTCI, Telecom ParisTech, Paris, France (P.G.); Department of Radiotherapy, Gustave Roussy University Hospital, Villejuif, France (F.D.); and Department of Neuropathology, Sainte-Anne Hospital, Paris, France (F.C., E.L., P.V.)
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Thakur SP, Doshi J, Pati S, Ha SM, Sako C, Talbar S, Kulkarni U, Davatzikos C, Erus G, Bakas S. Skull-Stripping of Glioblastoma MRI Scans Using 3D Deep Learning. BRAINLESION : GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES. BRAINLES (WORKSHOP) 2019; 11992:57-68. [PMID: 32577629 PMCID: PMC7311100 DOI: 10.1007/978-3-030-46640-4_6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Skull-stripping is an essential pre-processing step in computational neuro-imaging directly impacting subsequent analyses. Existing skull-stripping methods have primarily targeted non-pathologicallyaffected brains. Accordingly, they may perform suboptimally when applied on brain Magnetic Resonance Imaging (MRI) scans that have clearly discernible pathologies, such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. Here we present a performance evaluation of publicly available implementations of established 3D Deep Learning architectures for semantic segmentation (namely DeepMedic, 3D U-Net, FCN), with a particular focus on identifying a skull-stripping approach that performs well on brain tumor scans, and also has a low computational footprint. We have identified a retrospective dataset of 1,796 mpMRI brain tumor scans, with corresponding manually-inspected and verified gold-standard brain tissue segmentations, acquired during standard clinical practice under varying acquisition protocols at the Hospital of the University of Pennsylvania. Our quantitative evaluation identified DeepMedic as the best performing method (Dice = 97.9, Hausdorf f 95 = 2.68). We release this pre-trained model through the Cancer Imaging Phenomics Toolkit (CaPTk) platform.
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Affiliation(s)
- Siddhesh P Thakur
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Shri Guru Gobind Singhji Institute of Engineering and Technology (SGGS), Nanded, Maharashtra, India
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sung Min Ha
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sanjay Talbar
- Shri Guru Gobind Singhji Institute of Engineering and Technology (SGGS), Nanded, Maharashtra, India
| | - Uday Kulkarni
- Shri Guru Gobind Singhji Institute of Engineering and Technology (SGGS), Nanded, Maharashtra, India
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Fathi Kazerooni A, Bakas S, Saligheh Rad H, Davatzikos C. Imaging signatures of glioblastoma molecular characteristics: A radiogenomics review. J Magn Reson Imaging 2019; 52:54-69. [PMID: 31456318 DOI: 10.1002/jmri.26907] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 08/09/2019] [Indexed: 02/06/2023] Open
Abstract
Over the past few decades, the advent and development of genomic assessment methods and computational approaches have raised the hopes for identifying therapeutic targets that may aid in the treatment of glioblastoma. However, the targeted therapies have barely been successful in their effort to cure glioblastoma patients, leaving them with a grim prognosis. Glioblastoma exhibits high heterogeneity, both spatially and temporally. The existence of different genetic subpopulations in glioblastoma allows this tumor to adapt itself to environmental forces. Therefore, patients with glioblastoma respond poorly to the prescribed therapies, as treatments are directed towards the whole tumor and not to the specific genetic subregions. Genomic alterations within the tumor develop distinct radiographic phenotypes. In this regard, MRI plays a key role in characterizing molecular signatures of glioblastoma, based on regional variations and phenotypic presentation of the tumor. Radiogenomics has emerged as a (relatively) new field of research to explore the connections between genetic alterations and imaging features. Radiogenomics offers numerous advantages, including noninvasive and global assessment of the tumor and its response to therapies. In this review, we summarize the potential role of radiogenomic techniques to stratify patients according to their specific tumor characteristics with the goal of designing patient-specific therapies. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:54-69.
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Affiliation(s)
- Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Hamidreza Saligheh Rad
- Quantitative MR Imaging and Spectroscopy Group (QMISG), Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Prasanna P, Mitra J, Beig N, Nayate A, Patel J, Ghose S, Thawani R, Partovi S, Madabhushi A, Tiwari P. Mass Effect Deformation Heterogeneity (MEDH) on Gadolinium-contrast T1-weighted MRI is associated with decreased survival in patients with right cerebral hemisphere Glioblastoma: A feasibility study. Sci Rep 2019; 9:1145. [PMID: 30718547 PMCID: PMC6362117 DOI: 10.1038/s41598-018-37615-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Accepted: 12/04/2018] [Indexed: 12/04/2022] Open
Abstract
Subtle tissue deformations caused by mass-effect in Glioblastoma (GBM) are often not visually evident, and may cause neurological deficits, impacting survival. Radiomic features provide sub-visual quantitative measures to uncover disease characteristics. We present a new radiomic feature to capture mass effect-induced deformations in the brain on Gadolinium-contrast (Gd-C) T1w-MRI, and their impact on survival. Our rationale is that larger variations in deformation within functionally eloquent areas of the contralateral hemisphere are likely related to decreased survival. Displacements in the cortical and subcortical structures were measured by aligning the Gd-C T1w-MRI to a healthy atlas. The variance of deformation magnitudes was measured and defined as Mass Effect Deformation Heterogeneity (MEDH) within the brain structures. MEDH values were then correlated with overall-survival of 89 subjects on the discovery cohort, with tumors on the right (n = 41) and left (n = 48) cerebral hemispheres, and evaluated on a hold-out cohort (n = 49 subjects). On both cohorts, decreased survival time was found to be associated with increased MEDH in areas of language comprehension, social cognition, visual perception, emotion, somato-sensory, cognitive and motor-control functions, particularly in the memory areas in the left-hemisphere. Our results suggest that higher MEDH in functionally eloquent areas of the left-hemisphere due to GBM in the right-hemisphere may be associated with poor-survival.
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Affiliation(s)
- Prateek Prasanna
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, USA
| | - Jhimli Mitra
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, USA.,General Electric Global Research, New York, USA
| | - Niha Beig
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, USA
| | - Ameya Nayate
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, USA
| | - Jay Patel
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, USA
| | - Soumya Ghose
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, USA
| | - Rajat Thawani
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, USA
| | - Sasan Partovi
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, USA
| | - Anant Madabhushi
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, USA
| | - Pallavi Tiwari
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, USA.
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35
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Elsheikh SSM, Bakas S, Mulder NJ, Chimusa ER, Davatzikos C, Crimi A. Multi-stage Association Analysis of Glioblastoma Gene Expressions with Texture and Spatial Patterns. BRAINLESION : GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES. BRAINLES (WORKSHOP) 2019; 11383:239-250. [PMID: 31482151 PMCID: PMC6719702 DOI: 10.1007/978-3-030-11723-8_24] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Glioblastoma is the most aggressive malignant primary brain tumor with a poor prognosis. Glioblastoma heterogeneous neuroimaging, pathologic, and molecular features provide opportunities for subclassification, prognostication, and the development of targeted therapies. Magnetic resonance imaging has the capability of quantifying specific phenotypic imaging features of these tumors. Additional insight into disease mechanism can be gained by exploring genetics foundations. Here, we use the gene expressions to evaluate the associations with various quantitative imaging phenomic features extracted from magnetic resonance imaging. We highlight a novel correlation by carrying out multi-stage genomewide association tests at the gene-level through a non-parametric correlation framework that allows testing multiple hypotheses about the integrated relationship of imaging phenotype-genotype more efficiently and less expensive computationally. Our result showed several novel genes previously associated with glioblastoma and other types of cancers, as the LRRC46 (chromosome 17), EPGN (chromosome 4) and TUBA1C (chromosome 12), all associated with our radiographic tumor features.
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Affiliation(s)
- Samar S M Elsheikh
- Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nicola J Mulder
- Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Emile R Chimusa
- Division of Human Genetics, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alessandro Crimi
- University Hospital of Zürich, Zürich, Switzerland
- African Institute for Mathematical Sciences, Biriwa, Ghana
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Rudie JD, Rauschecker AM, Bryan RN, Davatzikos C, Mohan S. Emerging Applications of Artificial Intelligence in Neuro-Oncology. Radiology 2019; 290:607-618. [PMID: 30667332 DOI: 10.1148/radiol.2018181928] [Citation(s) in RCA: 134] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Due to the exponential growth of computational algorithms, artificial intelligence (AI) methods are poised to improve the precision of diagnostic and therapeutic methods in medicine. The field of radiomics in neuro-oncology has been and will likely continue to be at the forefront of this revolution. A variety of AI methods applied to conventional and advanced neuro-oncology MRI data can already delineate infiltrating margins of diffuse gliomas, differentiate pseudoprogression from true progression, and predict recurrence and survival better than methods used in daily clinical practice. Radiogenomics will also advance our understanding of cancer biology, allowing noninvasive sampling of the molecular environment with high spatial resolution and providing a systems-level understanding of underlying heterogeneous cellular and molecular processes. By providing in vivo markers of spatial and molecular heterogeneity, these AI-based radiomic and radiogenomic tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and enable better dynamic treatment monitoring in this era of personalized medicine. Although substantial challenges remain, radiologic practice is set to change considerably as AI technology is further developed and validated for clinical use.
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Affiliation(s)
- Jeffrey D Rudie
- From the Department of Radiology, Division of Neuroradiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (J.D.R., C.D., S.M.); Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (A.M.R.); and Department of Diagnostic Medicine, Dell Medical School, University of Texas, Austin, Tex (R.N.B.)
| | - Andreas M Rauschecker
- From the Department of Radiology, Division of Neuroradiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (J.D.R., C.D., S.M.); Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (A.M.R.); and Department of Diagnostic Medicine, Dell Medical School, University of Texas, Austin, Tex (R.N.B.)
| | - R Nick Bryan
- From the Department of Radiology, Division of Neuroradiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (J.D.R., C.D., S.M.); Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (A.M.R.); and Department of Diagnostic Medicine, Dell Medical School, University of Texas, Austin, Tex (R.N.B.)
| | - Christos Davatzikos
- From the Department of Radiology, Division of Neuroradiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (J.D.R., C.D., S.M.); Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (A.M.R.); and Department of Diagnostic Medicine, Dell Medical School, University of Texas, Austin, Tex (R.N.B.)
| | - Suyash Mohan
- From the Department of Radiology, Division of Neuroradiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (J.D.R., C.D., S.M.); Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (A.M.R.); and Department of Diagnostic Medicine, Dell Medical School, University of Texas, Austin, Tex (R.N.B.)
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37
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Akbari H, Bakas S, Pisapia JM, Nasrallah MP, Rozycki M, Martinez-Lage M, Morrissette JJD, Dahmane N, O’Rourke DM, Davatzikos C. In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature. Neuro Oncol 2018; 20:1068-1079. [PMID: 29617843 PMCID: PMC6280148 DOI: 10.1093/neuonc/noy033] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Background Epidermal growth factor receptor variant III (EGFRvIII) is a driver mutation and potential therapeutic target in glioblastoma. Non-invasive in vivo EGFRvIII determination, using clinically acquired multiparametric MRI sequences, could assist in assessing spatial heterogeneity related to EGFRvIII, currently not captured via single-specimen analyses. We hypothesize that integration of subtle, yet distinctive, quantitative imaging/radiomic patterns using machine learning may lead to non-invasively determining molecular characteristics, and particularly the EGFRvIII mutation. Methods We integrated diverse imaging features, including the tumor's spatial distribution pattern, via support vector machines, to construct an imaging signature of EGFRvIII. This signature was evaluated in independent discovery (n = 75) and replication (n = 54) cohorts of de novo glioblastoma, and compared with the EGFRvIII status obtained through an assay based on next-generation sequencing. Results The cross-validated accuracy of the EGFRvIII signature in classifying the mutation status in individual patients of the independent discovery and replication cohorts was 85.3% (specificity = 86.3%, sensitivity = 83.3%, area under the curve [AUC] = 0.85) and 87% (specificity = 90%, sensitivity = 78.6%, AUC = 0.86), respectively. The signature was consistent with EGFRvIII+ tumors having increased neovascularization and cell density, as well as a distinctive spatial pattern involving relatively more frontal and parietal regions compared with EGFRvIII- tumors. Conclusions An imaging signature of EGFRvIII was found, revealing a complex, yet distinct macroscopic glioblastoma phenotype. By non-invasively capturing the tumor in its entirety, the proposed methodology can assist in evaluating the tumor's spatial heterogeneity, hence overcoming common spatial sampling limitations of tissue-based analyses. This signature can preoperatively stratify patients for EGFRvIII-targeted therapies, and potentially monitor dynamic mutational changes during treatment.
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Affiliation(s)
- Hamed Akbari
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
| | - Jared M Pisapia
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
| | - MacLean P Nasrallah
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
| | - Martin Rozycki
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
| | - Maria Martinez-Lage
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
| | - Jennifer J D Morrissette
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
| | - Nadia Dahmane
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
| | - Donald M O’Rourke
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
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Radiomic MRI signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics, offering prognostic value beyond IDH1. Sci Rep 2018; 8:5087. [PMID: 29572492 PMCID: PMC5865162 DOI: 10.1038/s41598-018-22739-2] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Accepted: 02/23/2018] [Indexed: 12/28/2022] Open
Abstract
The remarkable heterogeneity of glioblastoma, across patients and over time, is one of the main challenges in precision diagnostics and treatment planning. Non-invasive in vivo characterization of this heterogeneity using imaging could assist in understanding disease subtypes, as well as in risk-stratification and treatment planning of glioblastoma. The current study leveraged advanced imaging analytics and radiomic approaches applied to multi-parametric MRI of de novo glioblastoma patients (n = 208 discovery, n = 53 replication), and discovered three distinct and reproducible imaging subtypes of glioblastoma, with differential clinical outcome and underlying molecular characteristics, including isocitrate dehydrogenase-1 (IDH1), O6-methylguanine–DNA methyltransferase, epidermal growth factor receptor variant III (EGFRvIII), and transcriptomic subtype composition. The subtypes provided risk-stratification substantially beyond that provided by WHO classifications. Within IDH1-wildtype tumors, our subtypes revealed different survival (p < 0.001), thereby highlighting the synergistic consideration of molecular and imaging measures for prognostication. Moreover, the imaging characteristics suggest that subtype-specific treatment of peritumoral infiltrated brain tissue might be more effective than current uniform standard-of-care. Finally, our analysis found subtype-specific radiogenomic signatures of EGFRvIII-mutated tumors. The identified subtypes and their clinical and molecular correlates provide an in vivo portrait of phenotypic heterogeneity in glioblastoma, which points to the need for precision diagnostics and personalized treatment.
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Kfoury N, Sun T, Yu K, Rockwell N, Tinkum KL, Qi Z, Warrington NM, McDonald P, Roy A, Weir SJ, Mohila CA, Deneen B, Rubin JB. Cooperative p16 and p21 action protects female astrocytes from transformation. Acta Neuropathol Commun 2018; 6:12. [PMID: 29458417 PMCID: PMC5819173 DOI: 10.1186/s40478-018-0513-5] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 02/08/2018] [Indexed: 12/13/2022] Open
Abstract
Mechanisms underlying sex differences in cancer incidence are not defined but likely involve dimorphism (s) in tumor suppressor function at the cellular and organismal levels. As an example, sexual dimorphism in retinoblastoma protein (Rb) activity was shown to block transformation of female, but not male, murine astrocytes in which neurofibromin and p53 function was abrogated (GBM astrocytes). Correlated sex differences in gene expression in the murine GBM astrocytes were found to be highly concordant with sex differences in gene expression in male and female GBM patients, including in the expression of components of the Rb and p53 pathways. To define the basis of this phenomenon, we examined the functions of the cyclin dependent kinase (CDK) inhibitors, p16, p21 and p27 in murine GBM astrocytes under conditions that promote Rb-dependent growth arrest. We found that upon serum deprivation or etoposide-induced DNA damage, female, but not male GBM astrocytes, respond with increased p16 and p21 activity, and cell cycle arrest. In contrast, male GBM astrocytes continue to proliferate, accumulate chromosomal aberrations, exhibit enhanced clonogenic cell activity and in vivo tumorigenesis; all manifestations of broad sex differences in cell cycle regulation and DNA repair. Differences in tumorigenesis disappeared when female GBM astrocytes are also rendered null for p16 and p21. These data elucidate mechanisms underlying sex differences in cancer incidence and demonstrate sex-specific effects of cytotoxic and targeted therapeutics. This has critical implications for lab and clinical research.
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Rathore S, Bakas S, Pati S, Akbari H, Kalarot R, Sridharan P, Rozycki M, Bergman M, Tunc B, Verma R, Bilello M, Davatzikos C. Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma. BRAINLESION : GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES. BRAINLES (WORKSHOP) 2018; 10670:133-145. [PMID: 29733087 DOI: 10.1007/978-3-319-75238-9_12] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Quantitative research, especially in the field of radio(geno)mics, has helped us understand fundamental mechanisms of neurologic diseases. Such research is integrally based on advanced algorithms to derive extensive radiomic features and integrate them into diagnostic and predictive models. To exploit the benefit of such complex algorithms, their swift translation into clinical practice is required, currently hindered by their complicated nature. brain-CaPTk is a modular platform, with components spanning across image processing, segmentation, feature extraction, and machine learning, that facilitates such translation, enabling quantitative analyses without requiring substantial computational background. Thus, brain-CaPTk can be seamlessly integrated into the typical quantification, analysis and reporting workflow of a radiologist, underscoring its clinical potential. This paper describes currently available components of brain-CaPTk and example results from their application in glioblastoma.
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Affiliation(s)
- Saima Rathore
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Spyridon Bakas
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Sarthak Pati
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Hamed Akbari
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Ratheesh Kalarot
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Patmaa Sridharan
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Martin Rozycki
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Mark Bergman
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Birkan Tunc
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Ragini Verma
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
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Li HY, Sun CR, He M, Yin LC, Du HG, Zhang JM. Correlation Between Tumor Location and Clinical Properties of Glioblastomas in Frontal and Temporal Lobes. World Neurosurg 2018; 112:e407-e414. [PMID: 29355809 DOI: 10.1016/j.wneu.2018.01.055] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Revised: 01/06/2018] [Accepted: 01/11/2018] [Indexed: 10/18/2022]
Abstract
BACKGROUND Tumor location is a major prognostic factor in glioblastomas and may be associated with clinical properties. This study established and analyzed the correlation between tumor location and clinical properties of glioblastomas in frontal and temporal lobes. METHODS This retrospective study determined the location of glioblastomas in the frontal lobe (FL) or temporal lobe (TL) based on preoperative magnetic resonance imaging. Clinical, radiologic, and molecular characteristics of FL and TL glioblastomas were compared to define their clinical properties, including sex, age, sides, relationship to ventricle, imaging subtypes, volume, isocitrate dehydrogenase mutation, promoter methylation of O6-methylguanine-DNA methyltransferase, progression-free survival, and overall survival. RESULTS The study enrolled 406 patients (182 [44.83%] in FL group and 224 [55.17%] in TL group) with a mean age of 69.8 years. Compared with FL group, TL group had higher incidence of female patients (P = 0.024), tumor location distant to the ventricle (P = 0.006), isocitrate dehydrogenase mutations (P = 0.021), promoter methylation of O6-methylguanine-DNA methyltransferase (P = 0.012), and prolonged progression-free survival and overall survival (P < 0.05). No significant differences were observed between groups with respect to age ≥60 years at study entry (P = 0.668), sides (P = 0.879), imaging subtypes (P = 0.362), or volume (P = 0.709). CONCLUSIONS This study demonstrated that different tumor locations are associated with diverse clinical properties of glioblastomas in FL and TL. This information will aid in increasing understanding of glioblastoma biology for application in baseline comparisons in future clinical trials.
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Affiliation(s)
- Hong-Yu Li
- Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Department of Neurosurgery, Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Chong-Ran Sun
- Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Min He
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Li-Chun Yin
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Hang-Gen Du
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Jian-Min Zhang
- Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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Davatzikos C, Rathore S, Bakas S, Pati S, Bergman M, Kalarot R, Sridharan P, Gastounioti A, Jahani N, Cohen E, Akbari H, Tunc B, Doshi J, Parker D, Hsieh M, Sotiras A, Li H, Ou Y, Doot RK, Bilello M, Fan Y, Shinohara RT, Yushkevich P, Verma R, Kontos D. Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome. J Med Imaging (Bellingham) 2018; 5:011018. [PMID: 29340286 PMCID: PMC5764116 DOI: 10.1117/1.jmi.5.1.011018] [Citation(s) in RCA: 92] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Accepted: 12/05/2017] [Indexed: 12/26/2022] Open
Abstract
The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.
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Affiliation(s)
- Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Saima Rathore
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Mark Bergman
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Ratheesh Kalarot
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Patmaa Sridharan
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Aimilia Gastounioti
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Nariman Jahani
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Eric Cohen
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Hamed Akbari
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Birkan Tunc
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Drew Parker
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Michael Hsieh
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Aristeidis Sotiras
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Hongming Li
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Yangming Ou
- Massachusetts General Hospital, Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States
| | - Robert K. Doot
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Russell T. Shinohara
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
- University of Pennsylvania, Perelman School of Medicine, Center for Clinical Epidemiology and Biostatistics (CCEB), Department of Biostatistics, Epidemiology, and Informatics, Philadelphia, Pennsylvania, United States
| | - Paul Yushkevich
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Ragini Verma
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Despina Kontos
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
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Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci Data 2017; 4:170117. [PMID: 28872634 PMCID: PMC5685212 DOI: 10.1038/sdata.2017.117] [Citation(s) in RCA: 675] [Impact Index Per Article: 96.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Accepted: 07/14/2017] [Indexed: 01/04/2023] Open
Abstract
Gliomas belong to a group of central nervous system tumors, and consist of various sub-regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for both clinical and computational studies, including radiomic and radiogenomic analyses. Towards this end, we release segmentation labels and radiomic features for all pre-operative multimodal magnetic resonance imaging (MRI) (n=243) of the multi-institutional glioma collections of The Cancer Genome Atlas (TCGA), publicly available in The Cancer Imaging Archive (TCIA). Pre-operative scans were identified in both glioblastoma (TCGA-GBM, n=135) and low-grade-glioma (TCGA-LGG, n=108) collections via radiological assessment. The glioma sub-region labels were produced by an automated state-of-the-art method and manually revised by an expert board-certified neuroradiologist. An extensive panel of radiomic features was extracted based on the manually-revised labels. This set of labels and features should enable i) direct utilization of the TCGA/TCIA glioma collections towards repeatable, reproducible and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments, as well as ii) performance evaluation of computer-aided segmentation methods, and comparison to our state-of-the-art method.
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