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Kwon J, Kim J, Park H. Leveraging segmentation-guided spatial feature embedding for overall survival prediction in glioblastoma with multimodal magnetic resonance imaging. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 255:108338. [PMID: 39042996 DOI: 10.1016/j.cmpb.2024.108338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 07/17/2024] [Accepted: 07/17/2024] [Indexed: 07/25/2024]
Abstract
BACKGROUND AND OBJECTIVE Patients with glioblastoma have a five-year relative survival rate of less than 5 %. Thus, accurately predicting the overall survival (OS) of patients with glioblastoma is crucial for effective treatment planning. METHODS To fully leverage the imaging characteristics of glioblastomas, we propose a segmentation-guided regression method for predicting OS of patients with brain tumors using multimodal magnetic resonance imaging. Specifically, a brain tumor segmentation network was first pre-trained without leveraging survival information. Subsequently, the survival regression network was jointly trained with the guidance of brain tumor segmentation, focusing on tumor voxels and suppressing irrelevant backgrounds. RESULTS Our proposed framework, based on the well-known backbone of UNETR++, achieved a Dice score of 0.7910, Spearman correlation of 0.4112, and Harrell's concordance index of 0.6488. The model consistently showed promising results compared with baseline methods on two different datasets (BraTS and UCSF-PDGM). Furthermore, ablation studies on our training configurations demonstrated that both the pre-training segmentation network and contrastive loss significantly improved all metrics for OS prediction. CONCLUSIONS In this study, we propose a joint learning framework based on a pre-trained segmentation backbone for OS prediction by leveraging a brain tumor segmentation map. By utilizing a spatial feature map, our model can operate using a sliding-window approach, which can be adopted by varying the matrix sizes and resolutions of the input images.
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Affiliation(s)
- Junmo Kwon
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, 16419, South Korea
| | - Jonghun Kim
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Hyunjin Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, 16419, South Korea.
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De Roeck L, Blommaert J, Dupont P, Sunaert S, Sleurs C, Lambrecht M. Brain network topology and its cognitive impact in adult glioma survivors. Sci Rep 2024; 14:12782. [PMID: 38834633 DOI: 10.1038/s41598-024-63716-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 05/31/2024] [Indexed: 06/06/2024] Open
Abstract
Structural brain network topology can be altered in case of a brain tumor, due to both the tumor itself and its treatment. In this study, we explored the role of structural whole-brain and nodal network metrics and their association with cognitive functioning. Fifty WHO grade 2-3 adult glioma survivors (> 1-year post-therapy) and 50 matched healthy controls underwent a cognitive assessment, covering six cognitive domains. Raw cognitive assessment scores were transformed into w-scores, corrected for age and education. Furthermore, based on multi-shell diffusion-weighted MRI, whole-brain tractography was performed to create weighted graphs and to estimate whole-brain and nodal graph metrics. Hubs were defined based on nodal strength, betweenness centrality, clustering coefficient and shortest path length in healthy controls. Significant differences in these metrics between patients and controls were tested for the hub nodes (i.e. n = 12) and non-hub nodes (i.e. n = 30) in two mixed-design ANOVAs. Group differences in whole-brain graph measures were explored using Mann-Whitney U tests. Graph metrics that significantly differed were ultimately correlated with the cognitive domain-specific w-scores. Bonferroni correction was applied to correct for multiple testing. In survivors, the bilateral putamen were significantly less frequently observed as a hub (pbonf < 0.001). These nodes' assortativity values were positively correlated with attention (r(90) > 0.573, pbonf < 0.001), and proxy IQ (r(90) > 0.794, pbonf < 0.001). Attention and proxy IQ were significantly more often correlated with assortativity of hubs compared to non-hubs (pbonf < 0.001). Finally, the whole-brain graph measures of clustering coefficient (r = 0.685), global (r = 0.570) and local efficiency (r = 0.500) only correlated with proxy IQ (pbonf < 0.001). This study demonstrated potential reorganization of hubs in glioma survivors. Assortativity of these hubs was specifically associated with cognitive functioning, which could be important to consider in future modeling of cognitive outcomes and risk classification in glioma survivors.
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Affiliation(s)
- Laurien De Roeck
- Department of Radiotherapy and Oncology, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium.
- Department of Oncology, KU Leuven, Leuven, Belgium.
| | - Jeroen Blommaert
- Department of Oncology, KU Leuven, Leuven, Belgium
- Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Patrick Dupont
- Leuven Brain Institute, KU Leuven, Leuven, Belgium
- Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Stefan Sunaert
- Leuven Brain Institute, KU Leuven, Leuven, Belgium
- Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Charlotte Sleurs
- Department of Oncology, KU Leuven, Leuven, Belgium
- Department of Cognitive Neuropsychology, Tilburg University, Tilburg, the Netherlands
| | - Maarten Lambrecht
- Department of Radiotherapy and Oncology, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
- Leuven Brain Institute, KU Leuven, Leuven, Belgium
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Guo Y, Guo H, Tong H, Xue W, Xie T, Wang L, Tong H. The effect Of vascular related CeRNA genes and corresponding imaging biomarkers on survival in lower grade glioma. Ir J Med Sci 2024; 193:653-663. [PMID: 37801268 DOI: 10.1007/s11845-023-03536-x] [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: 08/06/2023] [Accepted: 09/22/2023] [Indexed: 10/07/2023]
Abstract
BACKGROUND & AIMS To investigate the differential expression of vascular related ceRNA regulatory genes in LGG with different mutations of IDH1 and MGMT, and to verify imaging gene markers that can be closely associated with vascular related ceRNA regulatory genes. METHOD Five hundred fifteen patients with LGG were collected from TCGA database. CeRNA network analysis, GO analysis and Cox risk regression were used to find vascular ceRNA regulatory genes and their genetic markers related to survival. The preoperative MRI image data and postoperative tumor tissues of 14 patients with WHO grade III glioma were collected for full transcriptome analysis. The correlation between image characteristics of LGG and survival related vascular ceRNA regulatory genes was compared using nonparametric U test and Pearson correlation coefficient analysis. RESULTS Vascular related genes ranked first in the functional enrichment analysis of differentially expressed genes in LGG. EPHA2, ETS1, YAP1 and MEIS1 could significantly affect the survival of patients in each group of LGG. The volume of enhanced region was negatively correlated with IDH1 (r = -0.622, P = 0.009) mutation and TMEM100 (r = -0.535, P = 0.024), and positively correlated with MEIS1 (r = 0.551, P = 0.021), rCBFmax value was negatively correlated with TMEM100 (r = -0.492, P = 0.037). CONCLUSIONS Under different IDH1 mutations, lncRNA-dominated vascular-related ceRNA regulatory genes were the first differentially expressed subset of each group, and could be used as an effective risk factor affecting the survival of LGG. The image characteristics of LGG was an ideal image gene marker. It was a reliable imaging biological marker which can truly reflect the pathophysiological characteristics of glioma.
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Affiliation(s)
- Yu Guo
- Department of Radiology, Army Medical Center of PLA, Army Medical University, 10# Changjiangzhilu, Yuzhong District, 400024, Chongqing, China
- Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China
| | - Hong Guo
- Department of Radiology, Army Medical Center of PLA, Army Medical University, 10# Changjiangzhilu, Yuzhong District, 400024, Chongqing, China
- Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China
| | - Haiyan Tong
- Zhoukou Central Hospital, Zhoukou, Henan, China
| | - Wei Xue
- Department of Radiology, The 940Th Hospital of Logistics Support Force of PLA, Lanzhou, China
| | - Tian Xie
- Department of Radiology, Army Medical Center of PLA, Army Medical University, 10# Changjiangzhilu, Yuzhong District, 400024, Chongqing, China
- Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China
| | - Lulu Wang
- Chongqing University Cancer Hospital, Chongqing Cancer Hospital, Chongqing, China.
| | - Haipeng Tong
- Department of Radiology, Army Medical Center of PLA, Army Medical University, 10# Changjiangzhilu, Yuzhong District, 400024, Chongqing, China.
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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Pasquini L, Peck KK, Jenabi M, Holodny A. Functional MRI in Neuro-Oncology: State of the Art and Future Directions. Radiology 2023; 308:e222028. [PMID: 37668519 PMCID: PMC10546288 DOI: 10.1148/radiol.222028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 05/15/2023] [Accepted: 05/26/2023] [Indexed: 09/06/2023]
Abstract
Since its discovery in the early 1990s, functional MRI (fMRI) has been used to study human brain function. One well-established application of fMRI in the clinical setting is the neurosurgical planning of patients with brain tumors near eloquent cortical areas. Clinical fMRI aims to preoperatively identify eloquent cortices that serve essential functions in daily life, such as hand movement and language. The primary goal of neurosurgery is to maximize tumor resection while sparing eloquent cortices adjacent to the tumor. When a lesion presents in the vicinity of an eloquent cortex, surgeons may use fMRI to plan their best surgical approach by determining the proximity of the lesion to regions of activation, providing guidance for awake brain surgery and intraoperative brain mapping. The acquisition of fMRI requires patient preparation prior to imaging, determination of functional paradigms, monitoring of patient performance, and both processing and analysis of images. Interpretation of fMRI maps requires a strong understanding of functional neuroanatomy and familiarity with the technical limitations frequently present in brain tumor imaging, including neurovascular uncoupling, patient compliance, and data analysis. This review discusses clinical fMRI in neuro-oncology, relevant ongoing research topics, and prospective future developments in this exciting discipline.
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Affiliation(s)
- Luca Pasquini
- From the Neuroradiology Service, Department of Radiology (L.P.,
K.K.P., M.J., A.H.), Department of Medical Physics (K.K.P.), and Brain Tumor
Center (A.H.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York,
NY 10065; Neuroradiology Unit, NESMOS Department, Sant'Andrea Hospital,
La Sapienza University, Rome, Italy (L.P.); Department of Radiology, Weill
Medical College of Cornell University, New York, NY (A.H.); and Department of
Neuroscience, Weill Cornell Medicine Graduate School of Medical Sciences, New
York, NY (A.H.)
| | - Kyung K. Peck
- From the Neuroradiology Service, Department of Radiology (L.P.,
K.K.P., M.J., A.H.), Department of Medical Physics (K.K.P.), and Brain Tumor
Center (A.H.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York,
NY 10065; Neuroradiology Unit, NESMOS Department, Sant'Andrea Hospital,
La Sapienza University, Rome, Italy (L.P.); Department of Radiology, Weill
Medical College of Cornell University, New York, NY (A.H.); and Department of
Neuroscience, Weill Cornell Medicine Graduate School of Medical Sciences, New
York, NY (A.H.)
| | - Mehrnaz Jenabi
- From the Neuroradiology Service, Department of Radiology (L.P.,
K.K.P., M.J., A.H.), Department of Medical Physics (K.K.P.), and Brain Tumor
Center (A.H.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York,
NY 10065; Neuroradiology Unit, NESMOS Department, Sant'Andrea Hospital,
La Sapienza University, Rome, Italy (L.P.); Department of Radiology, Weill
Medical College of Cornell University, New York, NY (A.H.); and Department of
Neuroscience, Weill Cornell Medicine Graduate School of Medical Sciences, New
York, NY (A.H.)
| | - Andrei Holodny
- From the Neuroradiology Service, Department of Radiology (L.P.,
K.K.P., M.J., A.H.), Department of Medical Physics (K.K.P.), and Brain Tumor
Center (A.H.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York,
NY 10065; Neuroradiology Unit, NESMOS Department, Sant'Andrea Hospital,
La Sapienza University, Rome, Italy (L.P.); Department of Radiology, Weill
Medical College of Cornell University, New York, NY (A.H.); and Department of
Neuroscience, Weill Cornell Medicine Graduate School of Medical Sciences, New
York, NY (A.H.)
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Luckett PH, Olufawo M, Lamichhane B, Park KY, Dierker D, Verastegui GT, Yang P, Kim AH, Chheda MG, Snyder AZ, Shimony JS, Leuthardt EC. Predicting survival in glioblastoma with multimodal neuroimaging and machine learning. J Neurooncol 2023; 164:309-320. [PMID: 37668941 PMCID: PMC10522528 DOI: 10.1007/s11060-023-04439-8] [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: 08/03/2023] [Accepted: 08/26/2023] [Indexed: 09/06/2023]
Abstract
PURPOSE Glioblastoma (GBM) is the most common and aggressive malignant glioma, with an overall median survival of less than two years. The ability to predict survival before treatment in GBM patients would lead to improved disease management, clinical trial enrollment, and patient care. METHODS GBM patients (N = 133, mean age 60.8 years, median survival 14.1 months, 57.9% male) were retrospectively recruited from the neurosurgery brain tumor service at Washington University Medical Center. All patients completed structural neuroimaging and resting state functional MRI (RS-fMRI) before surgery. Demographics, measures of cortical thickness (CT), and resting state functional network connectivity (FC) were used to train a deep neural network to classify patients based on survival (< 1y, 1-2y, >2y). Permutation feature importance identified the strongest predictors of survival based on the trained models. RESULTS The models achieved a combined cross-validation and hold out accuracy of 90.6% in classifying survival (< 1y, 1-2y, >2y). The strongest demographic predictors were age at diagnosis and sex. The strongest CT predictors of survival included the superior temporal sulcus, parahippocampal gyrus, pericalcarine, pars triangularis, and middle temporal regions. The strongest FC features primarily involved dorsal and inferior somatomotor, visual, and cingulo-opercular networks. CONCLUSION We demonstrate that machine learning can accurately classify survival in GBM patients based on multimodal neuroimaging before any surgical or medical intervention. These results were achieved without information regarding presentation symptoms, treatments, postsurgical outcomes, or tumor genomic information. Our results suggest GBMs have a global effect on the brain's structural and functional organization, which is predictive of survival.
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Affiliation(s)
- Patrick H Luckett
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA.
| | - Michael Olufawo
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Bidhan Lamichhane
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Center for Health Sciences, Oklahoma State University, Tulsa, OK, 74136, USA
| | - Ki Yun Park
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Donna Dierker
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Peter Yang
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Albert H Kim
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Brain Tumor Center at Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Milan G Chheda
- Brain Tumor Center at Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Abraham Z Snyder
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Brain Tumor Center at Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Eric C Leuthardt
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Brain Tumor Center at Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University in Saint Louis, St. Louis, MO, 63130, USA
- Department of Mechanical Engineering and Materials Science, Washington University in Saint Louis, St. Louis, MO, 63130, USA
- Center for Innovation in Neuroscience and Technology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Brain Laser Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
- National Center for Adaptive Neurotechnologies, Albany, USA
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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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Brain Tumor at Diagnosis: From Cognition and Behavior to Quality of Life. Diagnostics (Basel) 2023; 13:diagnostics13030541. [PMID: 36766646 PMCID: PMC9914203 DOI: 10.3390/diagnostics13030541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The present narrative review aims to discuss cognitive-emotional-behavioral symptoms in adults with brain tumors at the time of diagnosis. METHODS The PubMed database was searched considering glioma, pituitary adenoma, and meningioma in adulthood as pathologies, together with cognitive, neuropsychological, or behavioral aspects. RESULTS Although a significant number of studies describe cognitive impairment after surgery or treatment in adults with brain tumors, only few focus on cognitive-emotional-behavioral symptoms at diagnosis. Furthermore, the importance of an effective communication and its impact on patients' quality of life and compliance with treatment are seldom discussed. CONCLUSIONS Adults with brain tumors have needs in terms of cognitive-emotional-behavioral features that are detectable at the time of diagnosis; more research is needed to identify effective communication protocols in order to allow a higher perceived quality of life in these patients.
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Radiomics-Based Machine Learning to Predict Recurrence in Glioma Patients Using Magnetic Resonance Imaging. J Comput Assist Tomogr 2023; 47:129-135. [PMID: 36194851 DOI: 10.1097/rct.0000000000001386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Recurrence is a major factor in the poor prognosis of patients with glioma. The aim of this study was to predict glioma recurrence using machine learning based on radiomic features. METHODS We recruited 77 glioma patients, consisting of 57 newly diagnosed patients and 20 patients with recurrence. After extracting the radiomic features from T2-weighted images, the data set was randomly divided into training (58 patients) and testing (19 patients) cohorts. An automated machine learning method (the Tree-based Pipeline Optimization Tool) was applied to generate 10 independent recurrence prediction models. The final model was determined based on the area under the curve (AUC) and average specificity. Moreover, an independent validation set of 20 patients with glioma was used to verify the model performance. RESULTS Recurrence in glioma patients was successfully predicting by machine learning using radiomic features. Among the 10 recurrence prediction models, the best model achieved an accuracy of 0.81, an AUC value of 0.85, and a specificity of 0.69 in the testing cohort, but an accuracy of 0.75 and an AUC value of 0.87 in the independent validation set. CONCLUSIONS Our algorithm that is generated by machine learning exhibits promising power and may predict recurrence noninvasively, thereby offering potential value for the early development of interventions to delay or prevent recurrence in glioma patients.
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di Noia C, Grist JT, Riemer F, Lyasheva M, Fabozzi M, Castelli M, Lodi R, Tonon C, Rundo L, Zaccagna F. Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI. Diagnostics (Basel) 2022; 12:diagnostics12092125. [PMID: 36140526 PMCID: PMC9497964 DOI: 10.3390/diagnostics12092125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 08/05/2022] [Accepted: 08/17/2022] [Indexed: 11/24/2022] Open
Abstract
Given growing clinical needs, in recent years Artificial Intelligence (AI) techniques have increasingly been used to define the best approaches for survival assessment and prediction in patients with brain tumors. Advances in computational resources, and the collection of (mainly) public databases, have promoted this rapid development. This narrative review of the current state-of-the-art aimed to survey current applications of AI in predicting survival in patients with brain tumors, with a focus on Magnetic Resonance Imaging (MRI). An extensive search was performed on PubMed and Google Scholar using a Boolean research query based on MeSH terms and restricting the search to the period between 2012 and 2022. Fifty studies were selected, mainly based on Machine Learning (ML), Deep Learning (DL), radiomics-based methods, and methods that exploit traditional imaging techniques for survival assessment. In addition, we focused on two distinct tasks related to survival assessment: the first on the classification of subjects into survival classes (short and long-term or eventually short, mid and long-term) to stratify patients in distinct groups. The second focused on quantification, in days or months, of the individual survival interval. Our survey showed excellent state-of-the-art methods for the first, with accuracy up to ∼98%. The latter task appears to be the most challenging, but state-of-the-art techniques showed promising results, albeit with limitations, with C-Index up to ∼0.91. In conclusion, according to the specific task, the available computational methods perform differently, and the choice of the best one to use is non-univocal and dependent on many aspects. Unequivocally, the use of features derived from quantitative imaging has been shown to be advantageous for AI applications, including survival prediction. This evidence from the literature motivates further research in the field of AI-powered methods for survival prediction in patients with brain tumors, in particular, using the wealth of information provided by quantitative MRI techniques.
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Affiliation(s)
- Christian di Noia
- Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum—University of Bologna, 40125 Bologna, Italy
| | - James T. Grist
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford OX1 3PT, UK
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
- Oxford Centre for Clinical Magnetic Research Imaging, University of Oxford, Oxford OX3 9DU, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2SY, UK
| | - Frank Riemer
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, N-5021 Bergen, Norway
| | - Maria Lyasheva
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Miriana Fabozzi
- Centro Medico Polispecialistico (CMO), 80058 Torre Annunziata, Italy
| | - Mauro Castelli
- NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal
| | - Raffaele Lodi
- Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum—University of Bologna, 40125 Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, 40139 Bologna, Italy
| | - Caterina Tonon
- Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum—University of Bologna, 40125 Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, 40139 Bologna, Italy
| | - Leonardo Rundo
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy
| | - Fulvio Zaccagna
- Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum—University of Bologna, 40125 Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, 40139 Bologna, Italy
- Correspondence: ; Tel.: +39-0514969951
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10
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Jian A, Liu S, Di Ieva A. Artificial Intelligence for Survival Prediction in Brain Tumors on Neuroimaging. Neurosurgery 2022; 91:8-26. [PMID: 35348129 DOI: 10.1227/neu.0000000000001938] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 01/08/2022] [Indexed: 12/30/2022] Open
Abstract
Survival prediction of patients affected by brain tumors provides essential information to guide surgical planning, adjuvant treatment selection, and patient counseling. Current reliance on clinical factors, such as Karnofsky Performance Status Scale, and simplistic radiological characteristics are, however, inadequate for survival prediction in tumors such as glioma that demonstrate molecular and clinical heterogeneity with variable survival outcomes. Advances in the domain of artificial intelligence have afforded powerful tools to capture a large number of hidden high-dimensional imaging features that reflect abundant information about tumor structure and physiology. Here, we provide an overview of current literature that apply computational analysis tools such as radiomics and machine learning methods to the pipeline of image preprocessing, tumor segmentation, feature extraction, and construction of classifiers to establish survival prediction models based on neuroimaging. We also discuss challenges relating to the development and evaluation of such models and explore ethical issues surrounding the future use of machine learning predictions.
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Affiliation(s)
- Anne Jian
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
- Royal Melbourne Hospital, Melbourne, Australia
| | - Sidong Liu
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
- Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
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11
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Liu X, Li J, Xu Q, Zhang Q, Zhou X, Pan H, Wu N, Lu G, Zhang Z. RP-Rs-fMRIomics as a Novel Imaging Analysis Strategy to Empower Diagnosis of Brain Gliomas. Cancers (Basel) 2022; 14:2818. [PMID: 35740484 PMCID: PMC9220978 DOI: 10.3390/cancers14122818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/29/2022] [Accepted: 06/01/2022] [Indexed: 12/07/2022] Open
Abstract
Rs-fMRI can provide rich information about functional processes in the brain with a large array of imaging parameters and is also suitable for investigating the biological processes in cerebral gliomas. We aimed to propose an imaging analysis method of RP-Rs-fMRIomics by adopting omics analysis on rs-fMRI with exhaustive regional parameters and subsequently estimating its feasibility on the prediction diagnosis of gliomas. In this retrospective study, preoperative rs-fMRI data were acquired from patients confirmed with diffuse gliomas (n = 176). A total of 420 features were extracted through measuring 14 regional parameters of rs-fMRI as much as available currently in 10 specific narrow frequency bins and three parts of gliomas. With a randomly split training and testing dataset (ratio 7:3), four classifiers were implemented to construct and optimize RP-Rs-fMRIomics models for predicting glioma grade, IDH status and Karnofsky Performance Status scores. The RP-Rs-fMRIomics models (AUROC 0.988, 0.905, 0.801) were superior to the corresponding traditional single rs-fMRI index (AUROC 0.803, 0.731, 0.632) in predicting glioma grade, IDH and survival. The RP-Rs-fMRIomics analysis, featuring high interpretability, was competitive for prediction of glioma grading, IDH genotype and prognosis. The method expanded the clinical application of rs-fMRI and also contributed a new imaging analysis for brain tumor research.
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Affiliation(s)
- Xiaoxue Liu
- Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China; (X.L.); (J.L.); (Q.X.); (Q.Z.); (X.Z.); (G.L.)
| | - Jianrui Li
- Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China; (X.L.); (J.L.); (Q.X.); (Q.Z.); (X.Z.); (G.L.)
| | - Qiang Xu
- Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China; (X.L.); (J.L.); (Q.X.); (Q.Z.); (X.Z.); (G.L.)
| | - Qirui Zhang
- Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China; (X.L.); (J.L.); (Q.X.); (Q.Z.); (X.Z.); (G.L.)
| | - Xian Zhou
- Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China; (X.L.); (J.L.); (Q.X.); (Q.Z.); (X.Z.); (G.L.)
| | - Hao Pan
- Department of Neurosurgery, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China;
| | - Nan Wu
- Department of Pathology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China;
| | - Guangming Lu
- Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China; (X.L.); (J.L.); (Q.X.); (Q.Z.); (X.Z.); (G.L.)
- State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing 210093, China
| | - Zhiqiang Zhang
- Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China; (X.L.); (J.L.); (Q.X.); (Q.Z.); (X.Z.); (G.L.)
- State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing 210093, China
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12
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Jiang W, Merhar SL, Zeng Z, Zhu Z, Yin W, Zhou Z, Wang L, He L, Vannest J, Lin W. Neural alterations in opioid-exposed infants revealed by edge-centric brain functional networks. Brain Commun 2022; 4:fcac112. [PMID: 35602654 PMCID: PMC9117006 DOI: 10.1093/braincomms/fcac112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 03/29/2022] [Accepted: 05/03/2022] [Indexed: 12/02/2022] Open
Abstract
Prenatal opioid exposure has been linked to adverse effects spanning multiple neurodevelopmental domains, including cognition, motor development, attention, and vision. However, the neural basis of these abnormalities is largely unknown. A total of 49 infants, including 21 opioid-exposed and 28 controls, were enrolled and underwent MRI (43 ± 6 days old) after birth, including resting state functional MRI. Edge-centric functional networks based on dynamic functional connections were constructed, and machine-learning methods were employed to identify neural features distinguishing opioid-exposed infants from unexposed controls. An accuracy of 73.6% (sensitivity 76.25% and specificity 69.33%) was achieved using 10 times 10-fold cross-validation, which substantially outperformed those obtained using conventional static functional connections (accuracy 56.9%). More importantly, we identified that prenatal opioid exposure preferentially affects inter- rather than intra-network dynamic functional connections, particularly with the visual, subcortical, and default mode networks. Consistent results at the brain regional and connection levels were also observed, where the brain regions and connections associated with visual and higher order cognitive functions played pivotal roles in distinguishing opioid-exposed infants from controls. Our findings support the clinical phenotype of infants exposed to opioids in utero and may potentially explain the higher rates of visual and emotional problems observed in this population. Finally, our findings suggested that edge-centric networks could better capture the neural differences between opioid-exposed infants and controls by abstracting the intrinsic co-fluctuation along edges, which may provide a promising tool for future studies focusing on investigating the effects of prenatal opioid exposure on neurodevelopment.
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Affiliation(s)
- Weixiong Jiang
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Stephanie L. Merhar
- Perinatal Institute, Division of Neonatology, Cincinnati Children’s Hospital and University of Cincinnati Department of Pediatrics, Cincinnati OH, United States
| | - Zhuohao Zeng
- East Chapel Hill High School, Chapel Hill, North Carolina, United States
| | - Ziliang Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Weiyan Yin
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Zhen Zhou
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Li Wang
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Lili He
- Department of Radiology, Cincinnati Children’s Hospital and University of Cincinnati, Cincinnati OH, United States
| | - Jennifer Vannest
- Department of Communication Sciences and Disorders, University of Cincinnati, Cincinnati OH, United States
| | - Weili Lin
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
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13
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Cognitive deficits in adult patients with high-grade glioma: A systematic review. Clin Neurol Neurosurg 2022; 219:107296. [DOI: 10.1016/j.clineuro.2022.107296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 04/04/2022] [Accepted: 05/13/2022] [Indexed: 11/15/2022]
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14
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Zhang H, Zhou Z, Ding L, Wu C, Qiu M, Huang Y, Jin F, Shen T, Yang Y, Hsu LM, Wang J, Zhang H, Shen D, Peng D. Divergent and convergent imaging markers between bipolar and unipolar depression based on Machine Learning. IEEE J Biomed Health Inform 2022; 26:4100-4110. [PMID: 35412995 DOI: 10.1109/jbhi.2022.3166826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Distinguishing bipolar depression (BD) from unipolar depression (UD) based on symptoms only is challenging. Brain functional connectivity (FC), especially dynamic FC, has emerged as a promising approach to identify possible imaging markers for differentiating BD from UD. However, most of such studies utilized conventional FC and group-level statistical comparisons, which may not be sensitive enough to quantify subtle changes in the FC dynamics between BD and UD. In this paper, we present a more effective individualized differentiation model based on machine learning and the whole-brain high-order functional connectivity (HOFC) network. The HOFC, capturing temporal synchronization among the dynamic FC time series, a more complex chronnectome metric compared to the conventional FC, was used to classify 52 BD, 73 UD, and 76 healthy controls (HC). We achieved a satisfactory accuracy (70.40%) in BD vs. UD differentiation. The resultant contributing features revealed the involvement of the coordinated flexible interactions among sensory (e.g., olfaction, vision, and audition), motor, and cognitive systems. Despite sharing common chronnectome of cognitive and affective impairments, BD and UD also demonstrated unique dynamic FC synchronization patterns. UD is more associated with abnormal visual-somatomotor inter-network connections, while BD is more related to impaired ventral attention-frontoparietal inter-network connections. Moreover, we found that the illness duration modulated the BD vs. UD separation, with the differentiation performance hampered by the secondary disease effects. Our findings suggest that BD and UD may have divergent and convergent neural substrates, which further expand our knowledge of the two different mental disorders.
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15
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Gregorich M, Melograna F, Sunqvist M, Michiels S, Van Steen K, Heinze G. Individual-specific networks for prediction modelling – A scoping review of methods. BMC Med Res Methodol 2022; 22:62. [PMID: 35249534 PMCID: PMC8898441 DOI: 10.1186/s12874-022-01544-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 02/11/2022] [Indexed: 11/10/2022] Open
Abstract
Background Recent advances in biotechnology enable the acquisition of high-dimensional data on individuals, posing challenges for prediction models which traditionally use covariates such as clinical patient characteristics. Alternative forms of covariate representations for the features derived from these modern data modalities should be considered that can utilize their intrinsic interconnection. The connectivity information between these features can be represented as an individual-specific network defined by a set of nodes and edges, the strength of which can vary from individual to individual. Global or local graph-theoretical features describing the network may constitute potential prognostic biomarkers instead of or in addition to traditional covariates and may replace the often unsuccessful search for individual biomarkers in a high-dimensional predictor space. Methods We conducted a scoping review to identify, collate and critically appraise the state-of-art in the use of individual-specific networks for prediction modelling in medicine and applied health research, published during 2000–2020 in the electronic databases PubMed, Scopus and Embase. Results Our scoping review revealed the main application areas namely neurology and pathopsychology, followed by cancer research, cardiology and pathology (N = 148). Network construction was mainly based on Pearson correlation coefficients of repeated measurements, but also alternative approaches (e.g. partial correlation, visibility graphs) were found. For covariates measured only once per individual, network construction was mostly based on quantifying an individual’s contribution to the overall group-level structure. Despite the multitude of identified methodological approaches for individual-specific network inference, the number of studies that were intended to enable the prediction of clinical outcomes for future individuals was quite limited, and most of the models served as proof of concept that network characteristics can in principle be useful for prediction. Conclusion The current body of research clearly demonstrates the value of individual-specific network analysis for prediction modelling, but it has not yet been considered as a general tool outside the current areas of application. More methodological research is still needed on well-founded strategies for network inference, especially on adequate network sparsification and outcome-guided graph-theoretical feature extraction and selection, and on how networks can be exploited efficiently for prediction modelling. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-022-01544-6.
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16
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Chen Y, Zhou Z, Liang Y, Tan X, Li Y, Qin C, Feng Y, Ma X, Mo Z, Xia J, Zhang H, Qiu S, Shen D. Classification of type 2 diabetes mellitus with or without cognitive impairment from healthy controls using high-order functional connectivity. Hum Brain Mapp 2021; 42:4671-4684. [PMID: 34213081 PMCID: PMC8410559 DOI: 10.1002/hbm.25575] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 06/03/2021] [Accepted: 06/08/2021] [Indexed: 12/12/2022] Open
Abstract
Type 2 diabetes mellitus (T2DM) is associated with cognitive impairment and may progress to dementia. However, the brain functional mechanism of T2DM-related dementia is still less understood. Recent resting-state functional magnetic resonance imaging functional connectivity (FC) studies have proved its potential value in the study of T2DM with cognitive impairment (T2DM-CI). However, they mainly used a mass-univariate statistical analysis that was not suitable to reveal the altered FC "pattern" in T2DM-CI, due to lower sensitivity. In this study, we proposed to use high-order FC to reveal the abnormal connectomics pattern in T2DM-CI with a multivariate, machine learning-based strategy. We also investigated whether such patterns were different between T2DM-CI and T2DM without cognitive impairment (T2DM-noCI) to better understand T2DM-induced cognitive impairment, on 23 T2DM-CI and 27 T2DM-noCI patients, as well as 50 healthy controls (HCs). We first built the large-scale high-order brain networks based on temporal synchronization of the dynamic FC time series among multiple brain region pairs and then used this information to classify the T2DM-CI (as well as T2DM-noCI) from the matched HC based on support vector machine. Our model achieved an accuracy of 79.17% in T2DM-CI versus HC differentiation, but only 59.62% in T2DM-noCI versus HC classification. We found abnormal high-order FC patterns in T2DM-CI compared to HC, which was different from that in T2DM-noCI. Our study indicates that there could be widespread connectivity alterations underlying the T2DM-induced cognitive impairment. The results help to better understand the changes in the central neural system due to T2DM.
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Affiliation(s)
- Yuna Chen
- The First School of Clinical MedicineGuangzhou University of Chinese MedicineGuangzhouGuangdongChina
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Zhen Zhou
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Yi Liang
- Department of RadiologyThe First Affiliated Hospital of Guangzhou University of Chinese MedicineGuangzhouGuangdongChina
| | - Xin Tan
- Department of RadiologyThe First Affiliated Hospital of Guangzhou University of Chinese MedicineGuangzhouGuangdongChina
| | - Yifan Li
- The First School of Clinical MedicineGuangzhou University of Chinese MedicineGuangzhouGuangdongChina
| | - Chunhong Qin
- Department of RadiologyThe First Affiliated Hospital of Guangzhou University of Chinese MedicineGuangzhouGuangdongChina
| | - Yue Feng
- The First School of Clinical MedicineGuangzhou University of Chinese MedicineGuangzhouGuangdongChina
| | - Xiaomeng Ma
- The First School of Clinical MedicineGuangzhou University of Chinese MedicineGuangzhouGuangdongChina
| | - Zhanhao Mo
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
- Department of RadiologyChina‐Japan Union Hospital of Jilin UniversityChangchunJilinChina
| | - Jing Xia
- Institute of Brain‐Intelligence Technology, Zhangjiang LabShanghaiChina
| | - Han Zhang
- Institute of Brain‐Intelligence Technology, Zhangjiang LabShanghaiChina
| | - Shijun Qiu
- Department of RadiologyThe First Affiliated Hospital of Guangzhou University of Chinese MedicineGuangzhouGuangdongChina
| | - Dinggang Shen
- School of Biomedical EngineeringShanghaiTech UniversityShanghaiChina
- Shanghai United Imaging Intelligence Co., Ltd.ShanghaiChina
- Department of Artificial IntelligenceKorea UniversitySeoulRepublic of Korea
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17
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Wang C, Van Dyk K, Cho N, Raymond C, Choi J, Salamon N, Pope WB, Lai A, Cloughesy TF, Nghiemphu PL, Ellingson BM. Characterization of cognitive function in survivors of diffuse gliomas using resting-state functional MRI (rs-fMRI). Brain Imaging Behav 2021; 16:239-251. [PMID: 34350525 PMCID: PMC8825610 DOI: 10.1007/s11682-021-00497-6] [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] [Accepted: 07/05/2021] [Indexed: 11/29/2022]
Abstract
As treatments for diffuse gliomas have advanced, survival for patients with gliomas has also increased. However, there remains limited knowledge on the relationships between brain connectivity and the lasting changes to cognitive function that glioma survivors often experience long after completing treatment. This resting-state functional magnetic resonance imaging (rs-fMRI) study explored functional connectivity (FC) alterations associated with cognitive function in survivors of gliomas. In this pilot study, 22 patients (mean age 43.8 ± 11.9) with diffuse gliomas who completed treatment within the past 10 years were evaluated using rs-fMRI and neuropsychological measures. Novel rs-fMRI analysis methods were used to account for missing brain in the resection cavity. FC relationships were assessed between cognitively impaired and non-impaired glioma patients, along with self-reported cognitive impairment, non-work daily functioning, and time with surgery. In the cognitively non-impaired patients, FC was stronger in the medial prefrontal cortex, rostral prefrontal cortex, and intraparietal sulcus compared to the impaired survivors. When examining non-work daily functioning, a positive correlation with FC was observed between the accumbens and the intracalcarine cortices, while a negative correlation with FC was observed between the parietal operculum cortex and the cerebellum. Additionally, worse self-reported cognitive impairment and worse non-work daily functioning were associated with increased FC between regions involved in cognition and sensorimotor processing. These preliminary findings suggest that neural correlates for cognitive and daily functioning in glioma patients can be revealed using rs-fMRI. Resting-state network alterations may serve as a biomarker for patients’ cognition and functioning.
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Affiliation(s)
- Chencai Wang
- 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 Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Kathleen Van Dyk
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, Semel Institute, University of California Los Angeles, Los Angeles, CA, USA
| | - Nicholas Cho
- 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 Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Medical Scientist Training Program, 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 Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Justin Choi
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Albert Lai
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Timothy F Cloughesy
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Phioanh L Nghiemphu
- Department of Neurology, 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 Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. .,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, Semel Institute, University of California Los Angeles, Los Angeles, CA, USA.
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18
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Lamichhane B, Jayasekera D, Jakes R, Ray WZ, Leuthardt EC, Hawasli AH. Functional Disruptions of the Brain in Low Back Pain: A Potential Imaging Biomarker of Functional Disability. Front Neurol 2021; 12:669076. [PMID: 34335444 PMCID: PMC8317987 DOI: 10.3389/fneur.2021.669076] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 06/17/2021] [Indexed: 12/12/2022] Open
Abstract
Chronic low back pain (LBP) is one of the leading causes of disability worldwide. While LBP research has largely focused on the spine, many studies have demonstrated a restructuring of human brain architecture accompanying LBP and other chronic pain states. Brain imaging presents a promising source for discovering noninvasive biomarkers that can improve diagnostic and prognostication outcomes for chronic LBP. This study evaluated graph theory measures derived from brain resting-state functional connectivity (rsFC) as prospective noninvasive biomarkers of LBP. We also proposed and tested a hybrid feature selection method (Enet-subset) that combines Elastic Net and an optimal subset selection method. We collected resting-state functional MRI scans from 24 LBP patients and 27 age-matched healthy controls (HC). We then derived graph-theoretical features and trained a support vector machine (SVM) to classify patient group. The degree centrality (DC), clustering coefficient (CC), and betweenness centrality (BC) were found to be significant predictors of patient group. We achieved an average classification accuracy of 83.1% (p < 0.004) and AUC of 0.937 (p < 0.002), respectively. Similarly, we achieved a sensitivity and specificity of 87.0 and 79.7%. The classification results from this study suggest that graph matrices derived from rsFC can be used as biomarkers of LBP. In addition, our findings suggest that the proposed feature selection method, Enet-subset, might act as a better technique to remove redundant variables and improve the performance of the machine learning classifier.
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Affiliation(s)
- Bidhan Lamichhane
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States
| | - Dinal Jayasekera
- Department of Biomedical Engineering, Washington University in St. Louis McKelvey School of Engineering, St. Louis, MO, United States
| | - Rachel Jakes
- Department of Biomedical Engineering, Washington University in St. Louis McKelvey School of Engineering, St. Louis, MO, United States
| | - Wilson Z Ray
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States.,Department of Biomedical Engineering, Washington University in St. Louis McKelvey School of Engineering, St. Louis, MO, United States
| | - Eric C Leuthardt
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States.,Department of Biomedical Engineering, Washington University in St. Louis McKelvey School of Engineering, St. Louis, MO, United States
| | - Ammar H Hawasli
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States.,Meritas Health Neurosurgery, North Kansas City, MO, United States
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19
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Graph Theoretical Analysis of Brain Network Characteristics in Brain Tumor Patients: A Systematic Review. Neuropsychol Rev 2021; 32:651-675. [PMID: 34235627 DOI: 10.1007/s11065-021-09512-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 04/23/2021] [Indexed: 10/20/2022]
Abstract
Graph theory is a branch of mathematics that allows for the characterization of complex networks, and has rapidly grown in popularity in network neuroscience in recent years. Researchers have begun to use graph theory to describe the brain networks of individuals with brain tumors to shed light on disrupted networks. This systematic review summarizes the current literature on graph theoretical analysis of magnetic resonance imaging data in the brain tumor population with particular attention paid to treatment effects and other clinical factors. Included papers were published through June 24th, 2020. Searches were conducted on Pubmed, PsycInfo, and Web of Science using the search terms (graph theory OR graph analysis) AND (brain tumor OR brain tumour OR brain neoplasm) AND (MRI OR EEG OR MEG). Studies were eligible for inclusion if they: evaluated participants with a primary brain tumor, used graph theoretical analyses on structural or functional MRI data, MEG, or EEG, were in English, and were an empirical research study. Seventeen papers met criteria for inclusion. Results suggest alterations in network properties are often found in people with brain tumors, although the directions of differences are inconsistent and few studies reported effect sizes. The most consistent finding suggests increased network segregation. Changes are most prominent with more intense treatment, in hub regions, and with factors such as faster tumor growth. The use of graph theory to study brain tumor patients is in its infancy, though some conclusions can be drawn. Future studies should focus on treatment factors, changes over time, and correlations with functional outcomes to better identify those in need of early intervention.
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20
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Buchlak QD, Esmaili N, Leveque JC, Bennett C, Farrokhi F, Piccardi M. Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review. J Clin Neurosci 2021; 89:177-198. [PMID: 34119265 DOI: 10.1016/j.jocn.2021.04.043] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 04/30/2021] [Indexed: 12/13/2022]
Abstract
Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive diagnosis still relies on surgical pathology. Machine learning has been applied to the analysis of MRI data in glioma research and has the potential to change clinical practice and improve patient outcomes. This systematic review synthesizes and analyzes the current state of machine learning applications to glioma MRI data and explores the use of machine learning for systematic review automation. Various datapoints were extracted from the 153 studies that met inclusion criteria and analyzed. Natural language processing (NLP) analysis involved keyword extraction, topic modeling and document classification. Machine learning has been applied to tumor grading and diagnosis, tumor segmentation, non-invasive genomic biomarker identification, detection of progression and patient survival prediction. Model performance was generally strong (AUC = 0.87 ± 0.09; sensitivity = 0.87 ± 0.10; specificity = 0.0.86 ± 0.10; precision = 0.88 ± 0.11). Convolutional neural network, support vector machine and random forest algorithms were top performers. Deep learning document classifiers yielded acceptable performance (mean 5-fold cross-validation AUC = 0.71). Machine learning tools and data resources were synthesized and summarized to facilitate future research. Machine learning has been widely applied to the processing of MRI data in glioma research and has demonstrated substantial utility. NLP and transfer learning resources enabled the successful development of a replicable method for automating the systematic review article screening process, which has potential for shortening the time from discovery to clinical application in medicine.
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Affiliation(s)
- Quinlan D Buchlak
- School of Medicine, The University of Notre Dame Australia, Sydney, NSW, Australia.
| | - Nazanin Esmaili
- School of Medicine, The University of Notre Dame Australia, Sydney, NSW, Australia; Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia
| | | | - Christine Bennett
- School of Medicine, The University of Notre Dame Australia, Sydney, NSW, Australia
| | - Farrokh Farrokhi
- Neuroscience Institute, Virginia Mason Medical Center, Seattle, WA, USA
| | - Massimo Piccardi
- Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia
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21
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Lamichhane B, Daniel AGS, Lee JJ, Marcus DS, Shimony JS, Leuthardt EC. Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients. Front Neurol 2021; 12:642241. [PMID: 33692747 PMCID: PMC7937731 DOI: 10.3389/fneur.2021.642241] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 02/03/2021] [Indexed: 12/27/2022] Open
Abstract
Glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy. Due to its poor prognosis with currently available treatments, there is a pressing need for easily accessible, non-invasive techniques to help inform pre-treatment planning, patient counseling, and improve outcomes. In this study we determined the feasibility of resting-state functional connectivity (rsFC) to classify GBM patients into short-term and long-term survival groups with respect to reported median survival (14.6 months). We used a support vector machine with rsFC between regions of interest as predictive features. We employed a novel hybrid feature selection method whereby features were first filtered using correlations between rsFC and OS, and then using the established method of recursive feature elimination (RFE) to select the optimal feature subset. Leave-one-subject-out cross-validation evaluated the performance of models. Classification between short- and long-term survival accuracy was 71.9%. Sensitivity and specificity were 77.1 and 65.5%, respectively. The area under the receiver operating characteristic curve was 0.752 (95% CI, 0.62–0.88). These findings suggest that highly specific features of rsFC may predict GBM survival. Taken together, the findings of this study support that resting-state fMRI and machine learning analytics could enable a radiomic biomarker for GBM, augmenting care and planning for individual patients.
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Affiliation(s)
- Bidhan Lamichhane
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, United States
| | - Andy G S Daniel
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States
| | - John J Lee
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Daniel S Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Eric C Leuthardt
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, United States.,Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States.,Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, United States.,Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO, United States.,Center for Innovation in Neuroscience and Technology, Washington University School of Medicine, St. Louis, MO, United States.,Brain Laser Center, Washington University School of Medicine, St. Louis, MO, United States
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22
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Zhang B, Yan J, Chen W, Dong Y, Zhang L, Mo X, Chen Q, Cheng J, Liu X, Wang W, Zhang Z, Zhang S. Machine learning classifiers for predicting 3-year progression-free survival and overall survival in patients with gliomas after surgery. J Cancer 2021; 12:1604-1615. [PMID: 33613747 PMCID: PMC7890310 DOI: 10.7150/jca.52183] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 12/15/2020] [Indexed: 01/22/2023] Open
Abstract
Background: To develop machine-learning based models to predict the progression-free survival (PFS) and overall survival (OS) in patients with gliomas and explore the effect of different feature selection methods on the prediction. Methods: We included 505 patients (training cohort, n = 354; validation cohort, n = 151) with gliomas between January 1, 2011 and December 31, 2016. The clinical, neuroimaging, and molecular genetic data of patients were retrospectively collected. The multi-causes discovering with structure learning (McDSL) algorithm, least absolute shrinkage and selection operator regression (LASSO), and Cox proportional hazards regression model were employed to discover the predictors for 3-year PFS and OS, respectively. Eight machine learning classifiers with 5-fold cross-validation were developed to predict 3-year PFS and OS. The area under the curve (AUC) was used to evaluate the prognostic performance of classifiers. Results: McDSL identified four causal factors (tumor location, WHO grade, histologic type, and molecular genetic group) for 3-year PFS and OS, whereas LASSO and Cox identified wide-range number of factors associated with 3-year PFS and OS. The performance of each machine learning classifier based on McDSL, LASSO, and Cox was not significantly different. Logistic regression yielded the optimal performance in predicting 3-year PFS based on the McDSL (AUC, 0.872, 95% confidence interval [CI]: 0.828-0.916) and 3-year OS based on the LASSO (AUC, 0.901, 95% CI: 0.861-0.940). Conclusions: McDSL is more reproducible than LASSO and Cox model in the feature selection process. Logistic regression model may have the highest performance in predicting 3-year PFS and OS of gliomas.
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Affiliation(s)
- Bin Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Jing Yan
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Weiqi Chen
- Big Data Decision Institute, Jinan University, Guangzhou, Guangdong, China.,School of management, Jinan University. Department of Catheterization Lab, Guangdong Cardiovascular Institute, Guangdong, Provincial Key Laboratory of South China
| | - Yuhao Dong
- Structural Heart Disease, Guangdong Provincial; People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, P.R. China
| | - Lu Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Xiaokai Mo
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Qiuying Chen
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xianzhi Liu
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Weiwei Wang
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhenyu Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shuixing Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
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23
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Qiu X, Gao J, Yang J, Hu J, Hu W, Kong L, Lu JJ. A Comparison Study of Machine Learning (Random Survival Forest) and Classic Statistic (Cox Proportional Hazards) for Predicting Progression in High-Grade Glioma after Proton and Carbon Ion Radiotherapy. Front Oncol 2020; 10:551420. [PMID: 33194609 PMCID: PMC7662123 DOI: 10.3389/fonc.2020.551420] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 09/29/2020] [Indexed: 12/30/2022] Open
Abstract
Background Machine learning (ML) algorithms are increasingly explored in glioma prognostication. Random survival forest (RSF) is a common ML approach in analyzing time-to-event survival data. However, it is controversial which method between RSF and traditional cornerstone method Cox proportional hazards (CPH) is better fitted. The purpose of this study was to compare RSF and CPH in predicting tumor progression of high-grade glioma (HGG) after particle beam radiotherapy (PBRT). Methods The study enrolled 82 consecutive HGG patients who were treated with PBRT at Shanghai Proton and Heavy Ion Center between 6/2015 and 11/2019. The entire cohort was split into the training and testing set in an 80/20 ratio. Ten variables from patient-related, tumor-related and treatment-related information were utilized for developing CPH and RSF for predicting progression-free survival (PFS). The model performance was compared in concordance index (C-index) for discrimination (accuracy), brier score (BS) for calibration (precision) and variable importance for interpretability. Results The CPH model demonstrated a better performance in terms of integrated C-index (62.9%) and BS (0.159) compared to RSF model (C-index = 61.1%, BS = 0.174). In the context of variable importance, CPH model indicated that age (P = 0.024), WHO grade (P = 0.020), IDH gene (P = 0.019), and MGMT promoter status (P = 0.040) were significantly correlated with PFS in the univariate analysis; multivariate analysis showed that age (P = 0.041), surgical completeness (P = 0.084), IDH gene (P = 0.057), and MGMT promoter (P = 0.092) had a significant or trend toward the relation with PFS. RSF showed that merely IDH and age were of positive importance for predicting PFS. A final nomogram was developed to predict tumor progression at the individual level based on CPH model. Conclusions In a relatively small dataset with HGG patients treated with PBRT, CPH outperformed RSF for predicting tumor progression. A comprehensive criterion with accuracy, precision, and interpretability is recommended in evaluating ML prognostication approaches for clinical deployment.
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Affiliation(s)
- Xianxin Qiu
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China.,Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Shanghai, China
| | - Jing Gao
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China.,Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Shanghai, China
| | - Jing Yang
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China.,Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Shanghai, China
| | - Jiyi Hu
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China.,Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Shanghai, China
| | - Weixu Hu
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China.,Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Shanghai, China
| | - Lin Kong
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China.,Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Center, Shanghai, China
| | - Jiade J Lu
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China.,Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Shanghai, China
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24
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Yuan T, Ying J, Zuo Z, Jin L, Gui S, Gao Z, Li G, Wang R, Zhang Y, Li C. Contrahemispheric Cortex Predicts Survival and Molecular Markers in Patients With Unilateral High-Grade Gliomas. Front Oncol 2020; 10:953. [PMID: 32793462 PMCID: PMC7390929 DOI: 10.3389/fonc.2020.00953] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 05/15/2020] [Indexed: 11/13/2022] Open
Abstract
Background: Malignant high-grade gliomas are characterized by infiltration and destruction of surrounding brain tissue. Alterations in the contrahemispheric brain structure and their roles that may offer prognostically valuable information have not been investigated in high-grade gliomas. Methods: In total, 153 patients with unilateral glioma (low-grade, n = 77; high-grade, n = 76) and 115 healthy controls (HCs) were recruited and scanned with 3-D T1 imaging. The gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) volume in the contrahemisphere were examined. Partial correlation, logistic regression, and multivariate Cox's regression analyses were performed. Results: The contrahemispheric GM volume (CHGMV) in the high-grade glioma patients was significantly decreased compared to that in the HCs/low-grade gliomas (one-way ANOVA, Bonferroni corrected, p < 0.05). The CHGMV is significantly correlated with the WHO grade (r = -0.22, p < 0.05) and contrast-enhanced volume (r = -0.33, p < 0.01). In the high-grade gliomas, the binary logistic regression revealed that the CHGMV can independently predict isocitrate dehydrogenase 1 (IDH1) and P53 mutations. The survival curves revealed that the patients with a low CHGMV had a shorter overall survival (OS) than the patients with a high CHGMV (p = 0.001). The multivariate Cox's regression analysis showed that a low CHGMV can independently predict unfavorable OS with a hazard ratio of 2.883 (p = 0.035). Conclusions: Volume of the contrahemispheric cortex can be potentially used in clinical practice as an imaging biomarker to predict survival and molecular markers in patients with unilateral high-grade gliomas.
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Affiliation(s)
- Taoyang Yuan
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Jianyou Ying
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Zhentao Zuo
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Lu Jin
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Songbai Gui
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhixian Gao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Guilin Li
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Rui Wang
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yazhuo Zhang
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Beijing Institute for Brain Disorders Brain Tumor Center, Beijing, China
| | - Chuzhong Li
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Institute for Brain Disorders Brain Tumor Center, Beijing, China
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25
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Zhou Z, Chen X, Zhang Y, Hu D, Qiao L, Yu R, Yap P, Pan G, Zhang H, Shen D. A toolbox for brain network construction and classification (BrainNetClass). Hum Brain Mapp 2020; 41:2808-2826. [PMID: 32163221 PMCID: PMC7294070 DOI: 10.1002/hbm.24979] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 02/09/2020] [Accepted: 02/25/2020] [Indexed: 12/12/2022] Open
Abstract
Brain functional network has been increasingly used in understanding brain functions and diseases. While many network construction methods have been proposed, the progress in the field still largely relies on static pairwise Pearson's correlation-based functional network and group-level comparisons. We introduce a "Brain Network Construction and Classification (BrainNetClass)" toolbox to promote more advanced brain network construction methods to the filed, including some state-of-the-art methods that were recently developed to capture complex and high-order interactions among brain regions. The toolbox also integrates a well-accepted and rigorous classification framework based on brain connectome features toward individualized disease diagnosis in a hope that the advanced network modeling could boost the subsequent classification. BrainNetClass is a MATLAB-based, open-source, cross-platform toolbox with both graphical user-friendly interfaces and a command line mode targeting cognitive neuroscientists and clinicians for promoting reliability, reproducibility, and interpretability of connectome-based, computer-aided diagnosis. It generates abundant classification-related results from network presentations to contributing features that have been largely ignored by most studies to grant users the ability of evaluating the disease diagnostic model and its robustness and generalizability. We demonstrate the effectiveness of the toolbox on real resting-state functional MRI datasets. BrainNetClass (v1.0) is available at https://github.com/zzstefan/BrainNetClass.
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Affiliation(s)
- Zhen Zhou
- College of Computer Science and TechnologyZhejiang UniversityHangzhouChina
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Xiaobo Chen
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
- Automotive Engineering Research InstituteJiangsu UniversityZhenjiangChina
| | - Yu Zhang
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
- Department of Psychiatry and Behavior SciencesStanford UniversityStanfordCaliforniaUSA
| | - Dan Hu
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Lishan Qiao
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
- School of Mathematics ScienceLiaocheng UniversityLiaochengChina
| | - Renping Yu
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
- School of Electric EngineeringZhengzhou UniversityZhengzhouChina
| | - Pew‐Thian Yap
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Gang Pan
- College of Computer Science and TechnologyZhejiang UniversityHangzhouChina
| | - Han Zhang
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Dinggang Shen
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
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26
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Tang Z, Xu Y, Jin L, Aibaidula A, Lu J, Jiao Z, Wu J, Zhang H, Shen D. Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2100-2109. [PMID: 31905135 PMCID: PMC7289674 DOI: 10.1109/tmi.2020.2964310] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patients is highly desired. Recently, many machine learning-based methods have been adopted to predict overall survival (OS) time based on the pre-operative mono- or multi-modal imaging phenotype. The genotypic information of GBM has been proven to be strongly indicative of the prognosis; however, this has not been considered in the existing imaging-based OS prediction methods. The main reason is that the tumor genotype is unavailable pre-operatively unless deriving from craniotomy. In this paper, we propose a new deep learning-based OS prediction method for GBM patients, which can derive tumor genotype-related features from pre-operative multimodal magnetic resonance imaging (MRI) brain data and feed them to OS prediction. Specifically, we propose a multi-task convolutional neural network (CNN) to accomplish both tumor genotype and OS prediction tasks jointly. As the network can benefit from learning tumor genotype-related features for genotype prediction, the accuracy of predicting OS time can be prominently improved. In the experiments, multimodal MRI brain dataset of 120 GBM patients, with as many as four different genotypic/molecular biomarkers, are used to evaluate our method. Our method achieves the highest OS prediction accuracy compared to other state-of-the-art methods.
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27
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Sun L, Zhang S, Chen H, Luo L. Brain Tumor Segmentation and Survival Prediction Using Multimodal MRI Scans With Deep Learning. Front Neurosci 2019; 13:810. [PMID: 31474816 PMCID: PMC6707136 DOI: 10.3389/fnins.2019.00810] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 07/22/2019] [Indexed: 11/13/2022] Open
Abstract
Gliomas are the most common primary brain malignancies. Accurate and robust tumor segmentation and prediction of patients' overall survival are important for diagnosis, treatment planning and risk factor identification. Here we present a deep learning-based framework for brain tumor segmentation and survival prediction in glioma, using multimodal MRI scans. For tumor segmentation, we use ensembles of three different 3D CNN architectures for robust performance through a majority rule. This approach can effectively reduce model bias and boost performance. For survival prediction, we extract 4,524 radiomic features from segmented tumor regions, then, a decision tree and cross validation are used to select potent features. Finally, a random forest model is trained to predict the overall survival of patients. The 2018 MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS), ranks our method at 2nd and 5th place out of 60+ participating teams for survival prediction tasks and segmentation tasks respectively, achieving a promising 61.0% accuracy on the classification of short-survivors, mid-survivors and long-survivors.
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Affiliation(s)
- Li Sun
- School of Innovation and Entrepreneurship, Southern University of Science and Technology, Shenzhen, China
| | - Songtao Zhang
- School of Innovation and Entrepreneurship, Southern University of Science and Technology, Shenzhen, China
| | - Hang Chen
- School of Innovation and Entrepreneurship, Southern University of Science and Technology, Shenzhen, China
| | - Lin Luo
- School of Innovation and Entrepreneurship, Southern University of Science and Technology, Shenzhen, China.,College of Engineering, Peking University, Beijing, China
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