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Lee MD, Jain R. Harnessing generative AI for glioma diagnosis: A step forward in neuro-oncologic imaging. Neuro Oncol 2024; 26:1136-1137. [PMID: 38442275 PMCID: PMC11145459 DOI: 10.1093/neuonc/noae043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Indexed: 03/07/2024] Open
Affiliation(s)
- Matthew D Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, New York, USA
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, New York, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, New York, USA
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Moon HH, Jeong J, Park JE, Kim N, Choi C, Kim Y, Song SW, Hong CK, Kim JH, Kim HS. Generative AI in glioma: Ensuring diversity in training image phenotypes to improve diagnostic performance for IDH mutation prediction. Neuro Oncol 2024; 26:1124-1135. [PMID: 38253989 PMCID: PMC11145451 DOI: 10.1093/neuonc/noae012] [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: 10/09/2023] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND This study evaluated whether generative artificial intelligence (AI)-based augmentation (GAA) can provide diverse and realistic imaging phenotypes and improve deep learning-based classification of isocitrate dehydrogenase (IDH) type in glioma compared with neuroradiologists. METHODS For model development, 565 patients (346 IDH-wildtype, 219 IDH-mutant) with paired contrast-enhanced T1 and FLAIR MRI scans were collected from tertiary hospitals and The Cancer Imaging Archive. Performance was tested on internal (119, 78 IDH-wildtype, 41 IDH-mutant [IDH1 and 2]) and external test sets (108, 72 IDH-wildtype, 36 IDH-mutant). GAA was developed using a score-based diffusion model and ResNet50 classifier. The optimal GAA was selected in comparison with the null model. Two neuroradiologists (R1, R2) assessed realism, diversity of imaging phenotypes, and predicted IDH mutation. The performance of a classifier trained with optimal GAA was compared with that of neuroradiologists using the area under the receiver operating characteristics curve (AUC). The effect of tumor size and contrast enhancement on GAA performance was tested. RESULTS Generated images demonstrated realism (Turing's test: 47.5-50.5%) and diversity indicating IDH type. Optimal GAA was achieved with augmentation with 110 000 generated slices (AUC: 0.938). The classifier trained with optimal GAA demonstrated significantly higher AUC values than neuroradiologists in both the internal (R1, P = .003; R2, P < .001) and external test sets (R1, P < .01; R2, P < .001). GAA with large-sized tumors or predominant enhancement showed comparable performance to optimal GAA (internal test: AUC 0.956 and 0.922; external test: 0.810 and 0.749). CONCLUSIONS The application of generative AI with realistic and diverse images provided better diagnostic performance than neuroradiologists for predicting IDH type in glioma.
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Affiliation(s)
- Hye Hyeon Moon
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jiheon Jeong
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science of Technology, Asan Medical Center, College of Medicine, University of Ulsan, Seoul, Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Namkug Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Changyong Choi
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science of Technology, Asan Medical Center, College of Medicine, University of Ulsan, Seoul, Korea
| | - Young‑Hoon Kim
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Sang Woo Song
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Chang-Ki Hong
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jeong Hoon Kim
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
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Zhang J, Wang Y, Yang Y, Han Y, Yu Y, Hu Y, Liang S, Sun Q, Shang D, Bi J, Cui G, Yan L. Noninvasive Isocitrate Dehydrogenase 1 Status Prediction in Grade II/III Glioma Based on Magnetic Resonance Images: A Transfer Learning Strategy. J Comput Assist Tomogr 2024; 48:449-458. [PMID: 38271541 DOI: 10.1097/rct.0000000000001575] [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: 01/27/2024]
Abstract
OBJECTIVE The aim of this study was to evaluate transfer learning combined with various convolutional neural networks (TL-CNNs) in predicting isocitrate dehydrogenase 1 ( IDH1 ) status of grade II/III gliomas. METHODS Grade II/III glioma patients diagnosed at the Tangdu Hospital (August 2009 to May 2017) were retrospectively enrolled, including 54 patients with IDH1 mutant and 56 patients with wild-type IDH1 . Convolutional neural networks, AlexNet, GoogLeNet, ResNet, and VGGNet were fine-tuned with T2-weighted imaging (T2WI), fluid attenuation inversion recovery (FLAIR), and contrast-enhanced T1-weighted imaging (T1CE) images. The single-modal networks were integrated with averaged sigmoid probabilities, logistic regression, and support vector machine. FLAIR-T1CE-fusion (FC-fusion), T2WI-T1CE-fusion (TC-fusion), and FLAIR-T2WI-T1CE-fusion (FTC-fusion) were used for fine-tuning TL-CNNs. RESULTS IDH1 -mutant prediction accuracies using AlexNet, GoogLeNet, ResNet, and VGGNet achieved 70.0% (AUC = 0.660), 65.0% (AUC = 0.600), 70.0% (AUC = 0.700), and 80.0% (AUC = 0.730) for T2WI images, 70.0% (AUC = 0.660), 70.0% (AUC = 0.620), 70.0% (AUC = 0.710), and 80.0% (AUC = 0.720) for FLAIR images, and 73.7% (AUC = 0.744), 73.7% (AUC = 0.656), 73.7% (AUC = 0.633), and 73.7% (AUC = 0.700) for T1CE images, respectively. The highest AUC (0.800) was achieved using VGGNet and FC-fusion images. CONCLUSIONS TL-CNNs (especially VGGNet) had a potential predictive value for IDH1 -mutant status of grade II/III gliomas.
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Affiliation(s)
- Jin Zhang
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
| | - Yuyao Wang
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
| | - Yang Yang
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
| | - Yu Han
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
| | - Ying Yu
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
| | - Yuchuan Hu
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
| | - Shouheng Liang
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
| | - Qian Sun
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
| | - Danting Shang
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
| | - Jiajun Bi
- College of Basic Medicine, the Fourth Military Medical University (Air Force Medical University), Xi'an, Shaanxi, China
| | - Guangbin Cui
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
| | - Linfeng Yan
- From the Department of Radiology and Functional and Molecular Imaging, Key Lab of Shaanxi Province, Tangdu Hospital
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Chen M, Zhang M, Yin L, Ma L, Ding R, Zheng T, Yue Q, Lui S, Sun H. Medical image foundation models in assisting diagnosis of brain tumors: a pilot study. Eur Radiol 2024:10.1007/s00330-024-10728-1. [PMID: 38627290 DOI: 10.1007/s00330-024-10728-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 02/08/2024] [Accepted: 03/04/2024] [Indexed: 04/23/2024]
Abstract
OBJECTIVES To build self-supervised foundation models for multicontrast MRI of the whole brain and evaluate their efficacy in assisting diagnosis of brain tumors. METHODS In this retrospective study, foundation models were developed using 57,621 enhanced head MRI scans through self-supervised learning with a pretext task of cross-contrast context restoration with two different content dropout schemes. Downstream classifiers were constructed based on the pretrained foundation models and fine-tuned for brain tumor detection, discrimination, and molecular status prediction. Metrics including accuracy, sensitivity, specificity, and area under the ROC curve (AUC) were used to evaluate the performance. Convolutional neural networks trained exclusively on downstream task data were employed for comparative analysis. RESULTS The pretrained foundation models demonstrated their ability to extract effective representations from multicontrast whole-brain volumes. The best classifiers, endowed with pretrained weights, showed remarkable performance with accuracies of 94.9, 92.3, and 80.4%, and corresponding AUC values of 0.981, 0.972, and 0.852 on independent test datasets in brain tumor detection, discrimination, and molecular status prediction, respectively. The classifiers with pretrained weights outperformed the convolutional classifiers trained from scratch by approximately 10% in terms of accuracy and AUC across all tasks. The saliency regions in the correctly predicted cases are mainly clustered around the tumors. Classifiers derived from the two dropout schemes differed significantly only in the detection of brain tumors. CONCLUSIONS Foundation models obtained from self-supervised learning have demonstrated encouraging potential for scalability and interpretability in downstream brain tumor-related tasks and hold promise for extension to neurological diseases with diffusely distributed lesions. CLINICAL RELEVANCE STATEMENT The application of our proposed method to the prediction of key molecular status in gliomas is expected to improve treatment planning and patient outcomes. Additionally, the foundation model we developed could serve as a cornerstone for advancing AI applications in the diagnosis of brain-related diseases.
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Affiliation(s)
- Mengyao Chen
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | | | - Lijuan Yin
- Department of Pathology, West China Hospital of Sichuan University, Chengdu, China
| | - Lu Ma
- Department of Neurosurgery, West China Hospital of Sichuan University, Chengdu, China
| | - Renxing Ding
- IT center, West China Hospital of Sichuan University, Chengdu, China
| | - Tao Zheng
- IT center, West China Hospital of Sichuan University, Chengdu, China
| | - Qiang Yue
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Su Lui
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Huaiqiang Sun
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China.
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Ji X, Cheng J, Su J, Wen R, Zhang Q, Liu G, Peng Y, Mao J. PTPN7 mediates macrophage-polarization and determines immunotherapy in gliomas: A single-cell sequencing analysis. ENVIRONMENTAL TOXICOLOGY 2024. [PMID: 38581214 DOI: 10.1002/tox.24259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Revised: 03/09/2024] [Accepted: 03/23/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Protein tyrosine phosphatase non-receptor type 7 (PTPN7) is a signaling molecule that regulates a multitude of cellular processes, spanning cell proliferation, cellular differentiation, the mitotic cycle, and oncogenic metamorphosis. However, the characteristic of PTPN7 in the glioma microenvironment has yet to be elucidated. METHODS The prognostic value, genomic features, immune characteristics, chemotherapy prediction, and immunotherapy prediction of PTPN7 were systematically explored at the bulk sequencing level. The cell evolution trajectory, cell communication pattern, and cell metabolic activity related to PTPN7 were systematically explored at the single-cell sequencing level. HMC3 and M0 cells were cocultured with U251 and T98G cells, and flow cytometry was carried out to investigate the polarization of HMC3 and M0. Transwell assay and CCK-8 assay were performed to explore the migration and proliferation activity of U251 and T98G. RESULTS The expression level of PTPN7 is significantly elevated in glioma and indicates malignant features. PTPN7 expression predicts worse prognosis of glioma patients. PTPN7 is associated with genome alteration and immune infiltration. Besides, PTPN7 plays a crucial role in modulating metabolic and immunogenic processes, particularly by influencing the activity of microglia and macrophages through multiple signaling pathways involved in cellular communication. Specifically, PTPN7 actively mediates inflammation-resolving-polarization of macrophages and microglia and protects glioma from immune attack. PTPN7 could also predict the response of immunotherapy. CONCLUSIONS PTPN7 is critically involved in inflammation-resolving-polarization mediated by macrophage and microglia and promotes the immune escape of glioma cells.
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Affiliation(s)
- Xiang Ji
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Jingsong Cheng
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Jing Su
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Rong Wen
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Qi Zhang
- Department of Neurosurgery, Tongnan Hospital of TCM, Chongqing, China
| | - Guodong Liu
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Yun Peng
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Jinning Mao
- Health Management Center, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
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Zhang W, Zhang L, Dong H, Peng H. TGIF2 is a potential biomarker for diagnosis and prognosis of glioma. Front Immunol 2024; 15:1356833. [PMID: 38629068 PMCID: PMC11020094 DOI: 10.3389/fimmu.2024.1356833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 02/09/2024] [Indexed: 04/19/2024] Open
Abstract
Background TGFB-induced factor homeobox 2 (TGIF2), a member of the Three-Amino-acid-Loop-Extension (TALE) superfamily, has been implicated in various malignant tumors. However, its prognostic significance in glioma, impact on tumor immune infiltration, and underlying mechanisms in glioma development remain elusive. Methods The expression of TGIF2 in various human normal tissues, normal brain tissues, and gliomas was investigated using HPA, TCGA, GTEx, and GEO databases. The study employed several approaches, including Kaplan-Meier analysis, ROC analysis, logistic regression, Cox regression, GO analysis, KEGG analysis, and GSEA, to explore the relationship between TGIF2 expression and clinicopathologic features, prognostic value, and potential biological functions in glioma patients. The impact of TGIF2 on tumor immune infiltration was assessed through Estimate, ssGSEA, and Spearman analysis. Genes coexpressed with TGIF2 were identified, and the protein-protein interaction (PPI) network of these coexpressed genes were constructed using the STRING database and Cytoscape software. Hub genes were identified using CytoHubba plugin, and their clinical predictive value was explored. Furthermore, in vitro experiments were performed by knocking down and knocking out TGIF2 using siRNA and CRISPR/Cas9 gene editing, and the role of TGIF2 in glioma cell invasion and migration was analyzed using transwell assay, scratch wound-healing assay, RT-qPCR, and Western blot. Results TGIF2 mRNA was found to be upregulated in 21 cancers, including glioma. High expression of TGIF2 was associated with malignant phenotypes and poor prognosis in glioma patients, indicating its potential as an independent prognostic factor. Furthermore, elevated TGIF2 expression positively correlated with cell cycle regulation, DNA synthesis and repair, extracellular matrix (ECM) components, immune response, and several signaling pathways that promote tumor progression. TGIF2 showed correlations with Th2 cells, macrophages, and various immunoregulatory genes. The hub genes coexpressed with TGIF2 demonstrated significant predictive value. Additionally, in vitro experiments revealed that knockdown and knockout of TGIF2 inhibited glioma cell invasion, migration and suppressed the epithelial-mesenchymal transition (EMT) phenotype. Conclusion TGIF2 emerges as a potential biomarker for glioma, possibly linked to tumor immune infiltration and EMT.
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Affiliation(s)
- Wan Zhang
- Health Science Center of Xi’an Jiaotong University, Xi’an, Shaanxi, China
- Bone and Joints Research Center, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Long Zhang
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Huanhuan Dong
- Department of Thoracic Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Hang Peng
- Health Science Center of Xi’an Jiaotong University, Xi’an, Shaanxi, China
- Second Department of General Surgery, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, China
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Wei R, Lu S, Lai S, Liang F, Zhang W, Jiang X, Zhen X, Yang R. A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI. J Cancer Res Clin Oncol 2024; 150:73. [PMID: 38305926 PMCID: PMC10837235 DOI: 10.1007/s00432-023-05603-3] [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: 09/17/2023] [Accepted: 12/26/2023] [Indexed: 02/03/2024]
Abstract
PURPOSE To explore a subregion-based RadioFusionOmics (RFO) model for discrimination between adult-type grade 4 astrocytoma and glioblastoma according to the 2021 WHO CNS5 classification. METHODS 329 patients (40 grade 4 astrocytomas and 289 glioblastomas) with histologic diagnosis was retrospectively collected from our local institution and The Cancer Imaging Archive (TCIA). The volumes of interests (VOIs) were obtained from four multiparametric MRI sequences (T1WI, T1WI + C, T2WI, T2-FLAIR) using (1) manual segmentation of the non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE), and (2) K-means clustering of four habitats (H1: high T1WI + C, high T2-FLAIR; (2) H2: high T1WI + C, low T2-FLAIR; (3) H3: low T1WI + C, high T2-FLAIR; and (4) H4: low T1WI + C, low T2-FLAIR). The optimal VOI and best MRI sequence combination were determined. The performance of the RFO model was evaluated using the area under the precision-recall curve (AUPRC) and the best signatures were identified. RESULTS The two best VOIs were manual VOI3 (putative peritumoral edema) and clustering H34 (low T1WI + C, high T2-FLAIR (H3) combined with low T1WI + C and low T2-FLAIR (H4)). Features fused from four MRI sequences ([Formula: see text]) outperformed those from either a single sequence or other sequence combinations. The RFO model that was trained using fused features [Formula: see text] achieved the AUPRC of 0.972 (VOI3) and 0.976 (H34) in the primary cohort (p = 0.905), and 0.971 (VOI3) and 0.974 (H34) in the testing cohort (p = 0.402). CONCLUSION The performance of subregions defined by clustering was comparable to that of subregions that were manually defined. Fusion of features from the edematous subregions of multiple MRI sequences by the RFO model resulted in differentiation between grade 4 astrocytoma and glioblastoma.
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Affiliation(s)
- Ruili Wei
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, GuangZhou, China
| | - Songlin Lu
- School of Biomedical Engineering, Southern Medical University, GuangZhou, China
| | - Shengsheng Lai
- School of Medical Equipment, Guangdong Food and Drug Vocational College, Guangzhou, China
| | - Fangrong Liang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, GuangZhou, China
| | - Wanli Zhang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, GuangZhou, China
| | - Xinqing Jiang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, GuangZhou, China
| | - Xin Zhen
- School of Biomedical Engineering, Southern Medical University, GuangZhou, China.
| | - Ruimeng Yang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, GuangZhou, China.
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Wamelink IJHG, Azizova A, Booth TC, Mutsaerts HJMM, Ogunleye A, Mankad K, Petr J, Barkhof F, Keil VC. Brain Tumor Imaging without Gadolinium-based Contrast Agents: Feasible or Fantasy? Radiology 2024; 310:e230793. [PMID: 38319162 PMCID: PMC10902600 DOI: 10.1148/radiol.230793] [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: 03/29/2023] [Revised: 08/07/2023] [Accepted: 08/14/2023] [Indexed: 02/07/2024]
Abstract
Gadolinium-based contrast agents (GBCAs) form the cornerstone of current primary brain tumor MRI protocols at all stages of the patient journey. Though an imperfect measure of tumor grade, GBCAs are repeatedly used for diagnosis and monitoring. In practice, however, radiologists will encounter situations where GBCA injection is not needed or of doubtful benefit. Reducing GBCA administration could improve the patient burden of (repeated) imaging (especially in vulnerable patient groups, such as children), minimize risks of putative side effects, and benefit costs, logistics, and the environmental footprint. On the basis of the current literature, imaging strategies to reduce GBCA exposure for pediatric and adult patients with primary brain tumors will be reviewed. Early postoperative MRI and fixed-interval imaging of gliomas are examples of GBCA exposure with uncertain survival benefits. Half-dose GBCAs for gliomas and T2-weighted imaging alone for meningiomas are among options to reduce GBCA use. While most imaging guidelines recommend using GBCAs at all stages of diagnosis and treatment, non-contrast-enhanced sequences, such as the arterial spin labeling, have shown a great potential. Artificial intelligence methods to generate synthetic postcontrast images from decreased-dose or non-GBCA scans have shown promise to replace GBCA-dependent approaches. This review is focused on pediatric and adult gliomas and meningiomas. Special attention is paid to the quality and real-life applicability of the reviewed literature.
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Affiliation(s)
- Ivar J. H. G. Wamelink
- From the Department of Radiology and Nuclear Medicine, Amsterdam
University Medical Center, VUMC Site, De Boelelaan 1117, Amsterdam 1081 HV, the
Netherlands (I.J.H.G.W., A.A., H.J.M.M.M., J.P., F.B., V.C.K.); Department of
Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, the Netherlands
(I.J.H.G.W., A.A., H.J.M.M.M., V.C.K.); School of Biomedical Engineering and
Imaging Sciences, King’s College London, London, United Kingdom (T.C.B.);
Department of Neuroradiology, King’s College Hospital, NHS Foundation
Trust, London, UK (T.C.B.); Department of Brain Imaging, Amsterdam Neuroscience,
Amsterdam, the Netherlands (H.J.M.M.M., F.B., V.C.K.); Department of Radiology,
Lagos State University Teaching Hospital, Ikeja, Nigeria Radiology (A.O.);
Department of Radiology, Great Ormond Street Hospital for Children, NHS
Foundation Trust, London, United Kingdom (K.M.); Institute of
Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf,
Dresden, Germany (J.P.); and Queen Square Institute of Neurology and Centre for
Medical Image Computing, University College London, London, United Kingdom
(F.B.)
| | - Aynur Azizova
- From the Department of Radiology and Nuclear Medicine, Amsterdam
University Medical Center, VUMC Site, De Boelelaan 1117, Amsterdam 1081 HV, the
Netherlands (I.J.H.G.W., A.A., H.J.M.M.M., J.P., F.B., V.C.K.); Department of
Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, the Netherlands
(I.J.H.G.W., A.A., H.J.M.M.M., V.C.K.); School of Biomedical Engineering and
Imaging Sciences, King’s College London, London, United Kingdom (T.C.B.);
Department of Neuroradiology, King’s College Hospital, NHS Foundation
Trust, London, UK (T.C.B.); Department of Brain Imaging, Amsterdam Neuroscience,
Amsterdam, the Netherlands (H.J.M.M.M., F.B., V.C.K.); Department of Radiology,
Lagos State University Teaching Hospital, Ikeja, Nigeria Radiology (A.O.);
Department of Radiology, Great Ormond Street Hospital for Children, NHS
Foundation Trust, London, United Kingdom (K.M.); Institute of
Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf,
Dresden, Germany (J.P.); and Queen Square Institute of Neurology and Centre for
Medical Image Computing, University College London, London, United Kingdom
(F.B.)
| | - Thomas C. Booth
- From the Department of Radiology and Nuclear Medicine, Amsterdam
University Medical Center, VUMC Site, De Boelelaan 1117, Amsterdam 1081 HV, the
Netherlands (I.J.H.G.W., A.A., H.J.M.M.M., J.P., F.B., V.C.K.); Department of
Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, the Netherlands
(I.J.H.G.W., A.A., H.J.M.M.M., V.C.K.); School of Biomedical Engineering and
Imaging Sciences, King’s College London, London, United Kingdom (T.C.B.);
Department of Neuroradiology, King’s College Hospital, NHS Foundation
Trust, London, UK (T.C.B.); Department of Brain Imaging, Amsterdam Neuroscience,
Amsterdam, the Netherlands (H.J.M.M.M., F.B., V.C.K.); Department of Radiology,
Lagos State University Teaching Hospital, Ikeja, Nigeria Radiology (A.O.);
Department of Radiology, Great Ormond Street Hospital for Children, NHS
Foundation Trust, London, United Kingdom (K.M.); Institute of
Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf,
Dresden, Germany (J.P.); and Queen Square Institute of Neurology and Centre for
Medical Image Computing, University College London, London, United Kingdom
(F.B.)
| | - Henk J. M. M. Mutsaerts
- From the Department of Radiology and Nuclear Medicine, Amsterdam
University Medical Center, VUMC Site, De Boelelaan 1117, Amsterdam 1081 HV, the
Netherlands (I.J.H.G.W., A.A., H.J.M.M.M., J.P., F.B., V.C.K.); Department of
Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, the Netherlands
(I.J.H.G.W., A.A., H.J.M.M.M., V.C.K.); School of Biomedical Engineering and
Imaging Sciences, King’s College London, London, United Kingdom (T.C.B.);
Department of Neuroradiology, King’s College Hospital, NHS Foundation
Trust, London, UK (T.C.B.); Department of Brain Imaging, Amsterdam Neuroscience,
Amsterdam, the Netherlands (H.J.M.M.M., F.B., V.C.K.); Department of Radiology,
Lagos State University Teaching Hospital, Ikeja, Nigeria Radiology (A.O.);
Department of Radiology, Great Ormond Street Hospital for Children, NHS
Foundation Trust, London, United Kingdom (K.M.); Institute of
Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf,
Dresden, Germany (J.P.); and Queen Square Institute of Neurology and Centre for
Medical Image Computing, University College London, London, United Kingdom
(F.B.)
| | - Afolabi Ogunleye
- From the Department of Radiology and Nuclear Medicine, Amsterdam
University Medical Center, VUMC Site, De Boelelaan 1117, Amsterdam 1081 HV, the
Netherlands (I.J.H.G.W., A.A., H.J.M.M.M., J.P., F.B., V.C.K.); Department of
Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, the Netherlands
(I.J.H.G.W., A.A., H.J.M.M.M., V.C.K.); School of Biomedical Engineering and
Imaging Sciences, King’s College London, London, United Kingdom (T.C.B.);
Department of Neuroradiology, King’s College Hospital, NHS Foundation
Trust, London, UK (T.C.B.); Department of Brain Imaging, Amsterdam Neuroscience,
Amsterdam, the Netherlands (H.J.M.M.M., F.B., V.C.K.); Department of Radiology,
Lagos State University Teaching Hospital, Ikeja, Nigeria Radiology (A.O.);
Department of Radiology, Great Ormond Street Hospital for Children, NHS
Foundation Trust, London, United Kingdom (K.M.); Institute of
Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf,
Dresden, Germany (J.P.); and Queen Square Institute of Neurology and Centre for
Medical Image Computing, University College London, London, United Kingdom
(F.B.)
| | - Kshitij Mankad
- From the Department of Radiology and Nuclear Medicine, Amsterdam
University Medical Center, VUMC Site, De Boelelaan 1117, Amsterdam 1081 HV, the
Netherlands (I.J.H.G.W., A.A., H.J.M.M.M., J.P., F.B., V.C.K.); Department of
Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, the Netherlands
(I.J.H.G.W., A.A., H.J.M.M.M., V.C.K.); School of Biomedical Engineering and
Imaging Sciences, King’s College London, London, United Kingdom (T.C.B.);
Department of Neuroradiology, King’s College Hospital, NHS Foundation
Trust, London, UK (T.C.B.); Department of Brain Imaging, Amsterdam Neuroscience,
Amsterdam, the Netherlands (H.J.M.M.M., F.B., V.C.K.); Department of Radiology,
Lagos State University Teaching Hospital, Ikeja, Nigeria Radiology (A.O.);
Department of Radiology, Great Ormond Street Hospital for Children, NHS
Foundation Trust, London, United Kingdom (K.M.); Institute of
Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf,
Dresden, Germany (J.P.); and Queen Square Institute of Neurology and Centre for
Medical Image Computing, University College London, London, United Kingdom
(F.B.)
| | - Jan Petr
- From the Department of Radiology and Nuclear Medicine, Amsterdam
University Medical Center, VUMC Site, De Boelelaan 1117, Amsterdam 1081 HV, the
Netherlands (I.J.H.G.W., A.A., H.J.M.M.M., J.P., F.B., V.C.K.); Department of
Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, the Netherlands
(I.J.H.G.W., A.A., H.J.M.M.M., V.C.K.); School of Biomedical Engineering and
Imaging Sciences, King’s College London, London, United Kingdom (T.C.B.);
Department of Neuroradiology, King’s College Hospital, NHS Foundation
Trust, London, UK (T.C.B.); Department of Brain Imaging, Amsterdam Neuroscience,
Amsterdam, the Netherlands (H.J.M.M.M., F.B., V.C.K.); Department of Radiology,
Lagos State University Teaching Hospital, Ikeja, Nigeria Radiology (A.O.);
Department of Radiology, Great Ormond Street Hospital for Children, NHS
Foundation Trust, London, United Kingdom (K.M.); Institute of
Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf,
Dresden, Germany (J.P.); and Queen Square Institute of Neurology and Centre for
Medical Image Computing, University College London, London, United Kingdom
(F.B.)
| | - Frederik Barkhof
- From the Department of Radiology and Nuclear Medicine, Amsterdam
University Medical Center, VUMC Site, De Boelelaan 1117, Amsterdam 1081 HV, the
Netherlands (I.J.H.G.W., A.A., H.J.M.M.M., J.P., F.B., V.C.K.); Department of
Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, the Netherlands
(I.J.H.G.W., A.A., H.J.M.M.M., V.C.K.); School of Biomedical Engineering and
Imaging Sciences, King’s College London, London, United Kingdom (T.C.B.);
Department of Neuroradiology, King’s College Hospital, NHS Foundation
Trust, London, UK (T.C.B.); Department of Brain Imaging, Amsterdam Neuroscience,
Amsterdam, the Netherlands (H.J.M.M.M., F.B., V.C.K.); Department of Radiology,
Lagos State University Teaching Hospital, Ikeja, Nigeria Radiology (A.O.);
Department of Radiology, Great Ormond Street Hospital for Children, NHS
Foundation Trust, London, United Kingdom (K.M.); Institute of
Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf,
Dresden, Germany (J.P.); and Queen Square Institute of Neurology and Centre for
Medical Image Computing, University College London, London, United Kingdom
(F.B.)
| | - Vera C. Keil
- From the Department of Radiology and Nuclear Medicine, Amsterdam
University Medical Center, VUMC Site, De Boelelaan 1117, Amsterdam 1081 HV, the
Netherlands (I.J.H.G.W., A.A., H.J.M.M.M., J.P., F.B., V.C.K.); Department of
Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, the Netherlands
(I.J.H.G.W., A.A., H.J.M.M.M., V.C.K.); School of Biomedical Engineering and
Imaging Sciences, King’s College London, London, United Kingdom (T.C.B.);
Department of Neuroradiology, King’s College Hospital, NHS Foundation
Trust, London, UK (T.C.B.); Department of Brain Imaging, Amsterdam Neuroscience,
Amsterdam, the Netherlands (H.J.M.M.M., F.B., V.C.K.); Department of Radiology,
Lagos State University Teaching Hospital, Ikeja, Nigeria Radiology (A.O.);
Department of Radiology, Great Ormond Street Hospital for Children, NHS
Foundation Trust, London, United Kingdom (K.M.); Institute of
Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf,
Dresden, Germany (J.P.); and Queen Square Institute of Neurology and Centre for
Medical Image Computing, University College London, London, United Kingdom
(F.B.)
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9
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Cheng D, Zhuo Z, Du J, Weng J, Zhang C, Duan Y, Sun T, Wu M, Guo M, Hua T, Jin Y, Peng B, Li Z, Zhu M, Imami M, Bettegowda C, Sair H, Bai HX, Barkhof F, Liu X, Liu Y. A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images. Clin Cancer Res 2024; 30:150-158. [PMID: 37916978 DOI: 10.1158/1078-0432.ccr-23-1461] [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: 05/18/2023] [Revised: 09/11/2023] [Accepted: 10/31/2023] [Indexed: 11/03/2023]
Abstract
PURPOSE We aimed to develop and validate a deep learning (DL) model to automatically segment posterior fossa ependymoma (PF-EPN) and predict its molecular subtypes [Group A (PFA) and Group B (PFB)] from preoperative MR images. EXPERIMENTAL DESIGN We retrospectively identified 227 PF-EPNs (development and internal test sets) with available preoperative T2-weighted (T2w) MR images and molecular status to develop and test a 3D nnU-Net (referred to as T2-nnU-Net) for tumor segmentation and molecular subtype prediction. The network was externally tested using an external independent set [n = 40; subset-1 (n = 31) and subset-2 (n =9)] and prospectively enrolled cases [prospective validation set (n = 27)]. The Dice similarity coefficient was used to evaluate the segmentation performance. Receiver operating characteristic analysis for molecular subtype prediction was performed. RESULTS For tumor segmentation, the T2-nnU-Net achieved a Dice score of 0.94 ± 0.02 in the internal test set. For molecular subtype prediction, the T2-nnU-Net achieved an AUC of 0.93 and accuracy of 0.89 in the internal test set, an AUC of 0.99 and accuracy of 0.93 in the external test set. In the prospective validation set, the model achieved an AUC of 0.93 and an accuracy of 0.89. The predictive performance of T2-nnU-Net was superior or comparable to that of demographic and multiple radiologic features (AUCs ranging from 0.87 to 0.95). CONCLUSIONS A fully automated DL model was developed and validated to accurately segment PF-EPNs and predict molecular subtypes using only T2w MR images, which could help in clinical decision-making.
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Affiliation(s)
- Dan Cheng
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Zhizheng Zhuo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Jiang Du
- Department of Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Jinyuan Weng
- Department of Medical Imaging Product, Neusoft, Group Ltd., Shenyang, P.R. China
| | - Chengzhou Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, Shandong, P.R. China
| | - Yunyun Duan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Ting Sun
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Minghao Wu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Min Guo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Tiantian Hua
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Ying Jin
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | - Boyang Peng
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
| | | | - Mingwang Zhu
- Department of Radiology, Sanbo Brain Hospital, Capital Medical University, Beijing, P.R. China
| | - Maliha Imami
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Chetan Bettegowda
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Haris Sair
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Harrison X Bai
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Frederik Barkhof
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, United Kingdom
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, the Netherlands
| | - Xing Liu
- Department of Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China
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10
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Herr J, Stoyanova R, Mellon EA. Convolutional Neural Networks for Glioma Segmentation and Prognosis: A Systematic Review. Crit Rev Oncog 2024; 29:33-65. [PMID: 38683153 DOI: 10.1615/critrevoncog.2023050852] [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: 05/01/2024]
Abstract
Deep learning (DL) is poised to redefine the way medical images are processed and analyzed. Convolutional neural networks (CNNs), a specific type of DL architecture, are exceptional for high-throughput processing, allowing for the effective extraction of relevant diagnostic patterns from large volumes of complex visual data. This technology has garnered substantial interest in the field of neuro-oncology as a promising tool to enhance medical imaging throughput and analysis. A multitude of methods harnessing MRI-based CNNs have been proposed for brain tumor segmentation, classification, and prognosis prediction. They are often applied to gliomas, the most common primary brain cancer, to classify subtypes with the goal of guiding therapy decisions. Additionally, the difficulty of repeating brain biopsies to evaluate treatment response in the setting of often confusing imaging findings provides a unique niche for CNNs to help distinguish the treatment response to gliomas. For example, glioblastoma, the most aggressive type of brain cancer, can grow due to poor treatment response, can appear to grow acutely due to treatment-related inflammation as the tumor dies (pseudo-progression), or falsely appear to be regrowing after treatment as a result of brain damage from radiation (radiation necrosis). CNNs are being applied to separate this diagnostic dilemma. This review provides a detailed synthesis of recent DL methods and applications for intratumor segmentation, glioma classification, and prognosis prediction. Furthermore, this review discusses the future direction of MRI-based CNN in the field of neuro-oncology and challenges in model interpretability, data availability, and computation efficiency.
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Affiliation(s)
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Miami, Fl 33136, USA
| | - Eric Albert Mellon
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Miami, Fl 33136, USA
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11
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Wu M, Luan J, Zhang D, Fan H, Qiao L, Zhang C. Development and validation of a clinical prediction model for glioma grade using machine learning. Technol Health Care 2024; 32:1977-1990. [PMID: 38306068 DOI: 10.3233/thc-231645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
BACKGROUND Histopathological evaluation is currently the gold standard for grading gliomas; however, this technique is invasive. OBJECTIVE This study aimed to develop and validate a diagnostic prediction model for glioma by employing multiple machine learning algorithms to identify risk factors associated with high-grade glioma, facilitating the prediction of glioma grading. METHODS Data from 1114 eligible glioma patients were obtained from The Cancer Genome Atlas (TCGA) database, which was divided into a training set (n= 781) and a test set (n= 333). Fifty machine learning algorithms were employed, and the optimal algorithm was selected to construct a prediction model. The performance of the machine learning prediction model was compared to the clinical prediction model in terms of discrimination, calibration, and clinical validity to assess the performance of the prediction model. RESULTS The area under the curve (AUC) values of the machine learning prediction models (training set: 0.870 vs. 0.740, test set: 0.863 vs. 0.718) were significantly improved from the clinical prediction models. Furthermore, significant improvement in discrimination was observed for the Integrated Discrimination Improvement (IDI) (training set: 0.230, test set: 0.270) and Net Reclassification Index (NRI) (training set: 0.170, test set: 0.170) from the clinical prognostic model. Both models showed a high goodness of fit and an increased net benefit. CONCLUSION A strong prediction accuracy model can be developed using machine learning algorithms to screen for high-grade glioma risk predictors, which can serve as a non-invasive prediction tool for preoperative diagnostic grading of glioma.
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Affiliation(s)
- Mingzhen Wu
- Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University and Shandong Academy of Medical Sciences, Shandong, China
- Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University and Shandong Academy of Medical Sciences, Shandong, China
| | - Jixin Luan
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University and Shandong Academy of Medical Sciences, Shandong, China
| | - Di Zhang
- Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University and Shandong Academy of Medical Sciences, Shandong, China
| | - Hua Fan
- Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University and Shandong Academy of Medical Sciences, Shandong, China
| | - Lishan Qiao
- School of Mathematics, Liaocheng University, Shandong, China
| | - Chuanchen Zhang
- Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University and Shandong Academy of Medical Sciences, Shandong, China
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12
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Di Ieva A. Fractals, Pattern Recognition, Memetics, and AI: A Personal Journal in the Computational Neurosurgery. ADVANCES IN NEUROBIOLOGY 2024; 36:273-283. [PMID: 38468038 DOI: 10.1007/978-3-031-47606-8_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
In this chapter, the personal journey of the author in many countries, including Italy, Germany, Austria, the United Kingdom, Switzerland, the United States, Canada, and Australia, is summarized, aimed to merge different translational fields (such as neurosurgery and the clinical neurosciences in general, biomedical engineering, mathematics, computer science, and cognitive sciences) and lay the foundations of a new field defined computational neurosurgery, with fractals, pattern recognition, memetics, and artificial intelligence as the common key words of the journey.
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Affiliation(s)
- Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab & Macquarie Neurosurgery, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia.
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13
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Guillevin R, Naudin M, Fayolle P, Giraud C, Le Guillou X, Thomas C, Herpe G, Miranville A, Fernandez-Maloigne C, Pellerin L, Guillevin C. Diagnostic and Therapeutic Issues in Glioma Using Imaging Data: The Challenge of Numerical Twinning. J Clin Med 2023; 12:7706. [PMID: 38137775 PMCID: PMC10744312 DOI: 10.3390/jcm12247706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 11/28/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023] Open
Abstract
Glial tumors represent the leading etiology of primary brain tumors. Their particularities lie in (i) their location in a highly functional organ that is difficult to access surgically, including for biopsy, and (ii) their rapid, anisotropic mode of extension, notably via the fiber bundles of the white matter, which further limits the possibilities of resection. The use of mathematical tools enables the development of numerical models representative of the oncotype, genotype, evolution, and therapeutic response of lesions. The significant development of digital technologies linked to high-resolution NMR exploration, coupled with the possibilities offered by AI, means that we can envisage the creation of digital twins of tumors and their host organs, thus reducing the use of physical sampling.
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Affiliation(s)
- Rémy Guillevin
- Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
| | - Mathieu Naudin
- Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
| | - Pierre Fayolle
- Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
| | - Clément Giraud
- Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
| | - Xavier Le Guillou
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
- Department of Genetic, University Hospital Center of Poitiers, 86000 Poitiers, France
| | - Clément Thomas
- Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
| | - Guillaume Herpe
- Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
| | - Alain Miranville
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
| | | | - Luc Pellerin
- IRMETIST Laboratory, INSERM U1313, University of Poitiers and University Hospital Center of Poitiers, 86000 Poitiers, France
| | - Carole Guillevin
- Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
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14
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Nakhate V, Gonzalez Castro LN. Artificial intelligence in neuro-oncology. Front Neurosci 2023; 17:1217629. [PMID: 38161802 PMCID: PMC10755952 DOI: 10.3389/fnins.2023.1217629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 11/14/2023] [Indexed: 01/03/2024] Open
Abstract
Artificial intelligence (AI) describes the application of computer algorithms to the solution of problems that have traditionally required human intelligence. Although formal work in AI has been slowly advancing for almost 70 years, developments in the last decade, and particularly in the last year, have led to an explosion of AI applications in multiple fields. Neuro-oncology has not escaped this trend. Given the expected integration of AI-based methods to neuro-oncology practice over the coming years, we set to provide an overview of existing technologies as they are applied to the neuropathology and neuroradiology of brain tumors. We highlight current benefits and limitations of these technologies and offer recommendations on how to appraise novel AI-tools as they undergo consideration for integration into clinical workflows.
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Affiliation(s)
- Vihang Nakhate
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - L. Nicolas Gonzalez Castro
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- The Center for Neuro-Oncology, Dana–Farber Cancer Institute, Boston, MA, United States
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15
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Pan I, Huang RY. Artificial intelligence in neuroimaging of brain tumors: reality or still promise? Curr Opin Neurol 2023; 36:549-556. [PMID: 37973024 DOI: 10.1097/wco.0000000000001213] [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: 11/19/2023]
Abstract
PURPOSE OF REVIEW To provide an updated overview of artificial intelligence (AI) applications in neuro-oncologic imaging and discuss current barriers to wider clinical adoption. RECENT FINDINGS A wide variety of AI applications in neuro-oncologic imaging have been developed and researched, spanning tasks from pretreatment brain tumor classification and segmentation, preoperative planning, radiogenomics, prognostication and survival prediction, posttreatment surveillance, and differentiating between pseudoprogression and true disease progression. While earlier studies were largely based on data from a single institution, more recent studies have demonstrated that the performance of these algorithms are also effective on external data from other institutions. Nevertheless, most of these algorithms have yet to see widespread clinical adoption, given the lack of prospective studies demonstrating their efficacy and the logistical difficulties involved in clinical implementation. SUMMARY While there has been significant progress in AI and neuro-oncologic imaging, clinical utility remains to be demonstrated. The next wave of progress in this area will be driven by prospective studies measuring outcomes relevant to clinical practice and go beyond retrospective studies which primarily aim to demonstrate high performance.
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Affiliation(s)
- Ian Pan
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School
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16
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Chen Q, Wang L, Xing Z, Wang L, Hu X, Wang R, Zhu YM. Deep wavelet scattering orthogonal fusion network for glioma IDH mutation status prediction. Comput Biol Med 2023; 166:107493. [PMID: 37774558 DOI: 10.1016/j.compbiomed.2023.107493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 06/26/2023] [Accepted: 09/15/2023] [Indexed: 10/01/2023]
Abstract
Accurately predicting the isocitrate dehydrogenase (IDH) mutation status of gliomas is greatly significant for formulating appropriate treatment plans and evaluating the prognoses of gliomas. Although existing studies can accurately predict the IDH mutation status of gliomas based on multimodal magnetic resonance (MR) images and machine learning methods, most of these methods cannot fully explore multimodal information and effectively predict IDH status for datasets acquired from multiple centers. To address this issue, a novel wavelet scattering (WS)-based orthogonal fusion network (WSOFNet) was proposed in this work to predict the IDH mutation status of gliomas from multiple centers. First, transformation-invariant features were extracted from multimodal MR images with a WS network, and then the multimodal WS features were used instead of the original images as the inputs of WSOFNet and were fully fused through an adaptive multimodal feature fusion module (AMF2M) and an orthogonal projection module (OPM). Finally, the fused features were input into a fully connected classifier to predict IDH mutation status. In addition, to achieve improved prediction accuracy, four auxiliary losses were also used in the feature extraction modules. The comparison results showed that the prediction area under the curve (AUC) of WSOFNet on a single-center dataset was 0.9966 and that on a multicenter dataset was approximately 0.9655, which was at least 3.9% higher than that of state-of-the-art methods. Moreover, the ablation experimental results also proved that the adaptive multimodal feature fusion strategy based on orthogonal projection could effectively improve the prediction performance of the model, especially for an external validation dataset.
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Affiliation(s)
- Qijian Chen
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Lihui Wang
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China.
| | - Zhiyang Xing
- Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People's Hospital, Guiyang, 550002, China
| | - Li Wang
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Xubin Hu
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Rongpin Wang
- Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People's Hospital, Guiyang, 550002, China
| | - Yue-Min Zhu
- University Lyon, INSA Lyon, CNRS, Inserm, IRP Metislab CREATIS UMR5220, U1206, Lyon 69621, France
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17
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Wei Y, Chen X, Zhu L, Zhang L, Schonlieb CB, Price S, Li C. Multi-Modal Learning for Predicting the Genotype of Glioma. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3167-3178. [PMID: 37022918 DOI: 10.1109/tmi.2023.3244038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
The isocitrate dehydrogenase (IDH) gene mutation is an essential biomarker for the diagnosis and prognosis of glioma. It is promising to better predict glioma genotype by integrating focal tumor image and geometric features with brain network features derived from MRI. Convolutional neural networks show reasonable performance in predicting IDH mutation, which, however, cannot learn from non-Euclidean data, e.g., geometric and network data. In this study, we propose a multi-modal learning framework using three separate encoders to extract features of focal tumor image, tumor geometrics and global brain networks. To mitigate the limited availability of diffusion MRI, we develop a self-supervised approach to generate brain networks from anatomical multi-sequence MRI. Moreover, to extract tumor-related features from the brain network, we design a hierarchical attention module for the brain network encoder. Further, we design a bi-level multi-modal contrastive loss to align the multi-modal features and tackle the domain gap at the focal tumor and global brain. Finally, we propose a weighted population graph to integrate the multi-modal features for genotype prediction. Experimental results on the testing set show that the proposed model outperforms the baseline deep learning models. The ablation experiments validate the performance of different components of the framework. The visualized interpretation corresponds to clinical knowledge with further validation. In conclusion, the proposed learning framework provides a novel approach for predicting the genotype of glioma.
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18
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Gultekin MA, Peker AA, Oktay AB, Turk HM, Cesme DH, Shbair ATM, Yilmaz TF, Kaya A, Yasin AI, Seker M, Mayadagli A, Alkan A. Differentiation of lung and breast cancer brain metastases: Comparison of texture analysis and deep convolutional neural networks. JOURNAL OF CLINICAL ULTRASOUND : JCU 2023; 51:1579-1586. [PMID: 37688435 DOI: 10.1002/jcu.23558] [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: 07/30/2023] [Revised: 08/29/2023] [Accepted: 08/30/2023] [Indexed: 09/10/2023]
Abstract
PURPOSE Metastases are the most common neoplasm in the adult brain. In order to initiate the treatment, an extensive diagnostic workup is usually required. Radiomics is a discipline aimed at transforming visual data in radiological images into reliable diagnostic information. We aimed to examine the capability of deep learning methods to classify the origin of metastatic lesions in brain MRIs and compare the deep Convolutional Neural Network (CNN) methods with image texture based features. METHODS One hundred forty three patients with 157 metastatic brain tumors were included in the study. The statistical and texture based image features were extracted from metastatic tumors after manual segmentation process. Three powerful pre-trained CNN architectures and the texture-based features on both 2D and 3D tumor images were used to differentiate lung and breast metastases. Ten-fold cross-validation was used for evaluation. Accuracy, precision, recall, and area under curve (AUC) metrics were calculated to analyze the diagnostic performance. RESULTS The texture-based image features on 3D volumes achieved better discrimination results than 2D image features. The overall performance of CNN architectures with 3D inputs was higher than the texture-based features. Xception architecture, with 3D volumes as input, yielded the highest accuracy (0.85) while the AUC value was 0.84. The AUC values of VGG19 and the InceptionV3 architectures were 0.82 and 0.81, respectively. CONCLUSION CNNs achieved superior diagnostic performance in differentiating brain metastases from lung and breast malignancies than texture-based image features. Differentiation using 3D volumes as input exhibited a higher success rate than 2D sagittal images.
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Affiliation(s)
- Mehmet Ali Gultekin
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Abdusselim Adil Peker
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Ayse Betul Oktay
- Department of Computer Engineering, Yildiz Technical University, Istanbul, Turkey
| | - Haci Mehmet Turk
- Department of Medical Oncology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Dilek Hacer Cesme
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Abdallah T M Shbair
- Department of Medical Oncology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Temel Fatih Yilmaz
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Ahmet Kaya
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Ayse Irem Yasin
- Department of Medical Oncology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Mesut Seker
- Department of Medical Oncology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Alpaslan Mayadagli
- Department of Radiation Oncology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Alpay Alkan
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
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Sufyan M, Shokat Z, Ashfaq UA. Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective. Comput Biol Med 2023; 165:107356. [PMID: 37688994 DOI: 10.1016/j.compbiomed.2023.107356] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/21/2023] [Accepted: 08/12/2023] [Indexed: 09/11/2023]
Abstract
Artificial intelligence (AI) in healthcare plays a pivotal role in combating many fatal diseases, such as skin, breast, and lung cancer. AI is an advanced form of technology that uses mathematical-based algorithmic principles similar to those of the human mind for cognizing complex challenges of the healthcare unit. Cancer is a lethal disease with many etiologies, including numerous genetic and epigenetic mutations. Cancer being a multifactorial disease is difficult to be diagnosed at an early stage. Therefore, genetic variations and other leading factors could be identified in due time through AI and machine learning (ML). AI is the synergetic approach for mining the drug targets, their mechanism of action, and drug-organism interaction from massive raw data. This synergetic approach is also facing several challenges in data mining but computational algorithms from different scientific communities for multi-target drug discovery are highly helpful to overcome the bottlenecks in AI for drug-target discovery. AI and ML could be the epicenter in the medical world for the diagnosis, treatment, and evaluation of almost any disease in the near future. In this comprehensive review, we explore the immense potential of AI and ML when integrated with the biological sciences, specifically in the context of cancer research. Our goal is to illuminate the many ways in which AI and ML are being applied to the study of cancer, from diagnosis to individualized treatment. We highlight the prospective role of AI in supporting oncologists and other medical professionals in making informed decisions and improving patient outcomes by examining the intersection of AI and cancer control. Although AI-based medical therapies show great potential, many challenges must be overcome before they can be implemented in clinical practice. We critically assess the current hurdles and provide insights into the future directions of AI-driven approaches, aiming to pave the way for enhanced cancer interventions and improved patient care.
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Affiliation(s)
- Muhammad Sufyan
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
| | - Zeeshan Shokat
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
| | - Usman Ali Ashfaq
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
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20
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Liu Z, Hong X, Wang L, Ma Z, Guan F, Wang W, Qiu Y, Zhang X, Duan W, Wang M, Sun C, Zhao Y, Duan J, Sun Q, Liu L, Ding L, Ji Y, Yan D, Liu X, Cheng J, Zhang Z, Li ZC, Yan J. Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas. BMC Cancer 2023; 23:848. [PMID: 37697238 PMCID: PMC10496393 DOI: 10.1186/s12885-023-11338-8] [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: 10/25/2022] [Accepted: 08/25/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND We aimed to develop machine learning models for prediction of molecular subgroups (low-risk group and intermediate/high-risk group) and molecular marker (KIAA1549-BRAF fusion) of pediatric low-grade gliomas (PLGGs) based on radiomic features extracted from multiparametric MRI. METHODS 61 patients with PLGGs were included in this retrospective study, which were divided into a training set and an internal validation set at a ratio of 2:1 based on the molecular subgroups or the molecular marker. The patients were classified into low-risk and intermediate/high-risk groups, BRAF fusion positive and negative groups, respectively. We extracted 5929 radiomic features from multiparametric MRI. Thereafter, we removed redundant features, trained random forest models on the training set for predicting the molecular subgroups or the molecular marker, and validated their performance on the internal validation set. The performance of the prediction model was verified by 3-fold cross-validation. RESULTS We constructed the classification model differentiating low-risk PLGGs from intermediate/high-risk PLGGs using 4 relevant features, with an AUC of 0.833 and an accuracy of 76.2% in the internal validation set. In the prediction model for predicting KIAA1549-BRAF fusion using 4 relevant features, an AUC of 0.818 and an accuracy of 81.0% were achieved in the internal validation set. CONCLUSIONS The current study demonstrates that MRI radiomics is able to predict molecular subgroups of PLGGs and KIAA1549-BRAF fusion with satisfying sensitivity. TRIAL REGISTRATION This study was retrospectively registered at clinicaltrials.gov (NCT04217018).
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Grants
- 2019YFC0117704 the National Key R&D Program of China
- 202102310136, 202102310138, 202102310113, 202102310083 the Science and Technology Program of Henan Province
- 202102310136, 202102310138, 202102310113, 202102310083 the Science and Technology Program of Henan Province
- 202102310136, 202102310138, 202102310113, 202102310083 the Science and Technology Program of Henan Province
- 82102149, U20A20171, 61901458, 61571432, 81702465, 8217111948, U1804172, U1904148 the National Natural Science Foundation of China
- 82102149, U20A20171, 61901458, 61571432, 81702465, 8217111948, U1804172, U1904148 the National Natural Science Foundation of China
- 82102149, U20A20171, 61901458, 61571432, 81702465, 8217111948, U1804172, U1904148 the National Natural Science Foundation of China
- 82102149, U20A20171, 61901458, 61571432, 81702465, 8217111948, U1804172, U1904148 the National Natural Science Foundation of China
- 2021B0101420006 the Key-Area Research and Development Program of Guangdong Province
- YXKC2022061 the Excellent Youth Talent Cultivation Program of Innovation in Health Science and Technology of Henan Province
- SBGJ202002062 the Key Program of Medical Science and Technique Foundation of Henan Province
- the National Key R&D Program of China
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Affiliation(s)
- Zhen Liu
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Jian she Dong Road 1, Zhengzhou, 450052, Henan province, China
| | - Xuanke Hong
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Jian she Dong Road 1, Zhengzhou, 450052, Henan province, China
| | - Linglong Wang
- Yanjing Medical College of Capital Medical University, Beijing, China
| | - Zeyu Ma
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Jian she Dong Road 1, Zhengzhou, 450052, Henan province, China
| | - Fangzhan Guan
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Jian she Dong Road 1, Zhengzhou, 450052, Henan province, China
| | - Weiwei Wang
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yuning Qiu
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Jian she Dong Road 1, Zhengzhou, 450052, Henan province, China
| | - Xueping Zhang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Jian she Dong Road 1, Zhengzhou, 450052, Henan province, China
| | - Wenchao Duan
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Jian she Dong Road 1, Zhengzhou, 450052, Henan province, China
| | - Minkai Wang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Jian she Dong Road 1, Zhengzhou, 450052, Henan province, China
| | - Chen Sun
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Jian she Dong Road 1, Zhengzhou, 450052, Henan province, China
| | - Yuanshen Zhao
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jingxian Duan
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qiuchang Sun
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Lin Liu
- China-Japan Union Hospital of Jilin University, Changchun, Jilin, China
| | - Lei Ding
- China-Japan Union Hospital of Jilin University, Changchun, Jilin, China
| | - Yuchen Ji
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Jian she Dong Road 1, Zhengzhou, 450052, Henan province, China
| | - Dongming Yan
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Jian she Dong Road 1, Zhengzhou, 450052, Henan province, China
| | - Xianzhi Liu
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Jian she Dong Road 1, Zhengzhou, 450052, Henan province, China
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Jian she Dong Road 1, Zhengzhou, 450052, Henan province, China
| | - Zhenyu Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Jian she Dong Road 1, Zhengzhou, 450052, Henan province, China.
| | - Zhi-Cheng Li
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- University of Chinese Academy of Sciences, Beijing, China.
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, 518045, China.
| | - Jing Yan
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Jian she Dong Road 1, Zhengzhou, 450052, Henan province, China.
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21
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Lee MD, Patel SH, Mohan S, Akbari H, Bakas S, Nasrallah MP, Calabrese E, Rudie J, Villanueva-Meyer J, LaMontagne P, Marcus DS, Colen RR, Balana C, Choi YS, Badve C, Barnholtz-Sloan JS, Sloan AE, Booth TC, Palmer JD, Dicker AP, Flanders AE, Shi W, Griffith B, Poisson LM, Chakravarti A, Mahajan A, Chang S, Orringer D, Davatzikos C, Jain R. Association of partial T2-FLAIR mismatch sign and isocitrate dehydrogenase mutation in WHO grade 4 gliomas: results from the ReSPOND consortium. Neuroradiology 2023; 65:1343-1352. [PMID: 37468750 PMCID: PMC11058040 DOI: 10.1007/s00234-023-03196-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 07/07/2023] [Indexed: 07/21/2023]
Abstract
PURPOSE While the T2-FLAIR mismatch sign is highly specific for isocitrate dehydrogenase (IDH)-mutant, 1p/19q-noncodeleted astrocytomas among lower-grade gliomas, its utility in WHO grade 4 gliomas is not well-studied. We derived the partial T2-FLAIR mismatch sign as an imaging biomarker for IDH mutation in WHO grade 4 gliomas. METHODS Preoperative MRI scans of adult WHO grade 4 glioma patients (n = 2165) from the multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium were analyzed. Diagnostic performance of the partial T2-FLAIR mismatch sign was evaluated. Subset analyses were performed to assess associations of imaging markers with overall survival (OS). RESULTS One hundred twenty-one (5.6%) of 2165 grade 4 gliomas were IDH-mutant. Partial T2-FLAIR mismatch was present in 40 (1.8%) cases, 32 of which were IDH-mutant, yielding 26.4% sensitivity, 99.6% specificity, 80.0% positive predictive value, and 95.8% negative predictive value. Multivariate logistic regression demonstrated IDH mutation was significantly associated with partial T2-FLAIR mismatch (odds ratio [OR] 5.715, 95% CI [1.896, 17.221], p = 0.002), younger age (OR 0.911 [0.895, 0.927], p < 0.001), tumor centered in frontal lobe (OR 3.842, [2.361, 6.251], p < 0.001), absence of multicentricity (OR 0.173, [0.049, 0.612], p = 0.007), and presence of cystic (OR 6.596, [3.023, 14.391], p < 0.001) or non-enhancing solid components (OR 6.069, [3.371, 10.928], p < 0.001). Multivariate Cox analysis demonstrated cystic components (p = 0.024) and non-enhancing solid components (p = 0.003) were associated with longer OS, while older age (p < 0.001), frontal lobe center (p = 0.008), multifocality (p < 0.001), and multicentricity (p < 0.001) were associated with shorter OS. CONCLUSION Partial T2-FLAIR mismatch sign is highly specific for IDH mutation in WHO grade 4 gliomas.
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Affiliation(s)
- Matthew D Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA.
| | - Sohil H Patel
- Department of Radiology, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Suyash Mohan
- Department of Radiology, Division of Neuroradiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Hamed Akbari
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - MacLean P Nasrallah
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Glioblastoma Multiforme Translational Center of Excellence, Abramson Cancer Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Evan Calabrese
- Department of Radiology, Division of Neuroradiology, Duke University, Durham, NC, USA
| | - Jeffrey Rudie
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Pamela LaMontagne
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel S Marcus
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Rivka R Colen
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Carmen Balana
- Medical Oncology Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
| | - Yoon Seong Choi
- Department of Radiology, Section of Neuroradiology, Yonsei University Health System, Seoul, South Korea
| | - Chaitra Badve
- Department of Radiology, Case Western Reserve University and University Hospitals of Cleveland, Cleveland, OH, USA
| | - Jill S Barnholtz-Sloan
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, USA
- Trans-Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Andrew E Sloan
- Department of Neurosurgery, Case Western Reserve University and University Hospitals of Cleveland, Cleveland, OH, USA
- Seidman Cancer Center and Case Comprehensive Cancer Center, Cleveland, OH, USA
| | - Thomas C Booth
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, King's College Hospital NHS Foundation Trust, Ruskin WingLondon, UK
| | - Joshua D Palmer
- Department of Radiation Oncology and Neurosurgery, The James Cancer Hospital at the Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Adam E Flanders
- Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Wenyin Shi
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Brent Griffith
- Department of Radiology, Henry Ford Health, Detroit, MI, USA
| | - Laila M Poisson
- Department of Public Health Sciences, Center for Bioinformatics, Henry Ford Health, Detroit, MI, USA
| | - Arnab Chakravarti
- Department of Radiation Oncology and Neurosurgery, The James Cancer Hospital at the Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, UK
| | - Susan Chang
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Daniel Orringer
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
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22
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Li J, Zhang P, Qu L, Sun T, Duan Y, Wu M, Weng J, Li Z, Gong X, Liu X, Wang Y, Jia W, Su X, Yue Q, Li J, Zhang Z, Barkhof F, Huang RY, Chang K, Sair H, Ye C, Zhang L, Zhuo Z, Liu Y. Deep Learning for Noninvasive Assessment of H3 K27M Mutation Status in Diffuse Midline Gliomas Using MR Imaging. J Magn Reson Imaging 2023; 58:850-861. [PMID: 36692205 DOI: 10.1002/jmri.28606] [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: 10/21/2022] [Revised: 01/05/2023] [Accepted: 01/07/2023] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Determination of H3 K27M mutation in diffuse midline glioma (DMG) is key for prognostic assessment and stratifying patient subgroups for clinical trials. MRI can noninvasively depict morphological and metabolic characteristics of H3 K27M mutant DMG. PURPOSE This study aimed to develop a deep learning (DL) approach to noninvasively predict H3 K27M mutation in DMG using T2-weighted images. STUDY TYPE Retrospective and prospective. POPULATION For diffuse midline brain gliomas, 341 patients from Center-1 (27 ± 19 years, 184 males), 42 patients from Center-2 (33 ± 19 years, 27 males) and 35 patients (37 ± 18 years, 24 males). For diffuse spinal cord gliomas, 133 patients from Center-1 (30 ± 15 years, 80 males). FIELD STRENGTH/SEQUENCE 5T and 3T, T2-weighted turbo spin echo imaging. ASSESSMENT Conventional radiological features were independently reviewed by two neuroradiologists. H3 K27M status was determined by histopathological examination. The Dice coefficient was used to evaluate segmentation performance. Classification performance was evaluated using accuracy, sensitivity, specificity, and area under the curve. STATISTICAL TESTS Pearson's Chi-squared test, Fisher's exact test, two-sample Student's t-test and Mann-Whitney U test. A two-sided P value <0.05 was considered statistically significant. RESULTS In the testing cohort, Dice coefficients of tumor segmentation using DL were 0.87 for diffuse midline brain and 0.81 for spinal cord gliomas. In the internal prospective testing dataset, the predictive accuracies, sensitivities, and specificities of H3 K27M mutation status were 92.1%, 98.2%, 82.9% in diffuse midline brain gliomas and 85.4%, 88.9%, 82.6% in spinal cord gliomas. Furthermore, this study showed that the performance generalizes to external institutions, with predictive accuracies of 85.7%-90.5%, sensitivities of 90.9%-96.0%, and specificities of 82.4%-83.3%. DATA CONCLUSION In this study, an automatic DL framework was developed and validated for accurately predicting H3 K27M mutation using T2-weighted images, which could contribute to the noninvasive determination of H3 K27M status for clinical decision-making. EVIDENCE LEVEL 2 Technical Efficacy: Stage 2.
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Affiliation(s)
- Junjie Li
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Peng Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Liying Qu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Ting Sun
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Yunyun Duan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Minghao Wu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Jinyuan Weng
- Department of Medical Imaging Product, Neusoft, Group Ltd., Shenyang, People's Republic of China
| | - Zhaohui Li
- BioMind Inc., Beijing, People's Republic of China
| | - Xiaodong Gong
- Department of Medical Imaging Product, Neusoft, Group Ltd., Shenyang, People's Republic of China
| | - Xing Liu
- Department of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Yongzhi Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Wenqing Jia
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Xiaorui Su
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, People's Republic of China
| | - Qiang Yue
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, People's Republic of China
| | - Jianrui Li
- Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, People's Republic of China
| | - Zhiqiang Zhang
- Department of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, People's Republic of China
| | - Frederik Barkhof
- UCL Institutes of Neurology and Healthcare Engineering, London, UK
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ken Chang
- Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Haris Sair
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Chuyang Ye
- School of Information and Electronics, Beijing Institute of Technology, Beijing, People's Republic of China
| | - Liwei Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Zhizheng Zhuo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
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Liu Z, Duan T, Zhang Y, Weng S, Xu H, Ren Y, Zhang Z, Han X. Radiogenomics: a key component of precision cancer medicine. Br J Cancer 2023; 129:741-753. [PMID: 37414827 PMCID: PMC10449908 DOI: 10.1038/s41416-023-02317-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 05/02/2023] [Accepted: 06/12/2023] [Indexed: 07/08/2023] Open
Abstract
Radiogenomics, focusing on the relationship between genomics and imaging phenotypes, has been widely applied to address tumour heterogeneity and predict immune responsiveness and progression. It is an inevitable consequence of current trends in precision medicine, as radiogenomics costs less than traditional genetic sequencing and provides access to whole-tumour information rather than limited biopsy specimens. By providing voxel-by-voxel genetic information, radiogenomics can allow tailored therapy targeting a complete, heterogeneous tumour or set of tumours. In addition to quantifying lesion characteristics, radiogenomics can also be used to distinguish benign from malignant entities, as well as patient characteristics, to better stratify patients according to disease risk, thereby enabling more precise imaging and screening. Here, we have characterised the radiogenomic application in precision medicine using a multi-omic approach. we outline the main applications of radiogenomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalised medicine. Finally, we discuss the challenges in the field of radiogenomics and the scope and clinical applicability of these methods.
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Affiliation(s)
- Zaoqu Liu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
- Interventional Institute of Zhengzhou University, 450052, Zhengzhou, Henan, China
- Interventional Treatment and Clinical Research Center of Henan Province, 450052, Zhengzhou, Henan, China
| | - Tian Duan
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Yuyuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Siyuan Weng
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Hui Xu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Yuqing Ren
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Zhenyu Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China.
| | - Xinwei Han
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China.
- Interventional Institute of Zhengzhou University, 450052, Zhengzhou, Henan, China.
- Interventional Treatment and Clinical Research Center of Henan Province, 450052, Zhengzhou, Henan, China.
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Zhang H, Fan X, Zhang J, Wei Z, Feng W, Hu Y, Ni J, Yao F, Zhou G, Wan C, Zhang X, Wang J, Liu Y, You Y, Yu Y. Deep-learning and conventional radiomics to predict IDH genotyping status based on magnetic resonance imaging data in adult diffuse glioma. Front Oncol 2023; 13:1143688. [PMID: 37711207 PMCID: PMC10499353 DOI: 10.3389/fonc.2023.1143688] [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: 01/13/2023] [Accepted: 08/17/2023] [Indexed: 09/16/2023] Open
Abstract
Objectives In adult diffuse glioma, preoperative detection of isocitrate dehydrogenase (IDH) status helps clinicians develop surgical strategies and evaluate patient prognosis. Here, we aim to identify an optimal machine-learning model for prediction of IDH genotyping by combining deep-learning (DL) signatures and conventional radiomics (CR) features as model predictors. Methods In this study, a total of 486 patients with adult diffuse gliomas were retrospectively collected from our medical center (n=268) and the public database (TCGA, n=218). All included patients were randomly divided into the training and validation sets by using nested 10-fold cross-validation. A total of 6,736 CR features were extracted from four MRI modalities in each patient, namely T1WI, T1CE, T2WI, and FLAIR. The LASSO algorithm was performed for CR feature selection. In each MRI modality, we applied a CNN+LSTM-based neural network to extract DL features and integrate these features into a DL signature after the fully connected layer with sigmoid activation. Eight classic machine-learning models were analyzed and compared in terms of their prediction performance and stability in IDH genotyping by combining the LASSO-selected CR features and integrated DL signatures as model predictors. In the validation sets, the prediction performance was evaluated by using accuracy and the area under the curve (AUC) of the receiver operating characteristics, while the model stability was analyzed by using the relative standard deviation of the AUC (RSDAUC). Subgroup analyses of DL signatures and CR features were also individually conducted to explore their independent prediction values. Results Logistic regression (LR) achieved favorable prediction performance (AUC: 0.920 ± 0.043, accuracy: 0.843 ± 0.044), whereas support vector machine with the linear kernel (l-SVM) displayed low prediction performance (AUC: 0.812 ± 0.052, accuracy: 0.821 ± 0.050). With regard to stability, LR also showed high robustness against data perturbation (RSDAUC: 4.7%). Subgroup analyses showed that DL signatures outperformed CR features (DL, AUC: 0.915 ± 0.054, accuracy: 0.835 ± 0.061, RSDAUC: 5.9%; CR, AUC: 0.830 ± 0.066, accuracy: 0.771 ± 0.051, RSDAUC: 8.0%), while DL and DL+CR achieved similar prediction results. Conclusion In IDH genotyping, LR is a promising machine-learning classification model. Compared with CR features, DL signatures exhibit markedly superior prediction values and discriminative capability.
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Affiliation(s)
- Hongjian Zhang
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xiao Fan
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Junxia Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Zhiyuan Wei
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wei Feng
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yifang Hu
- Department of Geriatric Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jiaying Ni
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Fushen Yao
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Gaoxin Zhou
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
- Institute of Medical Informatics and Management, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Cheng Wan
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
- Institute of Medical Informatics and Management, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xin Zhang
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
- Institute of Medical Informatics and Management, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Junjie Wang
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
- Institute of Medical Informatics and Management, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yun Liu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
- Institute of Medical Informatics and Management, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yongping You
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yun Yu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
- Institute of Medical Informatics and Management, Nanjing Medical University, Nanjing, Jiangsu, China
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Kazemzadeh K, Akhlaghdoust M, Zali A. Advances in artificial intelligence, robotics, augmented and virtual reality in neurosurgery. Front Surg 2023; 10:1241923. [PMID: 37693641 PMCID: PMC10483402 DOI: 10.3389/fsurg.2023.1241923] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 08/11/2023] [Indexed: 09/12/2023] Open
Abstract
Neurosurgical practitioners undergo extensive and prolonged training to acquire diverse technical proficiencies, while neurosurgical procedures necessitate a substantial amount of pre-, post-, and intraoperative clinical data acquisition, making decisions, attention, and convalescence. The past decade witnessed an appreciable escalation in the significance of artificial intelligence (AI) in neurosurgery. AI holds significant potential in neurosurgery as it supplements the abilities of neurosurgeons to offer optimal interventional and non-interventional care to patients by improving prognostic and diagnostic outcomes in clinical therapy and assisting neurosurgeons in making decisions while surgical interventions to enhance patient outcomes. Other technologies including augmented reality, robotics, and virtual reality can assist and promote neurosurgical methods as well. Moreover, they play a significant role in generating, processing, as well as storing experimental and clinical data. Also, the usage of these technologies in neurosurgery is able to curtail the number of costs linked with surgical care and extend high-quality health care to a wider populace. This narrative review aims to integrate the results of articles that elucidate the role of the aforementioned technologies in neurosurgery.
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Affiliation(s)
- Kimia Kazemzadeh
- Students’ Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Network of Neurosurgery and Artificial Intelligence (NONAI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Meisam Akhlaghdoust
- Network of Neurosurgery and Artificial Intelligence (NONAI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- USERN Office, Functional Neurosurgery Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alireza Zali
- Network of Neurosurgery and Artificial Intelligence (NONAI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- USERN Office, Functional Neurosurgery Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Kalaroopan D, Lasocki A. MRI-based deep learning techniques for the prediction of isocitrate dehydrogenase and 1p/19q status in grade 2-4 adult gliomas. J Med Imaging Radiat Oncol 2023; 67:492-498. [PMID: 36919468 DOI: 10.1111/1754-9485.13522] [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: 11/24/2021] [Accepted: 02/16/2023] [Indexed: 03/16/2023]
Abstract
Molecular biomarkers are becoming increasingly important in the classification of intracranial gliomas. While tissue sampling remains the gold standard, there is growing interest in the use of deep learning (DL) techniques to predict these markers. This narrative review with a systematic approach identifies and synthesises the current published data on DL techniques using conventional MRI sequences for predicting isocitrate dehydrogenase (IDH) and 1p/19q-codeletion status in World Health Organisation grade 2-4 gliomas. Three databases were searched for relevant studies. In all, 13 studies met the inclusion criteria after exclusions. Key results, limitations and discrepancies between studies were synthesised. High accuracy has been reported in some studies, but the existing literature has several limitations, including generally small cohort sizes, a paucity of studies with independent testing cohorts and a lack of studies assessing IDH and 1p/19q together. While DL shows promise as a non-invasive means of predicting glioma genotype, addressing these limitations in future research will be important for facilitating clinical translation.
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Affiliation(s)
- Dinusha Kalaroopan
- Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Arian Lasocki
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
- Department of Radiology, The University of Melbourne, Melbourne, Victoria, Australia
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Zhong S, Ren JX, Yu ZP, Peng YD, Yu CW, Deng D, Xie Y, He ZQ, Duan H, Wu B, Li H, Yang WZ, Bai Y, Sai K, Chen YS, Guo CC, Li DP, Cheng Y, Zhang XH, Mou YG. Predicting glioblastoma molecular subtypes and prognosis with a multimodal model integrating convolutional neural network, radiomics, and semantics. J Neurosurg 2023; 139:305-314. [PMID: 36461822 DOI: 10.3171/2022.10.jns22801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 10/24/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVE The aim of this study was to build a convolutional neural network (CNN)-based prediction model of glioblastoma (GBM) molecular subtype diagnosis and prognosis with multimodal features. METHODS In total, 222 GBM patients were included in the training set from Sun Yat-sen University Cancer Center (SYSUCC) and 107 GBM patients were included in the validation set from SYSUCC, Xuanwu Hospital Capital Medical University, and the First Hospital of Jilin University. The multimodal model was trained with MR images (pre- and postcontrast T1-weighted images and T2-weighted images), corresponding MRI impression, and clinical patient information. First, the original images were segmented using the Multimodal Brain Tumor Image Segmentation Benchmark toolkit. Convolutional features were extracted using 3D residual deep neural network (ResNet50) and convolutional 3D (C3D). Radiomic features were extracted using pyradiomics. Report texts were converted to word embedding using word2vec. These three types of features were then integrated to train neural networks. Accuracy, precision, recall, and F1-score were used to evaluate the model performance. RESULTS The C3D-based model yielded the highest accuracy of 91.11% in the prediction of IDH1 mutation status. Importantly, the addition of semantics improved precision by 11.21% and recall in MGMT promoter methylation status prediction by 14.28%. The areas under the receiver operating characteristic curves of the C3D-based model in the IDH1, ATRX, MGMT, and 1-year prognosis groups were 0.976, 0.953, 0.955, and 0.976, respectively. In external validation, the C3D-based model showed significant improvement in accuracy in the IDH1, ATRX, MGMT, and 1-year prognosis groups, which were 88.30%, 76.67%, 85.71%, and 85.71%, respectively (compared with 3D ResNet50: 83.51%, 66.67%, 82.14%, and 70.79%, respectively). CONCLUSIONS The authors propose a novel multimodal model integrating C3D, radiomics, and semantics, which had a great performance in predicting IDH1, ATRX, and MGMT molecular subtypes and the 1-year prognosis of GBM.
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Affiliation(s)
- Sheng Zhong
- 1Department of Neurosurgery and Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- 2Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts
- 3Department of Bioinformatics, Harvard Medical School, Boston, Massachusetts
| | - Jia-Xin Ren
- 4Department of Neurology, Stroke Center, The First Hospital of Jilin University, Changchun, China
| | - Ze-Peng Yu
- 1Department of Neurosurgery and Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yi-Da Peng
- 5College of Computer Science and Technology, Jilin University, Changchun, China
| | - Cheng-Wei Yu
- 1Department of Neurosurgery and Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Davy Deng
- 2Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - YangYiran Xie
- 6Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Zhen-Qiang He
- 1Department of Neurosurgery and Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Hao Duan
- 1Department of Neurosurgery and Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Bo Wu
- Departments of7Orthopaedics
| | | | - Wen-Zhuo Yang
- 1Department of Neurosurgery and Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yang Bai
- 9Neurosurgery, The First Hospital of Jilin University, Changchun, China; and
| | - Ke Sai
- 1Department of Neurosurgery and Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yin-Sheng Chen
- 1Department of Neurosurgery and Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Cheng-Cheng Guo
- 1Department of Neurosurgery and Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - De-Pei Li
- 1Department of Neurosurgery and Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Ye Cheng
- 10Department of Neurosurgery, The Xuanwu Hospital Capital Medical University, Beijing, China
| | - Xiang-Heng Zhang
- 1Department of Neurosurgery and Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yong-Gao Mou
- 1Department of Neurosurgery and Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
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Pan R, Yang C, Li Z, Ren J, Duan Y. Magnetoencephalography-based approaches to epilepsy classification. Front Neurosci 2023; 17:1183391. [PMID: 37502686 PMCID: PMC10368885 DOI: 10.3389/fnins.2023.1183391] [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: 03/10/2023] [Accepted: 06/12/2023] [Indexed: 07/29/2023] Open
Abstract
Epilepsy is a chronic central nervous system disorder characterized by recurrent seizures. Not only does epilepsy severely affect the daily life of the patient, but the risk of premature death in patients with epilepsy is three times higher than that of the normal population. Magnetoencephalography (MEG) is a non-invasive, high temporal and spatial resolution electrophysiological data that provides a valid basis for epilepsy diagnosis, and used in clinical practice to locate epileptic foci in patients with epilepsy. It has been shown that MEG helps to identify MRI-negative epilepsy, contributes to clinical decision-making in recurrent seizures after previous epilepsy surgery, that interictal MEG can provide additional localization information than scalp EEG, and complete excision of the stimulation area defined by the MEG has prognostic significance for postoperative seizure control. However, due to the complexity of the MEG signal, it is often difficult to identify subtle but critical changes in MEG through visual inspection, opening up an important area of research for biomedical engineers to investigate and implement intelligent algorithms for epilepsy recognition. At the same time, the use of manual markers requires significant time and labor costs, necessitating the development and use of computer-aided diagnosis (CAD) systems that use classifiers to automatically identify abnormal activity. In this review, we discuss in detail the results of applying various different feature extraction methods on MEG signals with different classifiers for epilepsy detection, subtype determination, and laterality classification. Finally, we also briefly look at the prospects of using MEG for epilepsy-assisted localization (spike detection, high-frequency oscillation detection) due to the unique advantages of MEG for functional area localization in epilepsy, and discuss the limitation of current research status and suggestions for future research. Overall, it is hoped that our review will facilitate the reader to quickly gain a general understanding of the problem of MEG-based epilepsy classification and provide ideas and directions for subsequent research.
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Affiliation(s)
- Ruoyao Pan
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Chunlan Yang
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Zhimei Li
- Department of Internal Neurology, Tiantan Hospital, Beijing, China
| | - Jiechuan Ren
- Department of Internal Neurology, Tiantan Hospital, Beijing, China
| | - Ying Duan
- Beijing Universal Medical Imaging Diagnostic Center, Beijing, China
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Kumari S, Gupta R, Ambasta RK, Kumar P. Multiple therapeutic approaches of glioblastoma multiforme: From terminal to therapy. Biochim Biophys Acta Rev Cancer 2023; 1878:188913. [PMID: 37182666 DOI: 10.1016/j.bbcan.2023.188913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 04/24/2023] [Accepted: 05/10/2023] [Indexed: 05/16/2023]
Abstract
Glioblastoma multiforme (GBM) is an aggressive brain cancer showing poor prognosis. Currently, treatment methods of GBM are limited with adverse outcomes and low survival rate. Thus, advancements in the treatment of GBM are of utmost importance, which can be achieved in recent decades. However, despite aggressive initial treatment, most patients develop recurrent diseases, and the overall survival rate of patients is impossible to achieve. Currently, researchers across the globe target signaling events along with tumor microenvironment (TME) through different drug molecules to inhibit the progression of GBM, but clinically they failed to demonstrate much success. Herein, we discuss the therapeutic targets and signaling cascades along with the role of the organoids model in GBM research. Moreover, we systematically review the traditional and emerging therapeutic strategies in GBM. In addition, we discuss the implications of nanotechnologies, AI, and combinatorial approach to enhance GBM therapeutics.
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Affiliation(s)
- Smita Kumari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India
| | - Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India.
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Shi X, Li Y, Cheng J, Bai J, Zhao G, Chen YW. Multi-task Model for Glioma Segmentation and Isocitrate Dehydrogenase Status Prediction Using Global and Local Features. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38083206 DOI: 10.1109/embc40787.2023.10340355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
According to the 2021 World Health Organization IDH status prediction scheme for gliomas, isocitrate dehydrogenase (IDH) is a particularly important basis for glioma diagnosis. In general, 3D multimodal brain MRI is an effective diagnostic tool. However, only using brain MRI data is difficult for experienced doctors to predict the IDH status. Surgery is necessary to be performed for confirming the IDH. Previous studies have shown that brain MRI images of glioma areas contain a lot of useful information for diagnosis. These studies usually need to mark the glioma area in advance to complete the prediction of IDH status, which takes a long time and has high computational cost. The tumor segmentation task model can automatically segment and locate the tumor region, which is exactly the information needed for the IDH prediction task. In this study, we proposed a multi-task deep learning model using 3D multimodal brain MRI images to achieve glioma segmentation and IDH status prediction simultaneously, which improved the accuracy of both tasks effectively. Firstly, we used a segmentation model to segment the tumor region. Also, the whole MRI image and the segmented glioma region features as the global and local features were used to predict IDH status. The effectiveness of the proposed method was validated via a public glioma dataset from the BraTS2020. Our experimental results show that our proposed method outperformed state-of-the-art methods with a prediction accuracy of 88.5% and average dice of 79.8%. The improvements in prediction and segmentation are 3% and 1% compared with the state-of-the-art method, respectively.
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Nacul Mora NG, Akkurt BH, Kasap D, Blömer D, Heindel W, Mannil M, Musigmann M. Comparison of MRI Sequences to Predict ATRX Status Using Radiomics-Based Machine Learning. Diagnostics (Basel) 2023; 13:2216. [PMID: 37443610 DOI: 10.3390/diagnostics13132216] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/23/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
ATRX is an important molecular marker according to the 2021 WHO classification of adult-type diffuse glioma. We aim to predict the ATRX mutation status non-invasively using radiomics-based machine learning models on MRI and to determine which MRI sequence is best suited for this purpose. In this retrospective study, we used MRI images of patients with histologically confirmed glioma, including the sequences T1w without and with the administration of contrast agent, T2w, and the FLAIR. Radiomics features were extracted from the corresponding MRI images by hand-delineated regions of interest. Data partitioning into training data and independent test data was repeated 100 times to avoid random effects. Feature preselection and subsequent model development were performed using Lasso regression. The T2w sequence was found to be the most suitable and the FLAIR sequence the least suitable for predicting ATRX mutations using radiomics-based machine learning models. For the T2w sequence, our seven-feature model developed with Lasso regression achieved a mean AUC of 0.831, a mean accuracy of 0.746, a mean sensitivity of 0.772, and a mean specificity of 0.697. In conclusion, for the prediction of ATRX mutation using radiomics-based machine learning models, the T2w sequence is the most suitable among the commonly used MRI sequences.
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Affiliation(s)
- Nabila Gala Nacul Mora
- Clinic for Radiology, University of Münster and University Hospital Münster Muenster, Albert-Schweitzer-Campus 1, 48149 Muenster, Germany
| | - Burak Han Akkurt
- Clinic for Radiology, University of Münster and University Hospital Münster Muenster, Albert-Schweitzer-Campus 1, 48149 Muenster, Germany
| | - Dilek Kasap
- Clinic for Radiology, University of Münster and University Hospital Münster Muenster, Albert-Schweitzer-Campus 1, 48149 Muenster, Germany
| | - David Blömer
- Clinic for Radiology, University of Münster and University Hospital Münster Muenster, Albert-Schweitzer-Campus 1, 48149 Muenster, Germany
| | - Walter Heindel
- Clinic for Radiology, University of Münster and University Hospital Münster Muenster, Albert-Schweitzer-Campus 1, 48149 Muenster, Germany
| | - Manoj Mannil
- Clinic for Radiology, University of Münster and University Hospital Münster Muenster, Albert-Schweitzer-Campus 1, 48149 Muenster, Germany
| | - Manfred Musigmann
- Clinic for Radiology, University of Münster and University Hospital Münster Muenster, Albert-Schweitzer-Campus 1, 48149 Muenster, Germany
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Yuan Y, Yu Y, Chang J, Chu YH, Yu W, Hsu YC, Patrick LA, Liu M, Yue Q. Convolutional neural network to predict IDH mutation status in glioma from chemical exchange saturation transfer imaging at 7 Tesla. Front Oncol 2023; 13:1134626. [PMID: 37223677 PMCID: PMC10200907 DOI: 10.3389/fonc.2023.1134626] [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: 01/06/2023] [Accepted: 04/14/2023] [Indexed: 05/25/2023] Open
Abstract
Background and goal Noninvasive prediction of isocitrate dehydrogenase (IDH) mutation status in glioma guides surgical strategies and individualized management. We explored the capability on preoperatively identifying IDH status of combining a convolutional neural network (CNN) and a novel imaging modality, ultra-high field 7.0 Tesla (T) chemical exchange saturation transfer (CEST) imaging. Method We enrolled 84 glioma patients of different tumor grades in this retrospective study. Amide proton transfer CEST and structural Magnetic Resonance (MR) imaging at 7T were performed preoperatively, and the tumor regions are manually segmented, leading to the "annotation" maps that offers the location and shape information of the tumors. The tumor region slices in CEST and T1 images were further cropped out as samples and combined with the annotation maps, which were inputted to a 2D CNN model for generating IDH predictions. Further comparison analysis to radiomics-based prediction methods was performed to demonstrate the crucial role of CNN for predicting IDH based on CEST and T1 images. Results A fivefold cross-validation was performed on the 84 patients and 4090 slices. We observed a model based on only CEST achieved accuracy of 74.01% ± 1.15%, and the area under the curve (AUC) of 0.8022 ± 0.0147. When using T1 image only, the prediction performances dropped to accuracy of 72.52% ± 1.12% and AUC of 0.7904 ± 0.0214, which indicates no superiority of CEST over T1. However, when we combined CEST and T1 together with the annotation maps, the performances of the CNN model were further boosted to accuracy of 82.94% ± 1.23% and AUC of 0.8868 ± 0.0055, suggesting the importance of a joint analysis of CEST and T1. Finally, using the same inputs, the CNN-based predictions achieved significantly improved performances above those from radiomics-based predictions (logistic regression and support vector machine) by 10% to 20% in all metrics. Conclusion 7T CEST and structural MRI jointly offer improved sensitivity and specificity of preoperative non-invasive imaging for the diagnosis of IDH mutation status. As the first study of CNN model on imaging acquired at ultra-high field MR, our results could demonstrate the potential of combining ultra-high-field CEST and CNN for facilitating decision-making in clinical practice. However, due to the limited cases and B1 inhomogeneities, the accuracy of this model will be improved in our further study.
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Affiliation(s)
- Yifan Yuan
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
- Research Units of New Technologies of Micro-Endoscopy Combination in Skull Base Surgery (2018RU008), Chinese Academy of Medical Sciences (CAMS), Shanghai, China
| | - Yang Yu
- National Center for Neurological Disorders, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
- Research Units of New Technologies of Micro-Endoscopy Combination in Skull Base Surgery (2018RU008), Chinese Academy of Medical Sciences (CAMS), Shanghai, China
- Department of Radiology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jun Chang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
| | - Ying-Hua Chu
- Magnetic Resonance (MR) Collaboration, Siemens Healthineers Ltd., Shanghai, China
| | - Wenwen Yu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Yi-Cheng Hsu
- Magnetic Resonance (MR) Collaboration, Siemens Healthineers Ltd., Shanghai, China
| | | | - Mianxin Liu
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Qi Yue
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
- Research Units of New Technologies of Micro-Endoscopy Combination in Skull Base Surgery (2018RU008), Chinese Academy of Medical Sciences (CAMS), Shanghai, China
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Anagun Y. Smart brain tumor diagnosis system utilizing deep convolutional neural networks. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-27. [PMID: 37362644 PMCID: PMC10140727 DOI: 10.1007/s11042-023-15422-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/12/2023] [Accepted: 04/18/2023] [Indexed: 06/28/2023]
Abstract
The early diagnosis of cancer is crucial to provide prompt and adequate management of the diseases. Imaging tests, in particular magnetic resonance imaging (MRI), are the first preferred method for diagnosis. However, these tests have some limitations which can cause a delay in detection and diagnosis. The use of computer-aided intelligent systems can assist physicians in diagnosis. In this study, we established a Convolutional Neural Network (CNN)-based brain tumor diagnosis system using EfficientNetv2s architecture, which was improved with the Ranger optimization and extensive pre-processing. We also compared the proposed model with state-of-the-art deep learning architectures such as ResNet18, ResNet200d, and InceptionV4 in discriminating brain tumors based on their spatial features. We achieved the best micro-average results with 99.85% test accuracy, 99.89% Area under the Curve (AUC), 98.16% precision, 98.17% recall, and 98.21% f1-score. Furthermore, the experimental results of the improved model were compared to various CNN-based architectures using key performance metrics and were shown to have a strong impact on tumor categorization. The proposed system has been experimentally evaluated with different optimizers and compared with recent CNN architectures, on both augmented and original data. The results demonstrated a convincing performance in tumor detection and diagnosis.
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Affiliation(s)
- Yildiray Anagun
- Department of Computer Engineering, Eskisehir Osmangazi University, Eskisehir, Turkey
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Nishikawa T, Ohka F, Aoki K, Suzuki H, Motomura K, Yamaguchi J, Maeda S, Kibe Y, Shimizu H, Natsume A, Innan H, Saito R. Easy-to-use machine learning system for the prediction of IDH mutation and 1p/19q codeletion using MRI images of adult-type diffuse gliomas. Brain Tumor Pathol 2023; 40:85-92. [PMID: 36991274 DOI: 10.1007/s10014-023-00459-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 03/20/2023] [Indexed: 03/31/2023]
Abstract
Adult-type diffuse gliomas are divided into Astrocytoma, IDH-mutant, Oligodendroglioma, IDH-mutant and 1p/19q-codeleted and Glioblastoma, IDH-wildtype based on the IDH mutation, and 1p/19q codeletion status. To determine the treatment strategy for these tumors, pre-operative prediction of IDH mutation and 1p/19q codeletion status might be effective. Computer-aided diagnosis (CADx) systems using machine learning have been noted as innovative diagnostic methods. However, it is difficult to promote the clinical application of machine learning systems at each institute because the support of various specialists is essential. In this study, we established an easy-to-use computer-aided diagnosis system using Microsoft Azure Machine Learning Studio (MAMLS) to predict these statuses. We constructed an analysis model using 258 adult-type diffuse glioma cases from The Cancer Genome Atlas (TCGA) cohort. Using MRI T2-weighted images, the overall accuracy, sensitivity, and specificity for the prediction of IDH mutation and 1p/19q codeletion were 86.9%, 80.9%, and 92.0%, and 94.7%, 94.1%, and 95.1%, respectively. We also constructed an reliable analysis model for the prediction of IDH mutation and 1p/19q codeletion using an independent Nagoya cohort including 202 cases. These analysis models were established within 30 min. This easy-to-use CADx system might be useful for the clinical application of CADx in various institutes.
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Affiliation(s)
- Tomohide Nishikawa
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
| | - Fumiharu Ohka
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan.
| | - Kosuke Aoki
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
| | - Hiromichi Suzuki
- Division of Brain Tumor Translational Research, National Cancer Center Research Institute, Tokyo, Japan
| | - Kazuya Motomura
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
| | - Junya Yamaguchi
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
| | - Sachi Maeda
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
| | - Yuji Kibe
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
| | - Hiroki Shimizu
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
| | - Atsushi Natsume
- Institutes of Innovation for Future Society, Nagoya University, Nagoya, Japan
| | - Hideki Innan
- Department of Evolutionary Studies of Biosystems, The Graduate University for Advanced Studies, Hayama, Japan
| | - Ryuta Saito
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
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Luo J, Pan M, Mo K, Mao Y, Zou D. Emerging role of artificial intelligence in diagnosis, classification and clinical management of glioma. Semin Cancer Biol 2023; 91:110-123. [PMID: 36907387 DOI: 10.1016/j.semcancer.2023.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 03/05/2023] [Accepted: 03/08/2023] [Indexed: 03/12/2023]
Abstract
Glioma represents a dominant primary intracranial malignancy in the central nervous system. Artificial intelligence that mainly includes machine learning, and deep learning computational approaches, presents a unique opportunity to enhance clinical management of glioma through improving tumor segmentation, diagnosis, differentiation, grading, treatment, prediction of clinical outcomes (prognosis, and recurrence), molecular features, clinical classification, characterization of the tumor microenvironment, and drug discovery. A growing body of recent studies apply artificial intelligence-based models to disparate data sources of glioma, covering imaging modalities, digital pathology, high-throughput multi-omics data (especially emerging single-cell RNA sequencing and spatial transcriptome), etc. While these early findings are promising, future studies are required to normalize artificial intelligence-based models to improve the generalizability and interpretability of the results. Despite prominent issues, targeted clinical application of artificial intelligence approaches in glioma will facilitate the development of precision medicine of this field. If these challenges can be overcome, artificial intelligence has the potential to profoundly change the way patients with or at risk of glioma are provided with more rational care.
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Affiliation(s)
- Jiefeng Luo
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China
| | - Mika Pan
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China
| | - Ke Mo
- Clinical Research Center, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China
| | - Yingwei Mao
- Department of Biology, Pennsylvania State University, University Park, PA 16802, USA.
| | - Donghua Zou
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China; Clinical Research Center, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, Guangxi, China.
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Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology. Semin Cancer Biol 2023; 91:1-15. [PMID: 36801447 DOI: 10.1016/j.semcancer.2023.02.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 01/30/2023] [Accepted: 02/15/2023] [Indexed: 02/21/2023]
Abstract
Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically required single-gene sequencing, next-generation sequencing, or visual inspection of histopathology slides by experienced pathologists in a clinical context. In the past decade, advances in artificial intelligence (AI) technologies have demonstrated remarkable potential in assisting physicians with accurate diagnosis of oncology image-recognition tasks. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics, providing critical guidance for the stratification of patients in the context of precision therapy. Given that the mutation detection is unaffordable and time-consuming for a considerable number of patients, predicting gene mutations based on routine clinical radiological scans or whole-slide images of tissue with AI-based methods has become a hot issue in actual clinical practice. In this review, we synthesized the general framework of multimodal integration (MMI) for molecular intelligent diagnostics beyond standard techniques. Then we summarized the emerging applications of AI in the prediction of mutational and molecular profiles of common cancers (lung, brain, breast, and other tumor types) pertaining to radiology and histology imaging. Furthermore, we concluded that there truly exist multiple challenges of AI techniques in the way of its real-world application in the medical field, including data curation, feature fusion, model interpretability, and practice regulations. Despite these challenges, we still prospect the clinical implementation of AI as a highly potential decision-support tool to aid oncologists in future cancer treatment management.
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Jordan JT, Gerstner ER. Imaging of Brain Tumors. Continuum (Minneap Minn) 2023; 29:171-193. [PMID: 36795877 DOI: 10.1212/con.0000000000001202] [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: 02/18/2023]
Abstract
OBJECTIVE This article focuses on neuroimaging as an essential tool for diagnosing brain tumors and monitoring response to treatment. LATEST DEVELOPMENTS Neuroimaging is useful at all stages of brain tumor care. Technologic advances have improved the clinical diagnostic capability of neuroimaging as a vital complement to history, examination, and pathologic assessment. Presurgical evaluations are enriched by novel imaging techniques, through improved differential diagnosis and better surgical planning using functional MRI (fMRI) and diffusion tensor imaging. The common clinical challenge of differentiating tumor progression from treatment-related inflammatory change is aided by novel uses of perfusion imaging, susceptibility-weighted imaging (SWI), spectroscopy, and new positron emission tomography (PET) tracers. ESSENTIAL POINTS Using the most up-to-date imaging techniques will facilitate high-quality clinical practice in the care of patients with brain tumors.
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Chen Z, Zhang H, Zhang PJZ, Bai HX, Li X. Editorial: Advances of radiomics and artificial intelligence in the management of patients with central nervous system tumors. Front Oncol 2023; 13:1081301. [PMID: 36741005 PMCID: PMC9893481 DOI: 10.3389/fonc.2023.1081301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 01/11/2023] [Indexed: 01/20/2023] Open
Affiliation(s)
- Ziyan Chen
- Department of Neurosurgery, Xinagya hospital, Central South University, Changsha, China,Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Helen Zhang
- Department of Diagnostic Imaging, Rhode Island Hospital and Alpert Medical School of Brown University, Providence, RI, United States
| | - Paul J. Z. Zhang
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States
| | - Harrison X. Bai
- Department of Diagnostic Imaging, Rhode Island Hospital and Alpert Medical School of Brown University, Providence, RI, United States,Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Xuejun Li
- Department of Neurosurgery, Xinagya hospital, Central South University, Changsha, China,Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China,*Correspondence: Xuejun Li,
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Karami G, Pascuzzo R, Figini M, Del Gratta C, Zhang H, Bizzi A. Combining Multi-Shell Diffusion with Conventional MRI Improves Molecular Diagnosis of Diffuse Gliomas with Deep Learning. Cancers (Basel) 2023; 15:cancers15020482. [PMID: 36672430 PMCID: PMC9856805 DOI: 10.3390/cancers15020482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 12/21/2022] [Accepted: 01/03/2023] [Indexed: 01/14/2023] Open
Abstract
The WHO classification since 2016 confirms the importance of integrating molecular diagnosis for prognosis and treatment decisions of adult-type diffuse gliomas. This motivates the development of non-invasive diagnostic methods, in particular MRI, to predict molecular subtypes of gliomas before surgery. At present, this development has been focused on deep-learning (DL)-based predictive models, mainly with conventional MRI (cMRI), despite recent studies suggesting multi-shell diffusion MRI (dMRI) offers complementary information to cMRI for molecular subtyping. The aim of this work is to evaluate the potential benefit of combining cMRI and multi-shell dMRI in DL-based models. A model implemented with deep residual neural networks was chosen as an illustrative example. Using a dataset of 146 patients with gliomas (from grade 2 to 4), the model was trained and evaluated, with nested cross-validation, on pre-operative cMRI, multi-shell dMRI, and a combination of the two for the following classification tasks: (i) IDH-mutation; (ii) 1p/19q-codeletion; and (iii) three molecular subtypes according to WHO 2021. The results from a subset of 100 patients with lower grades gliomas (2 and 3 according to WHO 2016) demonstrated that combining cMRI and multi-shell dMRI enabled the best performance in predicting IDH mutation and 1p/19q codeletion, achieving an accuracy of 75 ± 9% in predicting the IDH-mutation status, higher than using cMRI and multi-shell dMRI separately (both 70 ± 7%). Similar findings were observed for predicting the 1p/19q-codeletion status, with the accuracy from combining cMRI and multi-shell dMRI (72 ± 4%) higher than from each modality used alone (cMRI: 65 ± 6%; multi-shell dMRI: 66 ± 9%). These findings remain when we considered all 146 patients for predicting the IDH status (combined: 81 ± 5% accuracy; cMRI: 74 ± 5%; multi-shell dMRI: 73 ± 6%) and for the diagnosis of the three molecular subtypes according to WHO 2021 (combined: 60 ± 5%; cMRI: 57 ± 8%; multi-shell dMRI: 56 ± 7%). Together, these findings suggest that combining cMRI and multi-shell dMRI can offer higher accuracy than using each modality alone for predicting the IDH and 1p/19q status and in diagnosing the three molecular subtypes with DL-based models.
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Affiliation(s)
- Golestan Karami
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele D’Annunzio University, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, Gabriele D’Annunzio University, 66100 Chieti, Italy
| | - Riccardo Pascuzzo
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy
- Correspondence:
| | - Matteo Figini
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Cosimo Del Gratta
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele D’Annunzio University, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, Gabriele D’Annunzio University, 66100 Chieti, Italy
| | - Hui Zhang
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Alberto Bizzi
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy
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Chao HS, Tsai CY, Chou CW, Shiao TH, Huang HC, Chen KC, Tsai HH, Lin CY, Chen YM. Artificial Intelligence Assisted Computational Tomographic Detection of Lung Nodules for Prognostic Cancer Examination: A Large-Scale Clinical Trial. Biomedicines 2023; 11:biomedicines11010147. [PMID: 36672655 PMCID: PMC9856020 DOI: 10.3390/biomedicines11010147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 12/23/2022] [Accepted: 12/29/2022] [Indexed: 01/11/2023] Open
Abstract
Low-dose computed tomography (LDCT) has emerged as a standard method for detecting early-stage lung cancer. However, the tedious computer tomography (CT) slide reading, patient-by-patient check, and lack of standard criteria to determine the vague but possible nodule leads to variable outcomes of CT slide interpretation. To determine the artificial intelligence (AI)-assisted CT examination, AI algorithm-assisted CT screening was embedded in the hospital picture archiving and communication system, and a 200 person-scaled clinical trial was conducted at two medical centers. With AI algorithm-assisted CT screening, the sensitivity of detecting nodules sized 4−5 mm, 6~10 mm, 11~20 mm, and >20 mm increased by 41%, 11.2%, 10.3%, and 18.7%, respectively. Remarkably, the overall sensitivity of detecting varied nodules increased by 20.7% from 67.7% to 88.4%. Furthermore, the sensitivity increased by 18.5% from 72.5% to 91% for detecting ground glass nodules (GGN), which is challenging for radiologists and physicians. The free-response operating characteristic (FROC) AI score was ≥0.4, and the AI algorithm standalone CT screening sensitivity reached >95% with an area under the localization receiver operating characteristic curve (LROC-AUC) of >0.88. Our study demonstrates that AI algorithm-embedded CT screening significantly ameliorates tedious LDCT practices for doctors.
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Affiliation(s)
- Heng-Sheng Chao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Chiao-Yun Tsai
- Division of Thoracic Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- Institute of Medicine, College of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
| | - Chung-Wei Chou
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Tsu-Hui Shiao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Hsu-Chih Huang
- Division of Thoracic Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- Institute of Medicine, College of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
| | - Kun-Chieh Chen
- Division of Pulmonary Medicine, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- Department of Applied Chemistry, National Chi Nan University, Nantou 545301, Taiwan
| | - Hao-Hung Tsai
- Institute of Medicine, College of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
- Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- School of Medicine, College of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
| | - Chin-Yu Lin
- Institute of New Drug Development, College of Medicine, China Medical University, Taichung 40402, Taiwan
- Tsuzuki Institute for Traditional Medicine, College of Pharmacy, China Medical University, Taichung 40402, Taiwan
- Department for Biomedical Engineering, Collage of Biomedical Engineering, China Medical University, Taichung 40402, Taiwan
| | - Yuh-Min Chen
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Correspondence: ; Tel.: +886-2-28712121 (ext. 7865)
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Wu B, Moeckel G. Application of digital pathology and machine learning in the liver, kidney and lung diseases. J Pathol Inform 2023; 14:100184. [PMID: 36714454 PMCID: PMC9874068 DOI: 10.1016/j.jpi.2022.100184] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/28/2022] [Accepted: 12/28/2022] [Indexed: 01/05/2023] Open
Abstract
The development of rapid and accurate Whole Slide Imaging (WSI) has paved the way for the application of Artificial Intelligence (AI) to digital pathology. The availability of WSI in the recent years allowed the rapid development of various AI technologies to blossom. WSI-based digital pathology combined with neural networks can automate arduous and time-consuming tasks of slide evaluation. Machine Learning (ML)-based AI has been demonstrated to outperform pathologists by eliminating inter- and intra-observer subjectivity, obtaining quantitative data from slide images, and extracting hidden image patterns that are relevant to disease subtype and progression. In this review, we outline the functionality of different AI technologies such as neural networks and deep learning and discover how aspects of different diseases make them benefit from the implementation of AI. AI has proven to be valuable in many different organs, with this review focusing on the liver, kidney, and lungs. We also discuss how AI and image analysis not only can grade diseases objectively but also discover aspects of diseases that have prognostic value. In the end, we review the current status of the integration of AI in pathology and share our vision on the future of digital pathology.
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Affiliation(s)
- Benjamin Wu
- Horace Mann School, Bronx, NY, USA,Corresponding author at: 950 Post Rd., Scarsdale, NY 10583, USA.
| | - Gilbert Moeckel
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
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Zhang H, Cao H, Luo H, Zhang N, Wang Z, Dai Z, Wu W, Liu G, Xie Z, Cheng Q, Cheng Y. RUNX1/CD44 axis regulates the proliferation, migration, and immunotherapy of gliomas: A single-cell sequencing analysis. Front Immunol 2023; 14:1086280. [PMID: 36776876 PMCID: PMC9909339 DOI: 10.3389/fimmu.2023.1086280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 01/10/2023] [Indexed: 01/27/2023] Open
Abstract
Background Glioma is one of the most common, primary, and lethal adult brain tumors because of its extreme aggressiveness and poor prognosis. Several recent studies relevant to the immune function of CD44, a transmembrane glycoprotein as a significant hyaluronic acid receptor, have achieved great success, revealing the critical role of CD44 in immune infiltration in gliomas. The overexpression of CD44 has been verified to correlate with cancer aggressiveness and migration, while the clinical and immune features of CD44 expression have not yet been thoroughly characterized in gliomas. Methods Molecular and clinical data of glioma collected from publicly available genomic databases were analyzed. Results CD44 was up-expressed in malignant gliomas, notably in the 1p/19q non-codeletion cases, isocitrate dehydrogenase (IDH) wild-type, and mesenchymal subtypes in GBM samples. CD44 expression level strongly correlates with stromal and immune cells, mainly infiltrating the glioma microenvironment by single-cell sequencing analysis. Meanwhile, CD44 can be a promising biomarker in predicting immunotherapy responses and mediating the expression of PD-L1. Finally, RUNX1/CD44 axis could promote the proliferation and migration of gliomas. Conclusions Therefore, CD44 was responsible for glioma growth and progression. It could potentially lead to a novel target for glioma immunotherapy or a prognostic biomarker.
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Affiliation(s)
- Hao Zhang
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Hui Cao
- Department of Psychiatry, Brain Hospital of Hunan Province, The Second People's Hospital of Hunan Province, Changsha, China.,The School of Clinical Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Hong Luo
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Nan Zhang
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Zeyu Wang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Ziyu Dai
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Wantao Wu
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Guodong Liu
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Zongyi Xie
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Quan Cheng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yuan Cheng
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
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Alom Z, Tran QT, Bag AK, Lucas JT, Orr BA. Predicting methylation class from diffusely infiltrating adult gliomas using multimodality MRI data. Neurooncol Adv 2023; 5:vdad045. [PMID: 37215955 PMCID: PMC10195196 DOI: 10.1093/noajnl/vdad045] [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] [Indexed: 05/24/2023] Open
Abstract
Background Radiogenomic studies of adult-type diffuse gliomas have used magnetic resonance imaging (MRI) data to infer tumor attributes, including abnormalities such as IDH-mutation status and 1p19q deletion. This approach is effective but does not generalize to tumor types that lack highly recurrent alterations. Tumors have intrinsic DNA methylation patterns and can be grouped into stable methylation classes even when lacking recurrent mutations or copy number changes. The purpose of this study was to prove the principle that a tumor's DNA-methylation class could be used as a predictive feature for radiogenomic modeling. Methods Using a custom DNA methylation-based classification model, molecular classes were assigned to diffuse gliomas in The Cancer Genome Atlas (TCGA) dataset. We then constructed and validated machine learning models to predict a tumor's methylation family or subclass from matched multisequence MRI data using either extracted radiomic features or directly from MRI images. Results For models using extracted radiomic features, we demonstrated top accuracies above 90% for predicting IDH-glioma and GBM-IDHwt methylation families, IDH-mutant tumor methylation subclasses, or GBM-IDHwt molecular subclasses. Classification models utilizing MRI images directly demonstrated average accuracies of 80.6% for predicting methylation families, compared to 87.2% and 89.0% for differentiating IDH-mutated astrocytomas from oligodendrogliomas and glioblastoma molecular subclasses, respectively. Conclusions These findings demonstrate that MRI-based machine learning models can effectively predict the methylation class of brain tumors. Given appropriate datasets, this approach could generalize to most brain tumor types, expanding the number and types of tumors that could be used to develop radiomic or radiogenomic models.
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Affiliation(s)
- Zahangir Alom
- Department of Pathology, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
| | - Quynh T Tran
- Department of Pathology, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
| | - Asim K Bag
- Department of Diagnostic Imaging, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
| | - John T Lucas
- Department of Radiation Oncology, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
| | - Brent A Orr
- Corresponding Author: Brent A. Orr MD, PhD, Department of Pathology, St. Jude Children’s Research Hospital, 262 Danny Thomas Place, MS 250, Memphis, TN 38-105-3678, USA ()
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Zuo Z, Liu W, Zeng Y, Fan X, Li L, Chen J, Zhou X, Jiang Y, Yang X, Feng Y, Lu Y. Multiparametric magnetic resonance imaging-derived deep learning network to determine ferroptosis-related gene signatures in gliomas. Front Neurosci 2022; 16:1082867. [PMID: 36605558 PMCID: PMC9808079 DOI: 10.3389/fnins.2022.1082867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 12/02/2022] [Indexed: 12/24/2022] Open
Abstract
Introduction Ferroptosis-related gene (FRG) signature is important for assessing novel therapeutic approaches and prognosis in glioma. We trained a deep learning network for determining FRG signatures using multiparametric magnetic resonance imaging (MRI). Methods FRGs of patients with glioma were acquired from public databases. FRG-related risk score stratifying prognosis was developed from The Cancer Genome Atlas (TCGA) and validated using the Chinese Glioma Genome Atlas. Multiparametric MRI-derived glioma images and the corresponding genomic information were obtained for 122 cases from TCGA and The Cancer Imaging Archive. The deep learning network was trained using 3D-Resnet, and threefold cross-validation was performed to evaluate the predictive performance. Results The FRG-related risk score was associated with poor clinicopathological features and had a high predictive value for glioma prognosis. Based on the FRG-related risk score, patients with glioma were successfully classified into two subgroups (28 and 94 in the high- and low-risk groups, respectively). The deep learning networks TC (enhancing tumor and non-enhancing portion of the tumor core) mask achieved an average cross-validation accuracy of 0.842 and an average AUC of 0.781, while the deep learning networks WT (whole tumor and peritumoral edema) mask achieved an average cross-validation accuracy of 0.825 and an average AUC of 0.781. Discussion Our findings indicate that FRG signature is a prognostic indicator of glioma. In addition, we developed a deep learning network that has high classification accuracy in automatically determining FRG signatures, which may be an important step toward the clinical translation of novel therapeutic approaches and prognosis of glioma.
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Affiliation(s)
- Zhichao Zuo
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, China
| | - Wen Liu
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Ying Zeng
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, China
| | - Xiaohong Fan
- The School of Mathematics and Computational Science, Xiangtan University, Xiangtan, Hunan, China
| | - Li Li
- Department of Radiology, Hunan Children’s Hospital, University of South China, Changsha, Hunan, China
| | - Jing Chen
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Xiao Zhou
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, China
| | - Yihong Jiang
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, China
| | - Xiuqi Yang
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, China
| | - Yujie Feng
- The School of Mathematics and Computational Science, Xiangtan University, Xiangtan, Hunan, China,*Correspondence: Yujie Feng,
| | - Yixin Lu
- Medical Imaging Department, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China,Yixin Lu,
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Noninvasive Classification of Glioma Subtypes Using Multiparametric MRI to Improve Deep Learning. Diagnostics (Basel) 2022; 12:diagnostics12123063. [PMID: 36553070 PMCID: PMC9776470 DOI: 10.3390/diagnostics12123063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/26/2022] [Accepted: 12/03/2022] [Indexed: 12/12/2022] Open
Abstract
Background: Deep learning (DL) methods can noninvasively predict glioma subtypes; however, there is no set paradigm for the selection of network structures and input data, including the image combination method, image processing strategy, type of numeric data, and others. Purpose: To compare different combinations of DL frameworks (ResNet, ConvNext, and vision transformer (VIT)), image preprocessing strategies, magnetic resonance imaging (MRI) sequences, and numerical data for increasing the accuracy of DL models for differentiating glioma subtypes prior to surgery. Methods: Our dataset consisted of 211 patients with newly diagnosed gliomas who underwent preoperative MRI with standard and diffusion-weighted imaging methods. Different data combinations were used as input for the three different DL classifiers. Results: The accuracy of the image preprocessing strategies, including skull stripping, segment addition, and individual treatment of slices, was 5%, 10%, and 12.5% higher, respectively, than that of the other strategies. The accuracy increased by 7.5% and 10% following the addition of ADC and numeric data, respectively. ResNet34 exhibited the best performance, which was 5% and 17.5% higher than that of ConvNext tiny and VIT-base, respectively. Data Conclusions: The findings demonstrated that the addition of quantitatively numeric data, ADC images, and effective image preprocessing strategies improved model accuracy for datasets of similar size. The performance of ResNet was superior for small or medium datasets.
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He A, Wang P, Zhu A, Liu Y, Chen J, Liu L. Predicting IDH Mutation Status in Low-Grade Gliomas Based on Optimal Radiomic Features Combined with Multi-Sequence Magnetic Resonance Imaging. Diagnostics (Basel) 2022; 12:diagnostics12122995. [PMID: 36553002 PMCID: PMC9776893 DOI: 10.3390/diagnostics12122995] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/24/2022] [Accepted: 11/25/2022] [Indexed: 12/03/2022] Open
Abstract
The IDH somatic mutation status is an important basis for the diagnosis and classification of gliomas. We proposed a "6-Step" general radiomics model to noninvasively predict the IDH mutation status by simultaneously tuning combined multi-sequence MRI and optimizing the full radiomics processing pipeline. Radiomic features (n = 3776) were extracted from multi-sequence MRI (T1, T2, FLAIR, and T1Gd) in low-grade gliomas (LGGs), and a total of 45,360 radiomics pipeline were investigated according to different settings. The predictive ability of the general radiomics model was evaluated with regards to accuracy, stability, and efficiency. Based on numerous experiments, we finally reached an optimal pipeline for classifying IDH mutation status, namely the T2+FLAIR combined multi-sequence with the wavelet image filter, mean data normalization, PCC dimension reduction, RFE feature selection, and SVM classifier. The mean and standard deviation of AUC, accuracy, sensitivity, and specificity were 0.873 ± 0.05, 0.876 ± 0.09, 0.875 ± 0.11, and 0.877 ± 0.15, respectively. Furthermore, 14 radiomic features that best distinguished the IDH mutation status of the T2+FLAIR multi-sequence were analyzed, and the gray level co-occurrence matrix (GLCM) features were shown to be of high importance. Apart from the promising prediction of the molecular subtypes, this study also provided a general tool for radiomics investigation.
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Affiliation(s)
- Ailing He
- Big Data Center, Affiliated Hospital of Jiangnan University, Wuxi 214122, China
| | - Peng Wang
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi 214122, China
| | - Aihua Zhu
- Department of Neurosurgery, Affiliated Hospital of Jiangnan University, Wuxi 214122, China
| | - Yankui Liu
- Department of Pathology, Affiliated Hospital of Jiangnan University, Wuxi 214122, China
| | - Jianhuan Chen
- Laboratory of Genomic and Precision Medicine, Wuxi School of Medicine, Jiangnan University, Wuxi 214122, China
- Correspondence: (J.C.); (L.L.)
| | - Li Liu
- Big Data Center, Affiliated Hospital of Jiangnan University, Wuxi 214122, China
- Correspondence: (J.C.); (L.L.)
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Validation of MRI-Based Models to Predict MGMT Promoter Methylation in Gliomas: BraTS 2021 Radiogenomics Challenge. Cancers (Basel) 2022; 14:cancers14194827. [PMID: 36230750 PMCID: PMC9562637 DOI: 10.3390/cancers14194827] [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: 08/11/2022] [Revised: 09/19/2022] [Accepted: 09/30/2022] [Indexed: 12/05/2022] Open
Abstract
Simple Summary O6-methylguanine-DNA methyl transferase (MGMT) methylation in glioblastoma is an important prognostic and predictive factor that requires an invasive surgical procedure for identification. In several recent studies, MGMT methylation prediction models were developed using MR images, and good diagnostic performance was achieved, which seems to indicate a promising future for radiogenomics. However, the diagnostic performance was not reproducible for numerous research teams when using a larger dataset in the RSNA-MICCAI Brain Tumor Radiogenomic Classification 2021 challenge. To our knowledge, there has been no study regarding the external validation of MGMT prediction models using large-scale multicenter datasets. We tested recent CNN architectures via extensive experiments to investigate whether MGMT methylation in gliomas can be predicted using MRI. With unexpected negative results, approximately 80% of the developed models showed no significant difference with the chance level of 50% in terms of external validation accuracy. In conclusion, MGMT methylation status of gliomas may not be predictable with preoperative MRI, even using deep learning. Abstract O6-methylguanine-DNA methyl transferase (MGMT) methylation prediction models were developed using only small datasets without proper external validation and achieved good diagnostic performance, which seems to indicate a promising future for radiogenomics. However, the diagnostic performance was not reproducible for numerous research teams when using a larger dataset in the RSNA-MICCAI Brain Tumor Radiogenomic Classification 2021 challenge. To our knowledge, there has been no study regarding the external validation of MGMT prediction models using large-scale multicenter datasets. We tested recent CNN architectures via extensive experiments to investigate whether MGMT methylation in gliomas can be predicted using MR images. Specifically, prediction models were developed and validated with different training datasets: (1) the merged (SNUH + BraTS) (n = 985); (2) SNUH (n = 400); and (3) BraTS datasets (n = 585). A total of 420 training and validation experiments were performed on combinations of datasets, convolutional neural network (CNN) architectures, MRI sequences, and random seed numbers. The first-place solution of the RSNA-MICCAI radiogenomic challenge was also validated using the external test set (SNUH). For model evaluation, the area under the receiver operating characteristic curve (AUROC), accuracy, precision, and recall were obtained. With unexpected negative results, 80.2% (337/420) and 60.0% (252/420) of the 420 developed models showed no significant difference with a chance level of 50% in terms of test accuracy and test AUROC, respectively. The test AUROC and accuracy of the first-place solution of the BraTS 2021 challenge were 56.2% and 54.8%, respectively, as validated on the SNUH dataset. In conclusion, MGMT methylation status of gliomas may not be predictable with preoperative MR images even using deep learning.
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Li Y, Qin Q, Zhang Y, Cao Y. Noninvasive Determination of the IDH Status of Gliomas Using MRI and MRI-Based Radiomics: Impact on Diagnosis and Prognosis. Curr Oncol 2022; 29:6893-6907. [PMID: 36290819 PMCID: PMC9600456 DOI: 10.3390/curroncol29100542] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 09/18/2022] [Accepted: 09/19/2022] [Indexed: 01/13/2023] Open
Abstract
Gliomas are the most common primary malignant brain tumors in adults. The fifth edition of the WHO Classification of Tumors of the Central Nervous System, published in 2021, provided molecular and practical approaches to CNS tumor taxonomy. Currently, molecular features are essential for differentiating the histological subtypes of gliomas, and recent studies have emphasized the importance of isocitrate dehydrogenase (IDH) mutations in stratifying biologically distinct subgroups of gliomas. IDH plays a significant role in gliomagenesis, and the association of IDH status with prognosis is very clear. Recently, there has been much progress in conventional MR imaging (cMRI), advanced MR imaging (aMRI), and radiomics, which are widely used in the study of gliomas. These advances have resulted in an improved correlation between MR signs and IDH mutation status, which will complement the prediction of the IDH phenotype. Although imaging cannot currently substitute for genetic tests, imaging findings have shown promising signs of diagnosing glioma subtypes and evaluating the efficacy and prognosis of individualized molecular targeted therapy. This review focuses on the correlation between MRI and MRI-based radiomics and IDH gene-phenotype prediction, discussing the value and application of these techniques in the diagnosis and evaluation of the prognosis of gliomas.
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Affiliation(s)
- Yurong Li
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing 210029, China
- The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, China
| | - Qin Qin
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing 210029, China
| | - Yumeng Zhang
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing 210029, China
| | - Yuandong Cao
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing 210029, China
- Correspondence:
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Wu J, Mayer AT, Li R. Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy. Semin Cancer Biol 2022; 84:310-328. [PMID: 33290844 PMCID: PMC8319834 DOI: 10.1016/j.semcancer.2020.12.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 11/29/2020] [Accepted: 12/02/2020] [Indexed: 02/07/2023]
Abstract
Radiological imaging is an integral component of cancer care, including diagnosis, staging, and treatment response monitoring. It contains rich information about tumor phenotypes that are governed not only by cancer cellintrinsic biological processes but also by the tumor microenvironment, such as the composition and function of tumor-infiltrating immune cells. By analyzing the radiological scans using a quantitative radiomics approach, robust relations between specific imaging and molecular phenotypes can be established. Indeed, a number of studies have demonstrated the feasibility of radiogenomics for predicting intrinsic molecular subtypes and gene expression signatures in breast cancer based on MRI. In parallel, promising results have been shown for inferring the amount of tumor-infiltrating lymphocytes, a key factor for the efficacy of cancer immunotherapy, from standard-of-care radiological images. Compared with the biopsy-based approach, radiogenomics offers a unique avenue to profile the molecular makeup of the tumor and immune microenvironment as well as its evolution in a noninvasive and holistic manner through longitudinal imaging scans. Here, we provide a systematic review of the state of the art radiogenomics studies in the era of immunotherapy and discuss emerging paradigms and opportunities in AI and deep learning approaches. These technical advances are expected to transform the radiogenomics field, leading to the discovery of reliable imaging biomarkers. This will pave the way for their clinical translation to guide precision cancer therapy.
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Affiliation(s)
- Jia Wu
- Department of Imaging Physics, MD Anderson Cancer Center, Texas, 77030, USA; Department of Thoracic/Head & Neck Medical Oncology, MD Anderson Cancer Center, Texas, 77030, USA.
| | - Aaron T Mayer
- Department of Bioengineering, Stanford University, Stanford, California, 94305, USA; Department of Radiology, Stanford University, Stanford, California, 94305, USA; Molecular Imaging Program at Stanford, Stanford University, Stanford, California, 94305, USA; BioX Program at Stanford, Stanford University, Stanford, California, 94305, USA
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University, Stanford, California, 94305, USA
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MR image normalization dilemma and the accuracy of brain tumor classification model. JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2022. [DOI: 10.1016/j.jrras.2022.05.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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