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Luan J, Zhang D, Liu B, Yang A, Lv K, Hu P, Yu H, Shmuel A, Zhang C, Ma G. Exploring the prognostic value and biological pathways of transcriptomics and radiomics patterns in glioblastoma multiforme. Heliyon 2024; 10:e33760. [PMID: 39071633 PMCID: PMC11283067 DOI: 10.1016/j.heliyon.2024.e33760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 06/20/2024] [Accepted: 06/26/2024] [Indexed: 07/30/2024] Open
Abstract
Objectives To develop a multi-omics prognostic model integrating transcriptomics and radiomics for predicting overall survival in patients with glioblastoma multiforme (GBM), and investigate the biological pathways of radiomics patterns. Materials and methods Transcription profiles of GBM patients and normal controls were used to obtain differentially expressed mRNAs and long non-coding RNAs (lncRNAs). Radiomics features were extracted from magnetic resonance imaging (MRI). Least absolute shrinkage and selection operator (LASSO) Cox regression was employed to select survival-associated features for the construction of transcriptomics and radiomics signatures. Genes associated with GBM prognosis were identified through the analysis of lncRNA-mRNA co-expression networks and Weighted Gene Co-expression Network Analysis (WGCNA), and their biological pathways were investigated using Genomes enrichment analysis. Transcriptomics, radiomics, and clinical data were integrated to evaluate the multi-omics prognostic model's performance. Results LASSO Cox regression yielded 21 survival-related features, including 19 transcriptomics features and 2 radiomics features. Based on transcriptomics and radiomics signature, GBM patients were classified as high-risk or low-risk. The genes obtained from the co-expression network screen were associated with microtubule binding, while those from the WGCNA screen were associated with growth factor receptor binding. In the training set, the AUC values for the multi-omics model and clinical model were 0.964 and 0.830, respectively, while in the validation set, they were 0.907 and 0.787. The multi-omics prognostic model outperformed the clinical prognostic model. Conclusions The co-expression network and WGCNA methods revealed genes associated with multiple biological pathways in GBM. The multi-omics prognostic model demonstrated excellent performance and indicated significant potential for clinical application.
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Affiliation(s)
- 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 & Peking Union Medical College, Beijing, China
| | - Di Zhang
- Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Shandong, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Aocai Yang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Kuan Lv
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Pianpian Hu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Hongwei Yu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Amir Shmuel
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Chuanchen Zhang
- Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Shandong, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
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Li T, Xu M, Yang S, Wang G, Liu Y, Liu K, Zhao K, Su X. Development and validation of [18 F]-PSMA-1007 PET-based radiomics model to predict biochemical recurrence-free survival following radical prostatectomy. Eur J Nucl Med Mol Imaging 2024; 51:2806-2818. [PMID: 38691111 DOI: 10.1007/s00259-024-06734-6] [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: 01/17/2024] [Accepted: 04/23/2024] [Indexed: 05/03/2024]
Abstract
PURPOSE Biochemical recurrence (BCR) following radical prostatectomy (RP) is a significant concern for patients with prostate cancer. Reliable prediction models are needed to identify patients at risk for BCR and facilitate appropriate management. This study aimed to develop and validate a clinical-radiomics model based on preoperative [18 F]PSMA-1007 PET for predicting BCR-free survival (BRFS) in patients who underwent RP for prostate cancer. MATERIALS AND METHODS A total of 236 patients with histologically confirmed prostate cancer who underwent RP were retrospectively analyzed. All patients had a preoperative [18 F]PSMA-1007 PET/CT scan. Radiomics features were extracted from the primary tumor region on PET images. A radiomics signature was developed using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The performance of the radiomics signature in predicting BRFS was assessed using Harrell's concordance index (C-index). The clinical-radiomics nomogram was constructed using the radiomics signature and clinical features. The model was externally validated in an independent cohort of 98 patients. RESULTS The radiomics signature comprised three features and demonstrated a C-index of 0.76 (95% CI: 0.60-0.91) in the training cohort and 0.71 (95% CI: 0.63-0.79) in the validation cohort. The radiomics signature remained an independent predictor of BRFS in multivariable analysis (HR: 2.48, 95% CI: 1.47-4.17, p < 0.001). The clinical-radiomics nomogram significantly improved the prediction performance (C-index: 0.81, 95% CI: 0.66-0.95, p = 0.007) in the training cohort and (C-index: 0.78 95% CI: 0.63-0.89, p < 0.001) in the validation cohort. CONCLUSION We developed and validated a novel [18 F]PSMA-1007 PET-based clinical-radiomics model that can predict BRFS following RP in prostate cancer patients. This model may be useful in identifying patients with a higher risk of BCR, thus enabling personalized risk stratification and tailored management strategies.
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Affiliation(s)
- Tiancheng Li
- Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, People's Republic of China
| | - Mimi Xu
- Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, People's Republic of China
| | - Shuye Yang
- Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, People's Republic of China
| | - Guolin Wang
- Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, People's Republic of China
| | - Yinuo Liu
- Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, People's Republic of China
| | - Kaifeng Liu
- Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, People's Republic of China
| | - Kui Zhao
- Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, People's Republic of China
| | - Xinhui Su
- Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, People's Republic of China.
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Truong NCD, Bangalore Yogananda CG, Wagner BC, Holcomb JM, Reddy D, Saadat N, Hatanpaa KJ, Patel TR, Fei B, Lee MD, Jain R, Bruce RJ, Pinho MC, Madhuranthakam AJ, Maldjian JA. Two-Stage Training Framework Using Multicontrast MRI Radiomics for IDH Mutation Status Prediction in Glioma. Radiol Artif Intell 2024; 6:e230218. [PMID: 38775670 PMCID: PMC11294953 DOI: 10.1148/ryai.230218] [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: 06/26/2023] [Revised: 03/18/2024] [Accepted: 04/25/2024] [Indexed: 06/21/2024]
Abstract
Purpose To develop a radiomics framework for preoperative MRI-based prediction of isocitrate dehydrogenase (IDH) mutation status, a crucial glioma prognostic indicator. Materials and Methods Radiomics features (shape, first-order statistics, and texture) were extracted from the whole tumor or the combination of nonenhancing, necrosis, and edema regions. Segmentation masks were obtained via the federated tumor segmentation tool or the original data source. Boruta, a wrapper-based feature selection algorithm, identified relevant features. Addressing the imbalance between mutated and wild-type cases, multiple prediction models were trained on balanced data subsets using random forest or XGBoost and assembled to build the final classifier. The framework was evaluated using retrospective MRI scans from three public datasets (The Cancer Imaging Archive [TCIA, 227 patients], the University of California San Francisco Preoperative Diffuse Glioma MRI dataset [UCSF, 495 patients], and the Erasmus Glioma Database [EGD, 456 patients]) and internal datasets collected from the University of Texas Southwestern Medical Center (UTSW, 356 patients), New York University (NYU, 136 patients), and University of Wisconsin-Madison (UWM, 174 patients). TCIA and UTSW served as separate training sets, while the remaining data constituted the test set (1617 or 1488 testing cases, respectively). Results The best performing models trained on the TCIA dataset achieved area under the receiver operating characteristic curve (AUC) values of 0.89 for UTSW, 0.86 for NYU, 0.93 for UWM, 0.94 for UCSF, and 0.88 for EGD test sets. The best performing models trained on the UTSW dataset achieved slightly higher AUCs: 0.92 for TCIA, 0.88 for NYU, 0.96 for UWM, 0.93 for UCSF, and 0.90 for EGD. Conclusion This MRI radiomics-based framework shows promise for accurate preoperative prediction of IDH mutation status in patients with glioma. Keywords: Glioma, Isocitrate Dehydrogenase Mutation, IDH Mutation, Radiomics, MRI Supplemental material is available for this article. Published under a CC BY 4.0 license. See also commentary by Moassefi and Erickson in this issue.
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Affiliation(s)
- Nghi C. D. Truong
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Chandan Ganesh Bangalore Yogananda
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Benjamin C. Wagner
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - James M. Holcomb
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Divya Reddy
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Niloufar Saadat
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Kimmo J. Hatanpaa
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Toral R. Patel
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Baowei Fei
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Matthew D. Lee
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Rajan Jain
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Richard J. Bruce
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Marco C. Pinho
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Ananth J. Madhuranthakam
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
| | - Joseph A. Maldjian
- From the Departments of Radiology (N.C.D.T., C.G.B.Y., B.C.W.,
J.M.H., D.R., N.S., B.F., M.C.P., A.J.M., J.A.M.), Pathology (K.J.H.), and
Neurologic Surgery (T.R.P.), The University of Texas Southwestern Medical
Center, 5323 Harry Hines Blvd, Dallas, TX 75390; Department of Bioengineering,
The University of Texas at Dallas, Richardson, Tex (B.F.); Departments of
Radiology (M.D.L., R.J.) and Neurosurgery (R.J.), New York University Grossman
School of Medicine, New York, NY; and Department of Radiology, University of
Wisconsin–Madison, Madison, Wis (R.J.B.)
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Liu C, Wang J, Shen J, Chen X, Ji N, Yue S. Accurate and rapid molecular subgrouping of high-grade glioma via deep learning-assisted label-free fiber-optic Raman spectroscopy. PNAS NEXUS 2024; 3:pgae208. [PMID: 38860145 PMCID: PMC11164103 DOI: 10.1093/pnasnexus/pgae208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 05/17/2024] [Indexed: 06/12/2024]
Abstract
Molecular genetics is highly related with prognosis of high-grade glioma. Accordingly, the latest WHO guideline recommends that molecular subgroups of the genes, including IDH, 1p/19q, MGMT, TERT, EGFR, Chromosome 7/10, CDKN2A/B, need to be detected to better classify glioma and guide surgery and treatment. Unfortunately, there is no preoperative or intraoperative technology available for accurate and comprehensive molecular subgrouping of glioma. Here, we develop a deep learning-assisted fiber-optic Raman diagnostic platform for accurate and rapid molecular subgrouping of high-grade glioma. Specifically, a total of 2,354 fingerprint Raman spectra was obtained from 743 tissue sites (astrocytoma: 151; oligodendroglioma: 150; glioblastoma (GBM): 442) of 44 high-grade glioma patients. The convolutional neural networks (ResNet) model was then established and optimized for molecular subgrouping. The mean area under receiver operating characteristic curves (AUC) for identifying the molecular subgroups of high-grade glioma reached 0.904, with mean sensitivity of 83.3%, mean specificity of 85.0%, mean accuracy of 83.3%, and mean time expense of 10.6 s. The diagnosis performance using ResNet model was shown to be superior to PCA-SVM and UMAP models, suggesting that high dimensional information from Raman spectra would be helpful. In addition, for the molecular subgroups of GBM, the mean AUC reached 0.932, with mean sensitivity of 87.8%, mean specificity of 83.6%, and mean accuracy of 84.1%. Furthermore, according to saliency maps, the specific Raman features corresponding to tumor-associated biomolecules (e.g. nucleic acid, tyrosine, tryptophan, cholesteryl ester, fatty acid, and collagen) were found to contribute to the accurate molecular subgrouping. Collectively, this study opens up new opportunities for accurate and rapid molecular subgrouping of high-grade glioma, which would assist optimal surgical resection and instant post-operative decision-making.
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Affiliation(s)
- Chang Liu
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Xueyuan Road 37, Beijing 100191, China
| | - Jiejun Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, South Fourth Ring West Road 119, Beijing 100050, China
| | - Jianghao Shen
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Xueyuan Road 37, Beijing 100191, China
| | - Xun Chen
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Xueyuan Road 37, Beijing 100191, China
- School of Engineering Medicine, Beihang University, Xueyuan Road 37, Beijing 100191, China
| | - Nan Ji
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, South Fourth Ring West Road 119, Beijing 100050, China
| | - Shuhua Yue
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Xueyuan Road 37, Beijing 100191, China
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5
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Luan J, Zhang D, Liu B, Yang A, Lv K, Hu P, Yu H, Shmuel A, Zhang C, Ma G. Immune-related lncRNAs signature and radiomics signature predict the prognosis and immune microenvironment of glioblastoma multiforme. J Transl Med 2024; 22:107. [PMID: 38279111 PMCID: PMC10821572 DOI: 10.1186/s12967-023-04823-y] [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: 06/17/2023] [Accepted: 12/22/2023] [Indexed: 01/28/2024] Open
Abstract
BACKGROUND Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor in adults. This study aimed to construct immune-related long non-coding RNAs (lncRNAs) signature and radiomics signature to probe the prognosis and immune infiltration of GBM patients. METHODS We downloaded GBM RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) project database, and MRI data were obtained from The Cancer Imaging Archive (TCIA). Then, we conducted a cox regression analysis to establish the immune-related lncRNAs signature and radiomics signature. Afterward, we employed a gene set enrichment analysis (GSEA) to explore the biological processes and pathways. Besides, we used CIBERSORT to estimate the abundance of tumor-infiltrating immune cells (TIICs). Furthermore, we investigated the relationship between the immune-related lncRNAs signature, radiomics signature and immune checkpoint genes. Finally, we constructed a multifactors prognostic model and compared it with the clinical prognostic model. RESULTS We identified four immune-related lncRNAs and two radiomics features, which show the ability to stratify patients into high-risk and low-risk groups with significantly different survival rates. The risk score curves and Kaplan-Meier curves confirmed that the immune-related lncRNAs signature and radiomics signature were a novel independent prognostic factor in GBM patients. The GSEA suggested that the immune-related lncRNAs signature were involved in L1 cell adhesion molecular (L1CAM) interactions and the radiomics signature were involved signaling by Robo receptors. Besides, the two signatures was associated with the infiltration of immune cells. Furthermore, they were linked with the expression of critical immune genes and could predict immunotherapy's clinical response. Finally, the area under the curve (AUC) (0.890,0.887) and C-index (0.737,0.817) of the multifactors prognostic model were greater than those of the clinical prognostic model in both the training and validation sets, indicated significantly improved discrimination. CONCLUSIONS We identified the immune-related lncRNAs signature and tradiomics signature that can predict the outcomes, immune cell infiltration, and immunotherapy response in patients with GBM.
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Affiliation(s)
- 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 & Peking Union Medical College, Beijing, China
| | - Di Zhang
- Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Liaocheng, Shandong, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Aocai Yang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Kuan Lv
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Pianpian Hu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Hongwei Yu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Amir Shmuel
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Chuanchen Zhang
- Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Liaocheng, Shandong, China.
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
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Zhao Z, Song Z, Wang Z, Zhang F, Ding Z, Fan T. Advances in Molecular Pathology, Diagnosis and Treatment of Spinal Cord Astrocytomas. Technol Cancer Res Treat 2024; 23:15330338241262483. [PMID: 39043042 PMCID: PMC11271101 DOI: 10.1177/15330338241262483] [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: 02/17/2024] [Revised: 05/23/2024] [Accepted: 05/28/2024] [Indexed: 07/25/2024] Open
Abstract
Spinal cord astrocytoma (SCA) is a rare subtype of astrocytoma, posing challenges in diagnosis and treatment. Low-grade SCA can achieve long-term survival solely through surgery, while high-grade has a disappointing prognosis even with comprehensive treatment. Diagnostic criteria and standard treatment of intracranial astrocytoma have shown obvious limitations in SCA. Research on the molecular mechanism in SCA is lagging far behind that on intracranial astrocytoma. In recent years, huge breakthroughs have been made in molecular pathology of astrocytoma, and novel techniques have emerged, including DNA methylation analysis and radiomics. These advances are now making it possible to provide a precise diagnosis and develop corresponding treatment strategies in SCA. Our aim is to review the current status of diagnosis and treatment of SCA, and summarize the latest research advancement, including tumor subtype, molecular characteristics, diagnostic technology, and potential therapy strategies, thus deepening our understanding of this uncommon tumor type and providing guidance for accurate diagnosis and treatment.
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Affiliation(s)
- Zijun Zhao
- Spine Center, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Zihan Song
- Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Zairan Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Fan Zhang
- Spine Center, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Ze Ding
- Spine Center, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Tao Fan
- Spine Center, Sanbo Brain Hospital, Capital Medical University, Beijing, China
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Cui N, Li J, Jiang Z, Long Z, Liu W, Yao H, Li M, Li W, Wang K. Development and validation of 18F-FDG PET/CT radiomics-based nomogram to predict visceral pleural invasion in solid lung adenocarcinoma. Ann Nucl Med 2023; 37:605-617. [PMID: 37598412 DOI: 10.1007/s12149-023-01861-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 08/07/2023] [Indexed: 08/22/2023]
Abstract
OBJECTIVES This study aimed to establish a radiomics model based on 18F-FDG PET/CT images to predict visceral pleural invasion (VPI) of solid lung adenocarcinoma preoperatively. METHODS We retrospectively enrolled 165 solid lung adenocarcinoma patients confirmed by histopathology with 18F-FDG PET/CT images. Patients were divided into training and validation at a ratio of 0.7. To find significant VPI predictors, we collected clinicopathological information and metabolic parameters measured from PET/CT images. Three-dimensional (3D) radiomics features were extracted from each PET and CT volume of interest (VOI). Receiver operating characteristic (ROC) curve was performed to determine the performance of the model. Accuracy, sensitivity, specificity and area under curve (AUC) were calculated. Finally, their performance was evaluated by concordance index (C-index) and decision curve analysis (DCA) in training and validation cohorts. RESULTS 165 patients were divided into training cohort (n = 116) and validation cohort (n = 49). Multivariate analysis showed that histology grade, maximum standardized uptake value (SUVmax), distance from the lesion to the pleura (DLP) and the radiomics features had statistically significant differences between patients with and without VPI (P < 0.05). A nomogram was developed based on the logistic regression method. The accuracy of ROC curve analysis of this model was 75.86% in the training cohort (AUC: 0.867; C-index: 0.867; sensitivity: 0.694; specificity: 0.889) and the accuracy rate in validation cohort was 71.55% (AUC: 0.889; C-index: 0.819; sensitivity: 0.654; specificity: 0.739). CONCLUSIONS A PET/CT-based radiomics model was developed with SUVmax, histology grade, DLP, and radiomics features. It can be easily used for individualized VPI prediction.
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Affiliation(s)
- Nan Cui
- PET-CT/MRI Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, Heilongjiang, China
| | - Jiatong Li
- PET-CT/MRI Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, Heilongjiang, China
| | - Zhiyun Jiang
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, Heilongjiang, China
| | - Zhiping Long
- Department of Epidemiology, School of Public Health, Harbin Medical University, 157 Baojian Road, Harbin, 150081, Heilongjiang, China
| | - Wei Liu
- PET-CT/MRI Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, Heilongjiang, China
| | - Hongyang Yao
- PET-CT/MRI Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, Heilongjiang, China
| | - Mingshan Li
- PET-CT/MRI Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, Heilongjiang, China
| | - Wei Li
- Interventional Vascular Surgery Department, The 4th Affiliated Hospital of Harbin Medical University, Harbin Medical University, 37 Yiyuan Road, Harbin, 150001, Heilongjiang, China
| | - Kezheng Wang
- PET-CT/MRI Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, Heilongjiang, China.
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Chen C, Du X, Yang L, Liu H, Li Z, Gou Z, Qi J. Research on application of radiomics in glioma: a bibliometric and visual analysis. Front Oncol 2023; 13:1083080. [PMID: 37771434 PMCID: PMC10523166 DOI: 10.3389/fonc.2023.1083080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 08/16/2023] [Indexed: 09/30/2023] Open
Abstract
Background With the continuous development of medical imaging informatics technology, radiomics has become a new and evolving field in medical applications. Radiomics aims to be an aid to support clinical decision making by extracting quantitative features from medical images and has a very wide range of applications. The purpose of this study was to perform a bibliometric and visual analysis of scientific results and research trends in the research application of radiomics in glioma. Methods We searched the Web of Science Core Collection (WOScc) for publications related to glioma radiomics. A bibliometric and visual analysis of online publications in this field related to countries/regions, authors, journals, references and keywords was performed using CiteSpace and R software. Results A total of 587 relevant literature published from 2012 to September 2022 were retrieved in WOScc, and finally a total of 484 publications were obtained according to the filtering criteria, including 393 (81.20%) articles and 91 (18.80%) reviews. The number of relevant publications increases year by year. The highest number of publications was from the USA (171 articles, 35.33%) and China (170 articles, 35.12%). The research institution with the highest number of publications was Chinese Acad Sci (24), followed by Univ Penn (22) and Fudan Univ (21). WANG Y (27) had the most publications, followed by LI Y (22), and WANG J (20). Among the 555 co-cited authors, LOUIS DN (207) and KICKINGEREDER P (207) were the most cited authors. FRONTIERS IN ONCOLOGY (42) was the most published journal and NEURO-ONCOLOGY (412) was the most co-cited journal. The most frequent keywords in all publications included glioblastoma (187), survival (136), classification (131), magnetic resonance imaging (113), machine learning (100), tumor (82), and feature (79), central nervous system (66), IDH (57), and radiomics (55). Cluster analysis was performed on the basis of keyword co-occurrence, and a total of 16 clusters were formed, indicating that these directions are the current hotspots of radiomics research applications in glioma and may be the future directions of continuous development. Conclusion In the past decade, radiomics has received much attention in the medical field and has been widely used in clinical research applications. Cooperation and communication between countries/regions need to be enhanced in future research to promote the development of radiomics in the field of medicine. In addition, the application of radiomics has improved the accuracy of pre-treatment diagnosis, efficacy prediction and prognosis assessment of glioma and helped to promote the development into precision medicine, the future still faces many challenges.
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Affiliation(s)
- Chunbao Chen
- Department of Neurosurgery, Afiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xue Du
- Department of Oncology, The People's Hospital of Hechuan, Chongqing, China
- Department of Oncology, North Sichuan Medical College, Nanchong, China
| | - Lu Yang
- Department of Oncology, Suining Central Hospital, Suining, China
| | - Hongjun Liu
- Department of Neurosurgery, Afiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Zhou Li
- Department of Neurosurgery, Nanchong Central Hospital, The Afiliated Nanchong Central Hospital of North Sichuan Medical College, Nanchong, China
| | - Zhangyang Gou
- Department of Neurosurgery, Afiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Jian Qi
- Department of Neurosurgery, Afiliated Hospital of North Sichuan Medical College, Nanchong, China
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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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Kocak B, Yardimci AH, Nazli MA, Yuzkan S, Mutlu S, Guzelbey T, Sam Ozdemir M, Akin M, Yucel S, Bulut E, Bayrak ON, Okumus AA. REliability of consensus-based segMentatIoN in raDiomic feature reproducibility (REMIND): A word of caution. Eur J Radiol 2023; 165:110893. [PMID: 37285646 DOI: 10.1016/j.ejrad.2023.110893] [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: 03/31/2023] [Revised: 05/01/2023] [Accepted: 05/23/2023] [Indexed: 06/09/2023]
Abstract
OBJECTIVE To evaluate the reliability of consensus-based segmentation in terms of reproducibility of radiomic features. METHODS In this retrospective study, three tumor data sets were investigated: breast cancer (n = 30), renal cell carcinoma (n = 30), and pituitary macroadenoma (n = 30). MRI was utilized for breast and pituitary data sets, while CT was used for renal data set. 12 readers participated in the segmentation process. Consensus segmentation was created by making corrections on a previous region or volume of interest. Four experiments were designed to evaluate the reproducibility of radiomic features. Reliability was assessed with intraclass correlation coefficient (ICC) with two cut-off values: 0.75 and 0.9. RESULTS Considering the lower bound of the 95% confidence interval and the ICC threshold of 0.90, at least 61% of the radiomic features were not reproducible in the inter-consensus analysis. In the susceptibility experiment, at least half (54%) became non-reproducible when the first reader is replaced with a different reader. In the intra-consensus analysis, at least about one-third (32%) were non-reproducible when the same second reader segmented the image over the same first reader two weeks later. Compared to inter-reader analysis based on independent single readers, the inter-consensus analysis did not statistically significantly improve the rates of reproducible features in all data sets and analyses. CONCLUSIONS Despite the positive connotation of the word "consensus", it is essential to REMIND that consensus-based segmentation has significant reproducibility issues. Therefore, the usage of consensus-based segmentation alone should be avoided unless a reliability analysis is performed, even if it is not practical in clinical settings.
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Affiliation(s)
- Burak Kocak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey.
| | - Aytul Hande Yardimci
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Mehmet Ali Nazli
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Sabahattin Yuzkan
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Samet Mutlu
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Tevfik Guzelbey
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Merve Sam Ozdemir
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Meliha Akin
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Serap Yucel
- Department of Radiology, Baskent University, Istanbul Hospital, Istanbul, Turkey
| | - Elif Bulut
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Osman Nuri Bayrak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Ahmet Arda Okumus
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
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Wang P, Chen K, Han Y, Zhao M, Abiyasi N, Peng H, Yan S, Shang J, Shang N, Meng W. Prediction model based on radiomics and clinical features for preoperative lymphovascular invasion in gastric cancer patients. Future Oncol 2023; 19:1613-1626. [PMID: 37377070 DOI: 10.2217/fon-2022-1025] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2023] Open
Abstract
Background: We explored whether a model based on contrast-enhanced computed tomography radiomics features and clinicopathological factors can evaluate preoperative lymphovascular invasion (LVI) in patients with gastric cancer (GC) with Lauren classification. Methods: Based on clinical and radiomic characteristics, we established three models: Clinical + Arterial phase_Radcore, Clinical + Venous phase_Radcore and a combined model. The relationship between Lauren classification and LVI was analyzed using a histogram. Results: We retrospectively analyzed 495 patients with GC. The areas under the curve of the combined model were 0.8629 and 0.8343 in the training and testing datasets, respectively. The combined model showed a superior performance to the other models. Conclusion: CECT-based radiomics models can effectively predict preoperative LVI in GC patients with Lauren classification.
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Affiliation(s)
- Ping Wang
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Kaige Chen
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Ying Han
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Min Zhao
- Pharmaceutical Diagnostics, GE Healthcare, Beijing, China, 1#Tongji South Road, Daxing District, Beijing, 100176, China
| | - Nanding Abiyasi
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Haiyong Peng
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Shaolei Yan
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Jiming Shang
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Naijian Shang
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Wei Meng
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
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Zheng J, Dong H, Li M, Lin X, Wang C. Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images. Clinics (Sao Paulo) 2023; 78:100238. [PMID: 37354775 DOI: 10.1016/j.clinsp.2023.100238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/19/2023] [Accepted: 06/06/2023] [Indexed: 06/26/2023] Open
Abstract
OBJECTIVE To investigate the value of a nomogram based on multiparametric and multiregional MR images to predict Isocitrate Dehydrogenase-1 (IDH1) gene mutations in glioma. DATA AND METHODS The authors performed a retrospective analysis of 110 MR images of surgically confirmed pathological gliomas; 33 patients with IDH1 gene Mutation (IDH1-M) and 77 patients with Wild-type IDH1 (IDH1-W) were divided into training and validation sets in a 7:3 ratio. The clinical features were statistically analyzed using SPSS and R software. Three glioma regions (rCET, rE, rNEC) were outlined using ITK-SNAP software and projected to four conventional sequences (T1, T2, Flair, T1C) for feature extraction using AI-Kit software. The extracted features were screened using R software. A logistic regression model was established, and a nomogram was generated using the selected clinical features. Eight models were developed based on different sequences and ROIs, and Receiver Operating Characteristic (ROC) curves were used to evaluate the predictive efficacy. Decision curve analysis was performed to assess the clinical usefulness. RESULTS Age was selected with Radscore to construct the nomogram. The Model 1 AUC values based on four sequences and three ROIs were the highest in these models, at 0.93 and 0.89, respectively. Decision curve analysis indicated that the net benefit of model 1 was higher than that of the other models for most Pt-values. CONCLUSION A nomogram based on multiparametric and multiregional MR images can predict the mutation status of the IDH1 gene accurately.
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Affiliation(s)
- Jinjing Zheng
- Department of Radiology, Ningbo Medical Center Lihuili Hospital, Ningbo University, China
| | - Haibo Dong
- Department of Radiology, Ningbo Medical Center Lihuili Hospital, Ningbo University, China.
| | - Ming Li
- Department of Radiology, Ningbo Medical Center Lihuili Hospital, Ningbo University, China
| | - Xueyao Lin
- Department of Radiology, Ningbo Medical Center Lihuili Hospital, Ningbo University, China
| | - Chaochao Wang
- Department of Radiology, Ningbo Medical Center Lihuili Hospital, Ningbo University, China
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Zhang S, Mu W, Dong D, Wei J, Fang M, Shao L, Zhou Y, He B, Zhang S, Liu Z, Liu J, Tian J. The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review. HEALTH DATA SCIENCE 2023; 3:0005. [PMID: 38487199 PMCID: PMC10877701 DOI: 10.34133/hds.0005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/05/2022] [Indexed: 03/17/2024]
Abstract
Importance Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity. Highlights We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma. Conclusion AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.
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Affiliation(s)
- Shuaitong Zhang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Wei Mu
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jingwei Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Mengjie Fang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Lizhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yu Zhou
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Bingxi He
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Song Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jianhua Liu
- Department of Oncology, Guangdong Provincial People's Hospital/Second Clinical Medical College of Southern Medical University/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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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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Diffusion-weighted imaging and arterial spin labeling radiomics features may improve differentiation between radiation-induced brain injury and glioma recurrence. Eur Radiol 2022; 33:3332-3342. [PMID: 36576544 DOI: 10.1007/s00330-022-09365-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 11/16/2022] [Accepted: 12/05/2022] [Indexed: 12/29/2022]
Abstract
OBJECTIVES To determine whether radiomics features derived from diffusion-weighted imaging (DWI) and arterial spin labeling (ASL) can improve the differentiation between radiation-induced brain injury (RIBI) and tumor recurrence (TR) in glioma patients. METHODS A total of 4199 radiomics features were extracted from conventional MRI, apparent diffusion coefficient (ADC), and cerebral blood flow (CBF) maps, obtained from 96 pathologically confirmed WHO grade 2~4 gliomas with enhancement after standard treatment. The intraclass correlation coefficient (ICC) was used to test segmentation stability between two doctors. Radiomics features were selected using the Mann-Whitney U test, LASSO regression, and RFE algorithms. Four machine learning classifiers were adopted to establish radiomics models. The diagnostic performance of multiparameter, conventional, and single-parameter MRI radiomics models was compared using the area under the curve (AUC). The models were evaluated in the subsequent independent validation set (n = 30). RESULTS Eight important radiomics features (3 from conventional MRI, 1 from ADC, and 4 from CBF) were selected. Support vector machine (SVM) was chosen as the optimal classifier. The diagnostic performance of the multiparameter MRI radiomics model (AUC 0.96) was higher than that of the conventional MRI (AUC 0.88), ADC (AUC 0.91), and CBF (AUC 0.95) radiomics models. For subgroup analysis, the multiparameter MRI radiomics model showed similar performance, with AUCs of 0.98 in WHO grade 2~3 and 0.96 in WHO grade 4. CONCLUSION The incorporation of noninvasive DWI and ASL into the MRI radiomics model improved the diagnostic performance in differentiating RIBI from TR; ASL, especially, played a significant role. KEY POINTS • The multiparameter MRI radiomics model was superior to the conventional MRI radiomics model in differentiating glioma recurrence from radiation-induced brain injury. • Diffusion and perfusion MRI could improve the ability of the radiomics model in predicting the progression in patients with glioma. • Arterial spin labeling played an important role in predicting glioma progression using radiomics models.
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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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17
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Piao S, Luo X, Bao Y, Hu B, Liu X, Zhu Y, Yang L, Geng D, Li Y. An MRI-based joint model of radiomics and spatial distribution differentiates autoimmune encephalitis from low-grade diffuse astrocytoma. Front Neurol 2022; 13:998279. [PMID: 36408523 PMCID: PMC9669344 DOI: 10.3389/fneur.2022.998279] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 10/12/2022] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND The differential diagnosis between autoimmune encephalitis and low-grade diffuse astrocytoma remains challenging. We aim to develop a quantitative model integrating radiomics and spatial distribution features derived from MRI for discriminating these two conditions. METHODS In our study, we included 188 patients with confirmed autoimmune encephalitis (n = 81) and WHO grade II diffuse astrocytoma (n = 107). Patients with autoimmune encephalitis (AE, n = 59) and WHO grade II diffuse astrocytoma (AS, n = 79) were divided into training and test sets, using stratified sampling according to MRI scanners. We further included an independent validation set (22 patients with AE and 28 patients with AS). Hyperintensity fluid-attenuated inversion recovery (FLAIR) lesions were segmented for each subject. Ten radiomics and eight spatial distribution features were selected via the least absolute shrinkage and selection operator (LASSO), and joint models were constructed by logistic regression for disease classification. Model performance was measured in the test set using the area under the receiver operating characteristic (ROC) curve (AUC). The discrimination performance of the joint model was compared with neuroradiologists. RESULTS The joint model achieved better performance (AUC 0.957/0.908, accuracy 0.914/0.840 for test and independent validation sets, respectively) than the radiomics and spatial distribution models. The joint model achieved lower performance than a senior neuroradiologist (AUC 0.917/0.875) but higher performance than a junior neuroradiologist (AUC 0.692/0.745) in the test and independent validation sets. CONCLUSION The joint model of radiomics and spatial distribution from a single FLAIR could effectively classify AE and AS, providing clinical decision support for the differential diagnosis between the two conditions.
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Affiliation(s)
- Sirong Piao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Xiao Luo
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Yifang Bao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Bin Hu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Xueling Liu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Yuqi Zhu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Liqin Yang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Daoying Geng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Yuxin Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
- Academy for Engineering and Technology, Fudan University, Shanghai, China
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18
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Li C, Li W, Liu C, Zheng H, Cai J, Wang S. Artificial intelligence in multi-parametric magnetic resonance imaging: A review. Med Phys 2022; 49:e1024-e1054. [PMID: 35980348 DOI: 10.1002/mp.15936] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/01/2022] [Accepted: 08/04/2022] [Indexed: 11/06/2022] Open
Abstract
Multi-parametric magnetic resonance imaging (mpMRI) is an indispensable tool in the clinical workflow for the diagnosis and treatment planning of various diseases. Machine learning-based artificial intelligence (AI) methods, especially those adopting the deep learning technique, have been extensively employed to perform mpMRI image classification, segmentation, registration, detection, reconstruction, and super-resolution. The current availability of increasing computational power and fast-improving AI algorithms have empowered numerous computer-based systems for applying mpMRI to disease diagnosis, imaging-guided radiotherapy, patient risk and overall survival time prediction, and the development of advanced quantitative imaging technology for magnetic resonance fingerprinting. However, the wide application of these developed systems in the clinic is still limited by a number of factors, including robustness, reliability, and interpretability. This survey aims to provide an overview for new researchers in the field as well as radiologists with the hope that they can understand the general concepts, main application scenarios, and remaining challenges of AI in mpMRI. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Cheng Li
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Wen Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Chenyang Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Shanshan Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.,Peng Cheng Laboratory, Shenzhen, 518066, China.,Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
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Qin D, Yang G, Jing H, Tan Y, Zhao B, Zhang H. Tumor Progression and Treatment-Related Changes: Radiological Diagnosis Challenges for the Evaluation of Post Treated Glioma. Cancers (Basel) 2022; 14:cancers14153771. [PMID: 35954435 PMCID: PMC9367286 DOI: 10.3390/cancers14153771] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 07/25/2022] [Accepted: 07/27/2022] [Indexed: 12/30/2022] Open
Abstract
Simple Summary Glioma is the most common primary malignant tumor of the adult central nervous system. Despite aggressive multimodal treatment, its prognosis remains poor. During follow-up, it remains challenging to distinguish treatment-related changes from tumor progression in treated patients with gliomas due to both share clinical symptoms and morphological imaging characteristics (with new and/or increasing enhancing mass lesions). The early effective identification of tumor progression and treatment-related changes is of great significance for the prognosis and treatment of gliomas. We believe that advanced neuroimaging techniques can provide additional information for distinguishing both at an early stage. In this article, we focus on the research of magnetic resonance imaging technology and artificial intelligence in tumor progression and treatment-related changes. Finally, it provides new ideas and insights for clinical diagnosis. Abstract As the most common neuro-epithelial tumors of the central nervous system in adults, gliomas are highly malignant and easy to recurrence, with a dismal prognosis. Imaging studies are indispensable for tracking tumor progression (TP) or treatment-related changes (TRCs). During follow-up, distinguishing TRCs from TP in treated patients with gliomas remains challenging as both share similar clinical symptoms and morphological imaging characteristics (with new and/or increasing enhancing mass lesions) and fulfill criteria for progression. Thus, the early identification of TP and TRCs is of great significance for determining the prognosis and treatment. Histopathological biopsy is currently the gold standard for TP and TRC diagnosis. However, the invasive nature of this technique limits its clinical application. Advanced imaging methods (e.g., diffusion magnetic resonance imaging (MRI), perfusion MRI, magnetic resonance spectroscopy (MRS), positron emission tomography (PET), amide proton transfer (APT) and artificial intelligence (AI)) provide a non-invasive and feasible technical means for identifying of TP and TRCs at an early stage, which have recently become research hotspots. This paper reviews the current research on using the abovementioned advanced imaging methods to identify TP and TRCs of gliomas. First, the review focuses on the pathological changes of the two entities to establish a theoretical basis for imaging identification. Then, it elaborates on the application of different imaging techniques and AI in identifying the two entities. Finally, the current challenges and future prospects of these techniques and methods are discussed.
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Affiliation(s)
- Danlei Qin
- College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China;
- Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School, Hospital of Stomatology, Taiyuan 030001, China
| | - Guoqiang Yang
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (Y.T.)
| | - Hui Jing
- Department of MRI, The Six Hospital, Shanxi Medical University, Taiyuan 030008, China;
| | - Yan Tan
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (Y.T.)
| | - Bin Zhao
- College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China;
- Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School, Hospital of Stomatology, Taiyuan 030001, China
- Correspondence: (B.Z.); (H.Z.)
| | - Hui Zhang
- College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China;
- Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (Y.T.)
- Intelligent Imaging Big Data and Functional Nano-imaging Engineering Research Center of Shanxi Province, Taiyuan 030001, China
- Correspondence: (B.Z.); (H.Z.)
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20
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Qi J, Gao A, Ma X, Song Y, zhao G, Bai J, Gao E, Zhao K, Wen B, Zhang Y, Cheng J. Differentiation of Benign From Malignant Parotid Gland Tumors Using Conventional MRI Based on Radiomics Nomogram. Front Oncol 2022; 12:937050. [PMID: 35898886 PMCID: PMC9309371 DOI: 10.3389/fonc.2022.937050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 06/20/2022] [Indexed: 12/12/2022] Open
Abstract
Objectives We aimed to develop and validate radiomic nomograms to allow preoperative differentiation between benign- and malignant parotid gland tumors (BPGT and MPGT, respectively), as well as between pleomorphic adenomas (PAs) and Warthin tumors (WTs). Materials and Methods This retrospective study enrolled 183 parotid gland tumors (68 PAs, 62 WTs, and 53 MPGTs) and divided them into training (n = 128) and testing (n = 55) cohorts. In total, 2553 radiomics features were extracted from fat-saturated T2-weighted images, apparent diffusion coefficient maps, and contrast-enhanced T1-weighted images to construct single-, double-, and multi-sequence combined radiomics models, respectively. The radiomics score (Rad-score) was calculated using the best radiomics model and clinical features to develop the radiomics nomogram. The receiver operating characteristic curve and area under the curve (AUC) were used to assess these models, and their performances were compared using DeLong’s test. Calibration curves and decision curve analysis were used to assess the clinical usefulness of these models. Results The multi-sequence combined radiomics model exhibited better differentiation performance (BPGT vs. MPGT, AUC=0.863; PA vs. MPGT, AUC=0.929; WT vs. MPGT, AUC=0.825; PA vs. WT, AUC=0.927) than the single- and double sequence radiomics models. The nomogram based on the multi-sequence combined radiomics model and clinical features attained an improved classification performance (BPGT vs. MPGT, AUC=0.907; PA vs. MPGT, AUC=0.961; WT vs. MPGT, AUC=0.879; PA vs. WT, AUC=0.967). Conclusions Radiomics nomogram yielded excellent diagnostic performance in differentiating BPGT from MPGT, PA from MPGT, and PA from WT.
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Affiliation(s)
- Jinbo Qi
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ankang Gao
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoyue Ma
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yang Song
- Magnetic Resonance Scientific Marketing, Siemens Healthineers Ltd., Shanghai, China
| | - Guohua zhao
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jie Bai
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Eryuan Gao
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kai Zhao
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Baohong Wen
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Baohong Wen, ; Yong Zhang, ; Jingliang Cheng,
| | - Yong Zhang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Baohong Wen, ; Yong Zhang, ; Jingliang Cheng,
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Baohong Wen, ; Yong Zhang, ; Jingliang Cheng,
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Tong Y, Li J, Chen J, Hu C, Xu Z, Duan S, Wang X, Yu R, Cheng X. A Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperative Prediction of DNA Mismatch Repair Deficiency in Gastric Adenocarcinoma. Front Oncol 2022; 12:865548. [PMID: 35912185 PMCID: PMC9327646 DOI: 10.3389/fonc.2022.865548] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 05/26/2022] [Indexed: 12/24/2022] Open
Abstract
Purpose To develop and validate a radiomics nomogram integrated with clinic-radiological features for preoperative prediction of DNA mismatch repair deficiency (dMMR) in gastric adenocarcinoma. Materials and Methods From March 2014 to August 2020, 161 patients with pathologically confirmed gastric adenocarcinoma were included from two centers (center 1 as the training and internal testing sets, n = 101; center 2 as the external testing sets, n = 60). All patients underwent preoperative contrast-enhanced computerized tomography (CT) examination. Radiomics features were extracted from portal-venous phase CT images. Max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods were used to select features, and then radiomics signature was constructed using logistic regression analysis. A radiomics nomogram was built incorporating the radiomics signature and independent clinical predictors. The model performance was assessed using receiver operating characteristic (ROC) curve analysis, calibration curve, and decision curve analysis (DCA). Results The radiomics signature, which was constructed using two selected features, was significantly associated with dMMR gastric adenocarcinoma in the training and internal testing sets (P < 0.05). The radiomics signature model showed a moderate discrimination ability with an area under the ROC curve (AUC) of 0.81 in the training set, which was confirmed with an AUC of 0.78 in the internal testing set. The radiomics nomogram consisting of the radiomics signature and clinical factors (age, sex, and location) showed excellent discrimination in the training, internal testing, and external testing sets with AUCs of 0.93, 0.82, and 0.83, respectively. Further, calibration curves and DCA analysis demonstrated good fit and clinical utility of the radiomics nomogram. Conclusions The radiomics nomogram combining radiomics signature and clinical characteristics (age, sex, and location) may be used to individually predict dMMR of gastric adenocarcinoma.
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Affiliation(s)
- Yahan Tong
- Department of Radiology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
| | - Jiaying Li
- Department of Radiology, The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Jieyu Chen
- Department of Radiology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Can Hu
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
- Department of Gastric Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
| | - Zhiyuan Xu
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
- Department of Gastric Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
| | - Shaofeng Duan
- Precision Health Institution, GE Healthcare, Shanghai, China
| | - Xiaojie Wang
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Risheng Yu
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Xiangdong Cheng, ; Risheng Yu,
| | - Xiangdong Cheng
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
- Department of Gastric Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- *Correspondence: Xiangdong Cheng, ; Risheng Yu,
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22
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A Survey of Radiomics in Precision Diagnosis and Treatment of Adult Gliomas. J Clin Med 2022; 11:jcm11133802. [PMID: 35807084 PMCID: PMC9267404 DOI: 10.3390/jcm11133802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/18/2022] [Accepted: 06/29/2022] [Indexed: 02/04/2023] Open
Abstract
Glioma is the most common primary malignant tumor of the adult central nervous system (CNS), which mostly shows invasive growth. In most cases, surgery is often difficult to completely remove, and the recurrence rate and mortality of patients are high. With the continuous development of molecular genetics and the great progress of molecular biology technology, more and more molecular biomarkers have been proved to have important guiding significance in the individualized diagnosis, treatment, and prognosis evaluation of glioma. With the updates of the World Health Organization (WHO) classification of tumors of the CNS in 2021, the diagnosis and treatment of glioma has entered the era of precision medicine in the true sense. Due to its ability to non-invasively achieve accurate identification of glioma from other intracranial tumors, and to predict the grade, genotyping, treatment response, and prognosis of glioma, which provides a scientific basis for the clinical application of individualized diagnosis and treatment model of glioma, radiomics has become a research hotspot in the field of precision medicine. This paper reviewed the research related to radiomics of adult gliomas published in recent years and summarized the research proceedings of radiomics in differential diagnosis, preoperative grading and genotyping, treatment and efficacy evaluation, and survival prediction of adult gliomas.
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23
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RP-Rs-fMRIomics as a Novel Imaging Analysis Strategy to Empower Diagnosis of Brain Gliomas. Cancers (Basel) 2022; 14:cancers14122818. [PMID: 35740484 PMCID: PMC9220978 DOI: 10.3390/cancers14122818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/29/2022] [Accepted: 06/01/2022] [Indexed: 12/07/2022] Open
Abstract
Rs-fMRI can provide rich information about functional processes in the brain with a large array of imaging parameters and is also suitable for investigating the biological processes in cerebral gliomas. We aimed to propose an imaging analysis method of RP-Rs-fMRIomics by adopting omics analysis on rs-fMRI with exhaustive regional parameters and subsequently estimating its feasibility on the prediction diagnosis of gliomas. In this retrospective study, preoperative rs-fMRI data were acquired from patients confirmed with diffuse gliomas (n = 176). A total of 420 features were extracted through measuring 14 regional parameters of rs-fMRI as much as available currently in 10 specific narrow frequency bins and three parts of gliomas. With a randomly split training and testing dataset (ratio 7:3), four classifiers were implemented to construct and optimize RP-Rs-fMRIomics models for predicting glioma grade, IDH status and Karnofsky Performance Status scores. The RP-Rs-fMRIomics models (AUROC 0.988, 0.905, 0.801) were superior to the corresponding traditional single rs-fMRI index (AUROC 0.803, 0.731, 0.632) in predicting glioma grade, IDH and survival. The RP-Rs-fMRIomics analysis, featuring high interpretability, was competitive for prediction of glioma grading, IDH genotype and prognosis. The method expanded the clinical application of rs-fMRI and also contributed a new imaging analysis for brain tumor research.
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Wu W, Wang Y, Xiang J, Li X, Wahafu A, Yu X, Bai X, Yan G, Wang C, Wang N, Du C, Xie W, Wang M, Wang J. A Novel Multi-Omics Analysis Model for Diagnosis and Survival Prediction of Lower-Grade Glioma Patients. Front Oncol 2022; 12:729002. [PMID: 35646656 PMCID: PMC9133344 DOI: 10.3389/fonc.2022.729002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 03/24/2022] [Indexed: 01/13/2023] Open
Abstract
Background Lower-grade gliomas (LGGs) are characterized by remarkable genetic heterogeneity and different clinical outcomes. Classification of LGGs is improved by the development of molecular stratification markers including IDH mutation and 1p/19q chromosomal integrity, which are used as a hallmark of survival and therapy sensitivity of LGG patients. However, the reproducibility and sensitivity of the current classification remain ambiguous. This study aimed to construct more accurate risk-stratification approaches. Methods According to bioinformatics, the sequencing profiles of methylation and transcription and imaging data derived from LGG patients were analyzed and developed predictable risk score and radiomics score. Moreover, the performance of predictable models was further validated. Results In this study, we determined a cluster of 6 genes that were correlated with IDH mutation/1p19q co-deletion status. Risk score model was calculated based on 6 genes and showed gratifying sensitivity and specificity for survival prediction and therapy response of LGG patients. Furthermore, a radiomics risk score model was established to noninvasively assist judgment of risk score in pre-surgery. Taken together, a predictable nomogram that combined transcriptional signatures and clinical characteristics was established and validated to be preferable to the histopathological classification. Our novel multi-omics nomograms showed a satisfying performance. To establish a user-friendly application, the nomogram was further developed into a web-based platform: https://drw576223193.shinyapps.io/Nomo/, which could be used as a supporting method in addition to the current histopathological-based classification of gliomas. Conclusions Our novel multi-omics nomograms showed the satisfying performance of LGG patients and assisted clinicians to draw up individualized clinical management.
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Affiliation(s)
- Wei Wu
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yichang Wang
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jianyang Xiang
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xiaodong Li
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Alafate Wahafu
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xiao Yu
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xiaobin Bai
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Ge Yan
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Chunbao Wang
- Department of Pathology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Ning Wang
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Changwang Du
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Wanfu Xie
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Maode Wang
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jia Wang
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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Di G, Tan M, Xu R, Zhou W, Duan K, Hu Z, Cao X, Zhang H, Jiang X. Altered Structural and Functional Patterns Within Executive Control Network Distinguish Frontal Glioma-Related Epilepsy. Front Neurosci 2022; 16:916771. [PMID: 35692418 PMCID: PMC9179179 DOI: 10.3389/fnins.2022.916771] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 05/05/2022] [Indexed: 11/27/2022] Open
Abstract
Background The tumor invasion of the frontal lobe induces changes in the executive control network (ECN). It remains unclear whether epileptic seizures in frontal glioma patients exacerbate the structural and functional alterations within the ECN, and whether these changes can be used to identify glioma-related seizures at an early stage. This study aimed to investigate the altered structural and functional patterns of ECN in frontal gliomas without epilepsy (non-FGep) and frontal gliomas with epilepsy (FGep) and to evaluate whether the patterns can accurately distinguish glioma-related epilepsy. Methods We measured gray matter (GM) volume, regional homogeneity (ReHo), and functional connectivity (FC) within the ECN to identify the structural and functional changes in 50 patients with frontal gliomas (29 non-FGep and 21 FGep) and 39 healthy controls (CN). We assessed the relationships between the structural and functional changes and cognitive function using partial correlation analysis. Finally, we applied a pattern classification approach to test whether structural and functional abnormalities within the ECN can distinguish non-FGep and FGep from CN subjects. Results Within the ECN, non-FGep and FGep showed increased local structure (GM) and function (ReHo), and decreased FC between brain regions compared to CN. Also, non-FGep and FGep showed differential patterns of structural and functional abnormalities within the ECN, and these abnormalities are more severe in FGep than in non-FGep. Lastly, FC between the right superior frontal gyrus and right dorsolateral prefrontal cortex was positively correlated with episodic memory scores in non-FGep and FGep. In particular, the support vector machine (SVM) classifier based on structural and functional abnormalities within ECN could accurately distinguish non-FGep and FGep from CN, and FGep from non-FGep on an individual basis with very high accuracy, area under the curve (AUC), sensitivity, and specificity. Conclusion Tumor invasion of the frontal lobe induces local structural and functional reorganization within the ECN, exacerbated by the accompanying epileptic seizures. The ECN abnormalities can accurately distinguish the presence or absence of epileptic seizures in frontal glioma patients. These findings suggest that differential ECN patterns can assist in the early identification and intervention of epileptic seizures in frontal glioma patients.
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Affiliation(s)
- Guangfu Di
- Department of Neurosurgery, The Translational Research Institute for Neurological Disorders of Wannan Medical College, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital of Wannan Medical College, Wuhu, China
| | - Mingze Tan
- Department of Neurosurgery, The Translational Research Institute for Neurological Disorders of Wannan Medical College, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital of Wannan Medical College, Wuhu, China
| | - Rui Xu
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital of Wannan Medical College, Wuhu, China
| | - Wei Zhou
- Department of Neurosurgery, The Translational Research Institute for Neurological Disorders of Wannan Medical College, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital of Wannan Medical College, Wuhu, China
| | - Kaiqiang Duan
- Department of Neurosurgery, The Translational Research Institute for Neurological Disorders of Wannan Medical College, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital of Wannan Medical College, Wuhu, China
| | - Zongwen Hu
- Department of Neurosurgery, The Translational Research Institute for Neurological Disorders of Wannan Medical College, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital of Wannan Medical College, Wuhu, China
| | - Xiaoxiang Cao
- Department of Neurosurgery, The Translational Research Institute for Neurological Disorders of Wannan Medical College, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital of Wannan Medical College, Wuhu, China
| | - Hongchuang Zhang
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital of Wannan Medical College, Wuhu, China
| | - Xiaochun Jiang
- Department of Neurosurgery, The Translational Research Institute for Neurological Disorders of Wannan Medical College, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital of Wannan Medical College, Wuhu, China
- *Correspondence: Xiaochun Jiang,
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Multiparametric Magnetic Resonance Imaging Information Fusion Using Graph Convolutional Network for Glioma Grading. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:7315665. [PMID: 35591941 PMCID: PMC9113909 DOI: 10.1155/2022/7315665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 03/31/2022] [Accepted: 04/01/2022] [Indexed: 11/17/2022]
Abstract
Accurate preoperative glioma grading is essential for clinical decision-making and prognostic evaluation. Multiparametric magnetic resonance imaging (mpMRI) serves as an important diagnostic tool for glioma patients due to its superior performance in describing noninvasively the contextual information in tumor tissues. Previous studies achieved promising glioma grading results with mpMRI data utilizing a convolutional neural network (CNN)-based method. However, these studies have not fully exploited and effectively fused the rich tumor contextual information provided in the magnetic resonance (MR) images acquired with different imaging parameters. In this paper, a novel graph convolutional network (GCN)-based mpMRI information fusion module (named MMIF-GCN) is proposed to comprehensively fuse the tumor grading relevant information in mpMRI. Specifically, a graph is constructed according to the characteristics of mpMRI data. The vertices are defined as the glioma grading features of different slices extracted by the CNN, and the edges reflect the distances between the slices in a 3D volume. The proposed method updates the information in each vertex considering the interaction between adjacent vertices. The final glioma grading is conducted by combining the fused information in all vertices. The proposed MMIF-GCN module can introduce an additional nonlinear representation learning step in the process of mpMRI information fusion while maintaining the positional relationship between adjacent slices. Experiments were conducted on two datasets, that is, a public dataset (named BraTS2020) and a private one (named GliomaHPPH2018). The results indicate that the proposed method can effectively fuse the grading information provided in mpMRI data for better glioma grading performance.
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Radiomics-Based Method for Predicting the Glioma Subtype as Defined by Tumor Grade, IDH Mutation, and 1p/19q Codeletion. Cancers (Basel) 2022; 14:cancers14071778. [PMID: 35406550 PMCID: PMC8997070 DOI: 10.3390/cancers14071778] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/25/2022] [Accepted: 03/28/2022] [Indexed: 02/01/2023] Open
Abstract
Gliomas are among the most common types of central nervous system (CNS) tumors. A prompt diagnosis of the glioma subtype is crucial to estimate the prognosis and personalize the treatment strategy. The objective of this study was to develop a radiomics pipeline based on the clinical Magnetic Resonance Imaging (MRI) scans to noninvasively predict the glioma subtype, as defined based on the tumor grade, isocitrate dehydrogenase (IDH) mutation status, and 1p/19q codeletion status. A total of 212 patients from the public retrospective The Cancer Genome Atlas Low Grade Glioma (TCGA-LGG) and The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) datasets were used for the experiments and analyses. Different settings in the radiomics pipeline were investigated to improve the classification, including the Z-score normalization, the feature extraction strategy, the image filter applied to the MRI images, the introduction of clinical information, ComBat harmonization, the classifier chain strategy, etc. Based on numerous experiments, we finally reached an optimal pipeline for classifying the glioma tumors. We then tested this final radiomics pipeline on the hold-out test data with 51 randomly sampled random seeds for reliable and robust conclusions. The results showed that, after tuning the radiomics pipeline, the mean AUC improved from 0.8935 (±0.0351) to 0.9319 (±0.0386), from 0.8676 (±0.0421) to 0.9283 (±0.0333), and from 0.6473 (±0.1074) to 0.8196 (±0.0702) in the test data for predicting the tumor grade, IDH mutation, and 1p/19q codeletion status, respectively. The mean accuracy for predicting the five glioma subtypes also improved from 0.5772 (±0.0816) to 0.6716 (±0.0655). Finally, we analyzed the characteristics of the radiomic features that best distinguished the glioma grade, the IDH mutation, and the 1p/19q codeletion status, respectively. Apart from the promising prediction of the glioma subtype, this study also provides a better understanding of the radiomics model development and interpretability. The results in this paper are replicable with our python codes publicly available in github.
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Guo W, She D, Xing Z, Lin X, Wang F, Song Y, Cao D. Multiparametric MRI-Based Radiomics Model for Predicting H3 K27M Mutant Status in Diffuse Midline Glioma: A Comparative Study Across Different Sequences and Machine Learning Techniques. Front Oncol 2022; 12:796583. [PMID: 35311083 PMCID: PMC8928064 DOI: 10.3389/fonc.2022.796583] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 02/08/2022] [Indexed: 11/17/2022] Open
Abstract
Objectives The performance of multiparametric MRI-based radiomics models for predicting H3 K27M mutant status in diffuse midline glioma (DMG) has not been thoroughly evaluated. The optimal combination of multiparametric MRI and machine learning techniques remains undetermined. We compared the performance of various radiomics models across different MRI sequences and different machine learning techniques. Methods A total of 102 patients with pathologically confirmed DMG were retrospectively enrolled (27 with H3 K27M-mutant and 75 with H3 K27M wild-type). Radiomics features were extracted from eight sequences, and 18 feature sets were conducted by independent combination. There were three feature matrix normalization algorithms, two dimensionality-reduction methods, four feature selectors, and seven classifiers, consisting of 168 machine learning pipelines. Radiomics models were established across different feature sets and machine learning pipelines. The performance of models was evaluated using receiver operating characteristic curves with area under the curve (AUC) and compared with DeLong’s test. Results The multiparametric MRI-based radiomics models could accurately predict the H3 K27M mutant status in DMG (highest AUC: 0.807–0.969, for different sequences or sequence combinations). However, the results varied significantly between different machine learning techniques. When suitable machine learning techniques were used, the conventional MRI-based radiomics models shared similar performance to advanced MRI-based models (highest AUC: 0.875–0.915 vs. 0.807–0.926; DeLong’s test, p > 0.05). Most models had a better performance when generated with a combination of MRI sequences. The optimal model in the present study used a combination of all sequences (AUC = 0.969). Conclusions The multiparametric MRI-based radiomics models could be useful for predicting H3 K27M mutant status in DMG, but the performance varied across different sequences and machine learning techniques.
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Affiliation(s)
- Wei Guo
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Dejun She
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Zhen Xing
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Xiang Lin
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Feng Wang
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers Ltd., Shanghai, China
| | - Dairong Cao
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Department of Radiology, Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.,Key Laboratory of Radiation Biology of Fujian Higher Education Institutions, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
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Wan S, He Y, Zhang X, Wei Y, Song B. Quantitative measurements of esophageal varices using computed tomography for prediction of severe varices and the risk of bleeding: a preliminary study. Insights Imaging 2022; 13:47. [PMID: 35286491 PMCID: PMC8921428 DOI: 10.1186/s13244-022-01189-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 02/10/2022] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND We aimed to assess whether the quantitative parameters of esophageal varices (EV) based on computed tomography (CT) can noninvasively predict severe EV and the risk of esophageal variceal bleeding (EVB). METHODS A total of 136 endoscopically confirmed EV patients were included in this retrospective study and were divided into a non-conspicuous (mild-to-moderate EV, n = 30) and a conspicuous EV group (severe EV, n = 106), a bleeding (n = 89) and a non-bleeding group (n = 47). EV grade (EVG), EV diameter (EVD), cross-sectional surface area (CSA), EV volume (EVV), spleen volume (SV), splenic vein (SNV), portal vein (PV), diameter of left gastric vein (DLGV), and the opening type of LGV were measured independently using 3D-slicer. Univariate and multivariate logistic analysis were used to determine the independent factors and the receiver operating characteristic (ROC) curves were performed to evaluate the diagnostic performance. RESULTS The difference of EVG, EVD, CSA, EVV, DLGV, SNV between the conspicuous and non-conspicuous EV group were statistically significant (p < 0.05), area under the curves (AUCs) of them for predicting severe EV were 0.72, 0.772, 0.704, 0.768, 0.707, 0.65, with corresponding sensitivities of 70.3%, 63.5%, 50%, 74.3%, 52.7%, 48.6%, specificities of 71.4%, 85.7%, 100%, 71.4%, 81%, 81%, respectively. EVG, CSA (odds ratio 3.258, 95% CI 1.597-6.647; 1.029, 95% CI 1.008-1.050) were found to be independent predictive factors. However, there was no significant difference of the included indices between the bleeding and non-bleeding group (p > 0.05). CONCLUSIONS CT can be used as a noninvasive method to predict the severity of EV, which may reduce the invasive screening of endoscopy.
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Affiliation(s)
- Shang Wan
- Department of Radiology, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu, 610041, People's Republic of China
| | - Yuhao He
- Department of Neurosurgery, Third People's Hospital of Chengdu, Chengdu, 610031, People's Republic of China
| | - Xin Zhang
- Pharmaceutical Diagnostic Team, GE Healthcare, Life Sciences, Beijing, 100176, People's Republic of China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu, 610041, People's Republic of China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu, 610041, People's Republic of China.
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Wang J, Hu Y, Zhou X, Bao S, Chen Y, Ge M, Jia Z. A radiomics model based on DCE-MRI and DWI may improve the prediction of estimating IDH1 mutation and angiogenesis in gliomas. Eur J Radiol 2022; 147:110141. [PMID: 34995947 DOI: 10.1016/j.ejrad.2021.110141] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 11/30/2021] [Accepted: 12/28/2021] [Indexed: 02/08/2023]
Abstract
PURPOSE To investigate the value of a radiomics model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion weighted imaging (DWI) in estimating isocitrate dehydrogenase 1 (IDH1) mutation and angiogenesis in gliomas. METHOD One hundred glioma patients with DCE-MRI and DWI were enrolled in this study (training and validation groups with a ratio of 7:3). The IDH1 genotypes and expression of vascular endothelial growth factor (VEGF) in gliomas were assessed by immunohistochemistry. Radiomics features were extracted by an open source software (3DSlicer) and reduced using Least absolute shrinkage and selection operator (Lasso). The support vector machine (SVM) model was developed based on the most useful predictive radiomics features. The conventional model was built by the selected clinical and morphological features. Finally, a combined model including radiomics signature, age and enhancement degree was established. Receiver operator characteristic (ROC) curve was implemented to assess the diagnostic performance of the three models. RESULTS For IDH1 mutation, the combined model achieved the highest area under curve (AUC) in comparison with the SVM and conventional models (training group, AUC = 0.967, 0.939 and 0.906; validation group, AUC = 0.909, 0.880 and 0.842). Furthermore, the SVM model showed good diagnostic performance in estimating gliomas VEGF expression (validation group, AUC = 0.919). CONCLUSIONS The radiomics model based on DCE-MRI and DWI can have a considerable effect on the evaluation of IDH1 mutation and angiogenesis in gliomas.
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Affiliation(s)
- Jie Wang
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China
| | - Yue Hu
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China
| | - Xuejun Zhou
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China
| | - Shanlei Bao
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China.
| | - Yue Chen
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China
| | - Min Ge
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China
| | - Zhongzheng Jia
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China.
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Wan S, Wei Y, Zhang X, Yang C, Song B. CT-derived quantitative liver volumetric parameters for prediction of severe esophageal varices and the risk of first variceal hemorrhage. Eur J Radiol 2021; 144:109984. [PMID: 34638080 DOI: 10.1016/j.ejrad.2021.109984] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 09/16/2021] [Accepted: 09/26/2021] [Indexed: 02/05/2023]
Abstract
PURPOSE To assess whether CT (computed tomography)-derived quantitative parameters of liver lobe volume can predict severe esophageal varices (EV) and the risk of first varicealhemorrhage (FVH) in patients with liver cirrhosis. METHODS A total of 217 endoscopically confirmed EV patients were included in this retrospective study and were divided into a low-risk EV group (mild-to-moderate EV, n = 83) and a high-risk EV group (severe EV, n = 134), a FVH group (n = 17) and a non-FVH group (n = 27), patients' clinical findings were recorded. The left, right, caudate lobe, total liver volume and the corresponding functional volume were measured respectively, and the ratio of caudate volume/total volume (CV/TV), caudate functional volume/total functional volume (CFV/TFV) were calculated. Univariate and multivariate logistic analysis were used to determine the independent factors and the receiver operating characteristic (ROC) curves were performed to evaluate the diagnostic performance. RESULTS CV, CFV, CV/TV, CFV/TFV were significantly different in the EV severity study and FVH study (p < 0.05). Multivariate analysis indicated that CV/TV and ascites were independent predictive factors for severe EV, a predictive model combing those two factors revealed a satisfactory diagnostic performance (area under the curve (AUC), 0.853, 95 %CI 0.797-0.905). Furthermore, CV/TV and the presence of red color sign under endoscopy were found to be independent predictive factors for FVH, and the former showed a better discriminative performance than the latter (AUC, 0.851 vs 0.779). CONCLUSIONS CT-derived quantitative parameters of CV, CFV, CV/TV, CFV/TFV may be used as an alternative to endoscopy in predicting severe varices and the risk of bleeding.
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Affiliation(s)
- Shang Wan
- Department of Radiology, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu 610041, PR China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu 610041, PR China
| | - Xin Zhang
- Pharmaceutical Diagnostic Team, GE Healthcare, Life Sciences, Beijing 100176, PR China
| | - Caiwei Yang
- Department of Radiology, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu 610041, PR China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu 610041, PR China.
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Predicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images. Sci Rep 2021; 11:16849. [PMID: 34413349 PMCID: PMC8377095 DOI: 10.1038/s41598-021-95948-x] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 08/02/2021] [Indexed: 02/07/2023] Open
Abstract
We developed end-to-end deep learning models using whole slide images of adults diagnosed with diffusely infiltrating, World Health Organization (WHO) grade 2 gliomas to predict prognosis and the mutation status of a somatic biomarker, isocitrate dehydrogenase (IDH) 1/2. The models, which utilize ResNet-18 as a backbone, were developed and validated on 296 patients from The Cancer Genome Atlas (TCGA) database. To account for the small sample size, repeated random train/test splits were performed for hyperparameter tuning, and the out-of-sample predictions were pooled for evaluation. Our models achieved a concordance- (C-) index of 0.715 (95% CI: 0.569, 0.830) for predicting prognosis and an area under the curve (AUC) of 0.667 (0.532, 0.784) for predicting IDH mutations. When combined with additional clinical information, the performance metrics increased to 0.784 (95% CI: 0.655, 0.880) and 0.739 (95% CI: 0.613, 0.856), respectively. When evaluated on the WHO grade 3 gliomas from the TCGA dataset, which were not used for training, our models predicted survival with a C-index of 0.654 (95% CI: 0.537, 0.768) and IDH mutations with an AUC of 0.814 (95% CI: 0.721, 0.897). If validated in a prospective study, our method could potentially assist clinicians in managing and treating patients with diffusely infiltrating gliomas.
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Kinoshita M, Kanemura Y, Narita Y, Kishima H. Reverse Engineering Glioma Radiomics to Conventional Neuroimaging. Neurol Med Chir (Tokyo) 2021; 61:505-514. [PMID: 34373429 PMCID: PMC8443974 DOI: 10.2176/nmc.ra.2021-0133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
A novel radiological research field pursuing comprehensive quantitative image, namely “Radiomics,” gained traction along with the advancement of computational technology and artificial intelligence. This novel concept for analyzing medical images brought extensive interest to the neuro-oncology and neuroradiology research community to build a diagnostic workflow to detect clinically relevant genetic alteration of gliomas noninvasively. Although quite a few promising results were published regarding MRI-based diagnosis of isocitrate dehydrogenase (IDH) mutation in gliomas, it has become clear that an ample amount of effort is still needed to render this technology clinically applicable. At the same time, many significant insights were discovered through this research project, some of which could be “reverse engineered” to improve conventional non-radiomic MR image acquisition. In this review article, the authors aim to discuss the recent advancements and encountering issues of radiomics, how we can apply the knowledge provided by radiomics to standard clinical images, and further expected technological advances in the realm of radiomics and glioma.
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Affiliation(s)
- Manabu Kinoshita
- Department of Neurosurgery, Asahikawa Medical University.,Department of Neurosurgery, Osaka University Graduate School of Medicine.,Department of Neurosurgery, Osaka International Cancer Institute
| | - Yonehiro Kanemura
- Department of Biomedical Research and Innovation, Institute for Clinical Research, National Hospital Organization Osaka National Hospital
| | - Yoshitaka Narita
- Department of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital
| | - Haruhiko Kishima
- Department of Neurosurgery, Osaka University Graduate School of Medicine
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Yan J, Zhang B, Zhang S, Cheng J, Liu X, Wang W, Dong Y, Zhang L, Mo X, Chen Q, Fang J, Wang F, Tian J, Zhang S, Zhang Z. Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients. NPJ Precis Oncol 2021; 5:72. [PMID: 34312469 PMCID: PMC8313682 DOI: 10.1038/s41698-021-00205-z] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 05/13/2021] [Indexed: 12/24/2022] Open
Abstract
Gliomas can be classified into five molecular groups based on the status of IDH mutation, 1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by biopsy or surgery. Thus, we aimed to use MRI-based radiomics to noninvasively predict the molecular groups and assess their prognostic value. We retrospectively identified 357 patients with gliomas and extracted radiomic features from their preoperative MRI images. Single-layered radiomic signatures were generated using a single MR sequence using Bayesian-regularization neural networks. Image fusion models were built by combing the significant radiomic signatures. By separately predicting the molecular markers, the predictive molecular groups were obtained. Prognostic nomograms were developed based on the predictive molecular groups and clinicopathologic data to predict progression-free survival (PFS) and overall survival (OS). The results showed that the image fusion model incorporating radiomic signatures from contrast-enhanced T1-weighted imaging (cT1WI) and apparent diffusion coefficient (ADC) achieved an AUC of 0.884 and 0.669 for predicting IDH and TERT status, respectively. cT1WI-based radiomic signature alone yielded favorable performance in predicting 1p/19q status (AUC = 0.815). The predictive molecular groups were comparable to actual ones in predicting PFS (C-index: 0.709 vs. 0.722, P = 0.241) and OS (C-index: 0.703 vs. 0.751, P = 0.359). Subgroup analyses by grades showed similar findings. The prognostic nomograms based on grades and the predictive molecular groups yielded a C-index of 0.736 and 0.735 in predicting PFS and OS, respectively. Accordingly, MRI-based radiomics may be useful for noninvasively detecting molecular groups and predicting survival in gliomas regardless of grades.
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Affiliation(s)
- Jing Yan
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bin Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Shuaitong Zhang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China.,CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Engineering Medicine, Beihang University, Beijing, China
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xianzhi Liu
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Weiwei Wang
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuhao Dong
- Department of Catheterization Lab, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Lu Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Xiaokai Mo
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Qiuying Chen
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Jin Fang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Fei Wang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Jie Tian
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China. .,CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China. .,School of Engineering Medicine, Beihang University, Beijing, China. .,Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shanxi, China.
| | - Shuixing Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.
| | - Zhenyu Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
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Safai A, Shinde S, Jadhav M, Chougule T, Indoria A, Kumar M, Santosh V, Jabeen S, Beniwal M, Konar S, Saini J, Ingalhalikar M. Developing a Radiomics Signature for Supratentorial Extra-Ventricular Ependymoma Using Multimodal MR Imaging. Front Neurol 2021; 12:648092. [PMID: 34367044 PMCID: PMC8339322 DOI: 10.3389/fneur.2021.648092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 06/14/2021] [Indexed: 11/25/2022] Open
Abstract
Rationale and Objectives: To build a machine learning-based diagnostic model that can accurately distinguish adult supratentorial extraventricular ependymoma (STEE) from similarly appearing high-grade gliomas (HGG) using quantitative radiomic signatures from a multi-parametric MRI framework. Materials and Methods: We computed radiomic features on the preprocessed and segmented tumor masks from a pre-operative multimodal MRI dataset [contrast-enhanced T1 (T1ce), T2, fluid-attenuated inversion recovery (FLAIR), apparent diffusion coefficient (ADC)] from STEE (n = 15), HGG-Grade IV (HGG-G4) (n = 24), and HGG-Grade III (HGG-G3) (n = 36) patients, followed by an optimum two-stage feature selection and multiclass classification. Performance of multiple classifiers were evaluated on both unimodal and multimodal feature sets and most discriminative radiomic features involved in classification of STEE from HGG subtypes were obtained. Results: Multimodal features demonstrated higher classification performance over unimodal feature set in discriminating STEE and HGG subtypes with an accuracy of 68% on test data and above 80% on cross validation, along with an overall above 90% specificity. Among unimodal feature sets, those extracted from FLAIR demonstrated high classification performance in delineating all three tumor groups. Texture-based radiomic features particularly from FLAIR were most important in discriminating STEE from HGG-G4, whereas first-order features from T2 and ADC consistently ranked higher in differentiating multiple tumor groups. Conclusions: This study illustrates the utility of radiomics-based multimodal MRI framework in accurately discriminating similarly appearing adult STEE from HGG subtypes. Radiomic features from multiple MRI modalities could capture intricate and complementary information for a robust and highly accurate multiclass tumor classification.
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Affiliation(s)
- Apoorva Safai
- Symbiosis Center for Medical Image Analysis, Symbiosis Institute of Technology, Symbiosis International University, Pune, India
| | - Sumeet Shinde
- Symbiosis Center for Medical Image Analysis, Symbiosis Institute of Technology, Symbiosis International University, Pune, India
| | - Manali Jadhav
- Symbiosis Center for Medical Image Analysis, Symbiosis Institute of Technology, Symbiosis International University, Pune, India
| | - Tanay Chougule
- Symbiosis Center for Medical Image Analysis, Symbiosis Institute of Technology, Symbiosis International University, Pune, India
| | - Abhilasha Indoria
- Department of Neuroimaging & Interventional Radiology, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Manoj Kumar
- Department of Neuroimaging & Interventional Radiology, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Vani Santosh
- Department of Neuropathology, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Shumyla Jabeen
- Department of Neuroimaging & Interventional Radiology, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Manish Beniwal
- Department of Neurosurgery, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Subhash Konar
- Department of Neurosurgery, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Jitender Saini
- Department of Neuroimaging & Interventional Radiology, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Madhura Ingalhalikar
- Symbiosis Center for Medical Image Analysis, Symbiosis Institute of Technology, Symbiosis International University, Pune, India
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La Greca Saint-Esteven A, Vuong D, Tschanz F, van Timmeren JE, Dal Bello R, Waller V, Pruschy M, Guckenberger M, Tanadini-Lang S. Systematic Review on the Association of Radiomics with Tumor Biological Endpoints. Cancers (Basel) 2021; 13:cancers13123015. [PMID: 34208595 PMCID: PMC8234501 DOI: 10.3390/cancers13123015] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/10/2021] [Accepted: 06/11/2021] [Indexed: 12/23/2022] Open
Abstract
Radiomics supposes an alternative non-invasive tumor characterization tool, which has experienced increased interest with the advent of more powerful computers and more sophisticated machine learning algorithms. Nonetheless, the incorporation of radiomics in cancer clinical-decision support systems still necessitates a thorough analysis of its relationship with tumor biology. Herein, we present a systematic review focusing on the clinical evidence of radiomics as a surrogate method for tumor molecular profile characterization. An extensive literature review was conducted in PubMed, including papers on radiomics and a selected set of clinically relevant and commonly used tumor molecular markers. We summarized our findings based on different cancer entities, additionally evaluating the effect of different modalities for the prediction of biomarkers at each tumor site. Results suggest the existence of an association between the studied biomarkers and radiomics from different modalities and different tumor sites, even though a larger number of multi-center studies are required to further validate the reported outcomes.
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Affiliation(s)
- Agustina La Greca Saint-Esteven
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
- Correspondence:
| | - Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Fabienne Tschanz
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Janita E. van Timmeren
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Riccardo Dal Bello
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Verena Waller
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Martin Pruschy
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
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Su CQ, Chen XT, Duan SF, Zhang JX, You YP, Lu SS, Hong XN. A radiomics-based model to differentiate glioblastoma from solitary brain metastases. Clin Radiol 2021; 76:629.e11-629.e18. [PMID: 34092362 DOI: 10.1016/j.crad.2021.04.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 04/21/2021] [Indexed: 11/18/2022]
Abstract
AIM To differentiate glioblastoma (GBM) from solitary brain metastases (MET) using radiomic analysis. MATERIALS AND METHODS Two hundred and fifty-three patients with solitary brain tumours (157 GBM and 98 solitary brain MET) were split into a training cohort (n=178) and a validation cohort (n=77) by stratified sampling using computer-generated random numbers at a ratio of 7:3. After feature extraction, minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used to build the radiomics signature on the training cohort and validation cohort. Performance was assessed by radiomics score (Rad-score), receiver operating characteristic (ROC) curve, calibration, and clinical usefulness. RESULTS Eleven radiomic features were selected as significant features in the training cohort. The Rad-score was significantly associated with the differentiation between GBM and solitary brain MET (p<0.001) both in the training and validation cohorts. The radiomics signature yielded area under the curve (AUC) values of 0.82 and 0.81 in the training and validation cohorts to distinguish between GBM and solitary brain MET. CONCLUSIONS The radiomics model might be a useful supporting tool for the preoperative differentiation of GBM from solitary brain MET, which could aid pretreatment decision-making.
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Affiliation(s)
- C-Q Su
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - X-T Chen
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - S-F Duan
- GE Healthcare China, NO.1, Huatuo Road, Pudong New Town, Shanghai 210000, China
| | - J-X Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - Y-P You
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - S-S Lu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China.
| | - X-N Hong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China.
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van Kempen EJ, Post M, Mannil M, Kusters B, ter Laan M, Meijer FJA, Henssen DJHA. Accuracy of Machine Learning Algorithms for the Classification of Molecular Features of Gliomas on MRI: A Systematic Literature Review and Meta-Analysis. Cancers (Basel) 2021; 13:cancers13112606. [PMID: 34073309 PMCID: PMC8198025 DOI: 10.3390/cancers13112606] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 05/17/2021] [Accepted: 05/18/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Glioma prognosis and treatment are based on histopathological characteristics and molecular profile. Following the World Health Organization (WHO) guidelines (2016), the most important molecular diagnostic markers include IDH1/2-genotype and 1p/19q codeletion status, although more recent publications also include ARTX genotype and TERT- and MGMT promoter methylation. Machine learning algorithms (MLAs), however, were described to successfully determine these molecular characteristics non-invasively by using magnetic resonance imaging (MRI) data. The aim of this review and meta-analysis was to define the diagnostic accuracy of MLAs with regard to these different molecular markers. We found high accuracies of MLAs to predict each individual molecular marker, with IDH1/2-genotype being the most investigated and the most accurate. Radiogenomics could therefore be a promising tool for discriminating genetically determined gliomas in a non-invasive fashion. Although encouraging results are presented here, large-scale, prospective trials with external validation groups are warranted. Abstract Treatment planning and prognosis in glioma treatment are based on the classification into low- and high-grade oligodendroglioma or astrocytoma, which is mainly based on molecular characteristics (IDH1/2- and 1p/19q codeletion status). It would be of great value if this classification could be made reliably before surgery, without biopsy. Machine learning algorithms (MLAs) could play a role in achieving this by enabling glioma characterization on magnetic resonance imaging (MRI) data without invasive tissue sampling. The aim of this study is to provide a performance evaluation and meta-analysis of various MLAs for glioma characterization. Systematic literature search and meta-analysis were performed on the aggregated data, after which subgroup analyses for several target conditions were conducted. This study is registered with PROSPERO, CRD42020191033. We identified 724 studies; 60 and 17 studies were eligible to be included in the systematic review and meta-analysis, respectively. Meta-analysis showed excellent accuracy for all subgroups, with the classification of 1p/19q codeletion status scoring significantly poorer than other subgroups (AUC: 0.748, p = 0.132). There was considerable heterogeneity among some of the included studies. Although promising results were found with regard to the ability of MLA-tools to be used for the non-invasive classification of gliomas, large-scale, prospective trials with external validation are warranted in the future.
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Affiliation(s)
- Evi J. van Kempen
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
| | - Max Post
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
| | - Manoj Mannil
- Clinic of Radiology, University Hospital Münster, WWU University of Münster, 48149 Münster, Germany;
| | - Benno Kusters
- Department of Pathology, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands;
| | - Mark ter Laan
- Department of Neurosurgery, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands;
| | - Frederick J. A. Meijer
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
| | - Dylan J. H. A. Henssen
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
- Correspondence:
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Buchlak QD, Esmaili N, Leveque JC, Bennett C, Farrokhi F, Piccardi M. Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review. J Clin Neurosci 2021; 89:177-198. [PMID: 34119265 DOI: 10.1016/j.jocn.2021.04.043] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 04/30/2021] [Indexed: 12/13/2022]
Abstract
Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive diagnosis still relies on surgical pathology. Machine learning has been applied to the analysis of MRI data in glioma research and has the potential to change clinical practice and improve patient outcomes. This systematic review synthesizes and analyzes the current state of machine learning applications to glioma MRI data and explores the use of machine learning for systematic review automation. Various datapoints were extracted from the 153 studies that met inclusion criteria and analyzed. Natural language processing (NLP) analysis involved keyword extraction, topic modeling and document classification. Machine learning has been applied to tumor grading and diagnosis, tumor segmentation, non-invasive genomic biomarker identification, detection of progression and patient survival prediction. Model performance was generally strong (AUC = 0.87 ± 0.09; sensitivity = 0.87 ± 0.10; specificity = 0.0.86 ± 0.10; precision = 0.88 ± 0.11). Convolutional neural network, support vector machine and random forest algorithms were top performers. Deep learning document classifiers yielded acceptable performance (mean 5-fold cross-validation AUC = 0.71). Machine learning tools and data resources were synthesized and summarized to facilitate future research. Machine learning has been widely applied to the processing of MRI data in glioma research and has demonstrated substantial utility. NLP and transfer learning resources enabled the successful development of a replicable method for automating the systematic review article screening process, which has potential for shortening the time from discovery to clinical application in medicine.
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Affiliation(s)
- Quinlan D Buchlak
- School of Medicine, The University of Notre Dame Australia, Sydney, NSW, Australia.
| | - Nazanin Esmaili
- School of Medicine, The University of Notre Dame Australia, Sydney, NSW, Australia; Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia
| | | | - Christine Bennett
- School of Medicine, The University of Notre Dame Australia, Sydney, NSW, Australia
| | - Farrokh Farrokhi
- Neuroscience Institute, Virginia Mason Medical Center, Seattle, WA, USA
| | - Massimo Piccardi
- Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia
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Jian A, Jang K, Manuguerra M, Liu S, Magnussen J, Di Ieva A. Machine Learning for the Prediction of Molecular Markers in Glioma on Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis. Neurosurgery 2021; 89:31-44. [PMID: 33826716 DOI: 10.1093/neuros/nyab103] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 01/24/2021] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Molecular characterization of glioma has implications for prognosis, treatment planning, and prediction of treatment response. Current histopathology is limited by intratumoral heterogeneity and variability in detection methods. Advances in computational techniques have led to interest in mining quantitative imaging features to noninvasively detect genetic mutations. OBJECTIVE To evaluate the diagnostic accuracy of machine learning (ML) models in molecular subtyping gliomas on preoperative magnetic resonance imaging (MRI). METHODS A systematic search was performed following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify studies up to April 1, 2020. Methodological quality of studies was assessed using the Quality Assessment for Diagnostic Accuracy Studies (QUADAS)-2. Diagnostic performance estimates were obtained using a bivariate model and heterogeneity was explored using metaregression. RESULTS Forty-four original articles were included. The pooled sensitivity and specificity for predicting isocitrate dehydrogenase (IDH) mutation in training datasets were 0.88 (95% CI 0.83-0.91) and 0.86 (95% CI 0.79-0.91), respectively, and 0.83 to 0.85 in validation sets. Use of data augmentation and MRI sequence type were weakly associated with heterogeneity. Both O6-methylguanine-DNA methyltransferase (MGMT) gene promoter methylation and 1p/19q codeletion could be predicted with a pooled sensitivity and specificity between 0.76 and 0.83 in training datasets. CONCLUSION ML application to preoperative MRI demonstrated promising results for predicting IDH mutation, MGMT methylation, and 1p/19q codeletion in glioma. Optimized ML models could lead to a noninvasive, objective tool that captures molecular information important for clinical decision making. Future studies should use multicenter data, external validation and investigate clinical feasibility of ML models.
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Affiliation(s)
- Anne Jian
- Computational NeuroSurgery (CNS) Lab, Department of Clinical Medicine, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia.,Melbourne Medical School, University of Melbourne, Melbourne, Australia
| | - Kevin Jang
- Computational NeuroSurgery (CNS) Lab, Department of Clinical Medicine, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia.,Discipline of Surgery, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Maurizio Manuguerra
- Department of Mathematics and Statistics, Faculty of Science and Engineering, Macquarie University, Sydney, Australia
| | - Sidong Liu
- Computational NeuroSurgery (CNS) Lab, Department of Clinical Medicine, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia.,Centre for Health Informatics, Macquarie University, Sydney, Australia
| | - John Magnussen
- Computational NeuroSurgery (CNS) Lab, Department of Clinical Medicine, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia.,Macquarie Medical Imaging, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Department of Clinical Medicine, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia.,Macquarie Neurosurgery, Macquarie University, Sydney, Australia
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Wang L, Li T, Hong J, Zhang M, Ouyang M, Zheng X, Tang K. 18F-FDG PET-based radiomics model for predicting occult lymph node metastasis in clinical N0 solid lung adenocarcinoma. Quant Imaging Med Surg 2021; 11:215-225. [PMID: 33392023 DOI: 10.21037/qims-20-337] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Background This study aimed to develop a preoperative positron emission tomography (PET)-based radiomics model for predicting occult lymph node metastasis (OLM) in clinical N0 (cN0) solid lung adenocarcinoma. Methods The preoperative fluorine-18-fludeoxyglucose (18F-FDG) PET images of 370 patients with cN0 lung adenocarcinoma confirmed by histopathological examination were retrospectively reviewed. Patients were divided into training and validation sets. Radiomics features and relevant data were extracted from PET images. A nomogram was developed in a training set via univariate and multivariate logistic analyses, and its performance was assessed by concordance-index (C-index), calibration curves, and decision curve analysis (DCA) in the training and validation sets. Results The multivariate logistic regression analysis showed that only carcinoembryonic antigen (CEA), metabolic tumor volume (MTV), and the radiomics signature had statistically significant differences between patients with and without OLM (P<0.05). A nomogram was developed based on the logistic analyses, and its C-index was 0.769 in the training set and 0.768 in the validation set. The calibration curve demonstrated good consistency between the nomogram-predicted probability of OLM and the actual rate. The DCA also confirmed the clinical utility of the nomogram. Conclusions A PET/computed tomography (CT)-based radiomics model including CEA, MTV, and the radiomics signature was developed and demonstrated adequate predictive accuracy and clinical net benefit in the present study, and was conveniently used to facilitate the individualized preoperative prediction of OLM.
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Affiliation(s)
- Lili Wang
- Department of PET/CT, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Tiancheng Li
- PET Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Junjie Hong
- Department of PET/CT, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Mingyue Zhang
- Department of Radiology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Mingli Ouyang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiangwu Zheng
- Department of PET/CT, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Kun Tang
- Department of PET/CT, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Hu Y, Chen Y, Wang J, Kang JJ, Shen DD, Jia ZZ. Non-Invasive Estimation of Glioma IDH1 Mutation and VEGF Expression by Histogram Analysis of Dynamic Contrast-Enhanced MRI. Front Oncol 2020; 10:593102. [PMID: 33425744 PMCID: PMC7793903 DOI: 10.3389/fonc.2020.593102] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Accepted: 10/30/2020] [Indexed: 12/28/2022] Open
Abstract
Objectives To investigate whether glioma isocitrate dehydrogenase (IDH) 1 mutation and vascular endothelial growth factor (VEGF) expression can be estimated by histogram analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods Chinese Glioma Genome Atlas (CGGA) database was wined for differential expression of VEGF in gliomas with different IDH genotypes. The VEGF expression and IDH1 genotypes of 56 glioma samples in our hospital were assessed by immunohistochemistry. Preoperative DCE-MRI data of glioma samples were reviewed. Regions of interest (ROIs) covering tumor parenchyma were delineated. Histogram parameters of volume transfer constant (Ktrans) and volume of extravascular extracellular space per unit volume of tissue (Ve) derived from DCE-MRI were obtained. Histogram parameters of Ktrans, Ve and VEGF expression of IDH1 mutant type (IDH1mut) gliomas were compared with the IDH1 wildtype (IDH1wt) gliomas. Receiver operating characteristic (ROC) curve analysis was performed to differentiate IDH1mut from IDH1wt gliomas. The correlation coefficients were determined between histogram parameters of Ktrans, Ve and VEGF expression in gliomas. Results In CGGA database, VEGF expression in IDHmut gliomas was lower as compared to wildtype counterpart. The immunohistochemistry of glioma samples in our hospital also confirmed the results. Comparisons demonstrated statistically significant differences in histogram parameters of Ktransand Ve [mean, standard deviation (SD), 50th, 75th, 90th. and 95th percentile] between IDH1mutand IDH1wtgliomas (P < 0.05, respectively). ROC curve analysis revealed that 50th percentile of Ktrans (0.019 min−1) and Ve (0.039) provided the perfect combination of sensitivity and specificity in differentiating gliomas with IDH1mutfrom IDH1wt. Irrespective of IDH1 mutation, histogram parameters of Ktransand Ve were correlated with VEGF expression in gliomas (P < 0.05, respectively). Conclusions VEGF expression is significantly lower in IDH1mut gliomas as compared to the wildtype counterpart, and it is non-invasively predictable with histogram analysis of DCE-MRI.
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Affiliation(s)
- Yue Hu
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Nantong, China
| | - Yue Chen
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Nantong, China
| | - Jie Wang
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Nantong, China
| | - Jin Juan Kang
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Nantong, China
| | - Dan Dan Shen
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Nantong, China
| | - Zhong Zheng Jia
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Nantong, China
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Peng H, Huo J, Li B, Cui Y, Zhang H, Zhang L, Ma L. Predicting Isocitrate Dehydrogenase (IDH) Mutation Status in Gliomas Using Multiparameter MRI Radiomics Features. J Magn Reson Imaging 2020; 53:1399-1407. [PMID: 33179832 DOI: 10.1002/jmri.27434] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 10/23/2020] [Accepted: 10/23/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Accurate and noninvasive detection of isocitrate dehydrogenase (IDH, including IDH1 and IDH2) status is clinically meaningful for molecular stratification of glioma, but remains challenging. PURPOSE To establish a model for classifying IDH status in gliomas based on multiparametric MRI. STUDY TYPE Retrospective, radiomics. POPULATION In all, 105 consecutive cases of grade II-IV glioma with 50 IDH1 or IDH2 mutant (IDHm) and 55 IDH wildtype (IDHw) were separated into a training cohort (n = 73) and a test cohort (n = 32). FIELD STRENGTH/SEQUENCE Contrast-enhanced T1 -weighted (CE-T1 W), T2 -weighted (T2 W), and arterial spin labeling (ASL) images were acquired at 3.0T. ASSESSMENT Two doctors manually labeled the volume of interest (VOI) on CE-T1 W, then T2 W and ASL were coregistered to CE-T1 W. A total of 851 radiomics features were extracted on each VOI of three sequences. From the training cohort, all radiomics features with age and gender were processed by the Mann-Whitney U-test, Pearson test, and least absolute shrinkage and selection operator to obtain optimal feature groups to train support vector machine models. The accuracy and area under curve (AUC) of all models for classifying the IDH status were calculated on the test cohort. Two subtasks were performed to verify the efficiency of texture features and the Pearson test in IDH status classification, respectively. STATISTICAL TESTS The permutation test with Bonferroni correction; chi-square test. RESULTS The accuracy and AUC of the classifier, which combines the features of all three sequences, achieved 0.823 and 0.770 (P < 0.05), respectively. The best model established by texture features only had an AUC of 0.819 and an accuracy of 0.761. The best model established without the Pearson test got an AUC of 0.747 and an accuracy of 0.719. DATA CONCLUSION IDH genotypes of glioma can be identified by radiomics features from multiparameter MRI. The Pearson test improved the performance of the IDH classification models. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Hong Peng
- Medical School of Chinese PLA, Beijing, China.,Department of Radiology, The 1st Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Jiaohua Huo
- School of Computer Science and Engineering, Xidian University, Xi'an, China
| | - Bo Li
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, China
| | - Yuanyuan Cui
- Medical School of Chinese PLA, Beijing, China.,Department of Radiology, The 1st Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Hao Zhang
- Medical School of Chinese PLA, Beijing, China.,Department of Radiology, The 1st Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Liang Zhang
- School of Computer Science and Engineering, Xidian University, Xi'an, China
| | - Lin Ma
- Medical School of Chinese PLA, Beijing, China.,Department of Radiology, The 1st Medical Centre, Chinese PLA General Hospital, Beijing, China
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Gao M, Huang S, Pan X, Liao X, Yang R, Liu J. Machine Learning-Based Radiomics Predicting Tumor Grades and Expression of Multiple Pathologic Biomarkers in Gliomas. Front Oncol 2020; 10:1676. [PMID: 33014836 PMCID: PMC7516282 DOI: 10.3389/fonc.2020.01676] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Accepted: 07/29/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The grading and pathologic biomarkers of glioma has important guiding significance for the individual treatment. In clinical, it is often necessary to obtain tumor samples through invasive operation for pathological diagnosis. The present study aimed to use conventional machine learning algorithms to predict the tumor grades and pathologic biomarkers on magnetic resonance imaging (MRI) data. METHODS The present study retrospectively collected a dataset of 367 glioma patients, who had pathological reports and underwent MRI scans between October 2013 and March 2019. The radiomic features were extracted from enhanced MRI images, and three frequently-used machine-learning models of LC, Support Vector Machine (SVM), and Random Forests (RF) were built for four predictive tasks: (1) glioma grades, (2) Ki67 expression level, (3) GFAP expression level, and (4) S100 expression level in gliomas. Each sub dataset was split into training and testing sets at a ratio of 4:1. The training sets were used for training and tuning models. The testing sets were used for evaluating models. According to the area under curve (AUC) and accuracy, the best classifier was chosen for each task. RESULTS The RF algorithm was found to be stable and consistently performed better than Logistic Regression and SVM for all the tasks. The RF classifier on glioma grades achieved a predictive performance (AUC: 0.79, accuracy: 0.81). The RF classifier also achieved a predictive performance on the Ki67 expression (AUC: 0.85, accuracy: 0.80). The AUC and accuracy score for the GFAP classifier were 0.72 and 0.81. The AUC and accuracy score for S100 expression levels are 0.60 and 0.91. CONCLUSION The machine-learning based radiomics approach can provide a non-invasive method for the prediction of glioma grades and expression levels of multiple pathologic biomarkers, preoperatively, with favorable predictive accuracy and stability.
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Affiliation(s)
- Min Gao
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, China
| | - Siying Huang
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia
| | - Xuequn Pan
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD, United States
| | - Xuan Liao
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, China
| | - Ru Yang
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, China
| | - Jun Liu
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, China
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Noninvasive Prediction of TERT Promoter Mutations in High-Grade Glioma by Radiomics Analysis Based on Multiparameter MRI. BIOMED RESEARCH INTERNATIONAL 2020; 2020:3872314. [PMID: 32509858 PMCID: PMC7245686 DOI: 10.1155/2020/3872314] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 04/22/2020] [Indexed: 11/17/2022]
Abstract
Objectives To investigate the predictors of telomerase reverse transcriptase (TERT) promoter mutations in adults suffered from high-grade glioma (HGG) through radiomics analysis, develop a noninvasive approach to evaluate TERT promoter mutations. Methods 126 adult patients with HGG (88 in the training cohort and 38 in the validation cohort) were retrospectively enrolled. Totally 5064 radiomics features were, respectively, extracted from three VOIs (necrosis, enhanced, and edema) in MRI. Firstly, an optimal radiomics signature (Radscore) was established based on LASSO regression. Secondly, univariate and multivariate logistic regression analyses were performed to investigate important potential variables as predictors of TERT promoter mutations. Besides, multiparameter models were established and evaluated. Eventually, an optimal model was visualized as radiomics nomogram for clinical evaluations. Results 6 radiomics features were selected to build Radscore signature through LASSO regression. Among them, 5 were from necrotic VOIs and 1 was from enhanced ones. With univariate and multivariate analysis, necrotic volume percentages of core (CNV), Age, Cho/Cr, Lac, and Radscore were significantly higher in TERTm than in TERTw (p < 0.05). 4 models were built in our study. Compared with Model B (Age, Cho/Cr, Lac, and Radscore), Model A (Age, Cho/Cr, Lac, Radscore, and CNV) has a larger AUC in both training (0.955 vs. 0.917, p = 0.049) and validation (0.889 vs. 0.868, p = 0.039) cohorts. It also has higher performances in net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) evaluation. Conclusively, Model A was visualized as a radiomics nomogram. Calibration curve shows a good agreement between estimated and actual probabilities. Conclusions Age, Cho/Cr, Lac, CNV, and Radscore are important indicators for TERT promoter mutation predictions in HGG. Tumor necrosis seems to be closely related to TERT promoter mutations. Radiomics nomogram based on multiparameter MRI and CNV has higher prediction accuracies.
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Liu S, Shah Z, Sav A, Russo C, Berkovsky S, Qian Y, Coiera E, Di Ieva A. Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learning. Sci Rep 2020; 10:7733. [PMID: 32382048 PMCID: PMC7206037 DOI: 10.1038/s41598-020-64588-y] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 04/15/2020] [Indexed: 01/07/2023] Open
Abstract
Mutations in isocitrate dehydrogenase genes IDH1 and IDH2 are frequently found in diffuse and anaplastic astrocytic and oligodendroglial tumours as well as in secondary glioblastomas. As IDH is a very important prognostic, diagnostic and therapeutic biomarker for glioma, it is of paramount importance to determine its mutational status. The haematoxylin and eosin (H&E) staining is a valuable tool in precision oncology as it guides histopathology-based diagnosis and proceeding patient's treatment. However, H&E staining alone does not determine the IDH mutational status of a tumour. Deep learning methods applied to MRI data have been demonstrated to be a useful tool in IDH status prediction, however the effectiveness of deep learning on H&E slides in the clinical setting has not been investigated so far. Furthermore, the performance of deep learning methods in medical imaging has been practically limited by small sample sizes currently available. Here we propose a data augmentation method based on the Generative Adversarial Networks (GAN) deep learning methodology, to improve the prediction performance of IDH mutational status using H&E slides. The H&E slides were acquired from 266 grade II-IV glioma patients from a mixture of public and private databases, including 130 IDH-wildtype and 136 IDH-mutant patients. A baseline deep learning model without data augmentation achieved an accuracy of 0.794 (AUC = 0.920). With GAN-based data augmentation, the accuracy of the IDH mutational status prediction was improved to 0.853 (AUC = 0.927) when the 3,000 GAN generated training samples were added to the original training set (24,000 samples). By integrating also patients' age into the model, the accuracy improved further to 0.882 (AUC = 0.931). Our findings show that deep learning methodology, enhanced by GAN data augmentation, can support physicians in gliomas' IDH status prediction.
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Affiliation(s)
- Sidong Liu
- Department of Clinical Medicine, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
- Computational NeuroSurgery (CNS) Lab, Department of Clinical Medicine, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
- Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
| | - Zubair Shah
- Computational NeuroSurgery (CNS) Lab, Department of Clinical Medicine, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
- Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Aydin Sav
- Department of Pathology, Yeditepe University, School of Medicine, Istanbul, Turkey
| | - Carlo Russo
- Computational NeuroSurgery (CNS) Lab, Department of Clinical Medicine, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
| | - Shlomo Berkovsky
- Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
| | - Yi Qian
- Department of Biomedical Sciences, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
| | - Enrico Coiera
- Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
| | - Antonio Di Ieva
- Department of Clinical Medicine, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia.
- Computational NeuroSurgery (CNS) Lab, Department of Clinical Medicine, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia.
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Implementation of eHealth and AI integrated diagnostics with multidisciplinary digitized data: are we ready from an international perspective? Eur Radiol 2020; 30:5510-5524. [PMID: 32377810 PMCID: PMC7476980 DOI: 10.1007/s00330-020-06874-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 03/18/2020] [Accepted: 04/08/2020] [Indexed: 12/18/2022]
Abstract
Digitization of medicine requires systematic handling of the increasing amount of health data to improve medical diagnosis. In this context, the integration of the versatile diagnostic information, e.g., from anamnesis, imaging, histopathology, and clinical chemistry, and its comprehensive analysis by artificial intelligence (AI)–based tools is expected to improve diagnostic precision and the therapeutic conduct. However, the complex medical environment poses a major obstacle to the translation of integrated diagnostics into clinical research and routine. There is a high need to address aspects like data privacy, data integration, interoperability standards, appropriate IT infrastructure, and education of staff. Besides this, a plethora of technical, political, and ethical challenges exists. This is complicated by the high diversity of approaches across Europe. Thus, we here provide insights into current international activities on the way to digital comprehensive diagnostics. This includes a technical view on challenges and solutions for comprehensive diagnostics in terms of data integration and analysis. Current data communications standards and common IT solutions that are in place in hospitals are reported. Furthermore, the international hospital digitalization scoring and the European funding situation were analyzed. In addition, the regional activities in radiomics and the related publication trends are discussed. Our findings show that prerequisites for comprehensive diagnostics have not yet been sufficiently established throughout Europe. The manifold activities are characterized by a heterogeneous digitization progress and they are driven by national efforts. This emphasizes the importance of clear governance, concerted investments, and cooperation at various levels in the health systems. Key Points • Europe is characterized by heterogeneity in its digitization progress with predominantly national efforts. Infrastructural prerequisites for comprehensive diagnostics are not given and not sufficiently funded throughout Europe, which is particularly true for data integration. • The clinical establishment of comprehensive diagnostics demands for a clear governance, significant investments, and cooperation at various levels in the healthcare systems. • While comprehensive diagnostics is on its way, concerted efforts should be taken in Europe to get consensus concerning interoperability and standards, security, and privacy as well as ethical and legal concerns.
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Wan S, Wei Y, Zhang X, Liu X, Zhang W, He Y, Yuan F, Yao S, Yue Y, Song B. Multiparametric radiomics nomogram may be used for predicting the severity of esophageal varices in cirrhotic patients. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:186. [PMID: 32309333 PMCID: PMC7154439 DOI: 10.21037/atm.2020.01.122] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Background To explore whether a multiparametric radiomics nomogram on computed tomography (CT) images based on radiomics and relevant parameters of esophageal varices (EV) can be used for predicting the EV severity in patients with cirrhotic livers. Methods From January 2016 to August 2018, 136 consecutive patients with clinicopathologically confirmed liver cirrhosis were included for the development of a predictive model. The patients were then divided into two groups, including non-conspicuous EV group (mild-to-moderate EV, n=30) and conspicuous EV group (severe EV, n=106) by using the endoscopic validation as the reference standard. The radiomic scores (Rad scores) were constructed using the binary logistic regression model from the radiomics features of regions of interest (ROIs) in the left liver (LL) and right liver (RL), respectively. The multiparametric nomogram combined the best performance Rad-score and EV-relevant factors, and the calibration, discrimination, and clinical usefulness of developed nomogram were evaluated using calibration curves, decision curve analysis (DCA) and net reclassification index (NRI) analysis respectively. Results The LL Rad-score calculated from radiomics features was selected with a relatively higher area under the curve (AUC) (AUC; 0.88, training cohort; 0.87, the validation cohort) compared with RL Rad-score (AUC; 0.86, training cohort; 0.83, the validation cohort). In addition, cross-sectional surface area (CSA) was identified as the important predictor (P<0.05), the multiparametric nomogram containing LL Rad-score and CSA was shown to have a better predictive performance and good calibration in the training model (C-index, 0.953, 95% CI, 0.892 to 0.973) and the validation cohort (C-index, 0.938, 95% CI, 0.841 to 0.961), resulting in an improved NRI (categorical NRI of 25.9%, P=0.0128; continuous NRI of 120%, P<0.001) and integrated discriminatory improvement (IDI) (IDI =13.9%, P<0.001). DCA demonstrated that the multiparametric radiomics nomogram was clinically useful. Conclusions A multiparametric radiomics nomogram, which incorporates the liver radiomics signature and EV-relevant indices, is a useful tool for noninvasively predicting EV severity and may complement the standard endoscopy for evaluating EV severity in patients with cirrhosis.
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Affiliation(s)
- Shang Wan
- Department of Radiology, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu 610041, China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu 610041, China
| | - Xin Zhang
- Pharmaceutical Diagnostic team, GE Healthcare, Life Sciences, Beijing 100176, China
| | - Xijiao Liu
- Department of Radiology, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu 610041, China
| | - Weiwei Zhang
- Department of Radiology, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu 610041, China
| | - Yuhao He
- Department of Neurosurgery, Third People's Hospital of Chengdu, Chengdu 610031, China
| | - Fang Yuan
- Department of Radiology, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu 610041, China
| | - Shan Yao
- Department of Radiology, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu 610041, China
| | - Yufeng Yue
- Department of Radiology, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu 610041, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu 610041, China
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Benzakoun J, Robert C, Legrand L, Pallud J, Meder JF, Oppenheim C, Dhermain F, Edjlali M. Anatomical and functional MR imaging to define tumoral boundaries and characterize lesions in neuro-oncology. Cancer Radiother 2020; 24:453-462. [PMID: 32278653 DOI: 10.1016/j.canrad.2020.03.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 03/04/2020] [Indexed: 12/19/2022]
Abstract
Neuroimaging and especially MRI has emerged as a necessary imaging modality to detect, measure, characterize and monitor brain tumours. Advanced MRI sequences such as perfusion MRI, diffusion MRI and spectroscopy as well as new post-processing techniques such as automatic segmentation of tumours and radiomics play a crucial role in characterization and follow up of brain tumours. The purpose of this review is to provide an overview on anatomical and functional MRI use for brain tumours boundaries determination and tumour characterization in the specific context of radiotherapy. The usefulness of anatomical and functional MRI on particular challenges posed by radiotherapy such as pseudo progression and pseudo esponse and new treatment strategies such as dose painting is also described.
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Affiliation(s)
- J Benzakoun
- Radiology Department, GHU de Paris, centre hospitalier Sainte-Anne, 1, rue Cabanis, 75014 Paris, France; Université de Paris, 85, boulevard Saint-Germain, 75006 Paris, France; Imabrain, Institut de psychiatrie et neurosciences de Paris (IPNP), 102-108, rue de la Santé, 75014 Paris, France; Inserm, U1266, 102, rue de la Santé, 75013 Paris, France.
| | - C Robert
- Medical Physics Department, Gustave-Roussy, 114, rue Édouard-Vaillant, 94805 Villejuif, France; Molecular Radiotherapy, Gustave-Roussy, 114, rue Édouard-Vaillant, 94805 Villejuif, France; Inserm, 114, rue Édouard-Vaillant, 94805 Villejuif, France; Paris-Sud University, Paris-Saclay University, 114, rue Édouard-Vaillant, 94805 Villejuif, France
| | - L Legrand
- Radiology Department, GHU de Paris, centre hospitalier Sainte-Anne, 1, rue Cabanis, 75014 Paris, France; Université de Paris, 85, boulevard Saint-Germain, 75006 Paris, France; Imabrain, Institut de psychiatrie et neurosciences de Paris (IPNP), 102-108, rue de la Santé, 75014 Paris, France; Inserm, U1266, 102, rue de la Santé, 75013 Paris, France
| | - J Pallud
- Université de Paris, 85, boulevard Saint-Germain, 75006 Paris, France; Imabrain, Institut de psychiatrie et neurosciences de Paris (IPNP), 102-108, rue de la Santé, 75014 Paris, France; Inserm, U1266, 102, rue de la Santé, 75013 Paris, France; Neurosurgery Department, GHU de Paris, centre hospitalier Sainte-Anne, 1, rue Cabanis, 75014 Paris, France
| | - J-F Meder
- Radiology Department, GHU de Paris, centre hospitalier Sainte-Anne, 1, rue Cabanis, 75014 Paris, France; Université de Paris, 85, boulevard Saint-Germain, 75006 Paris, France; Imabrain, Institut de psychiatrie et neurosciences de Paris (IPNP), 102-108, rue de la Santé, 75014 Paris, France; Inserm, U1266, 102, rue de la Santé, 75013 Paris, France
| | - C Oppenheim
- Radiology Department, GHU de Paris, centre hospitalier Sainte-Anne, 1, rue Cabanis, 75014 Paris, France; Université de Paris, 85, boulevard Saint-Germain, 75006 Paris, France; Imabrain, Institut de psychiatrie et neurosciences de Paris (IPNP), 102-108, rue de la Santé, 75014 Paris, France; Inserm, U1266, 102, rue de la Santé, 75013 Paris, France
| | - F Dhermain
- Radiotherapy Department, Gustave-Roussy, 114, rue Édouard-Vaillant, 94805 Villejuif, France
| | - M Edjlali
- Radiology Department, GHU de Paris, centre hospitalier Sainte-Anne, 1, rue Cabanis, 75014 Paris, France; Université de Paris, 85, boulevard Saint-Germain, 75006 Paris, France; Imabrain, Institut de psychiatrie et neurosciences de Paris (IPNP), 102-108, rue de la Santé, 75014 Paris, France; Inserm, U1266, 102, rue de la Santé, 75013 Paris, France
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Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis. Eur Radiol 2020; 30:4664-4674. [DOI: 10.1007/s00330-020-06717-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 01/22/2020] [Accepted: 02/05/2020] [Indexed: 12/24/2022]
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