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Śledzińska-Bebyn P, Furtak J, Bebyn M, Serafin Z. Beyond conventional imaging: Advancements in MRI for glioma malignancy prediction and molecular profiling. Magn Reson Imaging 2024; 112:63-81. [PMID: 38914147 DOI: 10.1016/j.mri.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/20/2024] [Accepted: 06/20/2024] [Indexed: 06/26/2024]
Abstract
This review examines the advancements in magnetic resonance imaging (MRI) techniques and their pivotal role in diagnosing and managing gliomas, the most prevalent primary brain tumors. The paper underscores the importance of integrating modern MRI modalities, such as diffusion-weighted imaging and perfusion MRI, which are essential for assessing glioma malignancy and predicting tumor behavior. Special attention is given to the 2021 WHO Classification of Tumors of the Central Nervous System, emphasizing the integration of molecular diagnostics in glioma classification, significantly impacting treatment decisions. The review also explores radiogenomics, which correlates imaging features with molecular markers to tailor personalized treatment strategies. Despite technological progress, MRI protocol standardization and result interpretation challenges persist, affecting diagnostic consistency across different settings. Furthermore, the review addresses MRI's capacity to distinguish between tumor recurrence and pseudoprogression, which is vital for patient management. The necessity for greater standardization and collaborative research to harness MRI's full potential in glioma diagnosis and personalized therapy is highlighted, advocating for an enhanced understanding of glioma biology and more effective treatment approaches.
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Affiliation(s)
- Paulina Śledzińska-Bebyn
- Department of Radiology, 10th Military Research Hospital and Polyclinic, 85-681 Bydgoszcz, Poland.
| | - Jacek Furtak
- Department of Clinical Medicine, Faculty of Medicine, University of Science and Technology, Bydgoszcz, Poland; Department of Neurosurgery, 10th Military Research Hospital and Polyclinic, 85-681 Bydgoszcz, Poland
| | - Marek Bebyn
- Department of Internal Diseases, 10th Military Clinical Hospital and Polyclinic, 85-681 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland
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Foltyn-Dumitru M, Mahmutoglu MA, Brugnara G, Kessler T, Sahm F, Wick W, Heiland S, Bendszus M, Vollmuth P, Schell M. Shape matters: unsupervised exploration of IDH-wildtype glioma imaging survival predictors. Eur Radiol 2024:10.1007/s00330-024-11042-6. [PMID: 39251442 DOI: 10.1007/s00330-024-11042-6] [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: 03/16/2024] [Revised: 06/15/2024] [Accepted: 08/07/2024] [Indexed: 09/11/2024]
Abstract
OBJECTIVES This study examines clustering based on shape radiomic features and tumor volume to identify IDH-wildtype glioma phenotypes and assess their impact on overall survival (OS). MATERIALS AND METHODS This retrospective study included 436 consecutive patients diagnosed with IDH-wt glioma who underwent preoperative MR imaging. Alongside the total tumor volume, nine distinct shape radiomic features were extracted using the PyRadiomics framework. Different imaging phenotypes were identified using partition around medoids (PAM) clustering on the training dataset (348/436). The prognostic efficacy of these phenotypes in predicting OS was evaluated on the test dataset (88/436). External validation was performed using the public UCSF glioma dataset (n = 397). A decision-tree algorithm was employed to determine the relevance of features associated with cluster affiliation. RESULTS PAM clustering identified two clusters in the training dataset: Cluster 1 (n = 233) had a higher proportion of patients with higher sphericity and elongation, while Cluster 2 (n = 115) had a higher proportion of patients with higher maximum 3D diameter, surface area, axis lengths, and tumor volume (p < 0.001 for each). OS differed significantly between clusters: Cluster 1 showed a median OS of 23.8 compared to 11.4 months of Cluster 2 in the holdout test dataset (p = 0.002). Multivariate Cox regression showed improved performance with cluster affiliation over clinical data alone (C index 0.67 vs 0.59, p = 0.003). Cluster-based models outperformed the models with tumor volume alone (evidence ratio: 5.16-5.37). CONCLUSION Data-driven clustering reveals imaging phenotypes, highlighting the improved prognostic power of combining shape-radiomics with tumor volume, thereby outperforming predictions based on tumor volume alone in high-grade glioma survival outcomes. CLINICAL RELEVANCE STATEMENT Shape-radiomics and volume-based cluster analyses of preoperative MRI scans can reveal imaging phenotypes that improve the prediction of OS in patients with IDH-wild type gliomas, outperforming currently known models based on tumor size alone or clinical parameters. KEY POINTS Shape radiomics and tumor volume clustering in IDH-wildtype gliomas are investigated for enhanced prognostic accuracy. Two distinct phenotypic clusters were identified with different median OSs. Integrating shape radiomics and volume-based clustering enhances OS prediction in IDH-wildtype glioma patients.
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Affiliation(s)
- Martha Foltyn-Dumitru
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Mustafa Ahmed Mahmutoglu
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Tobias Kessler
- Department of Neurology and Neurooncology Program, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wolfgang Wick
- Department of Neurology and Neurooncology Program, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Department of Neuroradiology, Bonn University Hospital, Bonn, Germany
| | - Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
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Han X, Maharjan S, Chen J, Zhao Y, Qi Y, White LE, Johnson GA, Wang N. High-resolution diffusion magnetic resonance imaging and spatial-transcriptomic in developing mouse brain. Neuroimage 2024; 297:120734. [PMID: 39032791 PMCID: PMC11377129 DOI: 10.1016/j.neuroimage.2024.120734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 07/06/2024] [Accepted: 07/11/2024] [Indexed: 07/23/2024] Open
Abstract
Brain development is a highly complex process regulated by numerous genes at the molecular and cellular levels. Brain tissue exhibits serial microstructural changes during the development process. High-resolution diffusion magnetic resonance imaging (dMRI) affords a unique opportunity to probe these changes in the developing brain non-destructively. In this study, we acquired multi-shell dMRI datasets at 32 µm isotropic resolution to investigate the tissue microstructure alterations, which we believe to be the highest spatial resolution dMRI datasets obtained for postnatal mouse brains. We adapted the Allen Developing Mouse Brain Atlas (ADMBA) to integrate quantitative MRI metrics and spatial transcriptomics. Diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and neurite orientation dispersion and density imaging (NODDI) metrics were used to quantify brain development at different postnatal days. We demonstrated that the differential evolutions of fiber orientation distributions contribute to the distinct development patterns in white matter (WM) and gray matter (GM). Furthermore, the genes enriched in the nervous system that regulate brain structure and function were expressed in spatial correlation with age-matched dMRI. This study is the first one providing high-resolution dMRI, including DTI, DKI, and NODDI models, to trace mouse brain microstructural changes in WM and GM during postnatal development. This study also highlighted the genotype-phenotype correlation of spatial transcriptomics and dMRI, which may improve our understanding of brain microstructure changes at the molecular level.
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Affiliation(s)
- Xinyue Han
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA; Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Surendra Maharjan
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA
| | - Jie Chen
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA
| | - Yi Zhao
- Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN, USA
| | - Yi Qi
- Center for In Vivo Microscopy, Department of Radiology, Duke University, Durham, NC, USA
| | - Leonard E White
- Department of Neurology, Duke University Medical Center, Durham, NC, USA
| | - G Allan Johnson
- Center for In Vivo Microscopy, Department of Radiology, Duke University, Durham, NC, USA; Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Nian Wang
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA; Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA; Stark Neurosciences Research Institute, Indiana University, Indianapolis, IN, USA.
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Lan T, Kuang S, Liang P, Ning C, Li Q, Wang L, Wang Y, Lin Z, Hu H, Yang L, Li J, Liu J, Li Y, Wu F, Chai H, Song X, Huang Y, Duan X, Zeng D, Li J, Cao H. MRI-based deep learning and radiomics for prediction of occult cervical lymph node metastasis and prognosis in early-stage oral and oropharyngeal squamous cell carcinoma: a diagnostic study. Int J Surg 2024; 110:4648-4659. [PMID: 38729119 PMCID: PMC11325978 DOI: 10.1097/js9.0000000000001578] [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: 12/21/2023] [Accepted: 04/25/2024] [Indexed: 05/12/2024]
Abstract
INTRODUCTION The incidence of occult cervical lymph node metastases (OCLNM) is reported to be 20-30% in early-stage oral cancer and oropharyngeal cancer. There is a lack of an accurate diagnostic method to predict occult lymph node metastasis and to help surgeons make precise treatment decisions. AIM To construct and evaluate a preoperative diagnostic method to predict OCLNM in early-stage oral and oropharyngeal squamous cell carcinoma (OC and OP SCC) based on deep learning features (DLFs) and radiomics features. METHODS A total of 319 patients diagnosed with early-stage OC or OP SCC were retrospectively enrolled and divided into training, test and external validation sets. Traditional radiomics features and DLFs were extracted from their MRI images. The least absolute shrinkage and selection operator (LASSO) analysis was employed to identify the most valuable features. Prediction models for OCLNM were developed using radiomics features and DLFs. The effectiveness of the models and their clinical applicability were evaluated using the area under the curve (AUC), decision curve analysis (DCA), and survival analysis. RESULTS Seventeen prediction models were constructed. The Resnet50 deep learning (DL) model based on the combination of radiomics and DL features achieves the optimal performance, with AUC values of 0.928 (95% CI: 0.881-0.975), 0.878 (95% CI: 0.766-0.990), 0.796 (95% CI: 0.666-0.927), and 0.834 (95% CI: 0.721-0.947) in the training, test, external validation set1, and external validation set2, respectively. Moreover, the Resnet50 model has great prediction value of prognosis in patients with early-stage OC and OP SCC. CONCLUSION The proposed MRI-based Resnet50 DL model demonstrated high capability in diagnosis of OCLNM and prognosis prediction in the early-stage OC and OP SCC. The Resnet50 model could help refine the clinical diagnosis and treatment of the early-stage OC and OP SCC.
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Affiliation(s)
- Tianjun Lan
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Shijia Kuang
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Peisheng Liang
- Guanghua School of Stomatology, Hospital of Stomatology, Guangdong Province Key Laboratory of Stomatology, Sun Yat-Sen University, Guangzhou
| | - Chenglin Ning
- School of Biomedical Engineering, Southern Medical University, Guangzhou
| | - Qunxing Li
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Liansheng Wang
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Youyuan Wang
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Zhaoyu Lin
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Huijun Hu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
| | - Lingjie Yang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
| | - Jintao Li
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Jingkang Liu
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Yanyan Li
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Fan Wu
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Hua Chai
- School of Mathematics and Big Data, Foshan University, Foshan, Guangdong
| | - Xinpeng Song
- School of Mathematics and Big Data, Foshan University, Foshan, Guangdong
| | - Yiqian Huang
- School of Mathematics and Big Data, Foshan University, Foshan, Guangdong
| | - Xiaohui Duan
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
| | - Dong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangzhou
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China
| | - Jinsong Li
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Haotian Cao
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
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Kersch CN, Kim M, Stoller J, Barajas RF, Park JE. Imaging Genomics of Glioma Revisited: Analytic Methods to Understand Spatial and Temporal Heterogeneity. AJNR Am J Neuroradiol 2024; 45:537-548. [PMID: 38548303 PMCID: PMC11288537 DOI: 10.3174/ajnr.a8148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 11/09/2023] [Indexed: 04/12/2024]
Abstract
An improved understanding of the cellular and molecular biologic processes responsible for brain tumor development, growth, and resistance to therapy is fundamental to improving clinical outcomes. Imaging genomics is the study of the relationships between microscopic, genetic, and molecular biologic features and macroscopic imaging features. Imaging genomics is beginning to shift clinical paradigms for diagnosing and treating brain tumors. This article provides an overview of imaging genomics in gliomas, in which imaging data including hallmarks such as IDH-mutation, MGMT methylation, and EGFR-mutation status can provide critical insights into the pretreatment and posttreatment stages. This article will accomplish the following: 1) review the methods used in imaging genomics, including visual analysis, quantitative analysis, and radiomics analysis; 2) recommend suitable analytic methods for imaging genomics according to biologic characteristics; 3) discuss the clinical applicability of imaging genomics; and 4) introduce subregional tumor habitat analysis with the goal of guiding future radiogenetics research endeavors toward translation into critically needed clinical applications.
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Affiliation(s)
- Cymon N Kersch
- From the Department of Radiation Medicine (C.N.K.), Oregon Health and Science University, Portland, Oregon
| | - Minjae Kim
- Department of Radiology and Research Institute of Radiology (M.K., J.E.P.), Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jared Stoller
- Department of Diagnostic Radiology (J.S., R.F.B.), Oregon Health and Science University, Portland, Oregon
| | - Ramon F Barajas
- Department of Diagnostic Radiology (J.S., R.F.B.), Oregon Health and Science University, Portland, Oregon
- Knight Cancer Institute (R.F.B.), Oregon Health and Science University, Portland, Oregon
- Advanced Imaging Research Center (R.F.B.), Oregon Health and Science University, Portland, Oregon
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology (M.K., J.E.P.), Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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Senger KPS, Kesavadas C, Thomas B, Singh A, Multani GS, AN D, Label M, Suchandrima B, Shin D. Experimenting with ASL-based arterialized cerebral blood volume as a novel imaging biomarker in grading glial neoplasms. Neuroradiol J 2023; 36:728-735. [PMID: 37548164 PMCID: PMC10649543 DOI: 10.1177/19714009231193163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/08/2023] Open
Abstract
BACKGROUND Perfusion imaging is one of the methods used to grade glial neoplasms, and in this study we evaluated the role of ASL perfusion in grading brain glioma. PURPOSE The aim is to evaluate the role of arterialized cerebral blood volume (aCBV) of multi-delay ASL perfusion for grading glial neoplasm. MATERIALS AND METHODS This study is a prospective observational study of 56 patients with glial neoplasms of the brain who underwent surgery, and only cases with positive diagnosis of glioma are included to evaluate the novel diagnostic parameter. RESULTS In the study, ASL-derived normalized aCBV (naCBV) and T2*DSC-derived normalized CBV (nCBV) are showing very high correlation (Pearson's correlation coefficient value of 0.94) in grading glial neoplasms. naCBV and nCBF are also showing very high correlation (Pearson's correlation coefficient value of 0.876). The study also provides cutoff values for differentiating LGG from HGG for normalized aCBV(naCBV) of ASL, normalized CBV (nCBV), and normalized nCBF derived from T2* DCS as 1.12, 1.254, and 1.31, respectively. ASL-derived aCBV also shows better diagnostic accuracy than ASL-derived CBF. CONCLUSION This study is one of its kind to the best of our knowledge where multi-delay ASL perfusion-derived aCBV is used as a novel imaging biomarker for grading glial neoplasms, and it has shown high statistical correlation with T2* DSC-derived perfusion parameters.
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Affiliation(s)
- Krishna Pratap Singh Senger
- 1Department of Imaging Sciences and Interventional Radiology, Sree Chita Institute of Medical Sciences and Technology, Trivandrum, Kerala, India
| | - C Kesavadas
- 1Department of Imaging Sciences and Interventional Radiology, Sree Chita Institute of Medical Sciences and Technology, Trivandrum, Kerala, India
| | - Bejoy Thomas
- 1Department of Imaging Sciences and Interventional Radiology, Sree Chita Institute of Medical Sciences and Technology, Trivandrum, Kerala, India
| | - Ankita Singh
- Department of Research, Army Hospital Research and Referral, New Delhi, India
| | - Gurpreet Singh Multani
- 1Department of Imaging Sciences and Interventional Radiology, Sree Chita Institute of Medical Sciences and Technology, Trivandrum, Kerala, India
| | - Deepti AN
- 1Department of Imaging Sciences and Interventional Radiology, Sree Chita Institute of Medical Sciences and Technology, Trivandrum, Kerala, India
| | - Marc Label
- Department of Research and Development, GEHealthcare, Calgary, AB, Canada
| | | | - David Shin
- Department of Research and Development, GEHealthcare, Calgary, AB, Canada
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Lee SJ, Park JE, Park SY, Kim YH, Hong CK, Kim JH, Kim HS. Imaging-Based Versus Pathologic Survival Stratifications of Diffuse Glioma According to the 2021 WHO Classification System. Korean J Radiol 2023; 24:772-783. [PMID: 37500578 PMCID: PMC10400365 DOI: 10.3348/kjr.2022.0919] [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: 11/21/2022] [Revised: 04/05/2023] [Accepted: 05/20/2023] [Indexed: 07/29/2023] Open
Abstract
OBJECTIVE Imaging-based survival stratification of patients with gliomas is important for their management, and the 2021 WHO classification system must be clinically tested. The aim of this study was to compare integrative imaging- and pathology-based methods for survival stratification of patients with diffuse glioma. MATERIALS AND METHODS This study included diffuse glioma cases from The Cancer Genome Atlas (training set: 141 patients) and Asan Medical Center (validation set: 131 patients). Two neuroradiologists analyzed presurgical CT and MRI to assign gliomas to five imaging-based risk subgroups (1 to 5) according to well-known imaging phenotypes (e.g., T2/FLAIR mismatch) and recategorized them into three imaging-based risk groups, according to the 2021 WHO classification: group 1 (corresponding to risk subgroup 1, indicating oligodendroglioma, isocitrate dehydrogenase [IDH]-mutant, and 1p19q-co-deleted), group 2 (risk subgroups 2 and 3, indicating astrocytoma, IDH-mutant), and group 3 (risk subgroups 4 and 5, indicating glioblastoma, IDHwt). The progression-free survival (PFS) and overall survival (OS) were estimated for each imaging risk group, subgroup, and pathological diagnosis. Time-dependent area-under-the receiver operating characteristic analysis (AUC) was used to compare the performance between imaging-based and pathology-based survival model. RESULTS Both OS and PFS were stratified according to the five imaging-based risk subgroups (P < 0.001) and three imaging-based risk groups (P < 0.001). The three imaging-based groups showed high performance in predicting PFS at one-year (AUC, 0.787) and five-years (AUC, 0.823), which was similar to that of the pathology-based prediction of PFS (AUC of 0.785 and 0.837). Combined with clinical predictors, the performance of the imaging-based survival model for 1- and 3-year PFS (AUC 0.813 and 0.921) was similar to that of the pathology-based survival model (AUC 0.839 and 0.889). CONCLUSION Imaging-based survival stratification according to the 2021 WHO classification demonstrated a performance similar to that of pathology-based survival stratification, especially in predicting PFS.
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Affiliation(s)
- So Jeong Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
| | - Seo Young Park
- Deparment of Statistics and Data Science, Korea National Open University, Seoul, Republic of Korea
| | - Young-Hoon Kim
- Department of Neurosurgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Chang Ki Hong
- Department of Neurosurgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jeong Hoon Kim
- Department of Neurosurgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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Mishra A, Ravina M, Kote R, Kumar A, Kashyap Y, Dasgupta S, Reddy M. Role of Textural Analysis of Pretreatment 18F Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Response Prediction in Esophageal Carcinoma Patients. Indian J Nucl Med 2023; 38:255-263. [PMID: 38046976 PMCID: PMC10693362 DOI: 10.4103/ijnm.ijnm_1_23] [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: 01/03/2023] [Accepted: 03/30/2023] [Indexed: 12/05/2023] Open
Abstract
Introduction Positron emission tomography/computed tomography (PET/CT) is routinely used for staging, response assessment, and surveillance in esophageal carcinoma patients. The aim of this study was to investigate whether textural features of pretreatment 18F-fluorodeoxyglucose (18F-FDG) PET/CT images can contribute to prognosis prediction in carcinoma oesophagus patients. Materials and Methods This is a retrospective study of 30 diagnosed carcinoma esophagus patients. These patients underwent pretreatment 18F-FDG PET/CT for staging. The images were processed in a commercially available textural analysis software. Region of interest was drawn over primary tumor with a 40% threshold and was processed further to derive 92 textural and radiomic parameters. These parameters were then compared between progression group and nonprogression group. The original dataset was subject separately to receiver operating curve analysis. Receiver operating characteristic (ROC) curves were used to identify the cutoff values for textural features with a P < 0.05 for statistical significance. Feature selection was done with principal component analysis. The selected features of each evaluator were subject to 4 machine-learning algorithms. The highest area under the curve (AUC) values was selected for 10 features. Results A retrospective study of 30 primary carcinoma esophagus patients was done. Patients were followed up after chemo-radiotherapy and they underwent follow-up PET/CT. On the basis of their response, patients were divided into progression group and nonprogression group. Among them, 15 patients showed disease progression and 15 patients were in the nonprogression group. Ten textural analysis parameters turned out to be significant in the prediction of disease progression. Cutoff values were calculated for these parameters according to the ROC curves, GLZLM_long zone emphasis (Gray Level Zone Length Matrix)_long zone emphasis (44.9), GLZLM_low gray level zone emphasis (0.006), GLZLM_short zone low gray level emphasis (0.0032), GLZLM_long zone low gray level emphasis (0.185), GLRLM_long run emphasis (Gray Level Run Length Matrix) (1.31), GLRLM_low gray level run emphasis (0.0058), GLRLM_short run low gray level emphasis (0.005496), GLRLM_long run low gray level emphasis (0.00727), NGLDM_Busyness (Neighborhood Gray Level Difference Matrix) (0.75), and gray level co-occurrence matrix_homogeneity (0.37). Feature selection by principal components analysis and feature classification by the K-nearest neighbor machine-learning model using independent training and test samples yielded the overall highest AUC. Conclusions Textural analysis parameters could provide prognostic information in carcinoma esophagus patients. Larger multicenter studies are needed for better clinical prognostication of these parameters.
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Affiliation(s)
- Ajit Mishra
- Department of Surgical Gastroenterology, DKS Multispeciality Hospital, Raipur, India
| | - Mudalsha Ravina
- Department of Nuclear Medicine, All India Institute of Medical Sciences, Raipur, India
| | - Rutuja Kote
- Department of Nuclear Medicine, All India Institute of Medical Sciences, Raipur, India
| | - Amit Kumar
- Department of Medical Oncology, All India Institute of Medical Sciences, Raipur, India
| | - Yashwant Kashyap
- Department of Medical Oncology, All India Institute of Medical Sciences, Raipur, India
| | - Subhajit Dasgupta
- Department of Nuclear Medicine, All India Institute of Medical Sciences, Raipur, India
| | - Moulish Reddy
- Department of Nuclear Medicine, All India Institute of Medical Sciences, Raipur, India
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9
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Gidwani M, Chang K, Patel JB, Hoebel KV, Ahmed SR, Singh P, Fuller CD, Kalpathy-Cramer J. Inconsistent Partitioning and Unproductive Feature Associations Yield Idealized Radiomic Models. Radiology 2023; 307:e220715. [PMID: 36537895 PMCID: PMC10068883 DOI: 10.1148/radiol.220715] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 10/19/2022] [Accepted: 11/01/2022] [Indexed: 12/24/2022]
Abstract
Background Radiomics is the extraction of predefined mathematic features from medical images for the prediction of variables of clinical interest. While some studies report superlative accuracy of radiomic machine learning (ML) models, the published methodology is often incomplete, and the results are rarely validated in external testing data sets. Purpose To characterize the type, prevalence, and statistical impact of methodologic errors present in radiomic ML studies. Materials and Methods Radiomic ML publications were reviewed for the presence of performance-inflating methodologic flaws. Common flaws were subsequently reproduced with randomly generated features interpolated from publicly available radiomic data sets to demonstrate the precarious nature of reported findings. Results In an assessment of radiomic ML publications, the authors uncovered two general categories of data analysis errors: inconsistent partitioning and unproductive feature associations. In simulations, the authors demonstrated that inconsistent partitioning augments radiomic ML accuracy by 1.4 times from unbiased performance and that correcting for flawed methodologic results in areas under the receiver operating characteristic curve approaching a value of 0.5 (random chance). With use of randomly generated features, the authors illustrated that unproductive associations between radiomic features and gene sets can imply false causality for biologic phenomenon. Conclusion Radiomic machine learning studies may contain methodologic flaws that undermine their validity. This study provides a review template to avoid such flaws. © RSNA, 2022 Supplemental material is available for this article. See also the editorial by Jacobs in this issue.
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Affiliation(s)
- Mishka Gidwani
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
| | - Ken Chang
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
| | - Jay Biren Patel
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
| | - Katharina Viktoria Hoebel
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
| | - Syed Rakin Ahmed
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
| | - Praveer Singh
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
| | - Clifton David Fuller
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
| | - Jayashree Kalpathy-Cramer
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
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10
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Abenavoli EM, Barbetti M, Linguanti F, Mungai F, Nassi L, Puccini B, Romano I, Sordi B, Santi R, Passeri A, Sciagrà R, Talamonti C, Cistaro A, Vannucchi AM, Berti V. Characterization of Mediastinal Bulky Lymphomas with FDG-PET-Based Radiomics and Machine Learning Techniques. Cancers (Basel) 2023; 15:cancers15071931. [PMID: 37046592 PMCID: PMC10093023 DOI: 10.3390/cancers15071931] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/11/2023] [Accepted: 03/20/2023] [Indexed: 04/14/2023] Open
Abstract
BACKGROUND This study tested the diagnostic value of 18F-FDG PET/CT (FDG-PET) volumetric and texture parameters in the histological differentiation of mediastinal bulky disease due to classical Hodgkin lymphoma (cHL), primary mediastinal B-cell lymphoma (PMBCL) and grey zone lymphoma (GZL), using machine learning techniques. METHODS We reviewed 80 cHL, 29 PMBCL and 8 GZL adult patients with mediastinal bulky disease and histopathological diagnoses who underwent FDG-PET pre-treatment. Volumetric and radiomic parameters were measured using FDG-PET both for bulky lesions (BL) and for all lesions (AL) using LIFEx software (threshold SUV ≥ 2.5). Binary and multiclass classifications were performed with various machine learning techniques fed by a relevant subset of radiomic features. RESULTS The analysis showed significant differences between the lymphoma groups in terms of SUVmax, SUVmean, MTV, TLG and several textural features of both first- and second-order grey level. Among machine learning classifiers, the tree-based ensembles achieved the best performance both for binary and multiclass classifications in histological differentiation. CONCLUSIONS Our results support the value of metabolic heterogeneity as an imaging biomarker, and the use of radiomic features for early characterization of mediastinal bulky lymphoma.
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Affiliation(s)
- Elisabetta Maria Abenavoli
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, 50139 Florence, Italy
| | - Matteo Barbetti
- Department of Information Engineering, University of Florence, 50134 Florence, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Florence Division, 50019 Sesto Fiorentino, Italy
| | - Flavia Linguanti
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, 50139 Florence, Italy
| | - Francesco Mungai
- Department of Radiology, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy
| | - Luca Nassi
- Hematology Department, Azienda Ospedaliero Universitaria Careggi, University of Florence, 50139 Florence, Italy
| | - Benedetta Puccini
- Hematology Department, Azienda Ospedaliero Universitaria Careggi, University of Florence, 50139 Florence, Italy
| | - Ilaria Romano
- Hematology Department, Azienda Ospedaliero Universitaria Careggi, University of Florence, 50139 Florence, Italy
| | - Benedetta Sordi
- Hematology Department, Azienda Ospedaliero Universitaria Careggi, University of Florence, 50139 Florence, Italy
- Department of Experimental and Clinical Medicine, CRIMM, Center Research and Innovation of Myeloproliferative Neoplasms, Azienda Ospedaliera Universitaria Careggi, University of Florence, 50139 Florence, Italy
| | - Raffaella Santi
- Pathology Section, Department of Health Sciences, University of Florence, 50139 Florence, Italy
| | - Alessandro Passeri
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, 50139 Florence, Italy
| | - Roberto Sciagrà
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, 50139 Florence, Italy
| | - Cinzia Talamonti
- Istituto Nazionale di Fisica Nucleare (INFN), Florence Division, 50019 Sesto Fiorentino, Italy
- Medical Physics Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, 50139 Florence, Italy
| | - Angelina Cistaro
- Nuclear Medicine Department, Salus Alliance Medical, 16128 Genoa, Italy
- Pediatric Study Group for Italian Association of Nuclear Medicine (AIMN), 20159 Milan, Italy
| | - Alessandro Maria Vannucchi
- Department of Experimental and Clinical Medicine, CRIMM, Center Research and Innovation of Myeloproliferative Neoplasms, Azienda Ospedaliera Universitaria Careggi, University of Florence, 50139 Florence, Italy
| | - Valentina Berti
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, 50139 Florence, Italy
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11
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Jamshidi N, Senthilvelan J, Dawson DW, Donahue TR, Kuo MD. Construction of a radiogenomic association map of pancreatic ductal adenocarcinoma. BMC Cancer 2023; 23:189. [PMID: 36843111 PMCID: PMC9969670 DOI: 10.1186/s12885-023-10658-z] [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: 08/30/2022] [Accepted: 02/17/2023] [Indexed: 02/28/2023] Open
Abstract
BACKGROUND Pancreatic adenocarcinoma (PDAC) persists as a malignancy with high morbidity and mortality that can benefit from new means to characterize and detect these tumors, such as radiogenomics. In order to address this gap in the literature, constructed a transcriptomic-CT radiogenomic (RG) map for PDAC. METHODS In this Institutional Review Board approved study, a cohort of subjects (n = 50) with gene expression profile data paired with histopathologically confirmed resectable or borderline resectable PDAC were identified. Studies with pre-operative contrast-enhanced CT images were independently assessed for a set of 88 predefined imaging features. Microarray gene expression profiling was then carried out on the histopathologically confirmed pancreatic adenocarcinomas and gene networks were constructed using Weighted Gene Correlation Network Analysis (WCGNA) (n = 37). Data were analyzed with bioinformatics analyses, multivariate regression-based methods, and Kaplan-Meier survival analyses. RESULTS Survival analyses identified multiple features of interest that were significantly associated with overall survival, including Tumor Height (P = 0.014), Tumor Contour (P = 0.033), Tumor-stroma Interface (P = 0.014), and the Tumor Enhancement Ratio (P = 0.047). Gene networks for these imaging features were then constructed using WCGNA and further annotated according to the Gene Ontology (GO) annotation framework for a biologically coherent interpretation of the imaging trait-associated gene networks, ultimately resulting in a PDAC RG CT-transcriptome map composed of 3 stage-independent imaging traits enriched in metabolic processes, telomerase activity, and podosome assembly (P < 0.05). CONCLUSIONS A CT-transcriptomic RG map for PDAC composed of semantic and quantitative traits with associated biology processes predictive of overall survival, was constructed, that serves as a reference for further mechanistic studies for non-invasive phenotyping of pancreatic tumors.
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Affiliation(s)
- Neema Jamshidi
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, 757 Westwood Ave, Suite 2125, Los Angeles, CA, 90095, USA. .,Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA.
| | - Jayasuriya Senthilvelan
- grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, 757 Westwood Ave, Suite 2125, Los Angeles, CA 90095 USA
| | - David W. Dawson
- grid.19006.3e0000 0000 9632 6718Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Pathology, University of California, Los Angeles, CA USA
| | - Timothy R. Donahue
- grid.19006.3e0000 0000 9632 6718Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Surgical Oncology, University of California, Los Angeles, CA USA
| | - Michael D. Kuo
- grid.194645.b0000000121742757Medical AI Laboratory Program, The University of Hong Kong, Hong Kong SAR, Hong Kong
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12
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Hartmann K, Sadée CY, Satwah I, Carrillo-Perez F, Gevaert O. Imaging genomics: data fusion in uncovering disease heritability. Trends Mol Med 2023; 29:141-151. [PMID: 36470817 PMCID: PMC10507799 DOI: 10.1016/j.molmed.2022.11.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 10/28/2022] [Accepted: 11/03/2022] [Indexed: 12/04/2022]
Abstract
Sequencing of the human genome in the early 2000s enabled probing of the genetic basis of disease on a scale previously unimaginable. Now, two decades later, after interrogating millions of markers in thousands of individuals, a significant portion of disease heritability still remains hidden. Recent efforts to unravel this 'missing heritability' have focused on garnering new insight from merging different data types, including medical imaging. Imaging offers promising intermediate phenotypes to bridge the gap between genetic variation and disease pathology. In this review we outline this fusion and provide examples of imaging genomics in a range of diseases, from oncology to cardiovascular and neurodegenerative disease. Finally, we discuss how ongoing revolutions in data science and sharing are primed to advance the field.
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Affiliation(s)
- Katherine Hartmann
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
| | - Christoph Y Sadée
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Ishan Satwah
- College of Medicine, Drexel University, Philadelphia, PA, USA
| | - Francisco Carrillo-Perez
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA; Department of Computer Architecture and Technology, University of Granada. C.I.T.I.C., Granada, Spain
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA.
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13
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Hooper GW, Ginat DT. MRI radiomics and potential applications to glioblastoma. Front Oncol 2023; 13:1134109. [PMID: 36874083 PMCID: PMC9982088 DOI: 10.3389/fonc.2023.1134109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 02/07/2023] [Indexed: 02/19/2023] Open
Abstract
MRI plays an important role in the evaluation of glioblastoma, both at initial diagnosis and follow up after treatment. Quantitative analysis via radiomics can augment the interpretation of MRI in terms of providing insights regarding the differential diagnosis, genotype, treatment response, and prognosis. The various MRI radiomic features of glioblastoma are reviewed in this article.
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Affiliation(s)
- Grayson W Hooper
- Landstuhl Regional Medical Center, Department of Radiology, Landstuhl, Germany
| | - Daniel T Ginat
- University of Chicago, Department of Radiology, Chicago, IL, United States
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14
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Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology. Biomedicines 2022; 10:biomedicines10123205. [PMID: 36551961 PMCID: PMC9775324 DOI: 10.3390/biomedicines10123205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/02/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022] Open
Abstract
Glioblastoma (GBM) is a malignant brain tumor exhibiting rapid and infiltrative growth, with less than 10% of patients surviving over 5 years, despite aggressive and multimodal treatments. The poor prognosis and the lack of effective pharmacological treatments are imputable to a remarkable histological and molecular heterogeneity of GBM, which has led, to date, to the failure of precision oncology and targeted therapies. Identification of molecular biomarkers is a paradigm for comprehensive and tailored treatments; nevertheless, biopsy sampling has proved to be invasive and limited. Radiogenomics is an emerging translational field of research aiming to study the correlation between radiographic signature and underlying gene expression. Although a research field still under development, not yet incorporated into routine clinical practice, it promises to be a useful non-invasive tool for future personalized/adaptive neuro-oncology. This review provides an up-to-date summary of the recent advancements in the use of magnetic resonance imaging (MRI) radiogenomics for the assessment of molecular markers of interest in GBM regarding prognosis and response to treatments, for monitoring recurrence, also providing insights into the potential efficacy of such an approach for survival prognostication. Despite a high sensitivity and specificity in almost all studies, accuracy, reproducibility and clinical value of radiomic features are the Achilles heel of this newborn tool. Looking into the future, investigators' efforts should be directed towards standardization and a disciplined approach to data collection, algorithms, and statistical analysis.
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15
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Impact of Tumour Segmentation Accuracy on Efficacy of Quantitative MRI Biomarkers of Radiotherapy Outcome in Brain Metastasis. Cancers (Basel) 2022; 14:cancers14205133. [PMID: 36291917 PMCID: PMC9601104 DOI: 10.3390/cancers14205133] [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: 09/20/2022] [Revised: 10/16/2022] [Accepted: 10/17/2022] [Indexed: 11/25/2022] Open
Abstract
Simple Summary Radiotherapy is a major treatment option for patients with brain metastasis. However, response to radiotherapy is highly varied among the patients, and it may take months before the response of brain metastasis to radiotherapy is apparent on standard follow-up imaging. This is not desirable, especially given the fact that patients diagnosed with brain metastasis suffer from a short median survival. Recent studies have shown the high potential of machine learning methods for analyzing quantitative imaging features (biomarkers) to predict the response of brain metastasis before or early after radiotherapy. However, these methods require manual delineation of individual tumours on imaging that is tedious and time-consuming, hindering further development and widespread application of these techniques. Here, we investigated the impact of using less accurate but automatically generated tumour outlines on the efficacy of the derived imaging biomarkers for radiotherapy response prediction. Our findings demonstrate that while the effect of tumour delineation accuracy is considerable for automatic contours with low accuracy, imaging biomarkers and prediction models are rather robust to imperfections in the produced tumour masks. The results of this study open the avenue to utilizing automatically generated tumour contours for discovering imaging biomarkers without sacrificing their accuracy. Abstract Significantly affecting patients’ clinical course and quality of life, a growing number of cancer cases are diagnosed with brain metastasis (BM) annually. Stereotactic radiotherapy is now a major treatment option for patients with BM. However, it may take months before the local response of BM to stereotactic radiation treatment is apparent on standard follow-up imaging. While machine learning in conjunction with radiomics has shown great promise in predicting the local response of BM before or early after radiotherapy, further development and widespread application of such techniques has been hindered by their dependency on manual tumour delineation. In this study, we explored the impact of using less-accurate automatically generated segmentation masks on the efficacy of radiomic features for radiotherapy outcome prediction in BM. The findings of this study demonstrate that while the effect of tumour delineation accuracy is substantial for segmentation models with lower dice scores (dice score ≤ 0.85), radiomic features and prediction models are rather resilient to imperfections in the produced tumour masks. Specifically, the selected radiomic features (six shared features out of seven) and performance of the prediction model (accuracy of 80% versus 80%, AUC of 0.81 versus 0.78) were fairly similar for the ground-truth and automatically generated segmentation masks, with dice scores close to 0.90. The positive outcome of this work paves the way for adopting high-throughput automatically generated tumour masks for discovering diagnostic and prognostic imaging biomarkers in BM without sacrificing accuracy.
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16
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Li Y, Qin Q, Zhang Y, Cao Y. Noninvasive Determination of the IDH Status of Gliomas Using MRI and MRI-Based Radiomics: Impact on Diagnosis and Prognosis. Curr Oncol 2022; 29:6893-6907. [PMID: 36290819 PMCID: PMC9600456 DOI: 10.3390/curroncol29100542] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 09/18/2022] [Accepted: 09/19/2022] [Indexed: 01/13/2023] Open
Abstract
Gliomas are the most common primary malignant brain tumors in adults. The fifth edition of the WHO Classification of Tumors of the Central Nervous System, published in 2021, provided molecular and practical approaches to CNS tumor taxonomy. Currently, molecular features are essential for differentiating the histological subtypes of gliomas, and recent studies have emphasized the importance of isocitrate dehydrogenase (IDH) mutations in stratifying biologically distinct subgroups of gliomas. IDH plays a significant role in gliomagenesis, and the association of IDH status with prognosis is very clear. Recently, there has been much progress in conventional MR imaging (cMRI), advanced MR imaging (aMRI), and radiomics, which are widely used in the study of gliomas. These advances have resulted in an improved correlation between MR signs and IDH mutation status, which will complement the prediction of the IDH phenotype. Although imaging cannot currently substitute for genetic tests, imaging findings have shown promising signs of diagnosing glioma subtypes and evaluating the efficacy and prognosis of individualized molecular targeted therapy. This review focuses on the correlation between MRI and MRI-based radiomics and IDH gene-phenotype prediction, discussing the value and application of these techniques in the diagnosis and evaluation of the prognosis of gliomas.
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Affiliation(s)
- Yurong Li
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing 210029, China
- The First School of Clinical Medicine, Nanjing Medical University, Nanjing 210029, China
| | - Qin Qin
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing 210029, China
| | - Yumeng Zhang
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing 210029, China
| | - Yuandong Cao
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Nanjing 210029, China
- Correspondence:
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Wu J, Mayer AT, Li R. Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy. Semin Cancer Biol 2022; 84:310-328. [PMID: 33290844 PMCID: PMC8319834 DOI: 10.1016/j.semcancer.2020.12.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 11/29/2020] [Accepted: 12/02/2020] [Indexed: 02/07/2023]
Abstract
Radiological imaging is an integral component of cancer care, including diagnosis, staging, and treatment response monitoring. It contains rich information about tumor phenotypes that are governed not only by cancer cellintrinsic biological processes but also by the tumor microenvironment, such as the composition and function of tumor-infiltrating immune cells. By analyzing the radiological scans using a quantitative radiomics approach, robust relations between specific imaging and molecular phenotypes can be established. Indeed, a number of studies have demonstrated the feasibility of radiogenomics for predicting intrinsic molecular subtypes and gene expression signatures in breast cancer based on MRI. In parallel, promising results have been shown for inferring the amount of tumor-infiltrating lymphocytes, a key factor for the efficacy of cancer immunotherapy, from standard-of-care radiological images. Compared with the biopsy-based approach, radiogenomics offers a unique avenue to profile the molecular makeup of the tumor and immune microenvironment as well as its evolution in a noninvasive and holistic manner through longitudinal imaging scans. Here, we provide a systematic review of the state of the art radiogenomics studies in the era of immunotherapy and discuss emerging paradigms and opportunities in AI and deep learning approaches. These technical advances are expected to transform the radiogenomics field, leading to the discovery of reliable imaging biomarkers. This will pave the way for their clinical translation to guide precision cancer therapy.
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Affiliation(s)
- Jia Wu
- Department of Imaging Physics, MD Anderson Cancer Center, Texas, 77030, USA; Department of Thoracic/Head & Neck Medical Oncology, MD Anderson Cancer Center, Texas, 77030, USA.
| | - Aaron T Mayer
- Department of Bioengineering, Stanford University, Stanford, California, 94305, USA; Department of Radiology, Stanford University, Stanford, California, 94305, USA; Molecular Imaging Program at Stanford, Stanford University, Stanford, California, 94305, USA; BioX Program at Stanford, Stanford University, Stanford, California, 94305, USA
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University, Stanford, California, 94305, USA
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Establishment of a Prediction Model for Overall Survival after Stereotactic Body Radiation Therapy for Primary Non-Small Cell Lung Cancer Using Radiomics Analysis. Cancers (Basel) 2022; 14:cancers14163859. [PMID: 36010853 PMCID: PMC9405862 DOI: 10.3390/cancers14163859] [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: 06/30/2022] [Revised: 08/04/2022] [Accepted: 08/09/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Lung cancer remains the leading cause of cancer-related mortality worldwide. Although early-stage non-small cell lung cancer (NSCLC) is likely to be controlled with stereotactic body radiation therapy (SBRT), approximately 18% of patients lead to recurrence. The aim of this study was to identify prognostic factors and establish a predictive model for survival outcomes of patients with non-metastatic NSCLC treated with SBRT. Several radiomic features were selected as predictive factors and two prediction models were established from the pre-treatment computed tomography images of 250 patients in the training cohort. One radiomic factor remained a significant prognostic factor of overall survival (OS) (p = 0.044), and one predicting model could estimate OS time (mean: 37.8 months) similar to the real OS time (33.7 months). In this study, we identified one radiomic factor and one prediction model that can be widely used. Abstract Stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (NSCLC) leads to recurrence in approximately 18% of patients. We aimed to extract the radiomic features, with which we predicted clinical outcomes and to establish predictive models. Patients with primary non-metastatic NSCLC who were treated with SBRT between 2002 and 2022 were retrospectively reviewed. The 358 primary tumors were randomly divided into a training cohort of 250 tumors and a validation cohort of 108 tumors. Clinical features and 744 radiomic features derived from primary tumor delineation on pre-treatment computed tomography were examined as prognostic factors of survival outcomes by univariate and multivariate analyses in the training cohort. Predictive models of survival outcomes were established from the results of the multivariate analysis in the training cohort. The selected radiomic features and prediction models were tested in a validation cohort. We found that one radiomic feature showed a significant difference in overall survival (OS) in the validation cohort (p = 0.044) and one predicting model could estimate OS time (mean: 37.8 months) similar to the real OS time (33.7 months). In this study, we identified one radiomic factor and one prediction model that can be widely used.
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Sansone G, Vivori N, Vivori C, Di Stefano AL, Picca A. Basic premises: searching for new targets and strategies in diffuse gliomas. Clin Transl Imaging 2022. [DOI: 10.1007/s40336-022-00507-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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20
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MRI Radiogenomics in Precision Oncology: New Diagnosis and Treatment Method. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2703350. [PMID: 35845886 PMCID: PMC9282990 DOI: 10.1155/2022/2703350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 05/04/2022] [Accepted: 05/25/2022] [Indexed: 11/21/2022]
Abstract
Precision medicine for cancer affords a new way for the most accurate and effective treatment to each individual cancer. Given the high time-evolving intertumor and intratumor heterogeneity features of personal medicine, there are still several obstacles hindering its diagnosis and treatment in clinical practice regardless of extensive exploration on it over the past years. This paper is to investigate radiogenomics methods in the literature for precision medicine for cancer focusing on the heterogeneity analysis of tumors. Based on integrative analysis of multimodal (parametric) imaging and molecular data in bulk tumors, a comprehensive analysis and discussion involving the characterization of tumor heterogeneity in imaging and molecular expression are conducted. These investigations are intended to (i) fully excavate the multidimensional spatial, temporal, and semantic related information regarding high-dimensional breast magnetic resonance imaging data, with integration of the highly specific structured data of genomics and combination of the diagnosis and cognitive process of doctors, and (ii) establish a radiogenomics data representation model based on multidimensional consistency analysis with multilevel spatial-temporal correlations.
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Liu H, Wang Y, Liu Y, Lin D, Zhang C, Zhao Y, Chen L, Li Y, Yuan J, Chen Z, Yu J, Kong W, Chen T. Contrast-Enhanced Computed Tomography–Based Radiogenomics Analysis for Predicting Prognosis in Gastric Cancer. Front Oncol 2022; 12:882786. [PMID: 35814414 PMCID: PMC9257248 DOI: 10.3389/fonc.2022.882786] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 05/16/2022] [Indexed: 12/12/2022] Open
Abstract
Objective The aim of this study is to identify prognostic imaging biomarkers and create a radiogenomics nomogram to predict overall survival (OS) in gastric cancer (GC). Material RNA sequencing data from 407 patients with GC and contrast-enhanced computed tomography (CECT) imaging data from 46 patients obtained from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) were utilized to identify radiogenomics biomarkers. A total of 392 patients with CECT images from the Nanfang Hospital database were obtained to create and validate a radiogenomics nomogram based on the biomarkers. Methods The prognostic imaging features that correlated with the prognostic gene modules (selected by weighted gene coexpression network analysis) were identified as imaging biomarkers. A nomogram that integrated the radiomics score and clinicopathological factors was created and validated in the Nanfang Hospital database. Nomogram discrimination, calibration, and clinical usefulness were evaluated. Results Three prognostic imaging biomarkers were identified and had a strong correlation with four prognostic gene modules (P < 0.05, FDR < 0.05). The radiogenomics nomogram (AUC = 0.838) resulted in better performance of the survival prediction than that of the TNM staging system (AUC = 0.765, P = 0.011; Delong et al.). In addition, the radiogenomics nomogram exhibited good discrimination, calibration, and clinical usefulness in both the training and validation cohorts. Conclusions The novel prognostic radiogenomics nomogram that was constructed achieved excellent correlation with prognosis in both the training and validation cohort of Nanfang Hospital patients with GC. It is anticipated that this work may assist in clinical preferential treatment decisions and promote the process of precision theranostics in the future.
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Affiliation(s)
- Han Liu
- Department of Ultrasound, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Yiyun Wang
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
| | - Yingqiao Liu
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
| | - Dingyi Lin
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Cangui Zhang
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
| | - Yuyun Zhao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Li Chen
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
| | - Yi Li
- Department of Radiology, Southern Medical University, Guangzhou, China
| | - Jianyu Yuan
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
| | - Zhao Chen
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
| | - Jiang Yu
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
| | - Wentao Kong
- Department of Ultrasound, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
- *Correspondence: Tao Chen, ; Wentao Kong,
| | - Tao Chen
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
- *Correspondence: Tao Chen, ; Wentao Kong,
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Liu G, Poon M, Zapala MA, Temple WC, Vo KT, Matthay KK, Mitra D, Seo Y. Incorporating Radiomics into Machine Learning Models to Predict Outcomes of Neuroblastoma. J Digit Imaging 2022; 35:605-612. [PMID: 35237892 PMCID: PMC9156639 DOI: 10.1007/s10278-022-00607-w] [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: 05/07/2021] [Revised: 02/08/2022] [Accepted: 02/11/2022] [Indexed: 12/15/2022] Open
Abstract
Neuroblastoma is one of the most common pediatric cancers. This study used machine learning (ML) to predict the mortality and a few other investigated intermediate outcomes of neuroblastoma patients non-invasively from CT images. Performances of multiple ML algorithms over retrospective CT images of 65 neuroblastoma patients are analyzed. An artificial neural network (ANN) is used on tumor radiomic features extracted from 3D CT images. A pre-trained 2D convolutional neural network (CNN) is used on slices of the same images. ML models are trained for various pathologically investigated outcomes of these patients. A subspecialty-trained pediatric radiologist independently reviewed the manually segmented primary tumors. Pyradiomics library is used to extract 105 radiomic features. Six ML algorithms are compared to predict the following outcomes: mortality, presence or absence of metastases, neuroblastoma differentiation, mitosis-karyorrhexis index (MKI), presence or absence of MYCN gene amplification, and presence of image-defined risk factors (IDRF). The prediction ranges over multiple experiments are measured using the area under the receiver operating characteristic (ROC-AUC) for comparison. Our results show that the radiomics-based ANN method slightly outperforms the other algorithms in predicting all outcomes except classification of the grade of neuroblastic differentiation, for which the elastic regression model performed the best. Contributions of the article are twofold: (1) noninvasive models for the prognosis from CT images of neuroblastoma, and (2) comparison of relevant ML models on this medical imaging problem.
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Affiliation(s)
- Gengbo Liu
- Department of Computer Engineering and Sciences, Florida Institute of Technology, Melbourne, FL USA
| | - Mini Poon
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA
| | - Matthew A. Zapala
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA
| | - William C. Temple
- Department of Pediatrics, University of California, San Francisco, CA USA
| | - Kieuhoa T. Vo
- Department of Pediatrics, University of California, San Francisco, CA USA
| | | | - Debasis Mitra
- Department of Computer Engineering and Sciences, Florida Institute of Technology, Melbourne, FL USA ,Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA
| | - Youngho Seo
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA USA
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Radiogenomics: A Valuable Tool for the Clinical Assessment and Research of Ovarian Cancer. J Comput Assist Tomogr 2022; 46:371-378. [DOI: 10.1097/rct.0000000000001279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Afridi M, Jain A, Aboian M, Payabvash S. Brain Tumor Imaging: Applications of Artificial Intelligence. Semin Ultrasound CT MR 2022; 43:153-169. [PMID: 35339256 PMCID: PMC8961005 DOI: 10.1053/j.sult.2022.02.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Artificial intelligence has become a popular field of research with goals of integrating it into the clinical decision-making process. A growing number of predictive models are being employed utilizing machine learning that includes quantitative, computer-extracted imaging features known as radiomic features, and deep learning systems. This is especially true in brain-tumor imaging where artificial intelligence has been proposed to characterize, differentiate, and prognostication. We reviewed current literature regarding the potential uses of machine learning-based, and deep learning-based artificial intelligence in neuro-oncology as it pertains to brain tumor molecular classification, differentiation, and treatment response. While there is promising evidence supporting the use of artificial intelligence in neuro-oncology, there are still more investigations needed on a larger, multicenter scale along with a streamlined and standardized image processing workflow prior to its introduction in routine clinical decision-making protocol.
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Affiliation(s)
- Muhammad Afridi
- School of Osteopathic Medicine, Rowan University, Stratford, NJ
| | - Abhi Jain
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Mariam Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT.
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25
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Peng X, Yang S, Zhou L, Mei Y, Shi L, Zhang R, Shan F, Liu L. Repeatability and Reproducibility of Computed Tomography Radiomics for Pulmonary Nodules: A Multicenter Phantom Study. Invest Radiol 2022; 57:242-253. [PMID: 34743134 PMCID: PMC8903219 DOI: 10.1097/rli.0000000000000834] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 08/31/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Radiomics can yield minable information from medical images, which can facilitate computer-aided diagnosis. However, the lack of repeatability and reproducibility of radiomic features (RFs) may hinder their generalizability in clinical applications. OBJECTIVES The aims of this study were to explore 3 main sources of variability in RFs, investigate their influencing magnitudes and patterns, and identify a subset of robust RFs for further studies. MATERIALS AND METHODS A chest phantom with nodules was scanned with different computed tomography (CT) scanners repeatedly with varying acquisition and reconstruction parameters (April-May 2019) to evaluate 3 sources of variability: test-retest, inter-CT, and intra-CT protocol variability. The robustness of the RFs was measured using the concordance correlation coefficient, dynamic range, and intraclass correlation coefficient (ICC). The influencing magnitudes and patterns were analyzed using the Friedman test and Spearman rank correlation coefficient. Stable and informative RFs were selected, and their redundancy was eliminated using hierarchical clustering. Clinical validation was also performed to verify the clinical effectiveness and potential enhancement of the generalizability of radiomics research. RESULTS A total of 1295 RFs that showed all 3 sources of variability were included. The reconstruction kernel and the iteration level showed the greatest (ICC, 0.35 ± 0.31) and the least (ICC, 0.63 ± 0.27) influence on magnitudes. The different sources of variability showed relatively consistent patterns of influence (false discovery rate <0.001). Finally, we obtained a subset of 19 stable, informative, and nonredundant RFs under all 3 sources of variability. These RFs exhibited clinical effectiveness and showed better prediction performance than unstable RFs in the validation dataset (P = 0.017, Delong test). CONCLUSIONS The stability of RFs was affected to different degrees by test-retest and differences in CT manufacturers and models and CT acquisition and reconstruction parameters, but the influences of these factors showed relatively consistent patterns. We also obtained a subset of 19 stable, informative, and nonredundant RFs that should be preferably used to enhance the generalizability of further radiomics research.
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Affiliation(s)
- Xueqing Peng
- From the Institutes of Biomedical Sciences, Fudan University, Shanghai
| | - Shuyi Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai
- Shanghai Institute of Medical Imaging, Shanghai
- Department of Medical Imaging, Shanghai Medical College, Fudan University, Shanghai
| | - Lingxiao Zhou
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, Guangdong Province
| | - Yu Mei
- Shanghai Mental Health Center, Shanghai
| | - Lili Shi
- From the Institutes of Biomedical Sciences, Fudan University, Shanghai
| | - Rengyin Zhang
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai
| | - Lei Liu
- From the Institutes of Biomedical Sciences, Fudan University, Shanghai
- School of Basic Medical Sciences, Fudan University, Shanghai, China
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T2-Fluid-Attenuated Inversion Recovery Mismatch Sign in Grade II and III Gliomas: Is There a Coexisting T2-Diffusion-Weighted Imaging Mismatch? J Comput Assist Tomogr 2022; 46:251-256. [PMID: 35297581 DOI: 10.1097/rct.0000000000001267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE To determine whether the T2 fluid-attenuated inversion recovery (T2-FLAIR) mismatch sign in diffuse gliomas is associated with an equivalent pattern of disparity in signal intensities when comparing T2- and diffusion-weighted imaging (DWI). METHODS The level of correspondence between T2-FLAIR and T2-DWI evaluations in 34 World Health Organization grade II/III gliomas and interreader agreement among 3 neuroradiologists were assessed by calculating intraclass correlation coefficient and κ statistics, respectively. Tumoral apparent diffusion coefficient values were compared using t test. RESULTS There was an almost perfect correspondence between the 2 mismatch signs (intraclass correlation coefficient = 0.824 [95% confidence interval, 0.68-0.91]) that were associated with higher mean tumoral apparent diffusion coefficient (P < 0.01). Interreader agreement was substantial for T2-FLAIR (Fleiss κ = 0.724) and moderate for T2-DWI comparisons (Fleiss κ = 0.589) (P < 0.001). CONCLUSIONS The T2-FLAIR mismatch sign is usually reflected by a distinct microstructural pattern on DWI. The management of this tumor subtype may benefit from specifically tailored imaging assessments.
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Anan N, Zainon R, Tamal M. A review on advances in 18F-FDG PET/CT radiomics standardisation and application in lung disease management. Insights Imaging 2022; 13:22. [PMID: 35124733 PMCID: PMC8817778 DOI: 10.1186/s13244-021-01153-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 12/23/2021] [Indexed: 02/06/2023] Open
Abstract
Radiomics analysis quantifies the interpolation of multiple and invisible molecular features present in diagnostic and therapeutic images. Implementation of 18-fluorine-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiomics captures various disorders in non-invasive and high-throughput manner. 18F-FDG PET/CT accurately identifies the metabolic and anatomical changes during cancer progression. Therefore, the application of 18F-FDG PET/CT in the field of oncology is well established. Clinical application of 18F-FDG PET/CT radiomics in lung infection and inflammation is also an emerging field. Combination of bioinformatics approaches or textual analysis allows radiomics to extract additional information to predict cell biology at the micro-level. However, radiomics texture analysis is affected by several factors associated with image acquisition and processing. At present, researchers are working on mitigating these interrupters and developing standardised workflow for texture biomarker establishment. This review article focuses on the application of 18F-FDG PET/CT in detecting lung diseases specifically on cancer, infection and inflammation. An overview of different approaches and challenges encountered on standardisation of 18F-FDG PET/CT technique has also been highlighted. The review article provides insights about radiomics standardisation and application of 18F-FDG PET/CT in lung disease management.
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Radiographic markers of breast cancer brain metastases: relation to clinical characteristics and postoperative outcome. Acta Neurochir (Wien) 2022; 164:439-449. [PMID: 34677686 PMCID: PMC8854251 DOI: 10.1007/s00701-021-05026-4] [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: 08/06/2021] [Accepted: 10/09/2021] [Indexed: 12/24/2022]
Abstract
Objective Occurrence of brain metastases BM is associated with poor prognosis in patients with breast cancer (BC). Magnetic resonance imaging (MRI) is the standard of care in the diagnosis of BM and determines further treatment strategy. The aim of the present study was to evaluate the association between the radiographic markers of BCBM on MRI with other patients’ characteristics and overall survival (OS). Methods We included 88 female patients who underwent BCBM surgery in our institution from 2008 to 2019. Data on demographic, clinical, and histopathological characteristics of the patients and postoperative survival were collected from the electronic health records. Radiographic features of BM were assessed upon the preoperative MRI. Univariable and multivariable analyses were performed. Results The median OS was 17 months. Of all evaluated radiographic markers of BCBM, only the presence of necrosis was independently associated with OS (14.5 vs 22.5 months, p = 0.027). In turn, intra-tumoral necrosis was more often in individuals with shorter time interval between BC and BM diagnosis (< 3 years, p = 0.035) and preoperative leukocytosis (p = 0.022). Moreover, dural affection of BM was more common in individuals with positive human epidermal growth factor receptor 2 status (p = 0.015) and supratentorial BM location (p = 0.024). Conclusion Intra-tumoral necrosis demonstrated significant association with OS after BM surgery in patients with BC. The radiographic pattern of BM on the preoperative MRI depends on certain tumor and clinical characteristics of patients. Supplementary Information The online version contains supplementary material available at 10.1007/s00701-021-05026-4.
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Wang S, Xiao F, Sun W, Yang C, Ma C, Huang Y, Xu D, Li L, Chen J, Li H, Xu H. Radiomics Analysis Based on Magnetic Resonance Imaging for Preoperative Overall Survival Prediction in Isocitrate Dehydrogenase Wild-Type Glioblastoma. Front Neurosci 2022; 15:791776. [PMID: 35153659 PMCID: PMC8833841 DOI: 10.3389/fnins.2021.791776] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 12/15/2021] [Indexed: 01/24/2023] Open
Abstract
PurposeThis study aimed to develop a radiomics signature for the preoperative prognosis prediction of isocitrate dehydrogenase (IDH)-wild-type glioblastoma (GBM) patients and to provide personalized assistance in the clinical decision-making for different patients.Materials and MethodsA total of 142 IDH-wild-type GBM patients classified using the new classification criteria of WHO 2021 from two centers were included in the study and randomly divided into a training set and a test set. Firstly, their clinical characteristics were screened using univariate Cox regression. Then, the radiomics features were extracted from the tumor and peritumoral edema areas on their contrast-enhanced T1-weighted image (CE-T1WI), T2-weighted image (T2WI), and T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) magnetic resonance imaging (MRI) images. Subsequently, inter- and intra-class correlation coefficient (ICC) analysis, Spearman’s correlation analysis, univariate Cox, and the least absolute shrinkage and selection operator (LASSO) Cox regression were used step by step for feature selection and the construction of a radiomics signature. The combined model was established by integrating the selected clinical factors. Kaplan–Meier analysis was performed for the validation of the discrimination ability of the model, and the C-index was used to evaluate consistency in the prediction. Finally, a Radiomics + Clinical nomogram was generated for personalized prognosis analysis and then validated using the calibration curve.ResultsAnalysis of the clinical characteristics resulted in the screening of four risk factors. The combination of ICC, Spearman’s correlation, and univariate and LASSO Cox resulted in the selection of eight radiomics features, which made up the radiomics signature. Both the radiomics and combined models can significantly stratify high- and low-risk patients (p < 0.001 and p < 0.05 for the training and test sets, respectively) and obtained good prediction consistency (C-index = 0.74–0.86). The calibration plots exhibited good agreement in both 1- and 2-year survival between the prediction of the model and the actual observation.ConclusionRadiomics is an independent preoperative non-invasive prognostic tool for patients who were newly classified as having IDH-wild-type GBM. The constructed nomogram, which combined radiomics features with clinical factors, can predict the overall survival (OS) of IDH-wild-type GBM patients and could be a new supplement to treatment guidelines.
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Affiliation(s)
- Shouchao Wang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Feng Xiao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Wenbo Sun
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Chao Yang
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Chao Ma
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yong Huang
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Dan Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Lanqing Li
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jun Chen
- Precision Health Institute, GE Healthcare, Shanghai, China
| | - Huan Li
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
- *Correspondence: Huan Li,
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
- Haibo Xu,
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30
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Aftab K, Aamir FB, Mallick S, Mubarak F, Pope WB, Mikkelsen T, Rock JP, Enam SA. Radiomics for precision medicine in glioblastoma. J Neurooncol 2022; 156:217-231. [PMID: 35020109 DOI: 10.1007/s11060-021-03933-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 12/20/2021] [Indexed: 11/26/2022]
Abstract
INTRODUCTION Being the most common primary brain tumor, glioblastoma presents as an extremely challenging malignancy to treat with dismal outcomes despite treatment. Varying molecular epidemiology of glioblastoma between patients and intra-tumoral heterogeneity explains the failure of current one-size-fits-all treatment modalities. Radiomics uses machine learning to identify salient features of the tumor on brain imaging and promises patient-specific management in glioblastoma patients. METHODS We performed a comprehensive review of the available literature on studies investigating the role of radiomics and radiogenomics models for the diagnosis, stratification, prognostication as well as treatment planning and monitoring of glioblastoma. RESULTS Classifiers based on a combination of various MRI sequences, genetic information and clinical data can predict non-invasive tumor diagnosis, overall survival and treatment response with reasonable accuracy. However, the use of radiomics for glioblastoma treatment remains in infancy as larger sample sizes, standardized image acquisition and data extraction techniques are needed to develop machine learning models that can be translated effectively into clinical practice. CONCLUSION Radiomics has the potential to transform the scope of glioblastoma management through personalized medicine.
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Affiliation(s)
- Kiran Aftab
- Section of Neurosurgery, Department of Surgery, Aga Khan University, Karachi, Pakistan
| | | | - Saad Mallick
- Medical College, Aga Khan University, Karachi, Pakistan
| | - Fatima Mubarak
- Department of Radiology, Aga Khan University, Karachi, Pakistan
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Tom Mikkelsen
- Departments of Neurology and Neurosurgery, Henry Ford Hospital, Detroit, MI, USA
| | - Jack P Rock
- Department of Neurosurgery, Henry Ford Health System, Detroit, MI, USA
| | - Syed Ather Enam
- Section of Neurosurgery, Department of Surgery, Aga Khan University, Karachi, Pakistan.
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Kayadibi Y, Kocak B, Ucar N, Akan YN, Akbas P, Bektas S. Radioproteomics in Breast Cancer: Prediction of Ki-67 Expression With MRI-based Radiomic Models. Acad Radiol 2022; 29 Suppl 1:S116-S125. [PMID: 33744071 DOI: 10.1016/j.acra.2021.02.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 01/28/2021] [Accepted: 02/02/2021] [Indexed: 12/12/2022]
Abstract
RATIONALE AND OBJECTIVES We aimed to investigate the value of magnetic resonance image (MRI)-based radiomics in predicting Ki-67 expression of breast cancer. METHODS In this retrospective study, 159 lesions from 154 patients were included. Radiomic features were extracted from contrast-enhanced T1-weighted MRI (C+MRI) and apparent diffusion coefficient (ADC) maps, with open-source software. Dimension reduction was done with reliability analysis, collinearity analysis, and feature selection. Two different Ki-67 expression cut-off values (14% vs 20%) were studied as reference standard for the classifications. Input for the models were radiomic features from individual MRI sequences or their combination. Classifications were performed using a generalized linear model. RESULTS Considering Ki-67 cut-off value of 14%, training and testing AUC values were 0.785 (standard deviation [SD], 0.193) and 0.849 for ADC; 0.696 (SD, 0.150) and 0.695 for C+MRI; 0.755 (SD, 0.171) and 0.635 for the combination of both sequences, respectively. Regarding Ki-67 cut-off value of 20%, training and testing AUC values were 0.744 (SD, 0.197) and 0.617 for ADC; 0.629 (SD, 0.251) and 0.741 for C+MRI; 0.761 (SD, 0.207) and 0.618 for the combination of both sequences, respectively. CONCLUSION ADC map-based selected radiomic features coupled with generalized linear modeling might be a promising non-invasive method to determine the Ki-67 expression level of breast cancer.
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Seow P, Hernowo AT, Narayanan V, Wong JHD, Bahuri NFA, Cham CY, Abdullah NA, Kadir KAA, Rahmat K, Ramli N. Neural Fiber Integrity in High- Versus Low-Grade Glioma using Probabilistic Fiber Tracking. Acad Radiol 2021; 28:1721-1732. [PMID: 33023809 DOI: 10.1016/j.acra.2020.09.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 09/05/2020] [Accepted: 09/07/2020] [Indexed: 02/02/2023]
Abstract
RATIONALE AND OBJECTIVES Gliomatous tumors are known to affect neural fiber integrity, either by displacement or destruction. The aim of this study is to investigate the integrity and distribution of the white matter tracts within and around the glioma regions using probabilistic fiber tracking. MATERIAL AND METHODS Forty-two glioma patients were subjected to MRI using a standard tumor protocol with diffusion tensor imaging (DTI). The tumor and peritumor regions were delineated using snake model with reference to structural and diffusion MRI. A preprocessing pipeline of the structural MRI image, DTI data, and tumor regions was implemented. Tractography was performed to delineate the white matter (WM) tracts in the selected tumor regions via probabilistic fiber tracking. DTI indices were investigated through comparative mapping of WM tracts and tumor regions in low-grade gliomas (LGG) and high-grade gliomas (HGG). RESULTS Significant differences were seen in the planar tensor (Cp) in peritumor regions; mean diffusivity, axial diffusivity and pure isotropic diffusion in solid-enhancing tumor regions; and fractional anisotropy, axial diffusivity, pure anisotropic diffusion (q), total magnitude of diffusion tensor (L), relative anisotropy, Cp and spherical tensor (Cs) in solid nonenhancing tumor regions for affected WM tracts. In most cases of HGG, the WM tracts were not completely destroyed, but found intact inside the tumor. DISCUSSION Probabilistic fiber tracking revealed the existence and distribution of WM tracts inside tumor core for both LGG and HGG groups. There were more DTI indices in the solid nonenhancing tumor region, which showed significant differences between LGG and HGG.
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Zhou Z. Artificial intelligence on MRI for molecular subtyping of diffuse gliomas: feature comparison, visualization, and correlation between radiomics and deep learning. Eur Radiol 2021; 32:745-746. [PMID: 34825932 DOI: 10.1007/s00330-021-08400-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 09/28/2021] [Accepted: 10/12/2021] [Indexed: 01/05/2023]
Affiliation(s)
- Zijian Zhou
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1400 Pressler St., Unit 1472, Houston, TX, 77030, USA.
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Jin Y, Xu Y, Li Y, Chen R, Cai W. Integrative Radiogenomics Approach for Risk Assessment of Postoperative and Adjuvant Chemotherapy Benefits for Gastric Cancer Patients. Front Oncol 2021; 11:755271. [PMID: 34804945 PMCID: PMC8602567 DOI: 10.3389/fonc.2021.755271] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Accepted: 09/27/2021] [Indexed: 12/24/2022] Open
Abstract
Gastric cancer (GC) is a typical heterogeneous malignant tumor, whose insensitivity to chemotherapy is a common cause of tumor recurrence and metastasis. There is no doubt regarding the effectiveness of adjuvant chemotherapy (ACT) for GC, but the population for whom it is indicated and the selection of specific options remain the focus of present research. The conventional pathological TNM prediction focuses on cancer cells to predict prognosis, while they do not provide sufficient prediction. Enhanced computed tomography (CT) scanning is a validated tool that assesses the involvement of careful identification of the tumor, lymph node involvement, and metastatic spread. Using the radiomics approach, we selected the least absolute shrinkage and selection operator (LASSO) Cox regression model to build a radiomics signature for predicting the overall survival (OS) and disease-free survival (DFS) of patients with complete postoperative gastric cancer and further identifying candidate benefits from ACT. The radiomics trait-associated genes captured clinically relevant molecular pathways and potential chemotherapeutic drug metabolism mechanisms. Our results of precise surrogates using radiogenomics can lead to additional benefit from adjuvant chemotherapy and then survival prediction in postoperative GC patients.
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Affiliation(s)
- Yin Jin
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yilun Xu
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yanyan Li
- Department of Urology, Second Affiliated Hospital & Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Renpin Chen
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Weiyang Cai
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Liang CH, Liu YC, Wan YL, Yun CH, Wu WJ, López-González R, Huang WM. Quantification of Cancer-Developing Idiopathic Pulmonary Fibrosis Using Whole-Lung Texture Analysis of HRCT Images. Cancers (Basel) 2021; 13:cancers13225600. [PMID: 34830759 PMCID: PMC8615829 DOI: 10.3390/cancers13225600] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 10/28/2021] [Accepted: 11/05/2021] [Indexed: 01/23/2023] Open
Abstract
Simple Summary Idiopathic pulmonary fibrosis (IPF) patients have a significantly higher risk of developing lung cancer. Traditional risk factors including age, male gender, smoking status, and emphysema have been reported. However, there are only limited data on radiomics features from HRCT images useful for risk stratification of IPF patients for lung cancer. In this study, we found that texture-based radiomics features can be differentiated between IPF patients with and without cancer development, and their diagnostic accuracy is not inferior to that of traditional risk factors. By combining radiomics features and traditional risk factors, the diagnostic accuracy can be improved. Abstract Idiopathic pulmonary fibrosis (IPF) patients have a significantly higher risk of developing lung cancer (LC). There is only limited evidence of the use of texture-based radiomics features from high-resolution computed tomography (HRCT) images for risk stratification of IPF patients for LC. We retrospectively enrolled subjects who suffered from IPF in this study. Clinical data including age, gender, smoking status, and pulmonary function were recorded. Non-contrast chest CT for fibrotic score calculation and determination of three dimensional measures of whole-lung texture and emphysema were performed using a promising deep learning imaging platform. The results revealed that among 116 subjects with IPF (90 non-cancer and 26 lung cancer cases), the radiomics features showed significant differences between non-cancer and cancer patients. In the training cohort, the diagnostic accuracy using selected radiomics features with AUC of 0.66–0.73 (sensitivity of 80.0–85.0% and specificity of 54.2–59.7%) was not inferior to that obtained using traditional risk factors, such as gender, smoking status, and emphysema (%). In the validation cohort, the combination of radiomics features and traditional risk factors produced a diagnostic accuracy of 0.87 AUC and an accuracy of 75.0%. In this study, we found that whole-lung CT texture analysis is a promising tool for LC risk stratification of IPF patients.
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Affiliation(s)
- Chia-Hao Liang
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei City 112, Taiwan;
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei City 110, Taiwan
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei City 116, Taiwan
| | - Yung-Chi Liu
- Department of Diagnostic Radiology, Xiamen Chang Gung Hospital, Xiamen 361028, China;
- Department of Imaging Technology Division, Xiamen Chang Gung Hospital, Xiamen 361028, China
| | - Yung-Liang Wan
- Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan;
| | - Chun-Ho Yun
- Department of Radiology, Mackay Memorial Hospital, Taipei City 104, Taiwan;
- Department of Medicine, Mackay Medical College, New Taipei City 252, Taiwan
- Mackay Junior College of Medicine, Nursing, and Management, New Taipei City 252, Taiwan
| | - Wen-Jui Wu
- Division of Pulmonary and Critical Care Medicine, Mackay Memorial Hospital, Taipei City 104, Taiwan;
| | | | - Wei-Ming Huang
- Department of Radiology, Mackay Memorial Hospital, Taipei City 104, Taiwan;
- Department of Medicine, Mackay Medical College, New Taipei City 252, Taiwan
- Mackay Junior College of Medicine, Nursing, and Management, New Taipei City 252, Taiwan
- Correspondence:
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Jaberipour M, Soliman H, Sahgal A, Sadeghi-Naini A. A priori prediction of local failure in brain metastasis after hypo-fractionated stereotactic radiotherapy using quantitative MRI and machine learning. Sci Rep 2021; 11:21620. [PMID: 34732781 PMCID: PMC8566533 DOI: 10.1038/s41598-021-01024-9] [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: 03/19/2021] [Accepted: 10/21/2021] [Indexed: 12/14/2022] Open
Abstract
This study investigated the effectiveness of pre-treatment quantitative MRI and clinical features along with machine learning techniques to predict local failure in patients with brain metastasis treated with hypo-fractionated stereotactic radiation therapy (SRT). The predictive models were developed using the data from 100 patients (141 lesions) and evaluated on an independent test set with data from 20 patients (30 lesions). Quantitative MRI radiomic features were derived from the treatment-planning contrast-enhanced T1w and T2-FLAIR images. A multi-phase feature reduction and selection procedure was applied to construct an optimal quantitative MRI biomarker for predicting therapy outcome. The performance of standard clinical features in therapy outcome prediction was evaluated using a similar procedure. Survival analyses were conducted to compare the long-term outcome of the two patient cohorts (local control/failure) identified based on prediction at pre-treatment, and standard clinical criteria at last patient follow-up after SRT. The developed quantitative MRI biomarker consists of four features with two features quantifying heterogeneity in the edema region, one feature characterizing intra-tumour heterogeneity, and one feature describing tumour morphology. The predictive models with the radiomic and clinical feature sets yielded an AUC of 0.87 and 0.62, respectively on the independent test set. Incorporating radiomic features into the clinical predictive model improved the AUC of the model by up to 16%, relatively. A statistically significant difference was observed in survival of the two patient cohorts identified at pre-treatment using the radiomics-based predictive model, and at post-treatment using the the RANO-BM criteria. Results of this study revealed a good potential for quantitative MRI radiomic features at pre-treatment in predicting local failure in relatively large brain metastases undergoing SRT, and is a step forward towards a precision oncology paradigm for brain metastasis.
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Affiliation(s)
- Majid Jaberipour
- grid.21100.320000 0004 1936 9430Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON Canada ,grid.413104.30000 0000 9743 1587Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON Canada
| | - Hany Soliman
- grid.413104.30000 0000 9743 1587Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON Canada ,grid.413104.30000 0000 9743 1587Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Department of Radiation Oncology, University of Toronto, Toronto, ON Canada
| | - Arjun Sahgal
- grid.413104.30000 0000 9743 1587Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON Canada ,grid.413104.30000 0000 9743 1587Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Department of Radiation Oncology, University of Toronto, Toronto, ON Canada
| | - Ali Sadeghi-Naini
- grid.21100.320000 0004 1936 9430Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON Canada ,grid.413104.30000 0000 9743 1587Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON Canada ,grid.413104.30000 0000 9743 1587Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Department of Medical Biophysics, University of Toronto, Toronto, ON Canada
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Ding H, Wu C, Liao N, Zhan Q, Sun W, Huang Y, Jiang Z, Li Y. Radiomics in Oncology: A 10-Year Bibliometric Analysis. Front Oncol 2021; 11:689802. [PMID: 34616671 PMCID: PMC8488302 DOI: 10.3389/fonc.2021.689802] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 08/27/2021] [Indexed: 02/05/2023] Open
Abstract
Objectives To date, radiomics has been applied in oncology for over a decade and has shown great progress. We used a bibliometric analysis to analyze the publications of radiomics in oncology to clearly illustrate the current situation and future trends and encourage more researchers to participate in radiomics research in oncology. Methods Publications for radiomics in oncology were downloaded from the Web of Science Core Collection (WoSCC). WoSCC data were collected, and CiteSpace was used for a bibliometric analysis of countries, institutions, journals, authors, keywords, and references pertaining to this field. The state of research and areas of focus were analyzed through burst detection. Results A total of 7,199 pieces of literature concerning radiomics in oncology were analyzed on CiteSpace. The number of publications has undergone rapid growth and continues to increase. The USA and Chinese Academy of Sciences are found to be the most prolific country and institution, respectively. In terms of journals and co-cited journals, Scientific Reports is ranked highest with respect to the number of publications, and Radiology is ranked highest among co-cited journals. Moreover, Jie Tian has published the most publications, and Phillipe Lambin is the most cited author. A paper published by Gillies et al. presents the highest citation counts. Artificial intelligence (AI), segmentation methods, and the use of radiomics for classification and diagnosis in oncology are major areas of focus in this field. Test-retest statistics, including reproducibility and statistical methods of radiomics research, the relation between genomics and radiomics, and applications of radiomics to sarcoma and intensity-modulated radiotherapy, are frontier areas of this field. Conclusion To our knowledge, this is the first study to provide an overview of the literature related to radiomics in oncology and may inspire researchers from multiple disciplines to engage in radiomics-related research.
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Affiliation(s)
- Haoran Ding
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Head and Neck Oncology Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Chenzhou Wu
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Head and Neck Oncology Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Nailin Liao
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Head and Neck Oncology Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Qi Zhan
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Head and Neck Oncology Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Weize Sun
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Head and Neck Oncology Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Yingzhao Huang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Head and Neck Oncology Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Zhou Jiang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Head and Neck Oncology Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Yi Li
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Head and Neck Oncology Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
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Peeken JC, Asadpour R, Specht K, Chen EY, Klymenko O, Akinkuoroye V, Hippe DS, Spraker MB, Schaub SK, Dapper H, Knebel C, Mayr NA, Gersing AS, Woodruff HC, Lambin P, Nyflot MJ, Combs SE. MRI-based delta-radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy. Radiother Oncol 2021; 164:73-82. [PMID: 34506832 DOI: 10.1016/j.radonc.2021.08.023] [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: 04/26/2021] [Revised: 08/15/2021] [Accepted: 08/27/2021] [Indexed: 02/09/2023]
Abstract
PURPOSE In high-grade soft-tissue sarcomas (STS) the standard of care encompasses multimodal therapy regimens. While there is a growing body of evidence for prognostic pretreatment radiomic models, we hypothesized that temporal changes in radiomic features following neoadjuvant treatment ("delta-radiomics") may be able to predict the pathological complete response (pCR). METHODS MRI scans (T1-weighted with fat-saturation and contrast-enhancement (T1FSGd) and T2-weighted with fat-saturation (T2FS)) of patients with STS of the extremities and trunk treated with neoadjuvant therapy were gathered from two independent institutions (training: 103, external testing: 53 patients). pCR was defined as <5% viable cells. After segmentation and preprocessing, 105 radiomic features were extracted. Delta-radiomic features were calculated by subtraction of features derived from MRI scans obtained before and after neoadjuvant therapy. After feature reduction, machine learning modeling was performed in 100 iterations of 3-fold nested cross-validation. Delta-radiomic models were compared with single timepoint models in the testing cohort. RESULTS The combined delta-radiomic models achieved the best area under the receiver operating characteristic curve (AUC) of 0.75. Pre-therapeutic tumor volume was the best conventional predictor (AUC 0.70). The T2FS-based delta-radiomic model had the most balanced classification performance with a balanced accuracy of 0.69. Delta-radiomic models achieved better reproducibility than single timepoint radiomic models, RECIST or the peri-therapeutic volume change. Delta-radiomic models were significantly associated with survival in multivariate Cox regression. CONCLUSION This exploratory analysis demonstrated that MRI-based delta-radiomics improves prediction of pCR over tumor volume and RECIST. Delta-radiomics may one day function as a biomarker for personalized treatment adaptations.
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Affiliation(s)
- Jan C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum, München, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany; Department of Radiation Oncology, University of Washington, Seattle, United States; Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, The Netherlands.
| | - Rebecca Asadpour
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany
| | - Katja Specht
- Institute of Pathology, Technical University of Munich, Germany
| | - Eleanor Y Chen
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, United States
| | - Olena Klymenko
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany
| | - Victor Akinkuoroye
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany
| | - Daniel S Hippe
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, United States
| | - Matthew B Spraker
- Department of Radiation Oncology, Washington University in St. Louis, United States
| | - Stephanie K Schaub
- Department of Radiation Oncology, University of Washington, Seattle, United States
| | - Hendrik Dapper
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany
| | - Carolin Knebel
- Department of Orthopedics and Sports Orthopedics, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany
| | - Nina A Mayr
- Department of Radiation Oncology, University of Washington, Seattle, United States
| | - Alexandra S Gersing
- Department of Radiology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany
| | - Henry C Woodruff
- Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, The Netherlands; Department of Radiology and Nuclear Imaging, GROW- School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands
| | - Philippe Lambin
- Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, The Netherlands; Department of Radiology and Nuclear Imaging, GROW- School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands
| | - Matthew J Nyflot
- Department of Radiation Oncology, University of Washington, Seattle, United States; Department of Radiology, University of Washington, Seattle, United States
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum, München, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany
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Advanced Imaging and Computational Techniques for the Diagnostic and Prognostic Assessment of Malignant Gliomas. Cancer J 2021; 27:344-352. [PMID: 34570448 DOI: 10.1097/ppo.0000000000000545] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
ABSTRACT Advanced imaging techniques provide a powerful tool to assess the intratumoral and intertumoral heterogeneity of gliomas. Advances in the molecular understanding of glioma subgroups may allow improved diagnostic assessment combining imaging and molecular tumor features, with enhanced prognostic utility and implications for patient treatment. In this article, a comprehensive overview of the physiologic basis for conventional and advanced imaging techniques is presented, and clinical applications before and after treatment are discussed. An introduction to the principles of radiomics and the advanced integration of imaging, clinical outcomes, and genomic data highlights the future potential for this field of research to better stratify and select patients for standard as well as investigational therapies.
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Dapash M, Castro B, Hou D, Lee-Chang C. Current Immunotherapeutic Strategies for the Treatment of Glioblastoma. Cancers (Basel) 2021; 13:4548. [PMID: 34572775 PMCID: PMC8467991 DOI: 10.3390/cancers13184548] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 08/24/2021] [Accepted: 08/24/2021] [Indexed: 12/17/2022] Open
Abstract
Glioblastoma (GBM) is a lethal primary brain tumor. Despite extensive effort in basic, translational, and clinical research, the treatment outcomes for patients with GBM are virtually unchanged over the past 15 years. GBM is one of the most immunologically "cold" tumors, in which cytotoxic T-cell infiltration is minimal, and myeloid infiltration predominates. This is due to the profound immunosuppressive nature of GBM, a tumor microenvironment that is metabolically challenging for immune cells, and the low mutational burden of GBMs. Together, these GBM characteristics contribute to the poor results obtained from immunotherapy. However, as indicated by an ongoing and expanding number of clinical trials, and despite the mostly disappointing results to date, immunotherapy remains a conceptually attractive approach for treating GBM. Checkpoint inhibitors, various vaccination strategies, and CAR T-cell therapy serve as some of the most investigated immunotherapeutic strategies. This review article aims to provide a general overview of the current state of glioblastoma immunotherapy. Information was compiled through a literature search conducted on PubMed and clinical trials between 1961 to 2021.
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Affiliation(s)
- Mark Dapash
- Pritzker School of Medicine, University of Chicago, Chicago, IL 60637, USA;
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (B.C.); (D.H.)
| | - Brandyn Castro
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (B.C.); (D.H.)
- Department of Neurosurgery, University of Chicago, Chicago, IL 60637, USA
| | - David Hou
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (B.C.); (D.H.)
| | - Catalina Lee-Chang
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (B.C.); (D.H.)
- Northwestern Medicine Malnati Brain Tumor Institute, Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
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Radiomics and radiogenomics in gliomas: a contemporary update. Br J Cancer 2021; 125:641-657. [PMID: 33958734 PMCID: PMC8405677 DOI: 10.1038/s41416-021-01387-w] [Citation(s) in RCA: 82] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 03/10/2021] [Accepted: 03/31/2021] [Indexed: 02/03/2023] Open
Abstract
The natural history and treatment landscape of primary brain tumours are complicated by the varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low-grade lesions), as well as the dilemmas with identification of radiation necrosis, tumour progression, and pseudoprogression on MRI. Radiomics and radiogenomics promise to offer precise diagnosis, predict prognosis, and assess tumour response to modern chemotherapy/immunotherapy and radiation therapy. This is achieved by a triumvirate of morphological, textural, and functional signatures, derived from a high-throughput extraction of quantitative voxel-level MR image metrics. However, the lack of standardisation of acquisition parameters and inconsistent methodology between working groups have made validations unreliable, hence multi-centre studies involving heterogenous study populations are warranted. We elucidate novel radiomic and radiogenomic workflow concepts and state-of-the-art descriptors in sub-visual MR image processing, with relevant literature on applications of such machine learning techniques in glioma management.
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Wölfl B, te Rietmole H, Salvioli M, Kaznatcheev A, Thuijsman F, Brown JS, Burgering B, Staňková K. The Contribution of Evolutionary Game Theory to Understanding and Treating Cancer. DYNAMIC GAMES AND APPLICATIONS 2021; 12:313-342. [PMID: 35601872 PMCID: PMC9117378 DOI: 10.1007/s13235-021-00397-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/05/2021] [Indexed: 05/05/2023]
Abstract
Evolutionary game theory mathematically conceptualizes and analyzes biological interactions where one's fitness not only depends on one's own traits, but also on the traits of others. Typically, the individuals are not overtly rational and do not select, but rather inherit their traits. Cancer can be framed as such an evolutionary game, as it is composed of cells of heterogeneous types undergoing frequency-dependent selection. In this article, we first summarize existing works where evolutionary game theory has been employed in modeling cancer and improving its treatment. Some of these game-theoretic models suggest how one could anticipate and steer cancer's eco-evolutionary dynamics into states more desirable for the patient via evolutionary therapies. Such therapies offer great promise for increasing patient survival and decreasing drug toxicity, as demonstrated by some recent studies and clinical trials. We discuss clinical relevance of the existing game-theoretic models of cancer and its treatment, and opportunities for future applications. Moreover, we discuss the developments in cancer biology that are needed to better utilize the full potential of game-theoretic models. Ultimately, we demonstrate that viewing tumors with evolutionary game theory has medically useful implications that can inform and create a lockstep between empirical findings and mathematical modeling. We suggest that cancer progression is an evolutionary competition between different cell types and therefore needs to be viewed as an evolutionary game.
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Affiliation(s)
- Benjamin Wölfl
- Department of Mathematics, University of Vienna, Vienna, Austria
- Vienna Graduate School of Population Genetics, Vienna, Austria
| | - Hedy te Rietmole
- Department of Molecular Cancer Research, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Monica Salvioli
- Department of Mathematics, University of Trento, Trento, Italy
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Artem Kaznatcheev
- Department of Biology, University of Pennsylvania, Philadelphia, USA
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Frank Thuijsman
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Joel S. Brown
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL USA
- Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL USA
| | - Boudewijn Burgering
- Department of Molecular Cancer Research, University Medical Center Utrecht, Utrecht, The Netherlands
- The Oncode Institute, Utrecht, The Netherlands
| | - Kateřina Staňková
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
- Department of Engineering Systems and Services, Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands
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Maggio I, Franceschi E, Gatto L, Tosoni A, Di Nunno V, Tonon C, Brandes AA. Radiomics, mirnomics, and radiomirRNomics in glioblastoma: defining tumor biology from shadow to light. Expert Rev Anticancer Ther 2021; 21:1265-1272. [PMID: 34433354 DOI: 10.1080/14737140.2021.1971518] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
INTRODUCTION Glioblastoma is a highly aggressive brain tumor with an extremely poor prognosis. Genetic characterization of this tumor has identified alterations with prognostic and therapeutic impact, and many efforts are being made to improve molecular knowledge on glioblastoma. Invasive procedures, such as tumor biopsy or radical resection, are needed to characterize the tumor. AREAS COVERED The role of microRNA in cancer is an expanding field of research as many microRNAs have been shown to correlate with patient prognosis and treatment response. Novel methodologies like radiomics, radiogenomics, and radiomiRNomics are under evaluation to improve the amount of prognostic and predictive biomarkers available. EXPERT OPINION The role of radiomics, radiogenomics, and radiomiRNomic for the characterization of glioblastoma will further improve in the coming years.
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Affiliation(s)
- Ilaria Maggio
- Medical Oncology Department, Azienda USL, Bologna, Italy
| | | | - Lidia Gatto
- Medical Oncology Department, Azienda USL, Bologna, Italy
| | - Alicia Tosoni
- Medical Oncology Department, Azienda USL, Bologna, Italy
| | | | - Caterina Tonon
- Ircss Istituto di Scienze Neurologiche di Bologna, Bologna, Italy
| | - Alba A Brandes
- Medical Oncology Department, Azienda USL, Bologna, Italy
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Abstract
The central role of MRI in neuro-oncology is undisputed. The technique is used, both in clinical practice and in clinical trials, to diagnose and monitor disease activity, support treatment decision-making, guide the use of focused treatments and determine response to treatment. Despite recent substantial advances in imaging technology and image analysis techniques, clinical MRI is still primarily used for the qualitative subjective interpretation of macrostructural features, as opposed to quantitative analyses that take into consideration multiple pathophysiological features. However, the field of quantitative imaging and imaging biomarker development is maturing. The European Imaging Biomarkers Alliance (EIBALL) and Quantitative Imaging Biomarkers Alliance (QIBA) are setting standards for biomarker development, validation and implementation, as well as promoting the use of quantitative imaging and imaging biomarkers by demonstrating their clinical value. In parallel, advanced imaging techniques are reaching the clinical arena, providing quantitative, commonly physiological imaging parameters that are driving the discovery, validation and implementation of quantitative imaging and imaging biomarkers in the clinical routine. Additionally, computational analysis techniques are increasingly being used in the research setting to convert medical images into objective high-dimensional data and define radiomic signatures of disease states. Here, I review the definition and current state of MRI biomarkers in neuro-oncology, and discuss the clinical potential of quantitative image analysis techniques.
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Chang FC, Wong TT, Wu KS, Lu CF, Weng TW, Liang ML, Wu CC, Guo WY, Chen CY, Hsieh KLC. Magnetic resonance radiomics features and prognosticators in different molecular subtypes of pediatric Medulloblastoma. PLoS One 2021; 16:e0255500. [PMID: 34324588 PMCID: PMC8321137 DOI: 10.1371/journal.pone.0255500] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 07/17/2021] [Indexed: 01/06/2023] Open
Abstract
PURPOSE Medulloblastoma (MB) is a highly malignant pediatric brain tumor. In the latest classification, medulloblastoma is divided into four distinct groups: wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4. We analyzed the magnetic resonance imaging radiomics features to find the imaging surrogates of the 4 molecular subgroups of MB. MATERIAL AND METHODS Frozen tissue, imaging data, and clinical data of 38 patients with medulloblastoma were included from Taipei Medical University Hospital and Taipei Veterans General Hospital. Molecular clustering was performed based on the gene expression level of 22 subgroup-specific signature genes. A total 253 magnetic resonance imaging radiomic features were generated from each subject for comparison between different molecular subgroups. RESULTS Our cohort consisted of 7 (18.4%) patients with WNT medulloblastoma, 12 (31.6%) with SHH tumor, 8 (21.1%) with Group 3 tumor, and 11 (28.9%) with Group 4 tumor. 8 radiomics gray-level co-occurrence matrix texture (GLCM) features were significantly different between 4 molecular subgroups of MB. In addition, for tumors with higher values in a gray-level run length matrix feature-Short Run Low Gray-Level Emphasis, patients have shorter survival times than patients with low values of this feature (p = 0.04). The receiver operating characteristic analysis revealed optimal performance of the preliminary prediction model based on GLCM features for predicting WNT, Group 3, and Group 4 MB (area under the curve = 0.82, 0.72, and 0.78, respectively). CONCLUSION The preliminary result revealed that 8 contrast-enhanced T1-weighted imaging texture features were significantly different between 4 molecular subgroups of MB. Together with the prediction models, the radiomics features may provide suggestions for stratifying patients with MB into different risk groups.
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Affiliation(s)
- Feng-Chi Chang
- Department of Radiology, School of Medicine, Taipei Veterans General Hospital and National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Tai-Tong Wong
- Department of Neurosurgery, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
- Department of Neurosurgery, Neurological Institute, School of Medicine, Taipei Veterans General Hospital and National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Kuo-Sheng Wu
- Department of Neurosurgery, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
| | - Chia-Feng Lu
- Department of Biomedical Imaging and Radiological Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ting-Wei Weng
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan
| | - Muh-Lii Liang
- Department of Neurosurgery, Mackay Memorial Hospital, Taipei, Taiwan
| | - Chih-Chun Wu
- Department of Radiology, School of Medicine, Taipei Veterans General Hospital and National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Wan Yuo Guo
- Department of Radiology, School of Medicine, Taipei Veterans General Hospital and National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Cheng-Yu Chen
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Kevin Li-Chun Hsieh
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
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Jiang Z, Dong Y, Yang L, Lv Y, Dong S, Yuan S, Li D, Liu L. CT-Based Hand-crafted Radiomic Signatures Can Predict PD-L1 Expression Levels in Non-small Cell Lung Cancer: a Two-Center Study. J Digit Imaging 2021; 34:1073-1085. [PMID: 34327623 DOI: 10.1007/s10278-021-00484-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 05/27/2021] [Accepted: 06/21/2021] [Indexed: 12/01/2022] Open
Abstract
Here, we used pre-treatment CT images to develop and evaluate a radiomic signature that can predict the expression of programmed death ligand 1 (PD-L1) in non-small cell lung cancer (NSCLC). We then verified its predictive performance by cross-referencing its results with clinical characteristics. This two-center retrospective analysis included 125 patients with histologically confirmed NSCLC. A total of 1287 hand-crafted radiomic features were observed from manually determined tumor regions. Valuable features were then selected with a ridge regression-based recursive feature elimination approach. Machine learning-based prediction models were then built from this and compared each other. The final radiomic signature was built using logistic regression in the primary cohort, and then tested in a validation cohort. Finally, we compared the efficacy of the radiomic signature to the clinical model and the radiomic-clinical nomogram. Among the 125 patients, 89 were classified as having PD-L1 positive expression. However, there was no significant difference in PD-L1 expression levels determined by clinical characteristics (P = 0.109-0.955). Upon selecting 9 radiomic features, we found that the logistic regression-based prediction model performed the best (AUC = 0.96, P < 0.001). In the external cohort, our radiomic signature showed an AUC of 0.85, which outperformed both the clinical model (AUC = 0.38, P < 0.001) and the radiomics-nomogram model (AUC = 0.61, P < 0.001). Our CT-based hand-crafted radiomic signature model can effectively predict PD-L1 expression levels, providing a noninvasive means of better understanding PD-L1 expression in patients with NSCLC.
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Affiliation(s)
- Zekun Jiang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, 250358, Shandong, China
| | - Yinjun Dong
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, Shandong, China.,Liaocheng People's Hospital, Liaocheng, 252002, Shandong, China.,Shandong University, Jinan, 250117, Shandong, China
| | - Linke Yang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, Shandong, China
| | - Yunhong Lv
- Department of Mathematics and Information Technology, Xingtai University, Xingtai, 054001, Hebei, China.,Department of Mathematics and Statistics, University of Windsor, Windsor, ON, N9B 3P4, Canada
| | - Shuai Dong
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, Shandong, China
| | - Shuanghu Yuan
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, Shandong, China.
| | - Dengwang Li
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, 250358, Shandong, China.
| | - Liheng Liu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
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Glutamate-Oxaloacetate Transaminase 1 Impairs Glycolysis by Interacting with Pyruvate Carboxylase and Further Inhibits the Malignant Phenotypes of Glioblastoma Cells. World Neurosurg 2021; 154:e616-e626. [PMID: 34325031 DOI: 10.1016/j.wneu.2021.07.097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 07/19/2021] [Accepted: 07/20/2021] [Indexed: 11/24/2022]
Abstract
BACKGROUND Glycolysis is an important metabolic manner in glioblastoma multiforme (GBM)'s rapid growth. It has been reported that glutamate-oxaloacetate transaminase 1 (GOT1) is low-expressed in GBM and patients with high-expressed GOT1 have better prognosis. However, the effect and mechanism of GOT1 on glycolysis and malignant phenotypes of GBM cells are still unclear. METHODS The expression differences of GOT1 between GBM parenchyma and adjacent tissues were detected. The prognosis and clinical data with different levels of GOT1 were also analyzed. The glucose consumption, production of lactate and pyruvate were measured after GOT1 was knocked down or overexpressed. The effects of GOT1 on GBM cell's malignant phenotypes were analyzed by Western blot, CCK-8 assay, and flow cytometry. The relationship between GOT1 and pyruvate carboxylase (PC) was examined by immunoprecipitation and immunofluorescence. RESULTS GOT1 was expressed little in GBM, and patients with highly expressed GOT1 had longer survival periods. Overexpressed GOT1 inhibited the glycolysis and malignant phenotypes of GBM cells. 2-DG treatment could partially reverse the enhancement of malignant phenotypes caused by knockdown of GOT1. The expression of GOT1 was positively correlated with PC. The inhibitory effect of GOT1 on glycolysis could be partially reversed by PC's knockdown. CONCLUSIONS GOT1 could impair glycolysis by interacting with PC and further inhibit the malignant phenotypes of GBM cells.
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Galijašević M, Steiger R, Radović I, Birkl-Toeglhofer AM, Birkl C, Deeg L, Mangesius S, Rietzler A, Regodić M, Stockhammer G, Freyschlag CF, Kerschbaumer J, Haybaeck J, Grams AE, Gizewski ER. Phosphorous Magnetic Resonance Spectroscopy and Molecular Markers in IDH1 Wild Type Glioblastoma. Cancers (Basel) 2021; 13:cancers13143569. [PMID: 34298788 PMCID: PMC8305039 DOI: 10.3390/cancers13143569] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 07/04/2021] [Accepted: 07/14/2021] [Indexed: 12/18/2022] Open
Abstract
Simple Summary Gliobastoma is one of the deadliest tumors overall, yet the most common malignant brain tumor. The new World Health Organization Classification of Brain Tumors brought changes in how we look at this type of malignancy. Now we know that glioblastoma is rather a spectrum of similar tumors, but with some distinct characteristics that include molecular footprint, response to therapy and with that overall survival, among others. We hypothesised that by employing phosphorous magnetic resonance we will be able to show differences in cellular energy metabolism in these various subtypes of glioblastoma. For example, we found indices of faster cell reproduction and tumor growth in MGMT-methylated and EGFR-amplified tumors. These tumors also could have reduced energetic state or tissue oxygenation due to the increased necrosis. Tumors with EGFR-amplification could have increased apoptotic activity regardless of their MGMT status. Our study indicated various differences in energetic metabolism in tumors with different molecular characteristics, which could potentially be important in future therapeutic strategies. Abstract The World Health Organisation’s (WHO) classification of brain tumors requires consideration of both histological appearance and molecular characteristics. Possible differences in brain energy metabolism could be important in designing future therapeutic strategies. Forty-three patients with primary, isocitrate dehydrogenase 1 (IDH1) wild type glioblastomas (GBMs) were included in this study. Pre-operative standard MRI was obtained with additional phosphorous magnetic resonance spectroscopy (31-P-MRS) imaging. Following microsurgical resection of the tumors, biopsy specimens underwent neuropathological diagnostics including standard molecular diagnosis. The spectroscopy results were correlated with epidermal growth factor (EGFR) and O6-Methylguanine-DNA methyltransferase (MGMT) status. EGFR amplified tumors had significantly lower phosphocreatine (PCr) to adenosine triphosphate (ATP)-PCr/ATP and PCr to inorganic phosphate (Pi)-PCr/Pi ratios, and higher Pi/ATP and phosphomonoesters (PME) to phosphodiesters (PDE)-PME/PDE ratio than those without the amplification. Patients with MGMT-methylated tumors had significantly higher cerebral magnesium (Mg) values and PME/PDE ratio, while their PCr/ATP and PCr/Pi ratios were lower than in patients without the methylation. In survival analysis, not-EGFR-amplified, MGMT-methylated GBMs showed the longest survival. This group had lower PCr/Pi ratio when compared to MGMT-methylated, EGFR-amplified group. PCr/Pi ratio was lower also when compared to the MGMT-unmethylated, EGFR not-amplified group, while PCr/ATP ratio was lower than all other examined groups. Differences in energy metabolism in various molecular subtypes of wild-type-GBMs could be important information in future precision medicine approach.
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Affiliation(s)
- Malik Galijašević
- Department of Neuroradiology, Medical University of Innsbruck, 6020 Innsbruck, Austria; (M.G.); (I.R.); (C.B.); (L.D.); (S.M.); (A.R.); (A.E.G.); (E.R.G.)
- Neuroimaging Research Core Facility, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Ruth Steiger
- Department of Neuroradiology, Medical University of Innsbruck, 6020 Innsbruck, Austria; (M.G.); (I.R.); (C.B.); (L.D.); (S.M.); (A.R.); (A.E.G.); (E.R.G.)
- Neuroimaging Research Core Facility, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Correspondence:
| | - Ivan Radović
- Department of Neuroradiology, Medical University of Innsbruck, 6020 Innsbruck, Austria; (M.G.); (I.R.); (C.B.); (L.D.); (S.M.); (A.R.); (A.E.G.); (E.R.G.)
| | - Anna Maria Birkl-Toeglhofer
- Institute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, 6020 Innsbruck, Austria; (A.M.B.-T.); (J.H.)
| | - Christoph Birkl
- Department of Neuroradiology, Medical University of Innsbruck, 6020 Innsbruck, Austria; (M.G.); (I.R.); (C.B.); (L.D.); (S.M.); (A.R.); (A.E.G.); (E.R.G.)
- Neuroimaging Research Core Facility, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Lukas Deeg
- Department of Neuroradiology, Medical University of Innsbruck, 6020 Innsbruck, Austria; (M.G.); (I.R.); (C.B.); (L.D.); (S.M.); (A.R.); (A.E.G.); (E.R.G.)
| | - Stephanie Mangesius
- Department of Neuroradiology, Medical University of Innsbruck, 6020 Innsbruck, Austria; (M.G.); (I.R.); (C.B.); (L.D.); (S.M.); (A.R.); (A.E.G.); (E.R.G.)
- Neuroimaging Research Core Facility, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Andreas Rietzler
- Department of Neuroradiology, Medical University of Innsbruck, 6020 Innsbruck, Austria; (M.G.); (I.R.); (C.B.); (L.D.); (S.M.); (A.R.); (A.E.G.); (E.R.G.)
- Neuroimaging Research Core Facility, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Milovan Regodić
- Department of Otorhinolaryngology, Medical University of Innsbruck, 6020 Innsbruck, Austria;
- Department of Radiation Oncology, Medical University of Vienna, 1010 Vienna, Austria
| | - Guenther Stockhammer
- Department of Neurology, Medical University of Innsbruck, 6020 Innsbruck, Austria;
| | | | - Johannes Kerschbaumer
- Department of Neurosurgery, Medical University of Innsbruck, 6020 Innsbruck, Austria; (C.F.F.); (J.K.)
| | - Johannes Haybaeck
- Institute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, 6020 Innsbruck, Austria; (A.M.B.-T.); (J.H.)
- Diagnostic and Research Center for Molecular Biomedicine, Institute of Pathology, Medical University of Graz, 8010 Graz, Austria
| | - Astrid Ellen Grams
- Department of Neuroradiology, Medical University of Innsbruck, 6020 Innsbruck, Austria; (M.G.); (I.R.); (C.B.); (L.D.); (S.M.); (A.R.); (A.E.G.); (E.R.G.)
- Neuroimaging Research Core Facility, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Elke Ruth Gizewski
- Department of Neuroradiology, Medical University of Innsbruck, 6020 Innsbruck, Austria; (M.G.); (I.R.); (C.B.); (L.D.); (S.M.); (A.R.); (A.E.G.); (E.R.G.)
- Neuroimaging Research Core Facility, Medical University of Innsbruck, 6020 Innsbruck, Austria
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Hoshino I, Yokota H. Radiogenomics of gastroenterological cancer: The dawn of personalized medicine with artificial intelligence-based image analysis. Ann Gastroenterol Surg 2021; 5:427-435. [PMID: 34337291 PMCID: PMC8316732 DOI: 10.1002/ags3.12437] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 11/29/2020] [Accepted: 01/08/2021] [Indexed: 12/14/2022] Open
Abstract
Radiogenomics is a new field of medical science that integrates two omics, radiomics and genomics, and may bring a major paradigm shift in traditional personalized medicine strategies that require tumor tissue samples. In addition, the acquisition of the data does not require special imaging equipment or special imaging conditions, and it is possible to use image information from computed tomography, magnetic resonance imaging, positron emission tomography-computed tomography in clinical practice, so the versatility and cost-effectiveness of radiogenomics are expected. So far, the field of radiogenomics has developed, especially in the fields of brain tumors and breast cancer, but recently, reports of radiogenomic research on gastroenterological cancer are increasing. This review provides an overview of radiogenomic research methods and summarizes the current radiogenomic research in gastroenterological cancer. In addition, the application of artificial intelligence is considered to be indispensable for the integrated analysis of enormous omics information in the future, and the future direction of this research, including the latest technologies, will be discussed.
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Affiliation(s)
- Isamu Hoshino
- Division of Gastroenterological SurgeryChiba Cancer CenterChibaJapan
| | - Hajime Yokota
- Department of Diagnostic Radiology and Radiation OncologyGraduate School of MedicineChiba UniversityChibaJapan
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Yang F, Xie Y, Tang J, Liu B, Luo Y, He Q, Zhang L, Xin L, Wang J, Wang S, Zhang S, Cao Q, Wang L, He L, Zhang L. Uncovering a Distinct Gene Signature in Endothelial Cells Associated With Contrast Enhancement in Glioblastoma. Front Oncol 2021; 11:683367. [PMID: 34222002 PMCID: PMC8245778 DOI: 10.3389/fonc.2021.683367] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 05/27/2021] [Indexed: 01/23/2023] Open
Abstract
Purpose Glioblastoma (GBM) is the most aggressive and lethal type of brain tumors. Magnetic resonance imaging (MRI) has been commonly used for GBM diagnosis. Contrast enhancement (CE) on T1-weighted sequences are presented in nearly all GBM as a result of high vascular permeability in glioblastomas. Although several radiomics studies indicated that CE is associated with distinct molecular signatures in tumors, the effects of vascular endothelial cells, the key component of blood brain barrier (BBB) controlling vascular permeability, on CE have not been thoroughly analyzed. Methods Endothelial cell enriched genes have been identified using transcriptome data from 128 patients by a systematic method based on correlation analysis. Distinct endothelial cell enriched genes associated with CE were identified by analyzing difference of correlation score between CE-high and CE–low GBM cases. Immunohistochemical staining was performed on in-house patient cohort to validate the selected genes associated with CE. Moreover, a survival analysis was conducted to uncover the relation between CE and patient survival. Results We illustrated that CE is associated with distinct vascular molecular imprints characterized by up-regulation of pro-inflammatory genes and deregulation of BBB related genes. Among them, PLVAP is up-regulated, whereas TJP1 and ABCG2 are down-regulated in the vasculature of GBM with high CE. In addition, we found that the high CE is associated with poor prognosis and GBM mesenchymal subtype. Conclusion We provide an additional insight to reveal the molecular trait for CE in MRI images with special focus on vascular endothelial cells, linking CE with BBB disruption in the molecular level. This study provides a potential new direction that may be applied for the treatment optimization based on MRI features.
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Affiliation(s)
- Fan Yang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin Neurological Institute, Key Laboratory of Post-Neuro-injury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education and Tianjin City, Tianjin, China
| | - Yuan Xie
- Key Laboratory of Ministry of Education for Medicinal Plant Resource and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Developing of Endangered Chinese Crude Drugs in Northwest of China, College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Jiefu Tang
- Trauma Center, The First Affiliated Hospital of Hunan University of Medicine, Huaihua, China
| | - Boxuan Liu
- Precision Medicine Center, The Second People's Hospital of Huaihua, Huaihua, China
| | - Yuancheng Luo
- School of Life Science, University of Liverpool, Liverpool, United Kingdom
| | - Qiyuan He
- Key Laboratory of Ministry of Education for Medicinal Plant Resource and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Developing of Endangered Chinese Crude Drugs in Northwest of China, College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Lingxue Zhang
- Key Laboratory of Ministry of Education for Medicinal Plant Resource and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Developing of Endangered Chinese Crude Drugs in Northwest of China, College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Lele Xin
- Key Laboratory of Ministry of Education for Medicinal Plant Resource and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Developing of Endangered Chinese Crude Drugs in Northwest of China, College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Jianhao Wang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin Neurological Institute, Key Laboratory of Post-Neuro-injury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education and Tianjin City, Tianjin, China
| | - Sinan Wang
- Key Laboratory of Ministry of Education for Medicinal Plant Resource and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Developing of Endangered Chinese Crude Drugs in Northwest of China, College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Shuqiang Zhang
- Key Laboratory of Ministry of Education for Medicinal Plant Resource and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Developing of Endangered Chinese Crude Drugs in Northwest of China, College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Qingze Cao
- Key Laboratory of Ministry of Education for Medicinal Plant Resource and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Developing of Endangered Chinese Crude Drugs in Northwest of China, College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Liang Wang
- Department of Neurosurgery, Tangdu Hospital of the Fourth Military Medical University (Air Force Medical University of PLA), Xi'an, China
| | - Liqun He
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin Neurological Institute, Key Laboratory of Post-Neuro-injury Neuro-Repair and Regeneration in Central Nervous System, Ministry of Education and Tianjin City, Tianjin, China.,Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, Uppsala, Sweden
| | - Lei Zhang
- Key Laboratory of Ministry of Education for Medicinal Plant Resource and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Developing of Endangered Chinese Crude Drugs in Northwest of China, College of Life Sciences, Shaanxi Normal University, Xi'an, China.,Precision Medicine Center, The Second People's Hospital of Huaihua, Huaihua, China
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