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Xie Y, Zhang S, Liu X, Luo Y, Zhou J. Whole-lesion iodine map histogram analysis in the risk classification of gastrointestinal stromal tumors: comparison with single-slice iodine concentration measurements. Abdom Radiol (NY) 2024:10.1007/s00261-024-04224-9. [PMID: 38472310 DOI: 10.1007/s00261-024-04224-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/20/2024] [Accepted: 01/23/2024] [Indexed: 03/14/2024]
Abstract
PURPOSE To evaluate and compare the diagnostic performances of whole-lesion iodine map (IM) histogram analysis and single-slice IM measurement in the risk classification of gastrointestinal stromal tumors (GISTs). METHODS Thirty-seven patients with GISTs, including 19 with low malignant underlying GISTs (LG-GISTs) and 18 with high malignant underlying GISTs (HG-GISTs), were evaluated with dual-energy computed tomography (DECT). Whole-lesion IM histogram parameters (mean; median; minimum; maximum; standard deviation; variance; 1st, 10th, 25th, 50th, 75th, 90th, and 99th percentile; kurtosis, skewness, and entropy) were computed for each lesion. In other sessions, iodine concentrations (ICs) were derived from the IM by placing regions of interest (ROIs) on the tumor slices and normalizing them to the iodine concentration in the aorta. Both quantitative analyses were performed on the venous phase images. The diagnostic accuracies of the two methods were assessed and compared. RESULTS The minimum, maximum, 1st, 10th, and 25th percentile of the whole-lesion IM histogram and the IC and normalized IC (NIC) of the single-slice IC measurement significantly differed between LG- and HG-GISTs (p < 0.001 - p = 0.042). The minimum value in the histogram analysis (AUC = 0.844) and the NIC in the single-slice measurement analysis (AUC = 0.886) showed the best diagnostic performances. The NIC of single-slice measurements had a diagnostic performance similar to that of the whole-lesion IM histogram analysis (p = 0.618). CONCLUSIONS Both whole-lesion IM histogram analysis and single-slice IC measurement can differentiate LG-GISTs and HG-GISTs with similar diagnostic performances.
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Affiliation(s)
- Yijing Xie
- Department of Radiology, The Second Hospital of Lanzhou University, Lanzhou, 730000, China
- The Second Clinical Medical School, Lanzhou University, Lanzhou, 730000, China
- Key Laboratory of Medical Imaging of Gansu Province, The Second Hospital of Lanzhou University, Lanzhou, 730000, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730000, China
| | - Shipeng Zhang
- Department of Radiology, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, 730000, China
| | - Xianwang Liu
- Department of Radiology, The Second Hospital of Lanzhou University, Lanzhou, 730000, China
- The Second Clinical Medical School, Lanzhou University, Lanzhou, 730000, China
- Key Laboratory of Medical Imaging of Gansu Province, The Second Hospital of Lanzhou University, Lanzhou, 730000, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730000, China
| | - Yongjun Luo
- Department of Nuclear Medicine, The Second Hospital of Lanzhou University, Lanzhou, 730000, China
- The Second Clinical Medical School, Lanzhou University, Lanzhou, 730000, China
- Key Laboratory of Medical Imaging of Gansu Province, The Second Hospital of Lanzhou University, Lanzhou, 730000, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730000, China
| | - Junlin Zhou
- Department of Radiology, The Second Hospital of Lanzhou University, Lanzhou, 730000, China.
- Key Laboratory of Medical Imaging of Gansu Province, The Second Hospital of Lanzhou University, Lanzhou, 730000, China.
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730000, China.
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Qiu J, Zhu M, Chen CY, Luo Y, Wen J. Diffusion heterogeneity and vascular perfusion in tumor and peritumoral areas for prediction of overall survival in patients with high-grade glioma. Magn Reson Imaging 2023; 104:23-28. [PMID: 37734575 DOI: 10.1016/j.mri.2023.09.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 08/24/2023] [Accepted: 09/17/2023] [Indexed: 09/23/2023]
Abstract
OBJECTIVE To evaluation of diffusion heterogeneity and vascular perfusion in tumor and peritumoral areas for prognostic prediction in high-grade glioma (HGG, WHO III/IV grade). METHODS Forty patients with HGG underwent diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), and arterial spin labeling (ASL) MRI before operation. After normalization, the parameters were divided into diffusion heterogeneity parameters (rD, rMD, rMK, rKr, rKa) and vascular perfusion parameters (rD*, rF, rCBF). Univariate and multivariate Cox regression analyses were used to evaluate associations between overall survival (OS) and the above parameters, clinical factors, and IDH1 status. The Mann-Whitney test was used to evaluate differences in the parameters between different IDH1 states. RESULTS In the univariate Cox regression analysis, OS was significantly associated with tumor resection range, IDH1 status, tumor heterogeneity parameters (rD, rMD, rMK, rKr, rKa), and rCBF in tumor area(all p < 0.05). In addition, rD and rCBF measured in the peritumoral region were also predictors of poor OS (both p < 0.01). Multivariate Cox regression analysis indicated that rMK in the tumor area and rCBF in the peritumoral area (hazard ratio = 7.900 and 5.466, respectively, for each 0.1 increase in the normalized value) were independent predictors of OS. CONCLUSION The rMK of tumor area and rCBF of peritumoral area had independent predictive value for OS in patients with HGG. ADVANCES IN KNOWLEDGE This study explored useful imaging biomarkers from the diffusion heterogeneity and vascular perfusion of tumor and peritumoral areas in HGG, which is useful to help clinician to make precise therapeutic plans, and predict the prognostic for glioma patients.
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Affiliation(s)
- Jun Qiu
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230036, China.
| | - Min Zhu
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230036, China
| | - Chuan Yu Chen
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230036, China
| | - Yi Luo
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230036, China
| | - Jie Wen
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230036, China.
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Heo D, Lee J, Yoo RE, Choi SH, Kim TM, Park CK, Park SH, Won JK, Lee JH, Lee ST, Choi KS, Lee JY, Hwang I, Kang KM, Yun TJ. Deep learning based on dynamic susceptibility contrast MR imaging for prediction of local progression in adult-type diffuse glioma (grade 4). Sci Rep 2023; 13:13864. [PMID: 37620555 PMCID: PMC10449894 DOI: 10.1038/s41598-023-41171-9] [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: 02/23/2023] [Accepted: 08/23/2023] [Indexed: 08/26/2023] Open
Abstract
Adult-type diffuse glioma (grade 4) has infiltrating nature, and therefore local progression is likely to occur within surrounding non-enhancing T2 hyperintense areas even after gross total resection of contrast-enhancing lesions. Cerebral blood volume (CBV) obtained from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) is a parameter that is well-known to be a surrogate marker of both histologic and angiographic vascularity in tumors. We built two nnU-Net deep learning models for prediction of early local progression in adult-type diffuse glioma (grade 4), one using conventional MRI alone and one using multiparametric MRI, including conventional MRI and DSC-PWI. Local progression areas were annotated in a non-enhancing T2 hyperintense lesion on preoperative T2 FLAIR images, using the follow-up contrast-enhanced (CE) T1-weighted (T1W) images as the reference standard. The sensitivity was doubled with the addition of nCBV (80% vs. 40%, P = 0.02) while the specificity was decreased nonsignificantly (29% vs. 48%, P = 0.39), suggesting that fewer cases of early local progression would be missed with the addition of nCBV. While the diagnostic performance of CBV model is still poor and needs improving, the multiparametric deep learning model, which presumably learned from the subtle difference in vascularity between early local progression and non-progression voxels within perilesional T2 hyperintensity, may facilitate risk-adapted radiotherapy planning in adult-type diffuse glioma (grade 4) patients.
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Affiliation(s)
- Donggeon Heo
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jisoo Lee
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Roh-Eul Yoo
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Department of Radiology, Seoul National University Hospital, 101, Daehangno, Jongno-Gu, Seoul, 03080, Republic of Korea.
| | - Seung Hong Choi
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Department of Radiology, Seoul National University Hospital, 101, Daehangno, Jongno-Gu, Seoul, 03080, Republic of Korea.
- Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, Republic of Korea.
- School of Chemical and Biological Engineering, Seoul National University, 1, Gwanak-Ro, Gwanak-Gu, Seoul, 302-909, Republic of Korea.
| | - Tae Min Kim
- Department of Internal Medicine, Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Chul-Kee Park
- Department of Neurosurgery, Biomedical Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Sung-Hye Park
- Department of Pathology, Seoul National University College of Medicine, Seoul, Korea
| | - Jae-Kyung Won
- Department of Pathology, Seoul National University College of Medicine, Seoul, Korea
| | - Joo Ho Lee
- Department of Radiation Oncology, Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Soon Tae Lee
- Department of Neurology, Seoul National University College of Medicine, Seoul, Korea
| | - Kyu Sung Choi
- Department of Radiology, Seoul National University Hospital, 101, Daehangno, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Ji Ye Lee
- Department of Radiology, Seoul National University Hospital, 101, Daehangno, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Inpyeong Hwang
- Department of Radiology, Seoul National University Hospital, 101, Daehangno, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Koung Mi Kang
- Department of Radiology, Seoul National University Hospital, 101, Daehangno, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Tae Jin Yun
- Department of Radiology, Seoul National University Hospital, 101, Daehangno, Jongno-Gu, Seoul, 03080, Republic of Korea
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Lovibond S, Gewirtz AN, Pasquini L, Krebs S, Graham MS. The promise of metabolic imaging in diffuse midline glioma. Neoplasia 2023; 39:100896. [PMID: 36944297 PMCID: PMC10036941 DOI: 10.1016/j.neo.2023.100896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 02/10/2023] [Accepted: 03/13/2023] [Indexed: 03/23/2023]
Abstract
Recent insights into histopathological and molecular subgroups of glioma have revolutionized the field of neuro-oncology by refining diagnostic categories. An emblematic example in pediatric neuro-oncology is the newly defined diffuse midline glioma (DMG), H3 K27-altered. DMG represents a rare tumor with a dismal prognosis. The diagnosis of DMG is largely based on clinical presentation and characteristic features on conventional magnetic resonance imaging (MRI), with biopsy limited by its delicate neuroanatomic location. Standard MRI remains limited in its ability to characterize tumor biology. Advanced MRI and positron emission tomography (PET) imaging offer additional value as they enable non-invasive evaluation of molecular and metabolic features of brain tumors. These techniques have been widely used for tumor detection, metabolic characterization and treatment response monitoring of brain tumors. However, their role in the realm of pediatric DMG is nascent. By summarizing DMG metabolic pathways in conjunction with their imaging surrogates, we aim to elucidate the untapped potential of such imaging techniques in this devastating disease.
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Affiliation(s)
- Samantha Lovibond
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alexandra N Gewirtz
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Luca Pasquini
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Simone Krebs
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Radiochemistry and Imaging Sciences Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Radiology, Weill Cornell Medical College, New York, NY 10065, USA
| | - Maya S Graham
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Henssen D, Meijer F, Verburg FA, Smits M. Challenges and opportunities for advanced neuroimaging of glioblastoma. Br J Radiol 2023; 96:20211232. [PMID: 36062962 PMCID: PMC10997013 DOI: 10.1259/bjr.20211232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 08/10/2022] [Accepted: 08/25/2022] [Indexed: 11/05/2022] Open
Abstract
Glioblastoma is the most aggressive of glial tumours in adults. On conventional magnetic resonance (MR) imaging, these tumours are observed as irregular enhancing lesions with areas of infiltrating tumour and cortical expansion. More advanced imaging techniques including diffusion-weighted MRI, perfusion-weighted MRI, MR spectroscopy and positron emission tomography (PET) imaging have found widespread application to diagnostic challenges in the setting of first diagnosis, treatment planning and follow-up. This review aims to educate readers with regard to the strengths and weaknesses of the clinical application of these imaging techniques. For example, this review shows that the (semi)quantitative analysis of the mentioned advanced imaging tools was found useful for assessing tumour aggressiveness and tumour extent, and aids in the differentiation of tumour progression from treatment-related effects. Although these techniques may aid in the diagnostic work-up and (post-)treatment phase of glioblastoma, so far no unequivocal imaging strategy is available. Furthermore, the use and further development of artificial intelligence (AI)-based tools could greatly enhance neuroradiological practice by automating labour-intensive tasks such as tumour measurements, and by providing additional diagnostic information such as prediction of tumour genotype. Nevertheless, due to the fact that advanced imaging and AI-diagnostics is not part of response assessment criteria, there is no harmonised guidance on their use, while at the same time the lack of standardisation severely hampers the definition of uniform guidelines.
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Affiliation(s)
- Dylan Henssen
- Department of Medical Imaging, Radboud university medical
center, Nijmegen, The Netherlands
| | - Frederick Meijer
- Department of Medical Imaging, Radboud university medical
center, Nijmegen, The Netherlands
| | - Frederik A. Verburg
- Department of Medical Imaging, Radboud university medical
center, Nijmegen, The Netherlands
| | - Marion Smits
- Department of Medical Imaging, Radboud university medical
center, Nijmegen, The Netherlands
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6
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de Godoy LL, Chen YJ, Chawla S, Viaene AN, Wang S, Loevner LA, Alonso-Basanta M, Poptani H, Mohan S. Prognostication of overall survival in patients with brain metastases using diffusion tensor imaging and dynamic susceptibility contrast-enhanced MRI. Br J Radiol 2022; 95:20220516. [PMID: 36354164 PMCID: PMC9733614 DOI: 10.1259/bjr.20220516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 08/23/2022] [Accepted: 09/30/2022] [Indexed: 11/11/2022] Open
Abstract
OBJECTIVES To investigate the prognostic utility of DTI and DSC-PWI perfusion-derived parameters in brain metastases patients. METHODS Retrospective analyses of DTI-derived parameters (MD, FA, CL, CP, and CS) and DSC-perfusion PWI-derived rCBVmax from 101 patients diagnosed with brain metastases prior to treatment were performed. Using semi-automated segmentation, DTI metrics and rCBVmax were quantified from enhancing areas of the dominant metastatic lesion. For each metric, patients were classified as short- and long-term survivors based on analysis of the best coefficient for each parameter and percentile to separate the groups. Kaplan-Meier analysis was used to compare mOS between these groups. Multivariate survival analysis was subsequently conducted. A correlative histopathologic analysis was performed in a subcohort (n = 10) with DTI metrics and rCBVmax on opposite ends of the spectrum. RESULTS Significant differences in mOS were observed for MDmin (p < 0.05), FA (p < 0.01), CL (p < 0.05), and CP (p < 0.01) and trend toward significance for rCBVmax (p = 0.07) between the two risk groups, in the univariate analysis. On multivariate analysis, the best predictive survival model was comprised of MDmin (p = 0.05), rCBVmax (p < 0.05), RPA (p < 0.0001), and number of lesions (p = 0.07). On histopathology, metastatic tumors showed significant differences in the amount of stroma depending on the combination of DTI metrics and rCBVmax values. Patients with high stromal content demonstrated poorer mOS. CONCLUSION Pretreatment DTI-derived parameters, notably MDmin and rCBVmax, are promising imaging markers for prognostication of OS in patients with brain metastases. Stromal cellularity may be a contributing factor to these differences. ADVANCES IN KNOWLEDGE The correlation of DTI-derived metrics and perfusion MRI with patient outcomes has not been investigated in patients with treatment naïve brain metastasis. DTI and DSC-PWI can aid in therapeutic decision-making by providing additional clinical guidance.
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Affiliation(s)
- Laiz Laura de Godoy
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, United States
| | - Yin Jie Chen
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, United States
| | - Sanjeev Chawla
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, United States
| | - Angela N Viaene
- Division of Anatomic Pathology, Children’s Hospital of Philadelphia, Philadelphia, United States
| | - Sumei Wang
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, United States
| | - Laurie A Loevner
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, United States
| | - Michelle Alonso-Basanta
- Department of Radiation Oncology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, United States
| | - Harish Poptani
- Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, United States
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7
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Pasquini L, Napolitano A, Pignatelli M, Tagliente E, Parrillo C, Nasta F, Romano A, Bozzao A, Di Napoli A. Synthetic Post-Contrast Imaging through Artificial Intelligence: Clinical Applications of Virtual and Augmented Contrast Media. Pharmaceutics 2022; 14:pharmaceutics14112378. [PMID: 36365197 PMCID: PMC9695136 DOI: 10.3390/pharmaceutics14112378] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/25/2022] [Accepted: 10/26/2022] [Indexed: 11/06/2022] Open
Abstract
Contrast media are widely diffused in biomedical imaging, due to their relevance in the diagnosis of numerous disorders. However, the risk of adverse reactions, the concern of potential damage to sensitive organs, and the recently described brain deposition of gadolinium salts, limit the use of contrast media in clinical practice. In recent years, the application of artificial intelligence (AI) techniques to biomedical imaging has led to the development of 'virtual' and 'augmented' contrasts. The idea behind these applications is to generate synthetic post-contrast images through AI computational modeling starting from the information available on other images acquired during the same scan. In these AI models, non-contrast images (virtual contrast) or low-dose post-contrast images (augmented contrast) are used as input data to generate synthetic post-contrast images, which are often undistinguishable from the native ones. In this review, we discuss the most recent advances of AI applications to biomedical imaging relative to synthetic contrast media.
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Affiliation(s)
- Luca Pasquini
- Neuroradiology Unit, Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children’s Hospital, IRCCS, Piazza di Sant’Onofrio, 4, 00165 Rome, Italy
- Correspondence:
| | - Matteo Pignatelli
- Radiology Department, Castelli Hospital, Via Nettunense Km 11.5, 00040 Ariccia, Italy
| | - Emanuela Tagliente
- Medical Physics Department, Bambino Gesù Children’s Hospital, IRCCS, Piazza di Sant’Onofrio, 4, 00165 Rome, Italy
| | - Chiara Parrillo
- Medical Physics Department, Bambino Gesù Children’s Hospital, IRCCS, Piazza di Sant’Onofrio, 4, 00165 Rome, Italy
| | - Francesco Nasta
- Medical Physics Department, Bambino Gesù Children’s Hospital, IRCCS, Piazza di Sant’Onofrio, 4, 00165 Rome, Italy
| | - Andrea Romano
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy
| | - Alessandro Bozzao
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy
| | - Alberto Di Napoli
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy
- Neuroimaging Lab, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
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8
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Pasquini L, Napolitano A, Lucignani M, Tagliente E, Dellepiane F, Rossi-Espagnet MC, Ritrovato M, Vidiri A, Villani V, Ranazzi G, Stoppacciaro A, Romano A, Di Napoli A, Bozzao A. AI and High-Grade Glioma for Diagnosis and Outcome Prediction: Do All Machine Learning Models Perform Equally Well? Front Oncol 2021; 11:601425. [PMID: 34888226 PMCID: PMC8649764 DOI: 10.3389/fonc.2021.601425] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 11/02/2021] [Indexed: 12/30/2022] Open
Abstract
Radiomic models outperform clinical data for outcome prediction in high-grade gliomas (HGG). However, lack of parameter standardization limits clinical applications. Many machine learning (ML) radiomic models employ single classifiers rather than ensemble learning, which is known to boost performance, and comparative analyses are lacking in the literature. We aimed to compare ML classifiers to predict clinically relevant tasks for HGG: overall survival (OS), isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor vIII (EGFR) amplification, and Ki-67 expression, based on radiomic features from conventional and advanced magnetic resonance imaging (MRI). Our objective was to identify the best algorithm for each task. One hundred fifty-six adult patients with pathologic diagnosis of HGG were included. Three tumoral regions were manually segmented: contrast-enhancing tumor, necrosis, and non-enhancing tumor. Radiomic features were extracted with a custom version of Pyradiomics and selected through Boruta algorithm. A Grid Search algorithm was applied when computing ten times K-fold cross-validation (K=10) to get the highest mean and lowest spread of accuracy. Model performance was assessed as AUC-ROC curve mean values with 95% confidence intervals (CI). Extreme Gradient Boosting (xGB) obtained highest accuracy for OS (74,5%), Adaboost (AB) for IDH mutation (87.5%), MGMT methylation (70,8%), Ki-67 expression (86%), and EGFR amplification (81%). Ensemble classifiers showed the best performance across tasks. High-scoring radiomic features shed light on possible correlations between MRI and tumor histology.
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Affiliation(s)
- Luca Pasquini
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Martina Lucignani
- Medical Physics Department, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Emanuela Tagliente
- Medical Physics Department, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Francesco Dellepiane
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
| | - Maria Camilla Rossi-Espagnet
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Matteo Ritrovato
- Unit of Health Technology Assessment (HTA), Biomedical Technology Risk Manager, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Antonello Vidiri
- Radiology and Diagnostic Imaging Department, Regina Elena National Cancer Institute, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Veronica Villani
- Neuro-Oncology Unit, Regina Elena National Cancer Institute, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Giulio Ranazzi
- Department of Clinical and Molecular Medicine, Surgical Pathology Units, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
| | - Antonella Stoppacciaro
- Department of Clinical and Molecular Medicine, Surgical Pathology Units, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
| | - Andrea Romano
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
| | - Alberto Di Napoli
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
- Radiology Department, Castelli Romani Hospital, Rome, Italy
| | - Alessandro Bozzao
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
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9
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Pasquini L, Di Napoli A, Napolitano A, Lucignani M, Dellepiane F, Vidiri A, Villani V, Romano A, Bozzao A. Glioblastoma radiomics to predict survival: Diffusion characteristics of surrounding nonenhancing tissue to select patients for extensive resection. J Neuroimaging 2021; 31:1192-1200. [PMID: 34231927 DOI: 10.1111/jon.12903] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 06/07/2021] [Accepted: 06/08/2021] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND PURPOSE Glioblastoma (GBM) is an aggressive primary CNS neoplasm with poor overall survival (OS) despite standard of care. On MRI, GBM is usually characterized by an enhancing portion (CET) (surgery target) and a nonenhancing surrounding (NET). Extent of resection is a long debated issue in GBM, with recent evidence suggesting that both CET and NET should be resected in <65 years old patients, regardless of other risk factors (i.e., molecular biomarkers). Our aim was to test a radiomic model for patient survival stratification in <65 years old patients, by analyzing MRI features of NET, to aid tumor resection. METHODS Sixty-eight <65 years old GBM patients, with extensive CET resection, were selected. Resection was evaluated by manually segmenting CET on volumetric T1-weighted MRI pre and postsurgery (within 72 h). All patients underwent the same treatment protocol including chemoradiation. NET radiomic features were extracted with a custom version of Pyradiomics. Feature selection was performed with principal component analysis (PCA) and its effect on survival tested with Cox regression model. Twelve months OS discrimination was tested by t-test followed by logistic regression. Statistical significance was set at p<0.05. The most relevant features were identified from the component matrix. RESULTS Five PCA components (PC1-5) explained 90% of the variance. PC5 resulted significant in the Cox model (p = 0.002; exp(B) = 0.686), at t-test (p = 0.002) and logistic regression analysis (p = 0.006). Apparent diffusion coefficient (ADC)-based features were the most significant for patient survival stratification. CONCLUSIONS ADC radiomic features on NET predict survival after standard therapy and could be used to improve patient selection for more extensive surgery.
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Affiliation(s)
- Luca Pasquini
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA.,Neuroradiology Unit, NESMOS Department, Sant'Andrea Hospital, La Sapienza University, Rome, Italy
| | - Alberto Di Napoli
- Neuroradiology Unit, NESMOS Department, Sant'Andrea Hospital, La Sapienza University, Rome, Italy.,Radiology Department, Castelli Romani Hospital, Rome, Italy
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Martina Lucignani
- Medical Physics Department, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Francesco Dellepiane
- Neuroradiology Unit, NESMOS Department, Sant'Andrea Hospital, La Sapienza University, Rome, Italy
| | - Antonello Vidiri
- Radiology and Diagnostic Imaging Department, Regina Elena National Cancer Institute, IRCCS, Rome, Italy
| | - Veronica Villani
- Neuro-Oncology Unit, Regina Elena National Cancer Institute, IRCCS, Rome, Italy
| | - Andrea Romano
- Neuroradiology Unit, NESMOS Department, Sant'Andrea Hospital, La Sapienza University, Rome, Italy
| | - Alessandro Bozzao
- Neuroradiology Unit, NESMOS Department, Sant'Andrea Hospital, La Sapienza University, Rome, Italy
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10
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Pasquini L, Napolitano A, Tagliente E, Dellepiane F, Lucignani M, Vidiri A, Ranazzi G, Stoppacciaro A, Moltoni G, Nicolai M, Romano A, Di Napoli A, Bozzao A. Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM. J Pers Med 2021; 11:290. [PMID: 33918828 PMCID: PMC8069494 DOI: 10.3390/jpm11040290] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/02/2021] [Accepted: 04/07/2021] [Indexed: 12/16/2022] Open
Abstract
Isocitrate dehydrogenase (IDH) mutant and wildtype glioblastoma multiforme (GBM) often show overlapping features on magnetic resonance imaging (MRI), representing a diagnostic challenge. Deep learning showed promising results for IDH identification in mixed low/high grade glioma populations; however, a GBM-specific model is still lacking in the literature. Our aim was to develop a GBM-tailored deep-learning model for IDH prediction by applying convoluted neural networks (CNN) on multiparametric MRI. We selected 100 adult patients with pathologically demonstrated WHO grade IV gliomas and IDH testing. MRI sequences included: MPRAGE, T1, T2, FLAIR, rCBV and ADC. The model consisted of a 4-block 2D CNN, applied to each MRI sequence. Probability of IDH mutation was obtained from the last dense layer of a softmax activation function. Model performance was evaluated in the test cohort considering categorical cross-entropy loss (CCEL) and accuracy. Calculated performance was: rCBV (accuracy 83%, CCEL 0.64), T1 (accuracy 77%, CCEL 1.4), FLAIR (accuracy 77%, CCEL 1.98), T2 (accuracy 67%, CCEL 2.41), MPRAGE (accuracy 66%, CCEL 2.55). Lower performance was achieved on ADC maps. We present a GBM-specific deep-learning model for IDH mutation prediction, with a maximal accuracy of 83% on rCBV maps. Highest predictivity achieved on perfusion images possibly reflects the known link between IDH and neoangiogenesis through the hypoxia inducible factor.
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Affiliation(s)
- Luca Pasquini
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy; (L.P.); (F.D.); (G.M.); (M.N.); (A.R.); (A.D.N.); (A.B.)
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children’s Hospital, IRCCS, Piazza di Sant’Onofrio, 4, 00165 Rome, Italy; (E.T.); (M.L.)
| | - Emanuela Tagliente
- Medical Physics Department, Bambino Gesù Children’s Hospital, IRCCS, Piazza di Sant’Onofrio, 4, 00165 Rome, Italy; (E.T.); (M.L.)
| | - Francesco Dellepiane
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy; (L.P.); (F.D.); (G.M.); (M.N.); (A.R.); (A.D.N.); (A.B.)
| | - Martina Lucignani
- Medical Physics Department, Bambino Gesù Children’s Hospital, IRCCS, Piazza di Sant’Onofrio, 4, 00165 Rome, Italy; (E.T.); (M.L.)
| | - Antonello Vidiri
- Radiology and Diagnostic Imaging Department, Regina Elena National Cancer Institute, IRCCS, Via Elio Chianesi 53, 00144 Rome, Italy;
| | - Giulio Ranazzi
- Surgical Pathology Unit, Department of Clinical and Molecular Medicine, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy; (G.R.); (A.S.)
| | - Antonella Stoppacciaro
- Surgical Pathology Unit, Department of Clinical and Molecular Medicine, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy; (G.R.); (A.S.)
| | - Giulia Moltoni
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy; (L.P.); (F.D.); (G.M.); (M.N.); (A.R.); (A.D.N.); (A.B.)
| | - Matteo Nicolai
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy; (L.P.); (F.D.); (G.M.); (M.N.); (A.R.); (A.D.N.); (A.B.)
| | - Andrea Romano
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy; (L.P.); (F.D.); (G.M.); (M.N.); (A.R.); (A.D.N.); (A.B.)
| | - Alberto Di Napoli
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy; (L.P.); (F.D.); (G.M.); (M.N.); (A.R.); (A.D.N.); (A.B.)
| | - Alessandro Bozzao
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy; (L.P.); (F.D.); (G.M.); (M.N.); (A.R.); (A.D.N.); (A.B.)
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11
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Zhang HW, Lyu GW, He WJ, Lei Y, Lin F, Wang MZ, Zhang H, Liang LH, Feng YN, Yang JH. DSC and DCE Histogram Analyses of Glioma Biomarkers, Including IDH, MGMT, and TERT, on Differentiation and Survival. Acad Radiol 2020; 27:e263-e271. [PMID: 31983532 DOI: 10.1016/j.acra.2019.12.010] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 12/06/2019] [Accepted: 12/07/2019] [Indexed: 12/25/2022]
Abstract
RATIONALE AND OBJECTIVES The World Health Organization 2016 classification of central nervous system tumors added the molecular classification of gliomas and has guiding significance for the operation and prognosis of glioma patients. At present, the perfusion technique plays an important role in judging the malignant degree of glioma. To evaluate the performance of dynamic susceptibility contrast (DSC)- and dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) histogram analyses in discriminating the states of molecular biomarkers and survival in glioma patients. MATERIALS AND METHODS Forty-three glioma patients who underwent DCE- and DSC-MRI were enrolled. Relevant molecular test results, including those on isocitrate dehydrogenase (IDH), O6-methylguanine-DNA methyltransferase (MGMT) and telomere reverse transcriptase (TERT), were collected. The mean relative cerebral blood volume of DSC-MRI and histogram parameters derived from DCE-MRI (volume transfer coefficient (Ktrans), fractional volume of the extravascular extracellular space (Ve), fractional blood plasma volume (Vp), rate constant between the extravascular extracellular space and blood plasma (Kep) and area under the curve (AUC)) were calculated. Differences in each parameter between gliomas with different expression states (IDH, MGMT, and TERT) were evaluated. The diagnostic efficiency of each parameter was analyzed. The overall survival of all patients was assessed. RESULTS The 10th percentile AUC (AUC = 0.830, sensitivity = 0.78, specificity = 0.80), the 90th percentile Ve (AUC = 0.816, sensitivity = 0.84, specificity = 0.79), and the mean Kep (AUC = 0.818, sensitivity = 0.76, specificity = 0.78) provided the highest differential efficiency for IDH, MGMT, and TERT, respectively. Kaplan-Meier curves showed a significant difference between subjects with a 10th percentile AUC higher or lower than 0.028 (log-rank = 7.535; p = 0.006) for IDH and between subjects with different 90th percentile Ve values (log-rank = 6.532; p = 0.011) for MGMT. CONCLUSION Histogram DCE-MRI demonstrates good diagnostic performance in identifying different molecular types and for the prognostic assessment of glioma.
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Affiliation(s)
- Han-Wen Zhang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
| | - Gui-Wen Lyu
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
| | - Wen-Jie He
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
| | - Yi Lei
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China.
| | - Fan Lin
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
| | - Meng-Zhu Wang
- Department of MR Scientific Marketing, Siemens Healthineers, Guangzhou, Guangdong Province, China
| | - Hong Zhang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
| | - Li-Hong Liang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
| | - Yu-Ning Feng
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen 518035, China
| | - Ji-Hu Yang
- Department of Neurosurgery, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
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12
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Meyer HJ, Schneider I, Emmer A, Kornhuber M, Surov A. Associations between magnetic resonance imaging and EMG findings in myopathies. Acta Neurol Scand 2020; 142:428-433. [PMID: 32436228 DOI: 10.1111/ane.13284] [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: 03/27/2020] [Revised: 05/03/2020] [Accepted: 05/14/2020] [Indexed: 11/27/2022]
Abstract
OBJECTIVES Magnetic resonance imaging (MRI) is a cornerstone in diagnosis of myopathies. The present study sought to elucidate possible associations between electromyography (EMG) findings and histogram parameters derived from clinical MRI in myositis and other myopathies. MATERIALS AND METHODS Twenty six patients with myopathies were included in this retrospective study. Clinical MRI was performed with a 1.5T MRI scanner including T2- and T1-weighted images. EMG analysis was performed during clinical diagnostic workup. The histogram parameters of the MRI sequences were obtained of the same muscle, which was investigated with EMG. RESULTS Several correlations were identified between mean duration of the motor unit potentials (MUP) and histogram parameters derived from T1- and T2-weighted images. The highest for T1-weighted images was mode (r = -.73, P < .0001) and for T2-weighted images was p25 (r = -.57, P = .022). There were significant differences for several histogram parameters between muscles with pathological spontaneous activity and without. So, for T1-weighted images, the best discrimination was achieved with mean (P = .096), and for T2-weighted images for p10 (P = .05). Mean SI values derived from T1-weighted images achieved an AUC of 0.84 with a sensitivity of 0.81 and a specificity of 0.86 to discriminate patients with and without pathological spontaneous activity (PSA). CONCLUSIONS The present study identified strong associations between histogram analysis derived from morphological MRI sequences and the duration of the MUP derived from EMG in myopathies strengthening the fact that both diagnostic modalities can reflect disease state in a similar fashion. Histogram parameters can predict muscles with PSA.
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Affiliation(s)
- Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Ilka Schneider
- Department of Neurology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Alexander Emmer
- Department of Neurology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Malte Kornhuber
- Department of Neurology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Magdeburg, Magdeburg, Germany
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13
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Latysheva A, Geier OM, Hope TR, Brunetti M, Micci F, Vik-Mo EO, Emblem KE, Server A. Diagnostic utility of Restriction Spectrum Imaging in the characterization of the peritumoral brain zone in glioblastoma: Analysis of overall and progression-free survival. Eur J Radiol 2020; 132:109289. [PMID: 33002815 DOI: 10.1016/j.ejrad.2020.109289] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Revised: 09/07/2020] [Accepted: 09/13/2020] [Indexed: 11/28/2022]
Abstract
PURPOSE We studied the ability of Restriction Spectrum Imaging (RSI), a novel advanced diffusion imaging technique, to estimate levels of cellularity in different glioblastoma regions, evaluated their prognostic value compared with established clinical diffusion metrics such as fractional anisotropy (FA) and mean diffusivity (MD). METHODS Forty-two patients with untreated glioblastoma, IDH-wildtype, were examined with an advanced MRI tumor protocol. The region of interest (ROI) was obtained from the contrast-enhancing part of tumor and the peritumoral brain zones and then co-registered with RSI-cellularity index, FA and MD maps. Histogram parameters of diffusion metrics were assessed for all ROI locations and compared to MGMT promoter methylation status and survival. The ability of RSI-cellularity index, FA, and MD to stratify survival and were assessed by Cox proportional hazard regression, adjusted for significant clinical predictors. RESULTS The highest RSI-cellularity index was measured in contrast-enhancing tumor core with a negative gradient from tumor core to the periphery of peritumoral zone with predictive accuracy 81 % (P < 0.001). Shorter overall survival was significant associated with higher RSI-cellularity index (hazard ratio (HR) 3.6, 95 % confidence interval (CI) 1.3-9.5, P = 0.002) with synchronal decrease in MD (HR 0.31, 95 %CI 0.1-0.8, P = 0.008) in the contrast-enhanced tumor core. This association was also consistent for RSI-cellularity index value measured in the peri-enhancing zone (HR 3.6, 95 % CI 1.0-12.3, P = 0.041). No statistically significant differences were noted between RSI-cellularity index, FA, nor MD and MGMT promoter methylation. CONCLUSION RSI-cellularity index may be used as prognostic biomarker to improve risk stratification in patients with glioblastoma.
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Affiliation(s)
- Anna Latysheva
- Section of Neuroradiology, Department of Radiology, Oslo University Hospital - Rikshospitalet, Oslo, Norway; Faculty of Medicine, University of Oslo, Oslo, Norway.
| | - Oliver Marcel Geier
- Department of Diagnostic Physics, Oslo University Hospital - Rikshospitalet, Oslo, Norway
| | - Tuva R Hope
- Department of Diagnostic Physics, Oslo University Hospital - Rikshospitalet, Oslo, Norway
| | - Marta Brunetti
- Section for Cancer Cytogenetics, Institute for Cancer Genetics and Informatics, Oslo University Hospital Rikshospitalet, Oslo, Norway
| | - Francesca Micci
- Section for Cancer Cytogenetics, Institute for Cancer Genetics and Informatics, Oslo University Hospital Rikshospitalet, Oslo, Norway
| | - Einar Osland Vik-Mo
- Department of Neurosurgery, Oslo University Hospital - Rikshospitalet, Oslo, Norway
| | - Kyrre E Emblem
- Department of Diagnostic Physics, Oslo University Hospital - Rikshospitalet, Oslo, Norway
| | - Andrés Server
- Section of Neuroradiology, Department of Radiology, Oslo University Hospital - Rikshospitalet, Oslo, Norway
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14
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Chelebian E, Fuster-Garcia E, Álvarez-Torres MDM, Juan-Albarracín J, García-Gómez JM. Higher vascularity at infiltrated peripheral edema differentiates proneural glioblastoma subtype. PLoS One 2020; 15:e0232500. [PMID: 33052913 PMCID: PMC7556526 DOI: 10.1371/journal.pone.0232500] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Accepted: 09/29/2020] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND AND PURPOSE Genetic classifications are crucial for understanding the heterogeneity of glioblastoma. Recently, perfusion MRI techniques have demonstrated associations molecular alterations. In this work, we investigated whether perfusion markers within infiltrated peripheral edema were associated with proneural, mesenchymal, classical and neural subtypes. MATERIALS AND METHODS ONCOhabitats open web services were used to obtain the cerebral blood volume at the infiltrated peripheral edema for MRI studies of 50 glioblastoma patients from The Cancer Imaging Archive: TCGA-GBM. ANOVA and Kruskal-Wallis tests were carried out in order to assess the association between vascular features and the Verhaak subtypes. For assessing specific differences, Mann-Whitney U-test was conducted. Finally, the association of overall survival with molecular and vascular features was assessed using univariate and multivariate Cox models. RESULTS ANOVA and Kruskal-Wallis tests for the maximum cerebral blood volume at the infiltrated peripheral edema between the four subclasses yielded false discovery rate corrected p-values of <0.001 and 0.02, respectively. This vascular feature was significantly higher (p = 0.0043) in proneural patients compared to the rest of the subtypes while conducting Mann-Whitney U-test. The multivariate Cox model pointed to redundant information provided by vascular features at the peripheral edema and proneural subtype when analyzing overall survival. CONCLUSIONS Higher relative cerebral blood volume at infiltrated peripheral edema is associated with proneural glioblastoma subtype suggesting underlying vascular behavior related to molecular composition in that area.
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Affiliation(s)
- Eduard Chelebian
- Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politècnica de València, València, Spain.,Department of Information Technology, Uppsala University, Uppsala, Sweden
| | | | - María Del Mar Álvarez-Torres
- Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politècnica de València, València, Spain
| | - Javier Juan-Albarracín
- Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politècnica de València, València, Spain
| | - Juan M García-Gómez
- Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politècnica de València, València, Spain
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15
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Brendle C, Klose U, Hempel JM, Schittenhelm J, Skardelly M, Tabatabai G, Ernemann U, Bender B. Association of dynamic susceptibility magnetic resonance imaging at initial tumor diagnosis with the prognosis of different molecular glioma subtypes. Neurol Sci 2020; 41:3625-3632. [PMID: 32462389 PMCID: PMC8203510 DOI: 10.1007/s10072-020-04474-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 05/15/2020] [Indexed: 12/17/2022]
Abstract
Purpose The updated 2016 CNS World Health Organization classification differentiates three main groups of diffuse glioma according to their molecular characteristics: astrocytic tumors with and without isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deleted oligodendrogliomas. The present study aimed to determine whether dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) is an independent prognostic marker within the molecular subgroups of diffuse glioma. Methods Fifty-six patients with treatment-naive gliomas and advanced preoperative MRI examination were assessed retrospectively. The mean and maximal normalized cerebral blood volume values from DSC-MRI within the tumors were measured. Optimal cutoff values for the 1-year progression-free survival (PFS) were defined, and Kaplan-Meier analyses were performed separately for the three glioma subgroups. Results IDH wild-type astrocytic tumors had a higher mean and maximal perfusion than IDH-mutant astrocytic tumors and oligodendrogliomas. Patients with IDH wild-type astrocytic tumors and a low mean or maximal perfusion had a significantly shorter PFS than patients of the same group with high perfusion (p = 0.0159/0.0112). Furthermore, they had a significantly higher risk for early progression (hazard ratio = 5.6/5.1). This finding was independent of the methylation status of O6-methylguanin-DNA-methyltransferase and variations of the therapy. Within the groups of IDH-mutant astrocytic tumors and oligodendrogliomas, the PFS of low and highly perfused tumors did not differ. Conclusion High perfusion upon initial diagnosis is not compellingly associated with worse short-term prognosis within the different molecular subgroups of diffuse glioma. Particularly, the overall highly perfused group of IDH wild-type astrocytic tumors contains tumors with low perfusion but unfavorable prognosis.
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Affiliation(s)
- Cornelia Brendle
- Diagnostic and Interventional Neuroradiology, Department of Radiology, Eberhard Karls University, Hoppe-Seyler-Straße 3, 72076, Tuebingen, Germany.
| | - Uwe Klose
- Diagnostic and Interventional Neuroradiology, Department of Radiology, Eberhard Karls University, Hoppe-Seyler-Straße 3, 72076, Tuebingen, Germany
| | - Johann-Martin Hempel
- Diagnostic and Interventional Neuroradiology, Department of Radiology, Eberhard Karls University, Hoppe-Seyler-Straße 3, 72076, Tuebingen, Germany
| | - Jens Schittenhelm
- Neuropathology, Department of Pathology and Neuropathology, Eberhard Karls University, Calwerstr. 3, 72076, Tuebingen, Germany
| | - Marco Skardelly
- University Hospital for Neurosurgery, Eberhard Karls University, Hoppe-Seyler-Straße 3, 72076, Tuebingen, Germany
| | - Ghazaleh Tabatabai
- Interdisciplinary Section of Neurooncology, Eberhard Karls University, Hoppe-Seyler-Straße 3, 72076, Tuebingen, Germany
| | - Ulrike Ernemann
- Diagnostic and Interventional Neuroradiology, Department of Radiology, Eberhard Karls University, Hoppe-Seyler-Straße 3, 72076, Tuebingen, Germany
| | - Benjamin Bender
- Diagnostic and Interventional Neuroradiology, Department of Radiology, Eberhard Karls University, Hoppe-Seyler-Straße 3, 72076, Tuebingen, Germany
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16
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Tabibkhooei A, Izadpanahi M, Arab A, Zare-Mirzaei A, Minaeian S, Rostami A, Mohsenian A. Profiling of novel circulating microRNAs as a non-invasive biomarker in diagnosis and follow-up of high and low-grade gliomas. Clin Neurol Neurosurg 2019; 190:105652. [PMID: 31896490 DOI: 10.1016/j.clineuro.2019.105652] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Revised: 12/24/2019] [Accepted: 12/26/2019] [Indexed: 12/26/2022]
Abstract
OBJECTIVE Glioblastoma (GBM) is the most common primary malignant neoplasm of the central nervous system (CNS). Despite the progress in therapeutic strategies such as surgical techniques, radiotherapy, chemotherapy, and targeted therapy, prognosis and therapeutically convenient monitoring tools in patients with GBM has not improved significantly up to now.Therefore, exosomal miRNAs as novel non-invasive biomarkers having high sensitivity and specificity are required to improve diagnosis and to develop new targeted therapy strategies for GBM patients. The aim of the present study was to investigate a novel miRNA signature as a predictive biomarker for diagnosis and measurement of response to therapeutic interventions in plasma of GBM patients versus traumatic brain injury and diffuse low-grade astrocytoma (LGA) patients. PATIENTS AND METHODS Plasma exosomal-microRNAs were isolated from GBM (n = 25), LGA (n = 25), and head trauma patients (n = 15) as non-glioma control from March 2017 to June 2018 in Department of Neurosurgery at Rasoul-e-Akram Hospital. Through a bioinformatics analysis, we used Miranda, TargetScan, mirBase, DIANA-microT-CDS, and KEGG database as well as microarray data analysis from GEO for microRNA candidates. Finally, miR-210, miR-185, miR-5194, and miR-449 were selected among those miRNAs because they were recorded to target the maximum number of genes in EGFR and c-MET signaling pathways. Then, exosomal microRNAs were extracted from plasma of patients and quantitated by locked nucleic acid real-time PCR in GBM, LGA, and trauma patients. RESULTS This result is the first report on the role of circulating miR-185, miR-449, and miR-5194 in GBM compared to LGA and trauma. The plasma expression of miR-210 as an oncogenic miR was upregulated in GBM and LGA groups (P < 0.0001). Otherwise, miR-185, miR-5194, and miR-449 were significantly downregulated (P ≤ 0.05) in GBM and LGA compared to trauma patients. There was no significant downregulation in the expression of miR-185 between GBM and LGA, while the expression of miR-5194 (P ≤ 0.05) and miR-449 (P ≤ 0.05) was significantly decreased in GBM patients compared with LGA. CONCLUSIONS These results indicate that the levels of miR-210, miR-449, and miR-5194 are a promising diagnostic and prognostic biomarker positively correlated with histopathological grade and invasiveness of GBM. These findings imply that circulating microRNA can be potentially used as novel biomarkers for glioma that might be beneficial in clinical management of glioma patients.
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Affiliation(s)
- Alireza Tabibkhooei
- Department of Neurosurgery, Iran University of Medical Sciences, Tehran, Iran.
| | - Maryam Izadpanahi
- Department of Neurosurgery, Iran University of Medical Sciences, Tehran, Iran.
| | - Abolfazl Arab
- Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
| | - Ali Zare-Mirzaei
- Department of Pathology, Iran University of Medical Sciences, Tehran, Iran
| | - Sara Minaeian
- Antimicrobial Resistance Research Center, Institute of Immunology and Infection Diseases, Iran University of Medical Sciences, Tehran, Iran
| | - Ali Rostami
- Department of Neurosurgery, Iran University of Medical Sciences, Tehran, Iran
| | - Alireza Mohsenian
- Department of Neurosurgery, Iran University of Medical Sciences, Tehran, Iran
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Chen X, Fang M, Dong D, Liu L, Xu X, Wei X, Jiang X, Qin L, Liu Z. Development and Validation of a MRI-Based Radiomics Prognostic Classifier in Patients with Primary Glioblastoma Multiforme. Acad Radiol 2019; 26:1292-1300. [PMID: 30660472 DOI: 10.1016/j.acra.2018.12.016] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 12/06/2018] [Accepted: 12/19/2018] [Indexed: 12/15/2022]
Abstract
RATIONALE AND OBJECTIVES Glioblastoma multiforme (GBM) is the most common and deadly type of primary malignant tumor of the central nervous system. Accurate risk stratification is vital for a more personalized approach in GBM management. The purpose of this study is to develop and validate a MRI-based prognostic quantitative radiomics classifier in patients with newly diagnosed GBM and to evaluate whether the classifier allows stratification with improved accuracy over the clinical and qualitative imaging features risk models. METHODS Clinical and MR imaging data of 127 GBM patients were obtained from the Cancer Genome Atlas and the Cancer Imaging Archive. Regions of interest corresponding to high signal intensity portions of tumor were drawn on postcontrast T1-weighted imaging (post-T1WI) on the 127 patients (allocated in a 2:1 ratio into a training [n = 85] or validation [n = 42] set), then 3824 radiomics features per patient were extracted. The dimension of these radiomics features were reduced using the minimum redundancy maximum relevance algorithm, then Cox proportional hazard regression model was used to build a radiomics classifier for predicting overall survival (OS). The value of the radiomics classifier beyond clinical (gender, age, Karnofsky performance status, radiation therapy, chemotherapy, and type of resection) and VASARI features for OS was assessed with multivariate Cox proportional hazards model. Time-dependent receiver operating characteristic curve analysis was used to assess the predictive accuracy. RESULTS A classifier using four post-T1WI-MRI radiomics features built on the training dataset could successfully separate GBM patients into low- or high-risk group with a significantly different OS in training (HR, 6.307 [95% CI, 3.475-11.446]; p < 0.001) and validation set (HR, 3.646 [95% CI, 1.709-7.779]; p < 0.001). The area under receiver operating characteristic curve of radiomics classifier (training, 0.799; validation, 0.815 for 12-month) was higher compared to that of the clinical risk model (Karnofsky performance status, radiation therapy; training, 0.749; validation, 0.670 for 12-month), and none of the qualitative imaging features was associated with OS. The predictive accuracy was further improved when combined the radiomics classifier with clinical data (training, 0.819; validation: 0.851 for 12-month). CONCLUSION A classifier using radiomics features allows preoperative prediction of survival and risk stratification of patients with GBM, and it shows improved performance compared to that of clinical and qualitative imaging features models.
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Affiliation(s)
- Xin Chen
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China; Department of Radiology, Harvard Medical School, Boston 02115, Massachusetts
| | - Mengjie Fang
- Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Di Dong
- Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Lingling Liu
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xiangdong Xu
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xinhua Wei
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xinqing Jiang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Lei Qin
- Department of Imaging, Dana-Farber Cancer Institute, Boston 02115, Massachusetts; Department of Radiology, Harvard Medical School, Boston 02115, Massachusetts.
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
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