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Pang JHW, Saffari SE, Lee GR, Yu WY, Lim CCT, Lim KC, Lee CC, Koh WY, Chia WTD, Chua KLM, Tham CK, Low YYS, Ng WH, Low CYD, Lin X. Tumour growth rate predicts overall survival in patients with recurrent WHO grade 4 glioma. BMC Med Imaging 2024; 24:125. [PMID: 38802734 PMCID: PMC11131225 DOI: 10.1186/s12880-024-01263-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 03/27/2024] [Indexed: 05/29/2024] Open
Abstract
PURPOSE Accurate prognostication may aid in the selection of patients who will benefit from surgery at recurrent WHO grade 4 glioma. This study aimed to evaluate the role of serial tumour volumetric measurements for prognostication at first tumour recurrence. METHODS We retrospectively analyzed patients with histologically-diagnosed WHO grade 4 glioma at initial and at first tumour recurrence at a tertiary hospital between May 2000 and September 2018. We performed auto-segmentation using ITK-SNAP software, followed by manual adjustment to measure serial contrast-enhanced T1W (CE-T1W) and T2W lesional volume changes on all MRI images performed between initial resection and repeat surgery. RESULTS Thirty patients met inclusion criteria; the median overall survival using Kaplan-Meier analysis from second surgery was 10.5 months. Seventeen (56.7%) patients received treatment post second surgery. Univariate cox regression analysis showed that greater rate of increase in lesional volume on CE-T1W (HR = 2.57; 95% CI [1.18, 5.57]; p = 0.02) in the last 2 MRI scans leading up to the second surgery was associated with a higher mortality likelihood. Patients with higher Karnofsky Performance Score (KPS) (HR = 0.97; 95% CI [0.95, 0.99]; p = 0.01) and who received further treatment following second surgery (HR = 0.43; 95% CI [0.19, 0.98]; p = 0.04) were shown to have a better survival. CONCLUSION Higher rate of CE-T1W lesional growth on the last 2 MRI images prior to surgery at recurrence was associated with increase mortality risk. A larger prospective study is required to determine and validate the threshold to distinguish rapidly progressive tumour with poor prognosis.
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Affiliation(s)
- Jeffer Hann Wei Pang
- Department of Neurology, National Neuroscience Institute, 11 Jalan Tan Tock Seng, 308433, Singapore, Singapore
| | - Seyed Ehsan Saffari
- Department of Neurology, National Neuroscience Institute, 11 Jalan Tan Tock Seng, 308433, Singapore, Singapore
- Centre of Quantitative Medicine, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Guan Rong Lee
- Department of Neurology, National Neuroscience Institute, 11 Jalan Tan Tock Seng, 308433, Singapore, Singapore
| | - Wai-Yung Yu
- Department of Neuroradiology, National Neuroscience Institute, Singapore, Singapore
| | | | - Kheng Choon Lim
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore, Singapore
| | - Chia Ching Lee
- Division of Radiation Oncology, National University Cancer Institute, Singapore, Singapore
| | - Wee Yao Koh
- Division of Radiation Oncology, National University Cancer Institute, Singapore, Singapore
| | - Wei Tsau David Chia
- Division of Radiation Oncology, National University Cancer Institute, Singapore, Singapore
| | - Kevin Lee Min Chua
- Department of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Chee Kian Tham
- Department of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Yin Yee Sharon Low
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
| | - Wai Hoe Ng
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
| | - Chyi Yeu David Low
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
| | - Xuling Lin
- Department of Neurology, National Neuroscience Institute, 11 Jalan Tan Tock Seng, 308433, Singapore, Singapore.
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Differentiation of Glioblastoma and Brain Metastases by MRI-Based Oxygen Metabolomic Radiomics and Deep Learning. Metabolites 2022; 12:metabo12121264. [PMID: 36557302 PMCID: PMC9781524 DOI: 10.3390/metabo12121264] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/05/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Glioblastoma (GB) and brain metastasis (BM) are the most frequent types of brain tumors in adults. Their therapeutic management is quite different and a quick and reliable initial characterization has a significant impact on clinical outcomes. However, the differentiation of GB and BM remains a major challenge in today's clinical neurooncology due to their very similar appearance in conventional magnetic resonance imaging (MRI). Novel metabolic neuroimaging has proven useful for improving diagnostic performance but requires artificial intelligence for implementation in clinical routines. Here; we investigated whether the combination of radiomic features from MR-based oxygen metabolism ("oxygen metabolic radiomics") and deep convolutional neural networks (CNNs) can support reliably pre-therapeutic differentiation of GB and BM in a clinical setting. A self-developed one-dimensional CNN combined with radiomic features from the cerebral metabolic rate of oxygen (CMRO2) was clearly superior to human reading in all parameters for classification performance. The radiomic features for tissue oxygen saturation (mitoPO2; i.e., tissue hypoxia) also showed better diagnostic performance compared to the radiologists. Interestingly, both the mean and median values for quantitative CMRO2 and mitoPO2 values did not differ significantly between GB and BM. This demonstrates that the combination of radiomic features and DL algorithms is more efficient for class differentiation than the comparison of mean or median values. Oxygen metabolic radiomics and deep neural networks provide insights into brain tumor phenotype that may have important diagnostic implications and helpful in clinical routine diagnosis.
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Stadlbauer A, Marhold F, Oberndorfer S, Heinz G, Buchfelder M, Kinfe TM, Meyer-Bäse A. Radiophysiomics: Brain Tumors Classification by Machine Learning and Physiological MRI Data. Cancers (Basel) 2022; 14:2363. [PMID: 35625967 PMCID: PMC9139355 DOI: 10.3390/cancers14102363] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/04/2022] [Accepted: 05/09/2022] [Indexed: 01/06/2023] Open
Abstract
The precise initial characterization of contrast-enhancing brain tumors has significant consequences for clinical outcomes. Various novel neuroimaging methods have been developed to increase the specificity of conventional magnetic resonance imaging (cMRI) but also the increased complexity of data analysis. Artificial intelligence offers new options to manage this challenge in clinical settings. Here, we investigated whether multiclass machine learning (ML) algorithms applied to a high-dimensional panel of radiomic features from advanced MRI (advMRI) and physiological MRI (phyMRI; thus, radiophysiomics) could reliably classify contrast-enhancing brain tumors. The recently developed phyMRI technique enables the quantitative assessment of microvascular architecture, neovascularization, oxygen metabolism, and tissue hypoxia. A training cohort of 167 patients suffering from one of the five most common brain tumor entities (glioblastoma, anaplastic glioma, meningioma, primary CNS lymphoma, or brain metastasis), combined with nine common ML algorithms, was used to develop overall 135 classifiers. Multiclass classification performance was investigated using tenfold cross-validation and an independent test cohort. Adaptive boosting and random forest in combination with advMRI and phyMRI data were superior to human reading in accuracy (0.875 vs. 0.850), precision (0.862 vs. 0.798), F-score (0.774 vs. 0.740), AUROC (0.886 vs. 0.813), and classification error (5 vs. 6). The radiologists, however, showed a higher sensitivity (0.767 vs. 0.750) and specificity (0.925 vs. 0.902). We demonstrated that ML-based radiophysiomics could be helpful in the clinical routine diagnosis of contrast-enhancing brain tumors; however, a high expenditure of time and work for data preprocessing requires the inclusion of deep neural networks.
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Affiliation(s)
- Andreas Stadlbauer
- Institute of Medical Radiology, University Clinic St. Pölten, Karl Landsteiner University of Health Sciences, A-3100 St. Pölten, Austria;
- Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, D-91054 Erlangen, Germany; (M.B.); (T.M.K.)
| | - Franz Marhold
- Department of Neurosurgery, University Clinic of St. Pölten, Karl Landsteiner University of Health Sciences, A-3100 St. Pölten, Austria;
| | - Stefan Oberndorfer
- Department of Neurology, University Clinic of St. Pölten, Karl Landsteiner University of Health Sciences, A-3100 St. Pölten, Austria;
| | - Gertraud Heinz
- Institute of Medical Radiology, University Clinic St. Pölten, Karl Landsteiner University of Health Sciences, A-3100 St. Pölten, Austria;
| | - Michael Buchfelder
- Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, D-91054 Erlangen, Germany; (M.B.); (T.M.K.)
| | - Thomas M. Kinfe
- Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, D-91054 Erlangen, Germany; (M.B.); (T.M.K.)
- Division of Functional Neurosurgery and Stereotaxy, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, D-91054 Erlangen, Germany
| | - Anke Meyer-Bäse
- Department of Scientific Computing, Florida State University, 400 Dirac Science Library, Tallahassee, FL 32306-4120, USA;
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Metabolic Tumor Microenvironment Characterization of Contrast Enhancing Brain Tumors Using Physiologic MRI. Metabolites 2021; 11:metabo11100668. [PMID: 34677383 PMCID: PMC8537028 DOI: 10.3390/metabo11100668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 11/16/2022] Open
Abstract
The tumor microenvironment is a critical regulator of cancer development and progression as well as treatment response and resistance in brain neoplasms. The available techniques for investigation, however, are not well suited for noninvasive in vivo characterization in humans. A total of 120 patients (59 females; 61 males) with newly diagnosed contrast-enhancing brain tumors (64 glioblastoma, 20 brain metastases, 15 primary central nervous system (CNS) lymphomas (PCNSLs), and 21 meningiomas) were examined with a previously established physiological MRI protocol including quantitative blood-oxygen-level-dependent imaging and vascular architecture mapping. Six MRI biomarker maps for oxygen metabolism and neovascularization were fused for classification of five different tumor microenvironments: glycolysis, oxidative phosphorylation (OxPhos), hypoxia with/without neovascularization, and necrosis. Glioblastoma showed the highest metabolic heterogeneity followed by brain metastasis with a glycolysis-to-OxPhos ratio of approximately 2:1 in both tumor entities. In addition, glioblastoma revealed a significant higher percentage of hypoxia (24%) compared to all three other brain tumor entities: brain metastasis (7%; p < 0.001), PCNSL (8%; p = 0.001), and meningioma (8%; p = 0.003). A more aggressive biological brain tumor behavior was associated with a higher percentage of hypoxia and necrosis and a lower percentage of remaining vital tumor tissue and aerobic glycolysis. The proportion of oxidative phosphorylation, however, was rather similar (17–26%) for all four brain tumor entities. Tumor microenvironment (TME) mapping provides insights into neurobiological differences of contrast-enhancing brain tumors and deserves further clinical cancer research attention. Although there is a long roadmap ahead, TME mapping may become useful in order to develop new diagnostic and therapeutic approaches.
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Differentiating Glioblastomas from Solitary Brain Metastases: An Update on the Current Literature of Advanced Imaging Modalities. Cancers (Basel) 2021; 13:cancers13122960. [PMID: 34199151 PMCID: PMC8231515 DOI: 10.3390/cancers13122960] [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: 05/14/2021] [Revised: 06/09/2021] [Accepted: 06/09/2021] [Indexed: 12/12/2022] Open
Abstract
Differentiating between glioblastomas and solitary brain metastases proves to be a challenging diagnosis for neuroradiologists, as both present with imaging patterns consisting of peritumoral hyperintensities with similar intratumoral texture on traditional magnetic resonance imaging sequences. Early diagnosis is paramount, as each pathology has completely different methods of clinical assessment. In the past decade, recent developments in advanced imaging modalities enabled providers to acquire a more accurate diagnosis earlier in the patient's clinical assessment, thus optimizing clinical outcome. Dynamic susceptibility contrast has been optimized for detecting relative cerebral blood flow and relative cerebral blood volume. Diffusion tensor imaging can be used to detect changes in mean diffusivity. Neurite orientation dispersion and density imaging is an innovative modality detecting changes in intracellular volume fraction, isotropic volume fraction, and extracellular volume fraction. Magnetic resonance spectroscopy is able to assist by providing a metabolic descriptor while detecting variable ratios of choline/N-acetylaspartate, choline/creatine, and N-acetylaspartate/creatine. Finally, radiomics and machine learning algorithms have been devised to assist in improving diagnostic accuracy while often utilizing more than one advanced imaging protocol per patient. In this review, we provide an update on all the current evidence regarding the identification and differentiation of glioblastomas from solitary brain metastases.
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Bowers DC, Verbruggen LC, Kremer LCM, Hudson MM, Skinner R, Constine LS, Sabin ND, Bhangoo R, Haupt R, Hawkins MM, Jenkinson H, Khan RB, Klimo P, Pretorius P, Ng A, Reulen RC, Ronckers CM, Sadighi Z, Scheinemann K, Schouten-van Meeteren N, Sugden E, Teepen JC, Ullrich NJ, Walter A, Wallace WH, Oeffinger KC, Armstrong GT, van der Pal HJH, Mulder RL. Surveillance for subsequent neoplasms of the CNS for childhood, adolescent, and young adult cancer survivors: a systematic review and recommendations from the International Late Effects of Childhood Cancer Guideline Harmonization Group. Lancet Oncol 2021; 22:e196-e206. [PMID: 33845037 DOI: 10.1016/s1470-2045(20)30688-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/28/2020] [Accepted: 11/06/2020] [Indexed: 11/17/2022]
Abstract
Exposure to cranial radiotherapy is associated with an increased risk of subsequent CNS neoplasms among childhood, adolescent, and young adult (CAYA) cancer survivors. Surveillance for subsequent neoplasms can translate into early diagnoses and interventions that could improve cancer survivors' health and quality of life. The practice guideline presented here by the International Late Effects of Childhood Cancer Guideline Harmonization Group was developed with an evidence-based method that entailed the gathering and appraisal of published evidence associated with subsequent CNS neoplasms among CAYA cancer survivors. The preparation of these guidelines showed a paucity of high-quality evidence and highlighted the need for additional research to inform survivorship care. The recommendations are based on careful consideration of the evidence supporting the benefits, risks, and harms of the surveillance interventions, clinical judgment regarding individual patient circumstances, and the need to maintain flexibility of application across different health-care systems. Currently, there is insufficient evidence to establish whether early detection of subsequent CNS neoplasms reduces morbidity and mortality, and therefore no recommendation can be formulated for or against routine MRI surveillance. The decision to start surveillance should be made by the CAYA cancer survivor and health-care provider after careful consideration of the potential harms and benefits of surveillance for CNS neoplasms, including meningioma.
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Affiliation(s)
- Daniel C Bowers
- Division of Pediatric Hematology/Oncology, Harold C Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | | | | | - Melissa M Hudson
- Department of Oncology, St Jude Children's Research Hospital, Memphis, TN, USA; Department of Epidemiology and Cancer Control, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Roderick Skinner
- Department of Paediatric and Adolescent Haematology and Oncology, Great North Children's Hospital, Newcastle upon Tyne, UK; Translational and Clinical Research Institute, Newcastle University Centre for Cancer, Newcastle University, Newcastle upon Tyne, UK
| | - Louis S Constine
- Department of Radiation Oncology, Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, USA
| | - Noah D Sabin
- Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Ranjeev Bhangoo
- Neurosurgical Department, King's College Hospital Foundation Trust, London, UK
| | - Riccardo Haupt
- Epidemiology and Biostatistics Unit and DOPO Clinic, IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - Mike M Hawkins
- Centre for Childhood Cancer Survivor Studies, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Helen Jenkinson
- Department of Paediatric Oncology, Birmingham Children's Hospital, Birmingham, UK
| | - Raja B Khan
- Department of Pediatrics, Division of Neurology, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Paul Klimo
- Department of Neurosurgery, University of Tennessee, Memphis, TN, USA
| | - Pieter Pretorius
- Department of Neuroradiology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Antony Ng
- Department of Paediatric Oncology, Royal Hospital for Children, Bristol, UK
| | - Raoul C Reulen
- Centre for Childhood Cancer Survivor Studies, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Cécile M Ronckers
- Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands; Institute for Biostatistics and Registry Research, Brandenburg Medical School, Neuruppin, Germany
| | - Zsila Sadighi
- Department of Pediatrics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Katrin Scheinemann
- Division of Oncology-Hematology, Department of Pediatrics, Kantonsspital Aarau, Switzerland; Division of Hematology & Oncology, University Children's Hospital Basel, University of Basel, Switzerland; Department of Pediatrics, McMaster Children's Hospital, McMaster University, Hamilton, ON, Canada
| | | | | | - Jop C Teepen
- Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands
| | - Nicole J Ullrich
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrew Walter
- Department of Pediatrics, Sidney Kimmel Medical College, Thomas Jefferson University, Wilmington, DE, USA; Department of Pediatrics, A I duPont Hospital for Children, Wilmington, DE, USA
| | - W Hamish Wallace
- Department of Paediatric Oncology, Royal Hospital for Sick Children, Edinburgh, UK
| | - Kevin C Oeffinger
- Department of Community and Family Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Gregory T Armstrong
- Department of Oncology, St Jude Children's Research Hospital, Memphis, TN, USA; Department of Epidemiology and Cancer Control, St Jude Children's Research Hospital, Memphis, TN, USA
| | | | - Renée L Mulder
- Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands
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Yuksel U, Ogden M, Ozdemir A, Kisa U, Bakar B. Predictive diagnostic and/or prognostic biomarkers obtained from routine blood biochemistry in patients with solitary intracranial tumor. J Med Biochem 2021; 40:67-73. [PMID: 33584142 PMCID: PMC7857846 DOI: 10.5937/jomb0-24722] [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: 12/20/2019] [Accepted: 04/15/2020] [Indexed: 11/02/2022] Open
Abstract
Background Radiological and/or laboratory tests may be sometimes inadequate distinguishing glioblastoma from metastatic brain tumors. The aim of this study was to find possible predictive biomarkers produced from routine blood biochemistry analysis results evaluated preoperatively in each patient with solitary brain tumor in distinguishing glioblastoma from metastatic brain tumors as well as revealing short-term prognosis. Methods Patients admitted to neurosurgery clinic between January 2015 and September 2018 were included in this study and they were divided into GLIOMA (n=12) and METASTASIS (n=17) groups. Patients' data consisted of age, gender, Glasgow Coma Scale scores, duration of stay in hospital, Glasgow Outcome Scale (GOS) scores and histopathological examination reports, hemoglobin level, leukocyte, neutrophil, lymphocyte, monocyte, eosinophil, basophil and platelet count results, neutrophil-lymphocyte ratio and platelet-lymphocyte ratio values, C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) levels were evaluated preoperatively. Results The CRP levels of METASTASIS group (143.10 mg/L) were higher than those of GLIOMA group (23.90 mg/L); and it was 82% sensitive and 75% specific in distinguishing metastatic brain tumor from glioblastoma if CRP value was >55.00 mg/L. A positive correlation was determined between GOS score and hemoglobin level and between ESR and CRP values. However, GOS scores were negatively correlated with the ESR level and duration of stay in hospital. Conclusions Study results demonstrated that CRP values could be predictive biomarker in distinguishing metastatic brain tumor from glioblastoma. In addition, ESR, CRP, hemoglobin levels and duration of stay in hospital could be prognostic biomarkers in predicting short-term prognosis of patients with solitary brain tumor.
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Affiliation(s)
- Ulas Yuksel
- Kirikkale University, Faculty of Medicine, Department of Neurosurgery, Yahsihan, Kirikkale, Turkey
| | - Mustafa Ogden
- Kirikkale University, Faculty of Medicine, Department of Neurosurgery, Yahsihan, Kirikkale, Turkey
| | - Alemiddin Ozdemir
- Kirikkale University, Faculty of Medicine, Department of Neurosurgery, Yahsihan, Kirikkale, Turkey
| | - Ucler Kisa
- Kirikkale University, Faculty of Medicine, Department of Biochemistry, Kirikkale, Turkey
| | - Bulent Bakar
- Kirikkale University, Faculty of Medicine, Department of Neurosurgery, Yahsihan, Kirikkale, Turkey
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Olson JJ, Ryken TC. Congress of neurological surgeons systematic review and evidence-based clinical practice parameter guidelines for the treatment of adults with newly diagnosed glioblastoma: Introduction and Methods. J Neurooncol 2020; 150:87-93. [DOI: 10.1007/s11060-020-03593-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Accepted: 08/07/2020] [Indexed: 10/22/2022]
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Lundy P, Domino J, Ryken T, Fouke S, McCracken DJ, Ormond DR, Olson JJ. The role of imaging for the management of newly diagnosed glioblastoma in adults: a systematic review and evidence-based clinical practice guideline update. J Neurooncol 2020; 150:95-120. [DOI: 10.1007/s11060-020-03597-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 08/08/2020] [Indexed: 12/11/2022]
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Romeo V, Stanzione A, Ugga L, Cuocolo R, Cocozza S, Ioannidou E, Brunetti A, Bisdas S. A Critical Appraisal of the Quality of Glioma Imaging Guidelines Using the AGREE II Tool: A EuroAIM Initiative. Front Oncol 2019; 9:472. [PMID: 31231610 PMCID: PMC6566105 DOI: 10.3389/fonc.2019.00472] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Accepted: 05/16/2019] [Indexed: 12/24/2022] Open
Abstract
Background: Following the EuroAIM initiative to assess the quality of medical imaging guidelines by using the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument, we aimed to evaluate the quality of the current imaging guidelines in patients with gliomas. Methods: A literature search was conducted to identify eligible imaging guidelines considered in the management of adult patients with gliomas. The selected guidelines were evaluated using the AGREE II instrument by four independent appraisers. The agreement among the four appraisers was estimated using the intraclass correlation coefficient (ICC) analysis. Results: Seven guidelines were selected for the appraisal. Six out of the seven guidelines showed an average level of quality with only one showing a low quality. The highest scores were found in Domain 1 “Scope and purpose” (mean score = 81.2%) and Domain 4 “Clarity of presentation” (mean score = 77.6%). The remaining domains showed a low level of quality and, in particular, Domain 5 “Applicability” was the most critical with a mean score of 41.7%, mainly related to a minor attention to barriers and facilitators as well as costs and resources implications of applying the guidelines. The ICC analysis showed a very good agreement among the four appraisers with ICC values ranging from 0.907 to 0.993. Conclusions: The available guidelines on glioma imaging emerged as of average quality according to the AGREE II tool analysis. Based on these results, further efforts should be made in order to involve different professional bodies and stakeholders and increase patient and public involvement in any future guideline drafting as well as to improve the applicability of these guidelines into the clinical practice.
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Affiliation(s)
- Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Renato Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Sirio Cocozza
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Evangelia Ioannidou
- Medical School, University of Ioannina, Ioannina, Greece.,Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London, United Kingdom
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Sotirios Bisdas
- Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London, United Kingdom.,Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, United Kingdom
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Swinburne NC, Schefflein J, Sakai Y, Oermann EK, Titano JJ, Chen I, Tadayon S, Aggarwal A, Doshi A, Nael K. Machine learning for semi-automated classification of glioblastoma, brain metastasis and central nervous system lymphoma using magnetic resonance advanced imaging. ANNALS OF TRANSLATIONAL MEDICINE 2019; 7:232. [PMID: 31317002 DOI: 10.21037/atm.2018.08.05] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Differentiating glioblastoma, brain metastasis, and central nervous system lymphoma (CNSL) on conventional magnetic resonance imaging (MRI) can present a diagnostic dilemma due to the potential for overlapping imaging features. We investigate whether machine learning evaluation of multimodal MRI can reliably differentiate these entities. Methods Preoperative brain MRI including diffusion weighted imaging (DWI), dynamic contrast enhanced (DCE), and dynamic susceptibility contrast (DSC) perfusion in patients with glioblastoma, lymphoma, or metastasis were retrospectively reviewed. Perfusion maps (rCBV, rCBF), permeability maps (K-trans, Kep, Vp, Ve), ADC, T1C+ and T2/FLAIR images were coregistered and two separate volumes of interest (VOIs) were obtained from the enhancing tumor and non-enhancing T2 hyperintense (NET2) regions. The tumor volumes obtained from these VOIs were utilized for supervised training of support vector classifier (SVC) and multilayer perceptron (MLP) models. Validation of the trained models was performed on unlabeled cases using the leave-one-subject-out method. Head-to-head and multiclass models were created. Accuracies of the multiclass models were compared against two human interpreters reviewing conventional and diffusion-weighted MR images. Results Twenty-six patients enrolled with histopathologically-proven glioblastoma (n=9), metastasis (n=9), and CNS lymphoma (n=8) were included. The trained multiclass ML models discriminated the three pathologic classes with a maximum accuracy of 69.2% accuracy (18 out of 26; kappa 0.540, P=0.01) using an MLP trained with the VpNET2 tumor volumes. Human readers achieved 65.4% (17 out of 26) and 80.8% (21 out of 26) accuracies, respectively. Using the MLP VpNET2 model as a computer-aided diagnosis (CADx) for cases in which the human reviewers disagreed with each other on the diagnosis resulted in correct diagnoses in 5 (19.2%) additional cases. Conclusions Our trained multiclass MLP using VpNET2 can differentiate glioblastoma, brain metastasis, and CNS lymphoma with modest diagnostic accuracy and provides approximately 19% increase in diagnostic yield when added to routine human interpretation.
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Affiliation(s)
| | - Javin Schefflein
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yu Sakai
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eric Karl Oermann
- Department of Neurological Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joseph J Titano
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Iris Chen
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Amit Aggarwal
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Amish Doshi
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kambiz Nael
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Differentiation of solitary brain metastasis from glioblastoma multiforme: a predictive multiparametric approach using combined MR diffusion and perfusion. Neuroradiology 2015; 57:697-703. [DOI: 10.1007/s00234-015-1524-6] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2014] [Accepted: 03/24/2015] [Indexed: 10/23/2022]
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Linskey ME, Olson JJ, Mitchell LS, Kalkanis SN. Clinical practice guidelines in the AANS/CNS Section on Tumors: past, present and future directions. J Neurooncol 2014; 119:557-68. [DOI: 10.1007/s11060-014-1497-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2014] [Accepted: 05/29/2014] [Indexed: 12/18/2022]
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Akhavan-Sigari R, Baf MMF, Ariabod V, Rohde V, Rahighi S. Connection between Cell Phone use, p53 Gene Expression in Different Zones of Glioblastoma Multiforme and Survival Prognoses. Rare Tumors 2014; 6:5350. [PMID: 25276320 PMCID: PMC4178273 DOI: 10.4081/rt.2014.5350] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2014] [Revised: 06/16/2014] [Accepted: 07/03/2014] [Indexed: 12/16/2022] Open
Abstract
The aim of this paper is to investigate p53 gene expression in the central and peripheral zones of glioblastoma multiforme using a real-time reverse transcription polymerase chain reaction (RT-PCR) technique in patients who use cell phones ≥3 hours a day and determine its relationship to clinicopathological findings and overall survival. Sixty-three patients (38 males and 25 females), diagnosed with glioblastoma multiforme (GBM), underwent tumor resection between 2008 and 2011. Patient ages ranged from 25 to 88 years, with a mean age of 55. The levels of expression of p53 in the central and peripheral zone of the GBM were quantified by RT-PCR. Data on p53 gene expression from the central and peripheral zone, the related malignancy and the clinicopatholagical findings (age, gender, tumor location and size), as well as overall survival, were analyzed. Forty-one out of 63 patients (65%) with the highest level of cell phone use (≥3 hours/day) had higher mutant type p53 expression in the peripheral zone of the glioblastoma; the difference was statistically significant (P=0.034). Results from the present study on the use of mobile phones for ≥3 hours a day show a consistent pattern of increased risk for the mutant type of p53 gene expression in the peripheral zone of the glioblastoma, and that this increase was significantly correlated with shorter overall survival time. The risk was not higher for ipsilateral exposure. We found that the mutant type of p53 gene expression in the peripheral zone of the glioblastoma was increased in 65% of patients using cell phones ≥3 hours a day.
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Affiliation(s)
- Reza Akhavan-Sigari
- Department of Neurosurgery, University Medical Center of Göttingen, Georg-August University of Göttingen, Germany
| | | | - Vahid Ariabod
- Department of Pathology, Islamic Azad University, Medical Branch, Mashhad, Iran
| | - Veit Rohde
- Department of Neurosurgery, University Medical Center of Göttingen, Georg-August University of Göttingen, Germany
| | - Saeed Rahighi
- Department of Neurosurgery, Islamic Azad University, Medical Branch, Mashhad, Iran
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The role of imaging in the management of progressive glioblastoma : a systematic review and evidence-based clinical practice guideline. J Neurooncol 2014; 118:435-60. [PMID: 24715656 DOI: 10.1007/s11060-013-1330-0] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2013] [Accepted: 12/27/2013] [Indexed: 10/25/2022]
Abstract
QUESTION Which imaging techniques most accurately differentiate true tumor progression from pseudo-progression or treatment related changes in patients with previously diagnosed glioblastoma? TARGET POPULATION These recommendations apply to adults with previously diagnosed glioblastoma who are suspected of experiencing progression of the neoplastic process. RECOMMENDATIONS LEVEL II Magnetic resonance imaging with and without gadolinium enhancement is recommended as the imaging surveillance method to detect the progression of previously diagnosed glioblastoma. LEVEL II Magnetic resonance spectroscopy is recommended as a diagnostic method to differentiate true tumor progression from treatment-related imaging changes or pseudo-progression in patients with suspected progressive glioblastoma. LEVEL III The routine use of positron emission tomography to identify progression of glioblastoma is not recommended. LEVEL III Single-photon emission computed tomography imaging is recommended as a diagnostic method to differentiate true tumor progression from treatment-related imaging changes or pseudo-progression in patients with suspected progressive glioblastoma.
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Kong J, Cooper L, Moreno C, Wang F, Kurc T, Saltz J, Brat D. In silico analysis of nuclei in glioblastoma using large-scale microscopy images improves prediction of treatment response. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2011; 2011:87-90. [PMID: 22254257 PMCID: PMC3292262 DOI: 10.1109/iembs.2011.6089903] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In this paper, we present a complete and novel workflow for quantitative nuclear feature analysis of glioblastoma using high-throughput whole-slide microscopy image processing as it relates to treatment response and patient survival. With a complete suite of computer algorithms, large numbers of micro-anatomical structures, in this case nuclei, are analyzed and represented efficiently from whole-slide digitized images with numerical features. With regard to endpoints of treatment response, the computerized analysis presents a better discrimination than traditional neuropathologic review. As a result, this analysis method shows potential to facilitate a better understanding of disease progression and patients' response to therapy for glioblastoma.
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Affiliation(s)
- Jun Kong
- Emory University, Center for Comprehensive Informatics, Atlanta, GA 30322
| | - Lee Cooper
- Emory University, Center for Comprehensive Informatics, Atlanta, GA 30322
| | - Carlos Moreno
- Emory University, Dept. of Pathology and Lab Medicine, Atlanta, GA 30322
| | - Fusheng Wang
- Emory University, Center for Comprehensive Informatics, Atlanta, GA 30322
| | - Tahsin Kurc
- Emory University, Center for Comprehensive Informatics, Atlanta, GA 30322
| | - Joel Saltz
- Emory University, Center for Comprehensive Informatics, Atlanta, GA 30322
| | - Daniel Brat
- Emory University, Dept. of Pathology and Lab Medicine, Atlanta, GA 30322
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Diagnostic examination performance by using microvascular leakage, cerebral blood volume, and blood flow derived from 3-T dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in the differentiation of glioblastoma multiforme and brain metastasis. Neuroradiology 2010; 53:319-30. [PMID: 20625709 DOI: 10.1007/s00234-010-0740-3] [Citation(s) in RCA: 65] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2010] [Accepted: 06/23/2010] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Conventional magnetic resonance (MR) imaging has limited capacity to differentiate between glioblastoma multiforme (GBM) and metastasis. The purposes of this study were: (1) to compare microvascular leakage (MVL), cerebral blood volume (CBV), and blood flow (CBF) in the distinction of metastasis from GBM using dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging (DSC-MRI), and (2) to estimate the diagnostic accuracy of perfusion and permeability MR imaging. METHODS A prospective study of 61 patients (40 GBMs and 21 metastases) was performed at 3 T using DSC-MRI. Normalized rCBV and rCBF from tumoral (rCBVt, rCBFt), peri-enhancing region (rCBVe, rCBFe), and by dividing the value in the tumor by the value in the peri-enhancing region (rCBVt/e, rCBFt/e), as well as MVL were calculated. Hemodynamic and histopathologic variables were analyzed statistically and Spearman/Pearson correlations. Receiver operating characteristic curve analysis was performed for each of the variables. RESULTS The rCBVe, rCBFe, and MVL were significantly greater in GBMs compared with those of metastases. The optimal cutoff value for differentiating GBM from metastasis was 0.80 which implies a sensitivity of 95%, a specificity of 92%, a positive predictive value of 86%, and a negative predictive value of 97% for rCBVe ratio. We found a modest correlation between rCBVt and rCBFt ratios. CONCLUSION MVL measurements in GBMs are significantly higher than those in metastases. Statistically, both rCBVe, rCBVt/e and rCBFe, rCBFt/e were useful in differentiating between GBMs and metastases, supporting the hypothesis that perfusion MR imaging can detect infiltration of tumor cells in the peri-enhancing region.
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Management of newly diagnosed glioblastoma: guidelines development, value and application. J Neurooncol 2009; 93:1-23. [DOI: 10.1007/s11060-009-9838-z] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2009] [Accepted: 02/23/2009] [Indexed: 11/25/2022]
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