1
|
Liu J, Jakary A, Villanueva-Meyer JE, Butowski NA, Saloner D, Clarke JL, Taylor JW, Oberheim Bush NA, Chang SM, Xu D, Lupo JM. Automatic Brain Tissue and Lesion Segmentation and Multi-Parametric Mapping of Contrast-Enhancing Gliomas without the Injection of Contrast Agents: A Preliminary Study. Cancers (Basel) 2024; 16:1524. [PMID: 38672606 PMCID: PMC11049314 DOI: 10.3390/cancers16081524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/08/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
This study aimed to develop a rapid, 1 mm3 isotropic resolution, whole-brain MRI technique for automatic lesion segmentation and multi-parametric mapping without using contrast by continuously applying balanced steady-state free precession with inversion pulses throughout incomplete inversion recovery in a single 6 min scan. Modified k-means clustering was performed for automatic brain tissue and lesion segmentation using distinct signal evolutions that contained mixed T1/T2/magnetization transfer properties. Multi-compartment modeling was used to derive quantitative multi-parametric maps for tissue characterization. Fourteen patients with contrast-enhancing gliomas were scanned with this sequence prior to the injection of a contrast agent, and their segmented lesions were compared to conventionally defined manual segmentations of T2-hyperintense and contrast-enhancing lesions. Simultaneous T1, T2, and macromolecular proton fraction maps were generated and compared to conventional 2D T1 and T2 mapping and myelination water fraction mapping acquired with MAGiC. The lesion volumes defined with the new method were comparable to the manual segmentations (r = 0.70, p < 0.01; t-test p > 0.05). The T1, T2, and macromolecular proton fraction mapping values of the whole brain were comparable to the reference values and could distinguish different brain tissues and lesion types (p < 0.05), including infiltrating tumor regions within the T2-lesion. Highly efficient, whole-brain, multi-contrast imaging facilitated automatic lesion segmentation and quantitative multi-parametric mapping without contrast, highlighting its potential value in the clinic when gadolinium is contraindicated.
Collapse
Affiliation(s)
- Jing Liu
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.J.); (D.X.)
| | - Angela Jakary
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.J.); (D.X.)
| | - Javier E. Villanueva-Meyer
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.J.); (D.X.)
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA; (N.A.B.); (J.L.C.); (S.M.C.)
| | - Nicholas A. Butowski
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA; (N.A.B.); (J.L.C.); (S.M.C.)
| | - David Saloner
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.J.); (D.X.)
- Radiology Service, VA Medical Center, San Francisco, CA 94121, USA
| | - Jennifer L. Clarke
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA; (N.A.B.); (J.L.C.); (S.M.C.)
- Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA
| | - Jennie W. Taylor
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA; (N.A.B.); (J.L.C.); (S.M.C.)
- Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA
| | - Nancy Ann Oberheim Bush
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA; (N.A.B.); (J.L.C.); (S.M.C.)
- Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA
| | - Susan M. Chang
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA; (N.A.B.); (J.L.C.); (S.M.C.)
| | - Duan Xu
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.J.); (D.X.)
- UCSF/UC Berkeley Graduate Program in Bioengineering, University of California San Francisco and Berkeley, San Francisco, CA 94143, USA
| | - Janine M. Lupo
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.J.); (D.X.)
- UCSF/UC Berkeley Graduate Program in Bioengineering, University of California San Francisco and Berkeley, San Francisco, CA 94143, USA
| |
Collapse
|
2
|
Lin Y, Zhang Y, Wang D, Yang B, Shen YQ. Computer especially AI-assisted drug virtual screening and design in traditional Chinese medicine. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2022; 107:154481. [PMID: 36215788 DOI: 10.1016/j.phymed.2022.154481] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 09/14/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Traditional Chinese medicine (TCM), as a significant part of the global pharmaceutical science, the abundant molecular compounds it contains is a valuable potential source of designing and screening new drugs. However, due to the un-estimated quantity of the natural molecular compounds and diversity of the related problems drug discovery such as precise screening of molecular compounds or the evaluation of efficacy, physicochemical properties and pharmacokinetics, it is arduous for researchers to design or screen applicable compounds through old methods. With the rapid development of computer technology recently, especially artificial intelligence (AI), its innovation in the field of virtual screening contributes to an increasing efficiency and accuracy in the process of discovering new drugs. PURPOSE This study systematically reviewed the application of computational approaches and artificial intelligence in drug virtual filtering and devising of TCM and presented the potential perspective of computer-aided TCM development. STUDY DESIGN We made a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Then screening the most typical articles for our research. METHODS The systematic review was performed by following the PRISMA guidelines. The databases PubMed, EMBASE, Web of Science, CNKI were used to search for publications that focused on computer-aided drug virtual screening and design in TCM. RESULT Totally, 42 corresponding articles were included in literature reviewing. Aforementioned studies were of great significance to the treatment and cost control of many challenging diseases such as COVID-19, diabetes, Alzheimer's Disease (AD), etc. Computational approaches and AI were widely used in virtual screening in the process of TCM advancing, which include structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS). Besides, computational technologies were also extensively applied in absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction of candidate drugs and new drug design in crucial course of drug discovery. CONCLUSIONS The applications of computer and AI play an important role in the drug virtual screening and design in the field of TCM, with huge application prospects.
Collapse
Affiliation(s)
- Yumeng Lin
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - You Zhang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Dongyang Wang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Bowen Yang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Ying-Qiang Shen
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
| |
Collapse
|
3
|
Kataria P, Dogra A, Sharma T, Goyal B. Trends in DNN Model Based Classification and Segmentation of Brain Tumor Detection. Open Neuroimag J 2022. [DOI: 10.2174/18744400-v15-e2206290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Background:
Due to the complexities of scrutinizing and diagnosing brain tumors from MR images, brain tumor analysis has become one of the most indispensable concerns. Characterization of a brain tumor before any treatment, such as radiotherapy, requires decisive treatment planning and accurate implementation. As a result, early detection of brain tumors is imperative for better clinical outcomes and subsequent patient survival.
Introduction:
Brain tumor segmentation is a crucial task in medical image analysis. Because of tumor heterogeneity and varied intensity patterns, manual segmentation takes a long time, limiting the use of accurate quantitative interventions in clinical practice. Automated computer-based brain tumor image processing has become more valuable with technological advancement. With various imaging and statistical analysis tools, deep learning algorithms offer a viable option to enable health care practitioners to rule out the disease and estimate the growth.
Methods:
This article presents a comprehensive evaluation of conventional machine learning models as well as evolving deep learning techniques for brain tumor segmentation and classification.
Conclusion:
In this manuscript, a hierarchical review has been presented for brain tumor segmentation and detection. It is found that the segmentation methods hold a wide margin of improvement in the context of the implementation of adaptive thresholding and segmentation methods, the feature training and mapping requires redundancy correction, the input data training needs to be more exhaustive and the detection algorithms are required to be robust in terms of handling online input data analysis/tumor detection.
Collapse
|
4
|
Gao P, Shan W, Guo Y, Wang Y, Sun R, Cai J, Li H, Chan WS, Liu P, Yi L, Zhang S, Li W, Jiang T, He K, Wu Z. Development and Validation of a Deep Learning Model for Brain Tumor Diagnosis and Classification Using Magnetic Resonance Imaging. JAMA Netw Open 2022; 5:e2225608. [PMID: 35939301 PMCID: PMC9361083 DOI: 10.1001/jamanetworkopen.2022.25608] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
IMPORTANCE Deep learning may be able to use patient magnetic resonance imaging (MRI) data to aid in brain tumor classification and diagnosis. OBJECTIVE To develop and clinically validate a deep learning system for automated identification and classification of 18 types of brain tumors from patient MRI data. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study was conducted using MRI data collected between 2000 and 2019 from 37 871 patients. A deep learning system for segmentation and classification of 18 types of intracranial tumors based on T1- and T2-weighted images and T2 contrast MRI sequences was developed and tested. The diagnostic accuracy of the system was tested using 1 internal and 3 external independent data sets. The clinical value of the system was assessed by comparing the tumor diagnostic accuracy of neuroradiologists with vs without assistance of the proposed system using a separate internal test data set. Data were analyzed from March 2019 through February 2020. MAIN OUTCOMES AND MEASURES Changes in neuroradiologist clinical diagnostic accuracy in brain MRI scans with vs without the deep learning system were evaluated. RESULTS A deep learning system was trained among 37 871 patients (mean [SD] age, 41.6 [11.4] years; 18 519 women [48.9%]). It achieved a mean area under the receiver operating characteristic curve of 0.92 (95% CI, 0.84-0.99) on 1339 patients from 4 centers' data sets in diagnosis and classification of 18 types of tumors. Higher outcomes were found compared with neuroradiologists for accuracy and sensitivity and similar outcomes for specificity (for 300 patients in the Tiantan Hospital test data set: accuracy, 73.3% [95% CI, 67.7%-77.7%] vs 60.9% [95% CI, 46.8%-75.1%]; sensitivity, 88.9% [95% CI, 85.3%-92.4%] vs 53.4% [95% CI, 41.8%-64.9%]; and specificity, 96.3% [95% CI, 94.2%-98.4%] vs 97.9%; [95% CI, 97.3%-98.5%]). With the assistance of the deep learning system, the mean accuracy of neuroradiologists among 1166 patients increased by 12.0 percentage points, from 63.5% (95% CI, 60.7%-66.2%) without assistance to 75.5% (95% CI, 73.0%-77.9%) with assistance. CONCLUSIONS AND RELEVANCE These findings suggest that deep learning system-based automated diagnosis may be associated with improved classification and diagnosis of intracranial tumors from MRI data among neuroradiologists.
Collapse
Affiliation(s)
- Peiyi Gao
- National Center for Clinical Medicine of Neurological Diseases, Beijing, People’s Republic of China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Wei Shan
- National Center for Clinical Medicine of Neurological Diseases, Beijing, People’s Republic of China
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Yue Guo
- National Center for Clinical Medicine of Neurological Diseases, Beijing, People’s Republic of China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Yinyan Wang
- National Center for Clinical Medicine of Neurological Diseases, Beijing, People’s Republic of China
- China National Clinical Research Center for Neurological Diseases, Beijing Hanalytics Artificial Intelligence Research Center for Neurological Disorders Beijing, PR China
| | - Rujing Sun
- National Center for Clinical Medicine of Neurological Diseases, Beijing, People’s Republic of China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Jinxiu Cai
- National Center for Clinical Medicine of Neurological Diseases, Beijing, People’s Republic of China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Hao Li
- National Center for Clinical Medicine of Neurological Diseases, Beijing, People’s Republic of China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Wei Sheng Chan
- China National Clinical Research Center for Neurological Diseases, Beijing Hanalytics Artificial Intelligence Research Center for Neurological Disorders Beijing, PR China
| | - Pan Liu
- China National Clinical Research Center for Neurological Diseases, Beijing Hanalytics Artificial Intelligence Research Center for Neurological Disorders Beijing, PR China
| | - Lei Yi
- Medical Imaging Department, Shenzhen Second People’s Hospital/the First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, People’s Republic of China
| | - Shaosen Zhang
- National Center for Clinical Medicine of Neurological Diseases, Beijing, People’s Republic of China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Weihua Li
- Medical Imaging Department, Shenzhen Second People’s Hospital/the First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, People’s Republic of China
| | - Tao Jiang
- National Center for Clinical Medicine of Neurological Diseases, Beijing, People’s Republic of China
- China National Clinical Research Center for Neurological Diseases, Beijing Hanalytics Artificial Intelligence Research Center for Neurological Disorders Beijing, PR China
| | - Kunlun He
- Translational Medicine Laboratory, Chinese People's Liberation Army General Hospital, Beijing, People’s Republic of China
- Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese People's Liberation Army General Hospital, Beijing, People’s Republic of China
| | - Zhenzhou Wu
- National Center for Clinical Medicine of Neurological Diseases, Beijing, People’s Republic of China
- China National Clinical Research Center for Neurological Diseases, Beijing Hanalytics Artificial Intelligence Research Center for Neurological Disorders Beijing, PR China
| |
Collapse
|
5
|
Tandel GS, Tiwari A, Kakde OG. Performance optimisation of deep learning models using majority voting algorithm for brain tumour classification. Comput Biol Med 2021; 135:104564. [PMID: 34217980 DOI: 10.1016/j.compbiomed.2021.104564] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 06/08/2021] [Accepted: 06/08/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND Although biopsy is the gold standard for tumour grading, being invasive, this procedure also proves fatal to the brain. Thus, non-invasive methods for brain tumour grading are urgently needed. Here, a magnetic resonance imaging (MRI)-based non-invasive brain tumour grading method has been proposed using deep learning (DL) and machine learning (ML) techniques. METHOD Four clinically applicable datasets were designed. The four datasets were trained and tested on five DL-based models (convolutional neural networks), AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50, and five ML-based models, Support Vector Machine, K-Nearest Neighbours, Naïve Bayes, Decision Tree, and Linear Discrimination using five-fold cross-validation. A majority voting (MajVot)-based ensemble algorithm has been proposed to optimise the overall classification performance of five DL and five ML-based models. RESULTS The average accuracy improvement of four datasets using the DL-based MajVot algorithm against AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50 models was 2.02%, 1.11%, 1.04%, 2.67%, and 1.65%, respectively. Further, a 10.12% improvement was seen in the average accuracy of four datasets using the DL method against ML. Furthermore, the proposed DL-based MajVot algorithm was validated on synthetic face data and improved the male versus female face image classification accuracy by 2.88%, 0.71%, 1.90%, 2.24%, and 0.35% against AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50, respectively. CONCLUSION The proposed MajVot algorithm achieved promising results for brain tumour classification and is able to utilise the combined potential of multiple models.
Collapse
Affiliation(s)
- Gopal S Tandel
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, 440010, India.
| | - Ashish Tiwari
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, 440010, India.
| | - O G Kakde
- Indian Institute of Information Technology, Nagpur, 440006, India.
| |
Collapse
|
6
|
Gryska E, Schneiderman J, Björkman-Burtscher I, Heckemann RA. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ Open 2021; 11:e042660. [PMID: 33514580 PMCID: PMC7849889 DOI: 10.1136/bmjopen-2020-042660] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 01/09/2021] [Accepted: 01/12/2021] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVES Medical image analysis practices face challenges that can potentially be addressed with algorithm-based segmentation tools. In this study, we map the field of automatic MR brain lesion segmentation to understand the clinical applicability of prevalent methods and study designs, as well as challenges and limitations in the field. DESIGN Scoping review. SETTING Three databases (PubMed, IEEE Xplore and Scopus) were searched with tailored queries. Studies were included based on predefined criteria. Emerging themes during consecutive title, abstract, methods and whole-text screening were identified. The full-text analysis focused on materials, preprocessing, performance evaluation and comparison. RESULTS Out of 2990 unique articles identified through the search, 441 articles met the eligibility criteria, with an estimated growth rate of 10% per year. We present a general overview and trends in the field with regard to publication sources, segmentation principles used and types of lesions. Algorithms are predominantly evaluated by measuring the agreement of segmentation results with a trusted reference. Few articles describe measures of clinical validity. CONCLUSIONS The observed reporting practices leave room for improvement with a view to studying replication, method comparison and clinical applicability. To promote this improvement, we propose a list of recommendations for future studies in the field.
Collapse
Affiliation(s)
- Emilia Gryska
- Medical Radiation Sciences, Goteborgs universitet Institutionen for kliniska vetenskaper, Goteborg, Sweden
| | - Justin Schneiderman
- Sektionen för klinisk neurovetenskap, Goteborgs Universitet Institutionen for Neurovetenskap och fysiologi, Goteborg, Sweden
| | | | - Rolf A Heckemann
- Medical Radiation Sciences, Goteborgs universitet Institutionen for kliniska vetenskaper, Goteborg, Sweden
| |
Collapse
|
7
|
Oltra-Sastre M, Fuster-Garcia E, Juan-Albarracin J, Sáez C, Perez-Girbes A, Sanz-Requena R, Revert-Ventura A, Mocholi A, Urchueguia J, Hervas A, Reynes G, Font-de-Mora J, Muñoz-Langa J, Botella C, Aparici F, Marti-Bonmati L, Garcia-Gomez JM. Multi-parametric MR Imaging Biomarkers Associated to Clinical Outcomes in Gliomas: A Systematic Review. Curr Med Imaging 2020; 15:933-947. [PMID: 32008521 DOI: 10.2174/1573405615666190109100503] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Revised: 11/27/2018] [Accepted: 12/13/2018] [Indexed: 12/20/2022]
Abstract
PURPOSE To systematically review evidence regarding the association of multiparametric biomarkers with clinical outcomes and their capacity to explain relevant subcompartments of gliomas. MATERIALS AND METHODS Scopus database was searched for original journal papers from January 1st, 2007 to February 20th, 2017 according to PRISMA. Four hundred forty-nine abstracts of papers were reviewed and scored independently by two out of six authors. Based on those papers we analyzed associations between biomarkers, subcompartments within the tumor lesion, and clinical outcomes. From all the articles analyzed, the twenty-seven papers with the highest scores were highlighted to represent the evidence about MR imaging biomarkers associated with clinical outcomes. Similarly, eighteen studies defining subcompartments within the tumor region were also highlighted to represent the evidence of MR imaging biomarkers. Their reports were critically appraised according to the QUADAS-2 criteria. RESULTS It has been demonstrated that multi-parametric biomarkers are prepared for surrogating diagnosis, grading, segmentation, overall survival, progression-free survival, recurrence, molecular profiling and response to treatment in gliomas. Quantifications and radiomics features obtained from morphological exams (T1, T2, FLAIR, T1c), PWI (including DSC and DCE), diffusion (DWI, DTI) and chemical shift imaging (CSI) are the preferred MR biomarkers associated to clinical outcomes. Subcompartments relative to the peritumoral region, invasion, infiltration, proliferation, mass effect and pseudo flush, relapse compartments, gross tumor volumes, and highrisk regions have been defined to characterize the heterogeneity. For the majority of pairwise cooccurrences, we found no evidence to assert that observed co-occurrences were significantly different from their expected co-occurrences (Binomial test with False Discovery Rate correction, α=0.05). The co-occurrence among terms in the studied papers was found to be driven by their individual prevalence and trends in the literature. CONCLUSION Combinations of MR imaging biomarkers from morphological, PWI, DWI and CSI exams have demonstrated their capability to predict clinical outcomes in different management moments of gliomas. Whereas morphologic-derived compartments have been mostly studied during the last ten years, new multi-parametric MRI approaches have also been proposed to discover specific subcompartments of the tumors. MR biomarkers from those subcompartments show the local behavior within the heterogeneous tumor and may quantify the prognosis and response to treatment of gliomas.
Collapse
Affiliation(s)
- Miquel Oltra-Sastre
- Instituto de Aplicaciones de las Tecnologias de la Informaciony de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
| | - Elies Fuster-Garcia
- Instituto de Aplicaciones de las Tecnologias de la Informaciony de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
| | - Javier Juan-Albarracin
- Instituto de Aplicaciones de las Tecnologias de la Informaciony de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
| | - Carlos Sáez
- Instituto de Aplicaciones de las Tecnologias de la Informaciony de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
| | - Alexandre Perez-Girbes
- GIBI230 (Grupo de Investigacion Biomedica en Imagen), Instituto de Investigacion Sanitaria (IIS), Hospital la Fe, Valencia, Spain
| | | | | | - Antonio Mocholi
- Instituto de Aplicaciones de las Tecnologias de la Informaciony de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
| | - Javier Urchueguia
- Instituto de Aplicaciones de las Tecnologias de la Informaciony de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
| | - Antonio Hervas
- Instituto de Matematica Multidisciplinar (IMM), Universitat Politecnica de Valencia, Valencia, Spain
| | - Gaspar Reynes
- Grupo de Investigacion Clinica y Traslacional del Cancer, Instituto de Investigacion Sanitaria (IIS), Hospital la Fe, Valencia, Spain
| | - Jaime Font-de-Mora
- Grupo de Investigacion Clinica y Traslacional del Cancer, Instituto de Investigacion Sanitaria (IIS), Hospital la Fe, Valencia, Spain
| | - Jose Muñoz-Langa
- GIBI230 (Grupo de Investigacion Biomedica en Imagen), Instituto de Investigacion Sanitaria (IIS), Hospital la Fe, Valencia, Spain
| | - Carlos Botella
- GIBI230 (Grupo de Investigacion Biomedica en Imagen), Instituto de Investigacion Sanitaria (IIS), Hospital la Fe, Valencia, Spain
| | - Fernando Aparici
- GIBI230 (Grupo de Investigacion Biomedica en Imagen), Instituto de Investigacion Sanitaria (IIS), Hospital la Fe, Valencia, Spain
| | - Luis Marti-Bonmati
- GIBI230 (Grupo de Investigacion Biomedica en Imagen), Instituto de Investigacion Sanitaria (IIS), Hospital la Fe, Valencia, Spain
| | - Juan M Garcia-Gomez
- Instituto de Aplicaciones de las Tecnologias de la Informaciony de las Comunicaciones Avanzadas (ITACA), Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
| |
Collapse
|
8
|
Tandel GS, Balestrieri A, Jujaray T, Khanna NN, Saba L, Suri JS. Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm. Comput Biol Med 2020; 122:103804. [DOI: 10.1016/j.compbiomed.2020.103804] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 05/01/2020] [Accepted: 05/02/2020] [Indexed: 12/18/2022]
|
9
|
Artzi M, Liberman G, Blumenthal DT, Aizenstein O, Bokstein F, Ben Bashat D. Differentiation between vasogenic edema and infiltrative tumor in patients with high-grade gliomas using texture patch-based analysis. J Magn Reson Imaging 2018; 48:729-736. [PMID: 29314345 DOI: 10.1002/jmri.25939] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2017] [Accepted: 12/14/2017] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND High-grade gliomas (HGGs) induce both vasogenic edema and extensive infiltration of tumor cells, both of which present with similar appearance on conventional MRI. Using current radiological criteria, differentiation between these tumoral and nontumoral areas within the nonenhancing lesion area remains challenging. PURPOSE To use radiomics patch-based analysis, based on conventional MRI, for the classification of the nonenhancing lesion area in patients with HGG into tumoral and nontumoral components. STUDY TYPE Prospective. SUBJECTS In all, 179 MRI scans were obtained from 102 patients: 67 patients with HGG and 35 patients with brain metastases. A subgroup of 15 patients with HGG were scanned before and following administration of bevacizumab. FIELD STRENGTH/SEQUENCE Pre and postcontrast agent T1 -weighted-imaging (WI), T2 WI, FLAIR, diffusion-tensor-imaging (DTI), and dynamic-contrast-enhanced (DCE)-MRI at 3T. ASSESSMENT A total of 225 histograms and gray-level-co-occurrence matrix-based features were extracted from the nonenhancing lesion area. Tumoral volumes of interest (VOIs) were defined at the peritumoral area in patients with HGG; nontumoral VOIs were defined in patients with brain metastasis. Twenty machine-learning algorithms including support-vector-machine (SVM), k-nearest neighbor, decision-trees, and ensemble classifiers were tested. The best classifier was trained on the entire labeled data, and was used to classify the entire data. STATISTICAL TESTS Dimensional reduction was performed on the 225 features using principal component analysis. Classification results were evaluated based on the sensitivity, specificity, and accuracy of each of the 20 classifiers, first based on a training and testing dataset (80% of the labeled data) in a 5-fold manner, and next by applying the best classifier to the validation data (the remaining 20% of the labeled data). Results were additionally evaluated by assessing differences in dynamic-contrast-enhanced plasma-volume (vp ) and volume-transfer-constant (ktrans ) values between the two components using Mann-Whitney U-test/t-test. RESULTS The best classification into tumoral and nontumoral lesion components was obtained using a linear SVM classifier, with average accuracy of 87%, sensitivity 86%, and specificity of 89% (for the training and testing data). Significantly higher vp and ktrans values (P < 0.0001) were detected in the tumoral compared to the nontumoral component. Preliminary classification results in a subgroup of patients treated with bevacizumab demonstrated a reduction mainly in the nontumoral component following administration of bevacizumab, enabling early assessment of disease progression in some patients. DATA CONCLUSION A radiomics patch-based analysis enables classification of the nonenhancing lesion area in patients with HGG. Preliminary results were promising and the proposed method has the potential to assist in clinical decision-making and to improve therapy response assessment in patients with HGG. LEVEL OF EVIDENCE 1 Technical Efficacy Stage 4 J. Magn. Reson. Imaging 2018.
Collapse
Affiliation(s)
- Moran Artzi
- Functional Brain Center, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Gilad Liberman
- Department of Chemical Physics, Weizmann Institute, Rehovot, Israel
| | - Deborah T Blumenthal
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Neuro-Oncology Service, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Orna Aizenstein
- Functional Brain Center, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Felix Bokstein
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Neuro-Oncology Service, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Dafna Ben Bashat
- Functional Brain Center, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| |
Collapse
|
10
|
Mohan G, Subashini MM. MRI based medical image analysis: Survey on brain tumor grade classification. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.07.007] [Citation(s) in RCA: 196] [Impact Index Per Article: 32.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
11
|
Ngen EJ, Bar-Shir A, Jablonska A, Liu G, Song X, Ansari R, Bulte JWM, Janowski M, Pearl M, Walczak P, Gilad AA. Imaging the DNA Alkylator Melphalan by CEST MRI: An Advanced Approach to Theranostics. Mol Pharm 2016; 13:3043-53. [PMID: 27398883 DOI: 10.1021/acs.molpharmaceut.6b00130] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Brain tumors are among the most lethal types of tumors. Therapeutic response variability and failure in patients have been attributed to several factors, including inadequate drug delivery to tumors due to the blood-brain barrier (BBB). Consequently, drug delivery strategies are being developed for the local and targeted delivery of drugs to brain tumors. These drug delivery strategies could benefit from new approaches to monitor the delivery of drugs to tumors. Here, we evaluated the feasibility of imaging 4-[bis(2-chloroethyl)amino]-l-phenylalanine (melphalan), a clinically used DNA alkylating agent, using chemical exchange saturation transfer magnetic resonance imaging (CEST MRI), for theranostic applications. We evaluated the physicochemical parameters that affect melphalan's CEST contrast and demonstrated the feasibility of imaging the unmodified drug by saturating its exchangeable amine protons. Melphalan generated a CEST signal despite its reactivity in an aqueous milieu. The maximum CEST signal was observed at pH 6.2. This CEST contrast trend was then used to monitor therapeutic responses to melphalan in vitro. Upon cell death, the decrease in cellular pH from ∼7.4 to ∼6.4 caused an amplification of the melphalan CEST signal. This is contrary to what has been reported for other CEST contrast agents used for imaging cell death, where a decrease in the cellular pH following cell death results in a decrease in the CEST signal. Ultimately, this method could be used to noninvasively monitor melphalan delivery to brain tumors and also to validate therapeutic responses to melphalan clinically.
Collapse
Affiliation(s)
- Ethel J Ngen
- Division of Magnetic Resonance Research, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine , Baltimore, Maryland 21205, United States.,Cellular Imaging Section and Vascular Biology Program, Institute for Cellular Engineering, The Johns Hopkins University School of Medicine , Baltimore, Maryland 21205, United States
| | - Amnon Bar-Shir
- Division of Magnetic Resonance Research, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine , Baltimore, Maryland 21205, United States.,Cellular Imaging Section and Vascular Biology Program, Institute for Cellular Engineering, The Johns Hopkins University School of Medicine , Baltimore, Maryland 21205, United States
| | - Anna Jablonska
- Division of Magnetic Resonance Research, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine , Baltimore, Maryland 21205, United States.,Cellular Imaging Section and Vascular Biology Program, Institute for Cellular Engineering, The Johns Hopkins University School of Medicine , Baltimore, Maryland 21205, United States
| | - Guanshu Liu
- Division of Magnetic Resonance Research, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine , Baltimore, Maryland 21205, United States.,F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute , Baltimore, Maryland 21205, United States
| | - Xiaolei Song
- Division of Magnetic Resonance Research, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine , Baltimore, Maryland 21205, United States.,F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute , Baltimore, Maryland 21205, United States
| | | | - Jeff W M Bulte
- Division of Magnetic Resonance Research, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine , Baltimore, Maryland 21205, United States.,Cellular Imaging Section and Vascular Biology Program, Institute for Cellular Engineering, The Johns Hopkins University School of Medicine , Baltimore, Maryland 21205, United States.,F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute , Baltimore, Maryland 21205, United States
| | - Miroslaw Janowski
- Division of Magnetic Resonance Research, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine , Baltimore, Maryland 21205, United States.,Cellular Imaging Section and Vascular Biology Program, Institute for Cellular Engineering, The Johns Hopkins University School of Medicine , Baltimore, Maryland 21205, United States.,NeuroRepair Department, Mossakowski Medical Research Centre, PAS , 02106 Warsaw, Poland.,Department of Neurosurgery, Mossakowski Medical Research Centre, PAS , 02106 Warsaw, Poland
| | - Monica Pearl
- Division of Interventional Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Sciences, The Johns Hopkins University School of Medicine , Baltimore, Maryland 21205, United States.,Department of Radiology, Children's National Medical Center , Washington, D.C. 20010, United States
| | - Piotr Walczak
- Division of Magnetic Resonance Research, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine , Baltimore, Maryland 21205, United States.,Cellular Imaging Section and Vascular Biology Program, Institute for Cellular Engineering, The Johns Hopkins University School of Medicine , Baltimore, Maryland 21205, United States.,Department of Radiology, Faculty of Medical Sciences, University of Warmia and Mazury , Olsztyn, Poland
| | - Assaf A Gilad
- Division of Magnetic Resonance Research, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine , Baltimore, Maryland 21205, United States.,Cellular Imaging Section and Vascular Biology Program, Institute for Cellular Engineering, The Johns Hopkins University School of Medicine , Baltimore, Maryland 21205, United States.,F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute , Baltimore, Maryland 21205, United States
| |
Collapse
|
12
|
Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry. Sci Rep 2016; 6:23376. [PMID: 27001047 PMCID: PMC4802217 DOI: 10.1038/srep23376] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2015] [Accepted: 03/04/2016] [Indexed: 11/16/2022] Open
Abstract
Information about the size of a tumor and its temporal evolution is needed for diagnosis as well as treatment of brain tumor patients. The aim of the study was to investigate the potential of a fully-automatic segmentation method, called BraTumIA, for longitudinal brain tumor volumetry by comparing the automatically estimated volumes with ground truth data acquired via manual segmentation. Longitudinal Magnetic Resonance (MR) Imaging data of 14 patients with newly diagnosed glioblastoma encompassing 64 MR acquisitions, ranging from preoperative up to 12 month follow-up images, was analysed. Manual segmentation was performed by two human raters. Strong correlations (R = 0.83–0.96, p < 0.001) were observed between volumetric estimates of BraTumIA and of each of the human raters for the contrast-enhancing (CET) and non-enhancing T2-hyperintense tumor compartments (NCE-T2). A quantitative analysis of the inter-rater disagreement showed that the disagreement between BraTumIA and each of the human raters was comparable to the disagreement between the human raters. In summary, BraTumIA generated volumetric trend curves of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments comparable to estimates of human raters. These findings suggest the potential of automated longitudinal tumor segmentation to substitute manual volumetric follow-up of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments.
Collapse
|
13
|
Simi V, Joseph J. Segmentation of Glioblastoma Multiforme from MR Images – A comprehensive review. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2015. [DOI: 10.1016/j.ejrnm.2015.08.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
|
14
|
Lu-Emerson C, Duda DG, Emblem KE, Taylor JW, Gerstner ER, Loeffler JS, Batchelor TT, Jain RK. Lessons from anti-vascular endothelial growth factor and anti-vascular endothelial growth factor receptor trials in patients with glioblastoma. J Clin Oncol 2015; 33:1197-213. [PMID: 25713439 PMCID: PMC4517055 DOI: 10.1200/jco.2014.55.9575] [Citation(s) in RCA: 133] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Treatment of glioblastoma (GBM), the most common primary malignant brain tumor in adults, remains a significant unmet need in oncology. Historically, cytotoxic treatments provided little durable benefit, and tumors recurred within several months. This has spurred a substantial research effort to establish more effective therapies for both newly diagnosed and recurrent GBM. In this context, antiangiogenic therapy emerged as a promising treatment strategy because GBMs are highly vascular tumors. In particular, GBMs overexpress vascular endothelial growth factor (VEGF), a proangiogenic cytokine. Indeed, many studies have demonstrated promising radiographic response rates, delayed tumor progression, and a relatively safe profile for anti-VEGF agents. However, randomized phase III trials conducted to date have failed to show an overall survival benefit for antiangiogenic agents alone or in combination with chemoradiotherapy. These results indicate that antiangiogenic agents may not be beneficial in unselected populations of patients with GBM. Unfortunately, biomarker development has lagged behind in the process of drug development, and no validated biomarker exists for patient stratification. However, hypothesis-generating data from phase II trials that reveal an association between increased perfusion and/or oxygenation (ie, consequences of vascular normalization) and survival suggest that early imaging biomarkers could help identify the subset of patients who most likely will benefit from anti-VEGF agents. In this article, we discuss the lessons learned from the trials conducted to date and how we could potentially use recent advances in GBM biology and imaging to improve outcomes of patients with GBM who receive antiangiogenic therapy.
Collapse
Affiliation(s)
- Christine Lu-Emerson
- All authors, Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA
| | - Dan G Duda
- All authors, Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA
| | - Kyrre E Emblem
- All authors, Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA
| | - Jennie W Taylor
- All authors, Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA
| | - Elizabeth R Gerstner
- All authors, Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA
| | - Jay S Loeffler
- All authors, Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA
| | - Tracy T Batchelor
- All authors, Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA
| | - Rakesh K Jain
- All authors, Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA.
| |
Collapse
|
15
|
Artzi M, Blumenthal DT, Bokstein F, Nadav G, Liberman G, Aizenstein O, Ben Bashat D. Classification of tumor area using combined DCE and DSC MRI in patients with glioblastoma. J Neurooncol 2014; 121:349-57. [PMID: 25370705 DOI: 10.1007/s11060-014-1639-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2014] [Accepted: 10/18/2014] [Indexed: 10/24/2022]
Abstract
This study proposes an automatic method for identification and quantification of different tissue components: the non-enhanced infiltrative tumor, vasogenic edema and enhanced tumor areas, at the subject level, in patients with glioblastoma (GB) based on dynamic contrast enhancement (DCE) and dynamic susceptibility contrast (DSC) MRI. Nineteen MR data sets, obtained from 12 patients with GB, were included. Seven patients were scanned before and 8 weeks following bevacizumab initiation. Segmentation of the tumor area was performed based on the temporal data of DCE and DSC at the group-level using k-means algorithm, and further at the subject-level using support vector machines algorithm. The obtained components were associated to different tissues types based on their temporal characteristics, calculated perfusion and permeability values and MR-spectroscopy. The method enabled the segmentation of the tumor area into the enhancing permeable component; the non-enhancing hypoperfused component, associated with vasogenic edema; and the non-enhancing hyperperfused component, associated with infiltrative tumor. Good agreement was obtained between the group-level, unsupervised and subject-level, supervised classification results, with significant correlation (r = 0.93, p < 0.001) and average symmetric root-mean-square surface distance of 2.5 ± 5.1 mm. Longitudinal changes in the volumes of the three components were assessed alongside therapy. Tumor area segmentation using DCE and DSC can be used to differentiate between vasogenic edema and infiltrative tumors in patients with GB, which is of major clinical importance in therapy response assessment.
Collapse
Affiliation(s)
- Moran Artzi
- Functional Brain Center, The Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, 6 Weizman St, 64239, Tel Aviv, Israel
| | | | | | | | | | | | | |
Collapse
|
16
|
Keunen O, Taxt T, Grüner R, Lund-Johansen M, Tonn JC, Pavlin T, Bjerkvig R, Niclou SP, Thorsen F. Multimodal imaging of gliomas in the context of evolving cellular and molecular therapies. Adv Drug Deliv Rev 2014; 76:98-115. [PMID: 25078721 DOI: 10.1016/j.addr.2014.07.010] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2014] [Revised: 07/14/2014] [Accepted: 07/22/2014] [Indexed: 01/18/2023]
Abstract
The vast majority of malignant gliomas relapse after surgery and standard radio-chemotherapy. Novel molecular and cellular therapies are thus being developed, targeting specific aspects of tumor growth. While histopathology remains the gold standard for tumor classification, neuroimaging has over the years taken a central role in the diagnosis and treatment follow up of brain tumors. It is used to detect and localize lesions, define the target area for biopsies, plan surgical and radiation interventions and assess tumor progression and treatment outcome. In recent years the application of novel drugs including anti-angiogenic agents that affect the tumor vasculature, has drastically modulated the outcome of brain tumor imaging. To properly evaluate the effects of emerging experimental therapies and successfully support treatment decisions, neuroimaging will have to evolve. Multi-modal imaging systems with existing and new contrast agents, molecular tracers, technological advances and advanced data analysis can all contribute to the establishment of disease relevant biomarkers that will improve disease management and patient care. In this review, we address the challenges of glioma imaging in the context of novel molecular and cellular therapies, and take a prospective look at emerging experimental and pre-clinical imaging techniques that bear the promise of meeting these challenges.
Collapse
|
17
|
Artzi M, Bokstein F, Blumenthal DT, Aizenstein O, Liberman G, Corn BW, Ben Bashat D. Differentiation between vasogenic-edema versus tumor-infiltrative area in patients with glioblastoma during bevacizumab therapy: a longitudinal MRI study. Eur J Radiol 2014; 83:1250-1256. [PMID: 24809637 DOI: 10.1016/j.ejrad.2014.03.026] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2013] [Revised: 03/20/2014] [Accepted: 03/22/2014] [Indexed: 10/25/2022]
Abstract
BACKGROUND Treatment with bevacizumab is associated with substantial radiologic response in patients with glioblastoma (GB). However, following this initial response, changes in T2-weighted MRI signal may develop, suggesting an infiltrative pattern of tumor progression. The aim of this study was to differentiate between vasogenic-edema versus tumor-infiltrative area in GB patients. METHODS AND MATERIALS Fourteen patients with GB were longitudinally scanned, before and during intravenous bevacizumab therapy (5/10mg/kg every 2-weeks). A total of 40 MR scans including conventional, diffusion, dynamic susceptibility contrast, dynamic contrast enhancement imaging, and MR-spectroscopy (MRS) were analyzed. Classification of non-enhancing fluid-attenuation-inversion-recovery (FLAIR) area was performed based on mean diffusivity, cerebral blood volume and flow maps, and further characterized using multiple MRI parameters. RESULTS The non-enhancing FLAIR lesion area was classified into: vasogenic-edema, characterized by reduced perfusion and increased FLAIR values; or tumor-infiltrative area, characterized by increased perfusion. Tumor-infiltrative area demonstrated a higher malignant pattern on MRS compared to areas of vasogenic-edema. Substantial reductions of the enhanced T1-weighted (58 ± 10%) and hyperintense FLAIR (53 ± 9%) lesion volumes were detected mainly during the first weeks of therapy, with a shift to an infiltrative pattern of tumor progression thereafter, as detected by an increase in tumor-infiltrative area in the majority of patients, which correlated with progression-free survival (week 8: r=-0.86, p=0.003, week 16: r=-0.99, p=0.001). CONCLUSION Characterization of non-enhancing hyperintense FLAIR lesion area in GB patients can provide an MR-based biomarker, indicating a shift to an infiltrative progression pattern, and may improve therapy response assessment in patients following bevacizumab therapy.
Collapse
Affiliation(s)
- Moran Artzi
- Functional Brain Center, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Felix Bokstein
- Neuro-Oncology Service, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
| | | | - Orna Aizenstein
- Functional Brain Center, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
| | - Gilad Liberman
- Functional Brain Center, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Gonda Multidisciplinary Brain Research Center, Bar Ilan University, Ramat Gan, Israel.
| | - Benjamin W Corn
- Functional Brain Center, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Institute of Radiotherapy, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
| | - Dafna Ben Bashat
- Functional Brain Center, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
| |
Collapse
|
18
|
Zhang J, Barboriak DP, Hobbs H, Mazurowski MA. A fully automatic extraction of magnetic resonance image features in glioblastoma patients. Med Phys 2014; 41:042301. [DOI: 10.1118/1.4866218] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
|