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Parillo M, Vertulli D, Vaccarino F, Mallio CA, Beomonte Zobel B, Quattrocchi CC. The sensitivity of MIPs of 3D contrast-enhanced VIBE T1-weighted imaging for the detection of small brain metastases (≤ 5 mm) on 1.5 tesla MRI. Neuroradiol J 2024:19714009241260802. [PMID: 38861176 DOI: 10.1177/19714009241260802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024] Open
Abstract
OBJECTIVES To evaluate whether the use of Maximum Intensity Projection (MIP) images derived from contrast-enhanced 3D-T1-weighted volumetric interpolated breath-hold examination (VIBE) would allow more sensitive detection of small (≤5 mm) brain metastases (BM) compared with source as well as 2D-T1-weighted spin-echo (SE) images. METHODS We performed a single center retrospective study on subjects with BM who underwent 1.5 tesla brain magnetic resonance imaging. Two readers counted the number of small BM for each of the seven sets of contrast-enhanced images created: axial 2D-T1-weighted SE, 3D-T1-weighted VIBE, 2.5 mm-thick-MIP T1-weighted VIBE, and 5 mm-thick-MIP T1-weighted VIBE; sagittal 3D-T1-weighted VIBE, 2.5 mm-thick-MIP T1-weighted VIBE, and 5 mm-thick-MIP T1-weighted VIBE. Total number of lesions detected on each image type was compared. Sensitivity, the average rates of false negatives and false positives, and the mean discrepancy were evaluated. RESULTS A total of 403 small BM were identified in 49 patients. Significant differences were found: in the number of true positives and false negatives between the axial 2D-T1-weighted SE sequence and all other imaging techniques; in the number of false positives between the axial 2D-T1-weighted SE and the axial 3D-T1-weighted VIBE sequences. The two image types that combined offered the highest sensitivity were 2D-T1-weighted SE and axial 2.5 mm-thick-MIP T1-weighted VIBE. The axial 2D-T1-weighted SE sequence differed significantly in sensitivity from all other sequences. CONCLUSION MIP images did not show a significant difference in sensitivity for the detection of small BM compared with native images.
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Affiliation(s)
- Marco Parillo
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, Rome, Italy
- Research Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, Rome, Italy
| | - Daniele Vertulli
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, Rome, Italy
- Research Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, Rome, Italy
| | - Federica Vaccarino
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, Rome, Italy
- Research Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, Rome, Italy
| | - Carlo Augusto Mallio
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, Rome, Italy
- Research Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, Rome, Italy
| | - Bruno Beomonte Zobel
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, Rome, Italy
- Research Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, Rome, Italy
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Chartrand G, Emiliani RD, Pawlowski SA, Markel DA, Bahig H, Cengarle-Samak A, Rajakesari S, Lavoie J, Ducharme S, Roberge D. Automated Detection of Brain Metastases on T1-Weighted MRI Using a Convolutional Neural Network: Impact of Volume Aware Loss and Sampling Strategy. J Magn Reson Imaging 2022; 56:1885-1898. [PMID: 35624544 DOI: 10.1002/jmri.28274] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 05/13/2022] [Accepted: 05/13/2022] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Detection of brain metastases (BM) and segmentation for treatment planning could be optimized with machine learning methods. Convolutional neural networks (CNNs) are promising, but their trade-offs between sensitivity and precision frequently lead to missing small lesions. HYPOTHESIS Combining volume aware (VA) loss function and sampling strategy could improve BM detection sensitivity. STUDY TYPE Retrospective. POPULATION A total of 530 radiation oncology patients (55% women) were split into a training/validation set (433 patients/1460 BM) and an independent test set (97 patients/296 BM). FIELD STRENGTH/SEQUENCE 1.5 T and 3 T, contrast-enhanced three-dimensional (3D) T1-weighted fast gradient echo sequences. ASSESSMENT Ground truth masks were based on radiotherapy treatment planning contours reviewed by experts. A U-Net inspired model was trained. Three loss functions (Dice, Dice + boundary, and VA) and two sampling methods (label and VA) were compared. Results were reported with Dice scores, volumetric error, lesion detection sensitivity, and precision. A detected voxel within the ground truth constituted a true positive. STATISTICAL TESTS McNemar's exact test to compare detected lesions between models. Pearson's correlation coefficient and Bland-Altman analysis to compare volume agreement between predicted and ground truth volumes. Statistical significance was set at P ≤ 0.05. RESULTS Combining VA loss and VA sampling performed best with an overall sensitivity of 91% and precision of 81%. For BM in the 2.5-6 mm estimated sphere diameter range, VA loss reduced false negatives by 58% and VA sampling reduced it further by 30%. In the same range, the boundary loss achieved the highest precision at 81%, but a low sensitivity (24%) and a 31% Dice loss. DATA CONCLUSION Considering BM size in the loss and sampling function of CNN may increase the detection sensitivity regarding small BM. Our pipeline relying on a single contrast-enhanced T1-weighted MRI sequence could reach a detection sensitivity of 91%, with an average of only 0.66 false positives per scan. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
| | | | | | - Daniel A Markel
- Department of Radiation Oncology, Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada
| | - Houda Bahig
- Department of Radiation Oncology, Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada
| | | | - Selvan Rajakesari
- Department of Radiation Oncology, Hopital Charles Lemoyne, Greenfield Park, Québec, Canada
| | | | - Simon Ducharme
- AFX Medical Inc., Montréal, Canada.,Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montréal, Canada.,McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, Canada
| | - David Roberge
- Department of Radiation Oncology, Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada
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Abstract
Vessel wall MR imaging (VW-MRI) has been introduced into clinical practice and applied to a variety of diseases, and its usefulness has been reported. High-resolution VW-MRI is essential in the diagnostic workup and provides more information than other routine MR imaging protocols. VW-MRI is useful in assessing lesion location, morphology, and severity. Additional information, such as vessel wall enhancement, which is useful in the differential diagnosis of atherosclerotic disease and vasculitis could be assessed by this special imaging technique. This review describes the VW-MRI technique and its clinical applications in arterial disease, venous disease, vasculitis, and leptomeningeal disease.
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4
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From Dose Reduction to Contrast Maximization: Can Deep Learning Amplify the Impact of Contrast Media on Brain Magnetic Resonance Image Quality? A Reader Study. Invest Radiol 2022; 57:527-535. [PMID: 35446300 DOI: 10.1097/rli.0000000000000867] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
OBJECTIVES The aim of this study was to evaluate a deep learning method designed to increase the contrast-to-noise ratio in contrast-enhanced gradient echo T1-weighted brain magnetic resonance imaging (MRI) acquisitions. The processed images are quantitatively evaluated in terms of lesion detection performance. MATERIALS AND METHODS A total of 250 multiparametric brain MRIs, acquired between November 2019 and March 2021 at Gustave Roussy Cancer Campus (Villejuif, France), were considered for inclusion in this retrospective monocentric study. Independent training (107 cases; age, 55 ± 14 years; 58 women) and test (79 cases; age, 59 ± 14 years; 41 women) samples were defined. Patients had glioma, brain metastasis, meningioma, or no enhancing lesion. Gradient echo and turbo spin echo with variable flip angles postcontrast T1 sequences were acquired in all cases. For the cases that formed the training sample, "low-dose" postcontrast gradient echo T1 images using 0.025 mmol/kg injections of contrast agent were also acquired. A deep neural network was trained to synthetically enhance the low-dose T1 acquisitions, taking standard-dose T1 MRI as reference. Once trained, the contrast enhancement network was used to process the test gradient echo T1 images. A read was then performed by 2 experienced neuroradiologists to evaluate the original and processed T1 MRI sequences in terms of contrast enhancement and lesion detection performance, taking the turbo spin echo sequences as reference. RESULTS The processed images were superior to the original gradient echo and reference turbo spin echo T1 sequences in terms of contrast-to-noise ratio (44.5 vs 9.1 and 16.8; P < 0.001), lesion-to-brain ratio (1.66 vs 1.31 and 1.44; P < 0.001), and contrast enhancement percentage (112.4% vs 85.6% and 92.2%; P < 0.001) for cases with enhancing lesions. The overall image quality of processed T1 was preferred by both readers (graded 3.4/4 on average vs 2.7/4; P < 0.001). Finally, the proposed processing improved the average sensitivity of gradient echo T1 MRI from 88% to 96% for lesions larger than 10 mm (P = 0.008), whereas no difference was found in terms of the false detection rate (0.02 per case in both cases; P > 0.99). The same effect was observed when considering all lesions larger than 5 mm: sensitivity increased from 70% to 85% (P < 0.001), whereas false detection rates remained similar (0.04 vs 0.06 per case; P = 0.48). With all lesions included regardless of their size, sensitivities were 59% and 75% for original and processed T1 images, respectively (P < 0.001), and the corresponding false detection rates were 0.05 and 0.14 per case, respectively (P = 0.06). CONCLUSION The proposed deep learning method successfully amplified the beneficial effects of contrast agent injection on gradient echo T1 image quality, contrast level, and lesion detection performance. In particular, the sensitivity of the MRI sequence was improved by up to 16%, whereas the false detection rate remained similar.
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5
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Zhou Q, van den Berg NS, Rosenthal EL, Iv M, Zhang M, Vega Leonel JCM, Walters S, Nishio N, Granucci M, Raymundo R, Yi G, Vogel H, Cayrol R, Lee YJ, Lu G, Hom M, Kang W, Hayden Gephart M, Recht L, Nagpal S, Thomas R, Patel C, Grant GA, Li G. EGFR-targeted intraoperative fluorescence imaging detects high-grade glioma with panitumumab-IRDye800 in a phase 1 clinical trial. Theranostics 2021; 11:7130-7143. [PMID: 34158840 PMCID: PMC8210618 DOI: 10.7150/thno.60582] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 04/24/2021] [Indexed: 12/12/2022] Open
Abstract
Rationale: First-line therapy for high-grade gliomas (HGGs) includes maximal safe surgical resection. The extent of resection predicts overall survival, but current neuroimaging approaches lack tumor specificity. The epidermal growth factor receptor (EGFR) is a highly expressed HGG biomarker. We evaluated the safety and feasibility of an anti-EGFR antibody, panitumuab-IRDye800, at subtherapeutic doses as an imaging agent for HGG. Methods: Eleven patients with contrast-enhancing HGGs were systemically infused with panitumumab-IRDye800 at a low (50 mg) or high (100 mg) dose 1-5 days before surgery. Near-infrared fluorescence imaging was performed intraoperatively and ex vivo, to identify the optimal tumor-to-background ratio by comparing mean fluorescence intensities of tumor and histologically uninvolved tissue. Fluorescence was correlated with preoperative T1 contrast, tumor size, EGFR expression and other biomarkers. Results: No adverse events were attributed to panitumumab-IRDye800. Tumor fragments as small as 5 mg could be detected ex vivo and detection threshold was dose dependent. In tissue sections, panitumumab-IRDye800 was highly sensitive (95%) and specific (96%) for pathology confirmed tumor containing tissue. Cellular delivery of panitumumab-IRDye800 was correlated to EGFR overexpression and compromised blood-brain barrier in HGG, while normal brain tissue showed minimal fluorescence. Intraoperative fluorescence improved optical contrast in tumor tissue within and beyond the T1 contrast-enhancing margin, with contrast-to-noise ratios of 9.5 ± 2.1 and 3.6 ± 1.1, respectively. Conclusions: Panitumumab-IRDye800 provided excellent tumor contrast and was safe at both doses. Smaller fragments of tumor could be detected at the 100 mg dose and thus more suitable for intraoperative imaging.
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Affiliation(s)
- Quan Zhou
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
- Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Nynke S. van den Berg
- Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Eben L. Rosenthal
- Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Center, Stanford University, Stanford, CA, USA
| | - Michael Iv
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael Zhang
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Shannon Walters
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Naoki Nishio
- Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Monica Granucci
- Cancer Clinical Trials Office, Stanford University School of Medicine, Stanford, CA, USA
| | - Roan Raymundo
- Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
- Cancer Clinical Trials Office, Stanford University School of Medicine, Stanford, CA, USA
| | - Grace Yi
- Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
- Cancer Clinical Trials Office, Stanford University School of Medicine, Stanford, CA, USA
| | - Hannes Vogel
- Department of Neuropathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Romain Cayrol
- Department of Neuropathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Yu-Jin Lee
- Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Guolan Lu
- Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Marisa Hom
- Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Wenying Kang
- Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Larry Recht
- Department of Neurology, Stanford University School of Medicine, Stanford, CA, USA
| | - Seema Nagpal
- Department of Neurology, Stanford University School of Medicine, Stanford, CA, USA
| | - Reena Thomas
- Department of Neurology, Stanford University School of Medicine, Stanford, CA, USA
| | - Chirag Patel
- Department of Neurology, Stanford University School of Medicine, Stanford, CA, USA
| | - Gerald A. Grant
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Gordon Li
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
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6
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Kaufmann TJ, Smits M, Boxerman J, Huang R, Barboriak DP, Weller M, Chung C, Tsien C, Brown PD, Shankar L, Galanis E, Gerstner E, van den Bent MJ, Burns TC, Parney IF, Dunn G, Brastianos PK, Lin NU, Wen PY, Ellingson BM. Consensus recommendations for a standardized brain tumor imaging protocol for clinical trials in brain metastases. Neuro Oncol 2021; 22:757-772. [PMID: 32048719 PMCID: PMC7283031 DOI: 10.1093/neuonc/noaa030] [Citation(s) in RCA: 121] [Impact Index Per Article: 40.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
A recent meeting was held on March 22, 2019, among the FDA, clinical scientists, pharmaceutical and biotech companies, clinical trials cooperative groups, and patient advocacy groups to discuss challenges and potential solutions for increasing development of therapeutics for central nervous system metastases. A key issue identified at this meeting was the need for consistent tumor measurement for reliable tumor response assessment, including the first step of standardized image acquisition with an MRI protocol that could be implemented in multicenter studies aimed at testing new therapeutics. This document builds upon previous consensus recommendations for a standardized brain tumor imaging protocol (BTIP) in high-grade gliomas and defines a protocol for brain metastases (BTIP-BM) that addresses unique challenges associated with assessment of CNS metastases. The "minimum standard" recommended pulse sequences include: (i) parameter matched pre- and post-contrast inversion recovery (IR)-prepared, isotropic 3D T1-weighted gradient echo (IR-GRE); (ii) axial 2D T2-weighted turbo spin echo acquired after injection of gadolinium-based contrast agent and before post-contrast 3D T1-weighted images; (iii) axial 2D or 3D T2-weighted fluid attenuated inversion recovery; (iv) axial 2D, 3-directional diffusion-weighted images; and (v) post-contrast 2D T1-weighted spin echo images for increased lesion conspicuity. Recommended sequence parameters are provided for both 1.5T and 3T MR systems. An "ideal" protocol is also provided, which replaces IR-GRE with 3D TSE T1-weighted imaging pre- and post-gadolinium, and is best performed at 3T, for which dynamic susceptibility contrast perfusion is included. Recommended perfusion parameters are given.
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Affiliation(s)
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Jerrold Boxerman
- Department of Diagnostic Imaging, Rhode Island Hospital and Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Raymond Huang
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Daniel P Barboriak
- Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Michael Weller
- Department of Neurology & Brain Tumor Center, University Hospital and University of Zurich, Zurich, Switzerland
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Christina Tsien
- Department of Radiation Oncology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Paul D Brown
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | - Lalitha Shankar
- Division of Cancer Treatment and Diagnosis, National Cancer Institute (NCI), Bethesda, Maryland, USA
| | - Evanthia Galanis
- Division of Medical Oncology, Department of Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | - Elizabeth Gerstner
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Terry C Burns
- Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Ian F Parney
- Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Gavin Dunn
- Department of Neurological Surgery, Washington University, St Louis, Missouri, USA
| | - Priscilla K Brastianos
- Departments of Medicine and Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Nancy U Lin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Patrick Y Wen
- Center for Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Departments of Radiological Sciences and Psychiatry, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
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7
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Hainc N, Federau C, Tyndall A, Mittermeier A, Bink A, Stippich C, Schubert T. Evaluation of the clinical utility of maximum intensity projections of
3D contrast‐enhanced
,
T1
‐weighted imaging for the detection of brain metastases. Cancer Rep (Hoboken) 2020; 3:e1277. [DOI: 10.1002/cnr2.1277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 06/09/2020] [Accepted: 07/08/2020] [Indexed: 11/09/2022] Open
Affiliation(s)
- Nicolin Hainc
- Department of Medical Imaging, Division of Neuroradiology Toronto Western Hospital Toronto Ontario Canada
- Department of Neuroradiology, Clinical Neuroscience Center University Hospital Zurich, University of Zurich Zurich Switzerland
| | - Christian Federau
- Division of Diagnostic and Interventional Neuroradiology, Department of Radiology University Hospital Basel, University of Basel Basel Switzerland
- Institute for Biomedical Engineering Swiss Federal Institute of Technology Zurich Switzerland
| | - Anthony Tyndall
- Department of Neuroradiology, Clinical Neuroscience Center University Hospital Zurich, University of Zurich Zurich Switzerland
- Division of Diagnostic and Interventional Neuroradiology, Department of Radiology University Hospital Basel, University of Basel Basel Switzerland
| | - Andreas Mittermeier
- Department of Medical Imaging The Hospital for Sick Children, University of Toronto Toronto Canada
- Department of Radiology Ludwig‐Maximilians‐University Hospital Munich Munich Germany
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center University Hospital Zurich, University of Zurich Zurich Switzerland
- Division of Diagnostic and Interventional Neuroradiology, Department of Radiology University Hospital Basel, University of Basel Basel Switzerland
| | - Christoph Stippich
- Department of Neuroradiology, Clinical Neuroscience Center University Hospital Zurich, University of Zurich Zurich Switzerland
- Division of Diagnostic and Interventional Neuroradiology, Department of Radiology University Hospital Basel, University of Basel Basel Switzerland
| | - Tilman Schubert
- Department of Neuroradiology, Clinical Neuroscience Center University Hospital Zurich, University of Zurich Zurich Switzerland
- Division of Diagnostic and Interventional Neuroradiology, Department of Radiology University Hospital Basel, University of Basel Basel Switzerland
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8
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Dikici E, Ryu JL, Demirer M, Bigelow M, White RD, Slone W, Erdal BS, Prevedello LM. Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI. IEEE J Biomed Health Inform 2020; 24:2883-2893. [DOI: 10.1109/jbhi.2020.2982103] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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9
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Bapst B, Amegnizin JL, Vignaud A, Kauv P, Maraval A, Kalsoum E, Tuilier T, Benaissa A, Brugières P, Leclerc X, Hodel J. Post-contrast 3D T1-weighted TSE MR sequences (SPACE, CUBE, VISTA/BRAINVIEW, isoFSE, 3D MVOX): Technical aspects and clinical applications. J Neuroradiol 2020; 47:358-368. [DOI: 10.1016/j.neurad.2020.01.085] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2019] [Revised: 12/11/2019] [Accepted: 01/19/2020] [Indexed: 11/25/2022]
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10
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Kongpromsuk S, Pitakvej N, Jittapiromsak N, Prakkamakul S. Detection of brain metastases using alternative magnetic resonance imaging sequences: a comparison between SPACE and VIBE sequences. ASIAN BIOMED 2020. [DOI: 10.1515/abm-2020-0005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Abstract
Background
Accurate identification of brain metastases is crucial for cancer treatment.
Objectives
To compare the ability to detect brain metastases of two alternative types of contrast-enhanced three-dimensional (3D) T1-weighted sequences called SPACE (Sampling Perfection with Application optimized Contrasts using different flip angle Evolutions) and VIBE (Volumetric Interpolated Brain Sequence) on magnetic resonance imaging (MRI) at 3 tesla.
Methods
Between April 2017 and February 2018, 27 consecutive adult Thai patients with a total number of 424 brain metastases were retrospectively included. The patients underwent both contrast-enhanced 3D T1-weighted SPACE and 3D T1-weighted VIBE MRI sequences at 3 tesla. Two neuroradiology experts independently reviewed the images to determine the number of enhancing lesions on each sequence. Wilcoxon signed rank test was used to compare the difference between the numbers of detectable parenchymal enhancing lesions. Interobserver reliability was calculated using intraclass correlation.
Results
3D T1-weighted SPACE detected more parenchymal enhancing lesions than 3D T1-weighted VIBE (424 vs. 378 lesions, median 6 vs. 5, P = 0.008). Fifteen patients (55.6%) had equal number of parenchymal enhancing lesions between two sequences. 3D T1-weighted SPACE detected more parenchymal enhancing lesions (up to 9 more lesions) in 10 patients (37%), while 3D T1-weighted VIBE detected more enhancing lesions (up to 2 more lesions) in 2 patients (7.4%). Interobserver reliability between the readers was excellent.
Conclusion
Contrast-enhanced 3D T1-weighted SPACE sequence demonstrates a higher ability to detect brain metastases than contrast-enhanced 3D T1-weighted VIBE sequence at 3 tesla.
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Affiliation(s)
- Sutasinee Kongpromsuk
- Department of Radiology, King Chulalongkorn Memorial Hospital , Thai Red Cross Society , Bangkok , Thailand
- Department of Radiology and Nuclear Medicine, Faculty of Medicine , Burapha University , Chonburi , Thailand
| | - Nantaporn Pitakvej
- Department of Radiology, King Chulalongkorn Memorial Hospital , Thai Red Cross Society , Bangkok , Thailand
- Department of Radiology, Faculty of Medicine , Chulalongkorn University , Bangkok , Thailand
| | - Nutchawan Jittapiromsak
- Department of Radiology, King Chulalongkorn Memorial Hospital , Thai Red Cross Society , Bangkok , Thailand
- Department of Radiology, Faculty of Medicine , Chulalongkorn University , Bangkok , Thailand
| | - Supada Prakkamakul
- Department of Radiology, King Chulalongkorn Memorial Hospital , Thai Red Cross Society , Bangkok , Thailand
- Department of Radiology, Faculty of Medicine , Chulalongkorn University , Bangkok , Thailand
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11
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Tong E, McCullagh KL, Iv M. Advanced Imaging of Brain Metastases: From Augmenting Visualization and Improving Diagnosis to Evaluating Treatment Response. Front Neurol 2020; 11:270. [PMID: 32351445 PMCID: PMC7174761 DOI: 10.3389/fneur.2020.00270] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 03/24/2020] [Indexed: 12/11/2022] Open
Abstract
Early detection of brain metastases and differentiation from other neuropathologies is crucial. Although biopsy is often required for definitive diagnosis, imaging can provide useful information. After treatment commences, imaging is also performed to assess the efficacy of treatment. Contrast-enhanced magnetic resonance imaging (MRI) is the traditional imaging method for the evaluation of brain metastases, as it provides information about lesion size, morphology, and macroscopic properties. Newer MRI sequences have been developed to increase the conspicuity of detecting enhancing metastases. Other advanced MRI techniques, that have the capability to probe beyond the anatomic structure, are available to characterize micro-structures, cellularity, physiology, perfusion, and metabolism. Artificial intelligence provides powerful computational tools for detection, segmentation, classification, prediction, and prognosis. We highlight and review a few advanced MRI techniques for the assessment of brain metastases-specifically for (1) diagnosis, including differentiating between malignancy types and (2) evaluation of treatment response, including the differentiation between radiation necrosis and disease progression.
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Affiliation(s)
- Elizabeth Tong
- Stanford University Medical Center, Stanford, CA, United States
| | | | - Michael Iv
- Stanford University Medical Center, Stanford, CA, United States
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12
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Grøvik E, Yi D, Iv M, Tong E, Rubin D, Zaharchuk G. Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI. J Magn Reson Imaging 2019; 51:175-182. [PMID: 31050074 DOI: 10.1002/jmri.26766] [Citation(s) in RCA: 123] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Revised: 04/18/2019] [Accepted: 04/18/2019] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Detecting and segmenting brain metastases is a tedious and time-consuming task for many radiologists, particularly with the growing use of multisequence 3D imaging. PURPOSE To demonstrate automated detection and segmentation of brain metastases on multisequence MRI using a deep-learning approach based on a fully convolution neural network (CNN). STUDY TYPE Retrospective. POPULATION In all, 156 patients with brain metastases from several primary cancers were included. FIELD STRENGTH 1.5T and 3T. [Correction added on May 24, 2019, after first online publication: In the preceding sentence, the first field strength listed was corrected.] SEQUENCE: Pretherapy MR images included pre- and postgadolinium T1 -weighted 3D fast spin echo (CUBE), postgadolinium T1 -weighted 3D axial IR-prepped FSPGR (BRAVO), and 3D CUBE fluid attenuated inversion recovery (FLAIR). ASSESSMENT The ground truth was established by manual delineation by two experienced neuroradiologists. CNN training/development was performed using 100 and 5 patients, respectively, with a 2.5D network based on a GoogLeNet architecture. The results were evaluated in 51 patients, equally separated into those with few (1-3), multiple (4-10), and many (>10) lesions. STATISTICAL TESTS Network performance was evaluated using precision, recall, Dice/F1 score, and receiver operating characteristic (ROC) curve statistics. For an optimal probability threshold, detection and segmentation performance was assessed on a per-metastasis basis. The Wilcoxon rank sum test was used to test the differences between patient subgroups. RESULTS The area under the ROC curve (AUC), averaged across all patients, was 0.98 ± 0.04. The AUC in the subgroups was 0.99 ± 0.01, 0.97 ± 0.05, and 0.97 ± 0.03 for patients having 1-3, 4-10, and >10 metastases, respectively. Using an average optimal probability threshold determined by the development set, precision, recall, and Dice score were 0.79 ± 0.20, 0.53 ± 0.22, and 0.79 ± 0.12, respectively. At the same probability threshold, the network showed an average false-positive rate of 8.3/patient (no lesion-size limit) and 3.4/patient (10 mm3 lesion size limit). DATA CONCLUSION A deep-learning approach using multisequence MRI can automatically detect and segment brain metastases with high accuracy. LEVEL OF EVIDENCE 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:175-182.
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Affiliation(s)
- Endre Grøvik
- Department of Radiology, Stanford University, Stanford, California, USA
- Department for Diagnostic Physics, Oslo University Hospital, Oslo, Norway
| | - Darvin Yi
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Michael Iv
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Elizabeth Tong
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Daniel Rubin
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Greg Zaharchuk
- Department of Radiology, Stanford University, Stanford, California, USA
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