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Pandey S, Kutuk T, Abdalah MA, Stringfield O, Ravi H, Mills MN, Graham JA, Latifi K, Moreno WA, Ahmed KA, Raghunand N. Prediction of radiologic outcome-optimized dose plans and post-treatment magnetic resonance images: A proof-of-concept study in breast cancer brain metastases treated with stereotactic radiosurgery. Phys Imaging Radiat Oncol 2024; 31:100602. [PMID: 39040435 PMCID: PMC11261135 DOI: 10.1016/j.phro.2024.100602] [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: 11/20/2023] [Revised: 06/14/2024] [Accepted: 06/20/2024] [Indexed: 07/24/2024] Open
Abstract
Background and purpose Information in multiparametric Magnetic Resonance (mpMR) images is relatable to voxel-level tumor response to Radiation Treatment (RT). We have investigated a deep learning framework to predict (i) post-treatment mpMR images from pre-treatment mpMR images and the dose map ("forward models"), and, (ii) the RT dose map that will produce prescribed changes within the Gross Tumor Volume (GTV) on post-treatment mpMR images ("inverse model"), in Breast Cancer Metastases to the Brain (BCMB) treated with Stereotactic Radiosurgery (SRS). Materials and methods Local outcomes, planning computed tomography (CT) images, dose maps, and pre-treatment and post-treatment Apparent Diffusion Coefficient of water (ADC) maps, T1-weighted unenhanced (T1w) and contrast-enhanced (T1wCE), T2-weighted (T2w) and Fluid-Attenuated Inversion Recovery (FLAIR) mpMR images were curated from 39 BCMB patients. mpMR images were co-registered to the planning CT and intensity-calibrated. A 2D pix2pix architecture was used to train 5 forward models (ADC, T2w, FLAIR, T1w, T1wCE) and 1 inverse model on 1940 slices from 18 BCMB patients, and tested on 437 slices from another 9 BCMB patients. Results Root Mean Square Percent Error (RMSPE) within the GTV between predicted and ground-truth post-RT images for the 5 forward models, in 136 test slices containing GTV, were (mean ± SD) 0.12 ± 0.044 (ADC), 0.14 ± 0.066 (T2w), 0.08 ± 0.038 (T1w), 0.13 ± 0.058 (T1wCE), and 0.09 ± 0.056 (FLAIR). RMSPE within the GTV on the same 136 test slices, between the predicted and ground-truth dose maps, was 0.37 ± 0.20 for the inverse model. Conclusions A deep learning-based approach for radiologic outcome-optimized dose planning in SRS of BCMB has been demonstrated.
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Affiliation(s)
- Shraddha Pandey
- Department of Cancer Physiology, Moffitt Cancer Center, Tampa, FL 33612, USA
- Department of Electrical Engineering, University of South Florida, Tampa, FL 33612, USA
| | - Tugce Kutuk
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Mahmoud A. Abdalah
- Quantitative Imaging Shared Service, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Olya Stringfield
- Quantitative Imaging Shared Service, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Harshan Ravi
- Department of Cancer Physiology, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Matthew N. Mills
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Jasmine A. Graham
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
- Department of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USA
| | - Kujtim Latifi
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
- Department of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USA
| | - Wilfrido A. Moreno
- Department of Electrical Engineering, University of South Florida, Tampa, FL 33612, USA
| | - Kamran A. Ahmed
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
- Department of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USA
| | - Natarajan Raghunand
- Department of Cancer Physiology, Moffitt Cancer Center, Tampa, FL 33612, USA
- Department of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USA
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Ghaderi S, Mohammadi S, Mohammadi M, Pashaki ZNA, Heidari M, Khatyal R, Zafari R. A systematic review of brain metastases from lung cancer using magnetic resonance neuroimaging: Clinical and technical aspects. J Med Radiat Sci 2024; 71:269-289. [PMID: 38234262 PMCID: PMC11177032 DOI: 10.1002/jmrs.756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 01/06/2024] [Indexed: 01/19/2024] Open
Abstract
INTRODUCTION Brain metastases (BMs) are common in lung cancer (LC) and are associated with poor prognosis. Magnetic resonance imaging (MRI) plays a vital role in the detection, diagnosis and management of BMs. This review summarises recent advances in MRI techniques for BMs from LC. METHODS This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was conducted in three electronic databases: PubMed, Scopus and the Web of Science. The search was limited to studies published between January 2000 and March 2023. The quality of the included studies was evaluated using appropriate tools for different study designs. A narrative synthesis was carried out to describe the key findings of the included studies. RESULTS Sixty-five studies were included. Standard MRI sequences such as T1-weighted (T1w), T2-weighted (T2w) and fluid-attenuated inversion recovery (FLAIR) were commonly used. Advanced techniques included perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and radiomics analysis. DWI and PWI parameters could distinguish tumour recurrence from radiation necrosis. Radiomics models predicted genetic mutations and the risk of BMs. Diagnostic accuracy was improved with deep learning (DL) approaches. Prognostic factors such as performance status and concurrent chemotherapy impacted survival. CONCLUSION Advanced MRI techniques and specialised MRI methods have emerging roles in managing BMs from LC. PWI and DWI improve diagnostic accuracy in treated BMs. Radiomics and DL facilitate personalised prognosis and treatment. Magnetic resonance imaging plays a key role in the continuum of care for BMs of patients with LC, from screening to treatment monitoring.
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Affiliation(s)
- Sadegh Ghaderi
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in MedicineTehran University of Medical SciencesTehranIran
| | - Sana Mohammadi
- Department of Medical Sciences, School of MedicineIran University of Medical SciencesTehranIran
| | - Mahdi Mohammadi
- Department of Medical Physics and Biomedical Engineering, School of MedicineTehran University of Medical SciencesTehranIran
| | | | - Mehrsa Heidari
- Department of Medical Science, School of MedicineAhvaz Jundishapur University of Medical SciencesAhvazIran
| | - Rahim Khatyal
- Department of Radiology, Faculty of Allied Medical SciencesTabriz University of Medical SciencesTabrizIran
| | - Rasa Zafari
- School of MedicineTehran University of Medical SciencesTehranIran
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Romano A, Moltoni G, Blandino A, Palizzi S, Romano A, de Rosa G, De Blasi Palma L, Monopoli C, Guarnera A, Minniti G, Bozzao A. Radiosurgery for Brain Metastases: Challenges in Imaging Interpretation after Treatment. Cancers (Basel) 2023; 15:5092. [PMID: 37894459 PMCID: PMC10605307 DOI: 10.3390/cancers15205092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 10/13/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023] Open
Abstract
Stereotactic radiosurgery (SRS) has transformed the management of brain metastases by achieving local tumor control, reducing toxicity, and minimizing the need for whole-brain radiation therapy (WBRT). This review specifically investigates radiation-induced changes in patients treated for metastasis, highlighting the crucial role of magnetic resonance imaging (MRI) in the evaluation of treatment response, both at very early and late stages. The primary objective of the review is to evaluate the most effective imaging techniques for assessing radiation-induced changes and distinguishing them from tumor growth. The limitations of conventional imaging methods, which rely on size measurements, dimensional criteria, and contrast enhancement patterns, are critically evaluated. In addition, it has been investigated the potential of advanced imaging modalities to offer a more precise and comprehensive evaluation of treatment response. Finally, an overview of the relevant literature concerning the interpretation of brain changes in patients undergoing immunotherapies is provided.
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Affiliation(s)
- Andrea Romano
- NESMOS Department, U.O.C. Neuroradiology “Sant’Andrea” University Hospital, 00189 Rome, Italy; (A.R.); (G.M.); (A.B.); (S.P.); (A.R.); (G.d.R.); (L.D.B.P.); (C.M.); (A.G.); (A.B.)
| | - Giulia Moltoni
- NESMOS Department, U.O.C. Neuroradiology “Sant’Andrea” University Hospital, 00189 Rome, Italy; (A.R.); (G.M.); (A.B.); (S.P.); (A.R.); (G.d.R.); (L.D.B.P.); (C.M.); (A.G.); (A.B.)
| | - Antonella Blandino
- NESMOS Department, U.O.C. Neuroradiology “Sant’Andrea” University Hospital, 00189 Rome, Italy; (A.R.); (G.M.); (A.B.); (S.P.); (A.R.); (G.d.R.); (L.D.B.P.); (C.M.); (A.G.); (A.B.)
| | - Serena Palizzi
- NESMOS Department, U.O.C. Neuroradiology “Sant’Andrea” University Hospital, 00189 Rome, Italy; (A.R.); (G.M.); (A.B.); (S.P.); (A.R.); (G.d.R.); (L.D.B.P.); (C.M.); (A.G.); (A.B.)
| | - Allegra Romano
- NESMOS Department, U.O.C. Neuroradiology “Sant’Andrea” University Hospital, 00189 Rome, Italy; (A.R.); (G.M.); (A.B.); (S.P.); (A.R.); (G.d.R.); (L.D.B.P.); (C.M.); (A.G.); (A.B.)
| | - Giulia de Rosa
- NESMOS Department, U.O.C. Neuroradiology “Sant’Andrea” University Hospital, 00189 Rome, Italy; (A.R.); (G.M.); (A.B.); (S.P.); (A.R.); (G.d.R.); (L.D.B.P.); (C.M.); (A.G.); (A.B.)
| | - Lara De Blasi Palma
- NESMOS Department, U.O.C. Neuroradiology “Sant’Andrea” University Hospital, 00189 Rome, Italy; (A.R.); (G.M.); (A.B.); (S.P.); (A.R.); (G.d.R.); (L.D.B.P.); (C.M.); (A.G.); (A.B.)
| | - Cristiana Monopoli
- NESMOS Department, U.O.C. Neuroradiology “Sant’Andrea” University Hospital, 00189 Rome, Italy; (A.R.); (G.M.); (A.B.); (S.P.); (A.R.); (G.d.R.); (L.D.B.P.); (C.M.); (A.G.); (A.B.)
| | - Alessia Guarnera
- NESMOS Department, U.O.C. Neuroradiology “Sant’Andrea” University Hospital, 00189 Rome, Italy; (A.R.); (G.M.); (A.B.); (S.P.); (A.R.); (G.d.R.); (L.D.B.P.); (C.M.); (A.G.); (A.B.)
| | - Giuseppe Minniti
- Department of Radiological, Oncological and Pathological Sciences, “Sapienza” University of Rome, 00138 Rome, Italy
- IRCCS Neuromed, 86077 Pozzilli, Italy
| | - Alessandro Bozzao
- NESMOS Department, U.O.C. Neuroradiology “Sant’Andrea” University Hospital, 00189 Rome, Italy; (A.R.); (G.M.); (A.B.); (S.P.); (A.R.); (G.d.R.); (L.D.B.P.); (C.M.); (A.G.); (A.B.)
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Hu J, Xie X, Zhou W, Hu X, Sun X. The emerging potential of quantitative MRI biomarkers for the early prediction of brain metastasis response after stereotactic radiosurgery: a scoping review. Quant Imaging Med Surg 2023; 13:1174-1189. [PMID: 36819250 PMCID: PMC9929394 DOI: 10.21037/qims-22-412] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 11/23/2022] [Indexed: 01/05/2023]
Abstract
Background At present, the simple prognostic models based on clinical information for predicting the treatment outcomes of brain metastases (BMs) are subjective and delayed. Thus, we performed this systematic review of multiple studies to assess the potential of quantitative magnetic resonance imaging (MRI) biomarkers for the early prediction of treatment outcomes of brain metastases with stereotactic radiosurgery (SRS). Methods We systematically searched the PubMed, Embase, Cochrane, Web of Science, and Clinical Trials.gov databases for articles published between February 1, 1991, and April 11, 2022, with no language restrictions. We included studies involving patients with BMs receiving SRS; the included patients were required to have definite pathology of a primary tumor and complete imaging data (pre- and post-SRS). We excluded the articles that included patients who had undergone previous surgery and those that did not include regular follow-up or corresponding MRI scans. Results We identified 2,162 studies, of which 26 were included in our analysis, involving a total of 1,362 participants. All 26 studies explored the relevant MRI parameters to predict the prognosis of patients with BMs who received SRS. The outcomes were generalized according to the relationships between the anatomical/morphological, microstructural, vascular, and metabolic changes and SRS. Generally, with traditional MRI, there are several quantitative prognostic models based on preradiosurgical radiomics that predict the outcome of SRS treatment in local BM control. With the implementation of advanced MRI, the relative apparent diffusion coefficient (ADC), perfusion fraction (f), relative cerebral blood volume (rCBV), relative regional cerebral blood flow (rrCBF), interstitial fluid pressure (IFP), quadratic of time-dependent leakage (Ktrans 2), extracellular extravascular volume (ve), choline/creatine (Cho/Cr), nuclear Overhauser effect (NOE) peak, and intraextracellular water exchange rate constant (kIE ) were confirmed to be indicative of the therapeutic effect of SRS for BMs. Conclusions Quantitative MRI biomarkers extracted from traditional or advanced MRI at different time points, which can represent the anatomical/morphological, microstructural, vascular, and metabolic changes, respectively, have been proposed as promising markers for the early prediction of SRS response in those with BMs. There are some limitations in this review, including the risk of selection bias, the limited number of study objects, the incomparability of the total data, and the subjectivity of the review process.
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Affiliation(s)
- Jiamiao Hu
- Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University, School of Medicine, Hangzhou, China
| | - Xuyun Xie
- Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University, School of Medicine, Hangzhou, China
| | - Weiwen Zhou
- Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University, School of Medicine, Hangzhou, China
| | - Xiao Hu
- Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University, School of Medicine, Hangzhou, China
| | - Xiaonan Sun
- Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University, School of Medicine, Hangzhou, China
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Bottero M, Faiella A, Giannarelli D, Farneti A, D'Urso P, Bertini L, Landoni V, Vici P, Sanguineti G. A prospective study assessing the pattern of response of local disease at DCE-MRI after salvage radiotherapy for prostate cancer. Clin Transl Radiat Oncol 2022; 35:21-26. [PMID: 35516461 PMCID: PMC9065465 DOI: 10.1016/j.ctro.2022.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 04/18/2022] [Accepted: 04/25/2022] [Indexed: 10/29/2022] Open
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Quantitative Synthetic Magnetic Resonance Imaging for Brain Metastases: A Feasibility Study. Cancers (Basel) 2022; 14:cancers14112651. [PMID: 35681631 PMCID: PMC9179589 DOI: 10.3390/cancers14112651] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/16/2022] [Accepted: 05/20/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary This preliminary study aims to characterize brain metastases (BM) using T1 and T2 maps generated from newer, rapid, synthetic MRI (MAGnetic resonance image Compilation; MAGiC) in a clinical setting. In addition, synthetic MR could provide contrast images analogous to standard T1- and T2-weighted images. The reproducibility and repeatability of this method have been previously established for brain imaging. This study reports and analyzes the quantitative T1 and T2 values for 11 BM patients (17 BM lesions) with a total of 82 regions of interest (ROIs) delineated by an experienced neuroradiologist. The initial results, which need to be further validated in a larger patient cohort, demonstrated the ability of T1 and T2 metric values to characterize BMs and normal-appearing brain tissues. The T1 and T2 metrics could be potential surrogate biomarkers for BM free water content (cellularity) and tumor morphology, respectively. Abstract The present preliminary study aims to characterize brain metastases (BM) using T1 and T2 maps generated from newer, rapid, synthetic MRI (MAGnetic resonance image Compilation; MAGiC) in a clinical setting. We acquired synthetic MRI data from 11 BM patients on a 3T scanner. A multiple-dynamic multiple-echo (MDME) sequence was used for data acquisition and synthetic image reconstruction, including post-processing. MDME is a multi-contrast sequence that enables absolute quantification of physical tissue properties, including T1 and T2, independent of the scanner settings. In total, 82 regions of interest (ROIs) were analyzed, which were obtained from both normal-appearing brain tissue and BM lesions. The mean values obtained from the 48 normal-appearing brain tissue regions and 34 ROIs of BM lesions (T1 and T2) were analyzed using standard statistical methods. The mean T1 and T2 values were 1143 ms and 78 ms, respectively, for normal-appearing gray matter, 701 ms and 64 ms for white matter, and 4206 ms and 390 ms for cerebrospinal fluid. For untreated BMs, the mean T1 and T2 values were 1868 ms and 100 ms, respectively, and 2211 ms and 114 ms for the treated group. The quantitative T1 and T2 values generated from synthetic MRI can characterize BM and normal-appearing brain tissues.
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Jiang Z, Wang B, Han X, Zhao P, Gao M, Zhang Y, Wei P, Lan C, Liu Y, Li D. Multimodality MRI-based radiomics approach to predict the posttreatment response of lung cancer brain metastases to gamma knife radiosurgery. Eur Radiol 2022; 32:2266-2276. [PMID: 34978579 DOI: 10.1007/s00330-021-08368-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 08/31/2021] [Accepted: 09/28/2021] [Indexed: 12/29/2022]
Abstract
OBJECTIVES To develop and validate a multimodality MRI-based radiomics approach to predicting the posttreatment response of lung cancer brain metastases (LCBM) to gamma knife radiosurgery (GKRS). METHODS We retrospectively analyzed 213 lesions from 137 patients with LCBM who received GKRS between January 2017 and November 2020. The data were divided into a primary cohort (102 patients with 173 lesions) and an independent validation cohort (35 patients with 40 lesions) according to the time of treatment. Benefit result was defined using pretreatment and 3-month follow-up MRI images based on the Response Assessment in Neuro-Oncology Brain Metastases criteria. Valuable radiomics features were extracted from pretreatment multimodality MRI images using random forests. Prediction performance among the radiomics features of tumor core (RFTC) and radiomics features of peritumoral edema (RFPE) together was evaluated separately. Then, the random forest radiomics score and nomogram were developed through the primary cohort and evaluated through an independent validation cohort. Prediction performance was evaluated by ROC curve, calibration curve, and decision curve. RESULTS Gender (p = 0.018), histological subtype (p = 0.009), epidermal growth factor receptor mutation (p = 0.034), and targeted drug treatment (p = 0.021) were significantly associated with posttreatment response. Adding RFPE to RFTC showed improved prediction performance than RFTC alone in primary cohort (AUC = 0.848 versus AUC = 0.750; p < 0.001). Finally, the radiomics nomogram had an AUC of 0.930, a C-index of 0.930 (specificity of 83.1%, sensitivity of 87.3%) in primary cohort, and an AUC of 0.852, a C-index of 0.848 (specificity of 84.2%, sensitivity of 76.2%) in validation cohort. CONCLUSIONS Multimodality MRI-based radiomics models can predict the posttreatment response of LCBM to GKRS. KEY POINTS • Among the selected radiomics features, texture features basically contributed the dominant force in prediction tasks (80%), especially gray-level co-occurrence matrix features (40%). • Adding RFPE to RFTC showed improved prediction performance than RFTC alone in primary cohort (AUC = 0.848 versus AUC = 0.750; p < 0.001). • The multimodality MRI-based radiomics nomogram showed high accuracy for distinguishing the posttreatment response of LCBM to GKRS (AUC = 0.930, in primary cohort; AUC = 0.852, in validation cohort).
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Affiliation(s)
- Zekun Jiang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, East Wenhua Road 88, Jinan, 250014, Shandong, China
| | - Bao Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Xiao Han
- Department of Experiment, Tumor Hospital Affiliated to Guangxi Medical University, Nanning, Guangxi, China
| | - Peng Zhao
- Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jingwu Road 324, Jinan, 250021, Shandong, China
| | - Meng Gao
- Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jingwu Road 324, Jinan, 250021, Shandong, China
| | - Yi Zhang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Ping Wei
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Chuanjin Lan
- Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jingwu Road 324, Jinan, 250021, Shandong, China
| | - Yingchao Liu
- Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jingwu Road 324, Jinan, 250021, Shandong, China.
| | - Dengwang Li
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, East Wenhua Road 88, Jinan, 250014, Shandong, China.
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Abstract
Imaging of brain metastases (BMs) has advanced greatly over the past decade. In this review, we discuss the main challenges that BMs pose in clinical practice and describe the role of imaging.Firstly, we describe the increased incidence of BMs of different primary tumours and the rationale for screening. A challenge lies in selecting the right patients for screening: not all cancer patients develop BMs in their disease course.Secondly, we discuss the imaging techniques to detect BMs. A three-dimensional (3D) T1W MRI sequence is the golden standard for BM detection, but additional anatomical (susceptibility weighted imaging, diffusion weighted imaging), functional (perfusion MRI) and metabolic (MR spectroscopy, positron emission tomography) information can help to differentiate BMs from other intracranial aetiologies.Thirdly, we describe the role of imaging before, during and after treatment of BMs. For surgical resection, imaging is used to select surgical patients, but also to assist intraoperatively (neuronavigation, fluorescence-guided surgery, ultrasound). For treatment planning of stereotactic radiosurgery, MRI is combined with CT. For surveillance after both local and systemic therapies, conventional MRI is used. However, advanced imaging is increasingly performed to distinguish true tumour progression from pseudoprogression.FInally, future perspectives are discussed, including radiomics, new biomarkers, new endogenous contrast agents and theranostics.
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Affiliation(s)
- Sophie H A E Derks
- Department of Neuro-Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.,Department of Radiology & Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.,Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Astrid A M van der Veldt
- Department of Radiology & Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.,Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Marion Smits
- Department of Radiology & Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
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Magnetic Resonance Imaging Segmentation on the Basis of Boundary Tracking Algorithm in Lung Cancer Surgery. CONTRAST MEDIA & MOLECULAR IMAGING 2021; 2021:1368687. [PMID: 34858112 PMCID: PMC8592752 DOI: 10.1155/2021/1368687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 09/26/2021] [Accepted: 10/01/2021] [Indexed: 11/23/2022]
Abstract
This work was to study the guiding value of magnetic resonance imaging (MRI) based on the target region boundary tracking algorithm in lung cancer surgery. In this study, the traditional boundary tracking algorithm was optimized, and the target neighborhood point boundary tracking method was proposed. The iterative method was used to binarize the lung MRI image, which was applied to the MRI images of 50 lung cancer patients in hospital. The patients were divided into two groups as the progression-free survival (PFS) and overall survival (OS) of surgical treatment group (experimental group, n = 25) and nonsurgical treatment group (control group, n = 25). The experimental group received surgical resection, while the control group received systemic chemotherapy. The results showed that the traditional boundary tracking algorithm needed to manually rejudge whether the concave and convex parts of the image were missing. The target boundary tracking algorithm can effectively avoid the leakage of concave and convex parts and accurately locate the target image contour, fast operation, without manual intervention. The PFS time of the experimental group (325 days) was significantly higher than that of the control group (186 days) (P < 0.05). The OS time of the experimental group (697 days) was significantly higher than that of the control group (428 days) (P < 0.05). Fisher exact probability method was used to test the total survival time of patients in the two groups, and the tumor classification and treatment group had significant influence on the OS time (P < 0.05). The target boundary tracking algorithm in this study can effectively locate the contour of the target image, and the operation speed was fast. Surgical resection of lung cancer can improve the PFS and OS of patients.
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Gaebe K, Li AY, Das S. Clinical Biomarkers for Early Identification of Patients with Intracranial Metastatic Disease. Cancers (Basel) 2021; 13:cancers13235973. [PMID: 34885083 PMCID: PMC8656478 DOI: 10.3390/cancers13235973] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 11/25/2021] [Accepted: 11/25/2021] [Indexed: 12/18/2022] Open
Abstract
Simple Summary The development of brain metastases, or intracranial metastatic disease (IMD), is a serious and life-altering complication for many patients with cancer. While there have been substantial advancements in the treatments available for IMD and in our understanding of its pathogenesis, conventional methods remain insufficient to detect IMD at an early stage. In this review, we discuss current research on biomarkers specific to IMD. In particular, we highlight biomarkers that can be easily accessed via the bloodstream or cerebrospinal fluid, including circulating tumor cells and DNA, as well as advanced imaging techniques. The continued development of these assays could enable clinicians to detect IMD prior to the development of IMD-associated symptoms and ultimately improve patient prognosis and survival. Abstract Nearly 30% of patients with cancer will develop intracranial metastatic disease (IMD), and more than half of these patients will die within a few months following their diagnosis. In light of the profound effect of IMD on survival and quality of life, there is significant interest in identifying biomarkers that could facilitate the early detection of IMD or identify patients with cancer who are at high IMD risk. In this review, we will highlight early efforts to identify biomarkers of IMD and consider avenues for future investigation.
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Affiliation(s)
- Karolina Gaebe
- Institute of Medical Science, Faculty of Medicine, University of Toronto, 1 King’s College Circle, Toronto, ON M5S 3K1, Canada; (K.G.); (A.Y.L.)
| | - Alyssa Y. Li
- Institute of Medical Science, Faculty of Medicine, University of Toronto, 1 King’s College Circle, Toronto, ON M5S 3K1, Canada; (K.G.); (A.Y.L.)
| | - Sunit Das
- Institute of Medical Science, Faculty of Medicine, University of Toronto, 1 King’s College Circle, Toronto, ON M5S 3K1, Canada; (K.G.); (A.Y.L.)
- Division of Neurosurgery, St. Michael’s Hospital, University of Toronto, 30 Bond Street, Toronto, ON M5B 1W8, Canada
- Correspondence:
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11
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Filice S, Ortenzia O, Crisi G. How tissue T1-variability influences DCE-MRI perfusion parameters estimation of recurrent high-grade glioma after surgery followed by radiochemotherapy. Acta Radiol 2021; 63:1262-1269. [PMID: 34342495 DOI: 10.1177/02841851211035911] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Quantification of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) kinetic parameters (KPs) requires a determination of native tissue T1. Two approaches are adopted: (i) tissue T1-maps are acquired; and (ii) an a priori T1 value (fT1) is fixed for all patients (fT1-approach). Although it is more attractive, the fT1-approach might bias the results of KP calculations due to tissue T1 variability. PURPOSE To quantify the tissue T1 variability of recurrent high-grade glioma (HGG) and the error in KP estimation when the fT1-approach is adopted. MATERIAL AND METHODS We reviewed the postoperative MRI scans of 28 patients with recurrent HGG after radiochemotherapy. MRI study included T1-maps from multiple-dynamic multiple-echo imaging, DCE-MRI, and contrast enhanced T1-weighted images. KPs were calculated using T1-map and fT1-approach. RESULTS The tissue T1 variability of recurrent HGG was relevant. The absolute error in KP estimation, as a function of the deviation of fT1 from the true value, was 8% every 100 ms. The difference between the KPs obtained with fT1-approach from fT1 values of 1300, 1390, and 1500 ms and their reference values were mostly within the 95% confidence interval (± 1.96 standard deviation). Conversely, using fT1 values of 900, 1200, 1600, and 1900 ms causes a significant error in KP estimation (P<0.05). CONCLUSION Recurrent HGG is characterized by a substantial T1 variability. Although the fT1-approach does not account for this variability, it results in a minor effect on the KP estimations provided the fT1 value is in the range of 1300-1500 ms.
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Affiliation(s)
- Silvano Filice
- Medical Physics Unit, Azienda Ospedaliero-Universitaria of Parma, Parma, Italy
| | - Ornella Ortenzia
- Medical Physics Unit, Azienda Ospedaliero-Universitaria of Parma, Parma, Italy
| | - Girolamo Crisi
- Neuroradiology Unit, Azienda Ospedaliero-Universitaria of Parma, Parma, Italy
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12
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Xue J, Wang B, Ming Y, Liu X, Jiang Z, Wang C, Liu X, Chen L, Qu J, Xu S, Tang X, Mao Y, Liu Y, Li D. Deep learning-based detection and segmentation-assisted management of brain metastases. Neuro Oncol 2021; 22:505-514. [PMID: 31867599 DOI: 10.1093/neuonc/noz234] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Three-dimensional T1 magnetization prepared rapid acquisition gradient echo (3D-T1-MPRAGE) is preferred in detecting brain metastases (BM) among MRI. We developed an automatic deep learning-based detection and segmentation method for BM (named BMDS net) on 3D-T1-MPRAGE images and evaluated its performance. METHODS The BMDS net is a cascaded 3D fully convolution network (FCN) to automatically detect and segment BM. In total, 1652 patients with 3D-T1-MPRAGE images from 3 hospitals (n = 1201, 231, and 220, respectively) were retrospectively included. Manual segmentations were obtained by a neuroradiologist and a radiation oncologist in a consensus reading in 3D-T1-MPRAGE images. Sensitivity, specificity, and dice ratio of the segmentation were evaluated. Specificity and sensitivity measure the fractions of relevant segmented voxels. Dice ratio was used to quantitatively measure the overlap between automatic and manual segmentation results. Paired samples t-tests and analysis of variance were employed for statistical analysis. RESULTS The BMDS net can detect all BM, providing a detection result with an accuracy of 100%. Automatic segmentations correlated strongly with manual segmentations through 4-fold cross-validation of the dataset with 1201 patients: the sensitivity was 0.96 ± 0.03 (range, 0.84-0.99), the specificity was 0.99 ± 0.0002 (range, 0.99-1.00), and the dice ratio was 0.85 ± 0.08 (range, 0.62-0.95) for total tumor volume. Similar performances on the other 2 datasets also demonstrate the robustness of BMDS net in correctly detecting and segmenting BM in various settings. CONCLUSIONS The BMDS net yields accurate detection and segmentation of BM automatically and could assist stereotactic radiotherapy management for diagnosis, therapy planning, and follow-up.
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Affiliation(s)
- Jie Xue
- School of Business, Shandong Normal University, Jinan, China
| | - Bao Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Yang Ming
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xuejun Liu
- School of Business, Shandong Normal University, Jinan, China
| | - Zekun Jiang
- Shandong Key Laboratory of Medical Physics and Image Processing, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Chengwei Wang
- Department of Neurosurgery, the Second Hospital of Shandong University, Jinan, China
| | - Xiyu Liu
- Department of Radiology, the Affiliated Hospital of Qingdao University Medical College, Qingdao, China
| | - Ligang Chen
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jianhua Qu
- School of Business, Shandong Normal University, Jinan, China
| | - Shangchen Xu
- Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.,Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Xuqun Tang
- Department of Neurosurgery, Huashan Hospital Affiliated to Fudan University, Shanghai, China
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital Affiliated to Fudan University, Shanghai, China
| | - Yingchao Liu
- Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.,Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Dengwang Li
- Shandong Key Laboratory of Medical Physics and Image Processing, School of Physics and Electronics, Shandong Normal University, Jinan, China
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13
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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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14
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Galldiks N, Kocher M, Ceccon G, Werner JM, Brunn A, Deckert M, Pope WB, Soffietti R, Le Rhun E, Weller M, Tonn JC, Fink GR, Langen KJ. Imaging challenges of immunotherapy and targeted therapy in patients with brain metastases: response, progression, and pseudoprogression. Neuro Oncol 2021; 22:17-30. [PMID: 31437274 DOI: 10.1093/neuonc/noz147] [Citation(s) in RCA: 79] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The advent of immunotherapy using immune checkpoint inhibitors (ICIs) and targeted therapy (TT) has dramatically improved the prognosis of various cancer types. However, following ICI therapy or TT-either alone (especially ICI) or in combination with radiotherapy-imaging findings on anatomical contrast-enhanced MRI can be unpredictable and highly variable, and are often difficult to interpret regarding treatment response and outcome. This review aims at summarizing the imaging challenges related to TT and ICI monotherapy as well as combined with radiotherapy in patients with brain metastases, and to give an overview on advanced imaging techniques which potentially overcome some of these imaging challenges. Currently, major evidence suggests that imaging parameters especially derived from amino acid PET, perfusion-/diffusion-weighted MRI, or MR spectroscopy may provide valuable additional information for the differentiation of treatment-induced changes from brain metastases recurrence and the evaluation of treatment response.
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Affiliation(s)
- Norbert Galldiks
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Institute of Neuroscience and Medicine, Research Center Juelich, Juelich, Germany.,Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne, and Düsseldorf, Germany
| | - Martin Kocher
- Institute of Neuroscience and Medicine, Research Center Juelich, Juelich, Germany.,Department of Stereotaxy and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Garry Ceccon
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Jan-Michael Werner
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Anna Brunn
- Institute of Neuropathology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Martina Deckert
- Institute of Neuropathology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Riccardo Soffietti
- Department of Neuro-Oncology, University and City of Health and Science Hospital, Turin, Italy
| | - Emilie Le Rhun
- Neuro-Oncology, General and Stereotaxic Neurosurgery Service, University Hospital Lille, Lille, France.,Breast Cancer Department, Oscar Lambret Center, Lille, France.,Department of Neurology & Brain Tumor Center, University Hospital and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Department of Neurology & Brain Tumor Center, University Hospital and University of Zurich, Zurich, Switzerland
| | - Jörg C Tonn
- Department of Neurosurgery, Ludwig Maximilians University of Munich, Munich, Germany.,German Cancer Consortium, partner site Munich, Germany
| | - Gereon R Fink
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Institute of Neuroscience and Medicine, Research Center Juelich, Juelich, Germany
| | - Karl-Josef Langen
- Institute of Neuroscience and Medicine, Research Center Juelich, Juelich, Germany.,Department of Nuclear Medicine, University Hospital Aachen, Aachen, Germany
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15
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Imaging of Response to Radiosurgery and Immunotherapy in Brain Metastases: Quo Vadis? Curr Treat Options Neurol 2021. [DOI: 10.1007/s11940-021-00664-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Abstract
Purpose of Review
This review presents an overview of how advanced imaging techniques may help to overcome shortcomings of anatomical MRI for response assessment in patients with brain metastases who are undergoing stereotactic radiosurgery, immunotherapy, or combinations thereof.
Recent Findings
Study results suggest that parameters derived from amino acid PET, diffusion- and perfusion-weighted MRI, MR spectroscopy, and newer MRI methods are particularly helpful for the evaluation of the response to radiosurgery or checkpoint inhibitor immunotherapy and provide valuable information for the differentiation of radiotherapy-induced changes such as radiation necrosis from brain metastases. The evaluation of these imaging modalities is also of great interest in the light of emerging high-throughput analysis methods such as radiomics, which allow the acquisition of additional data at a low cost.
Summary
Preliminary results are promising and should be further evaluated. Shortcomings are different levels of PET and MRI standardization, the number of patients enrolled in studies, and the monocentric and retrospective character of most studies.
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16
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Shah AD, Shridhar Konar A, Paudyal R, Oh JH, LoCastro E, Nuñez DA, Swinburne N, Vachha B, Ulaner GA, Young RJ, Holodny AI, Beal K, Shukla-Dave A, Hatzoglou V. Diffusion and Perfusion MRI Predicts Response Preceding and Shortly After Radiosurgery to Brain Metastases: A Pilot Study. J Neuroimaging 2020; 31:317-323. [PMID: 33370467 DOI: 10.1111/jon.12828] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 11/20/2020] [Accepted: 12/06/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND AND PURPOSE To determine the ability of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict long-term response of brain metastases prior to and within 72 hours of stereotactic radiosurgery (SRS). METHODS In this prospective pilot study, multiple b-value DWI and T1-weighted DCE-MRI were performed in patients with brain metastases before and within 72 hours following SRS. Diffusion-weighted images were analyzed using the monoexponential and intravoxel incoherent motion (IVIM) models. DCE-MRI data were analyzed using the extended Tofts pharmacokinetic model. The parameters obtained with these methods were correlated with brain metastasis outcomes according to modified Response Assessment in Neuro-Oncology Brain Metastases criteria. RESULTS We included 25 lesions from 16 patients; 16 patients underwent pre-SRS MRI and 12 of 16 patients underwent both pre- and early (within 72 hours) post-SRS MRI. The perfusion fraction (f) derived from IVIM early post-SRS was higher in lesions demonstrating progressive disease than in lesions demonstrating stable disease, partial response, or complete response (q = .041). Pre-SRS extracellular extravascular volume fraction, ve , and volume transfer coefficient, Ktrans , derived from DCE-MRI were higher in nonresponders versus responders (q = .041). CONCLUSIONS Quantitative DWI and DCE-MRI are feasible imaging methods in the pre- and early (within 72 hours) post-SRS evaluation of brain metastases. DWI- and DCE-MRI-derived parameters demonstrated physiologic changes (tumor cellularity and vascularity) and offer potentially useful biomarkers that can predict treatment response. This allows for initiation of alternate therapies within an effective time window that may help prevent disease progression.
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Affiliation(s)
- Akash Deelip Shah
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Eve LoCastro
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - David Aramburu Nuñez
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Nathaniel Swinburne
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Behroze Vachha
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Gary A Ulaner
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Robert J Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Andrei I Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Kathryn Beal
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Amita Shukla-Dave
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
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17
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Swinburne N, LoCastro E, Paudyal R, Oh JH, Taunk NK, Shah A, Beal K, Vachha B, Young RJ, Holodny AI, Shukla-Dave A, Hatzoglou V. Computational Modeling of Interstitial Fluid Pressure and Velocity in Non-small Cell Lung Cancer Brain Metastases Treated With Stereotactic Radiosurgery. Front Neurol 2020; 11:402. [PMID: 32547470 PMCID: PMC7271672 DOI: 10.3389/fneur.2020.00402] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 04/17/2020] [Indexed: 12/11/2022] Open
Abstract
Background: Early imaging-based treatment response assessment of brain metastases following stereotactic radiosurgery (SRS) remains challenging. The aim of this study is to determine whether early (within 12 weeks) intratumoral changes in interstitial fluid pressure (IFP) and velocity (IFV) estimated from computational fluid modeling (CFM) using dynamic contrast-enhanced (DCE) MRI can predict long-term outcomes of lung cancer brain metastases (LCBMs) treated with SRS. Methods: Pre- and post-treatment T1-weighted DCE-MRI data were obtained in 41 patients treated with SRS for intact LCBMs. The imaging response was assessed using RANO-BM criteria. For each lesion, extravasation of contrast agent measured from Extended Tofts pharmacokinetic Model (volume transfer constant, Ktrans) was incorporated into a computational fluid model to estimate tumor IFP and IFV. Estimates of mean IFP and IFV and heterogeneity (skewness and kurtosis) were calculated for each lesion from pre- and post-SRS imaging. The Wilcoxon rank-sum test was utilized to assess for significant differences in IFP, IFV, and IFP/IFV change (Δ) between response groups. Results: Fifty-three lesions from 41 patients were included. Median follow-up time after SRS was 11 months. The objective response (OR) rate (partial or complete response) was 79%, with 21% demonstrating stable disease (SD) or progressive disease (PD). There were significant response group differences for multiple posttreatment and Δ CFM parameters: post-SRS IFP skewness (mean −0.405 vs. −0.691, p = 0.022), IFP kurtosis (mean 2.88 vs. 3.51, p = 0.024), and IFV mean (5.75e-09 vs. 4.19e-09 m/s, p = 0.027); and Δ IFP kurtosis (mean −2.26 vs. −0.0156, p = 0.017) and IFV mean (1.91e-09 vs. 2.38e-10 m/s, p = 0.013). Posttreatment and Δ thresholds predicted non-OR with high sensitivity (sens): post-SRS IFP skewness (−0.432, sens 84%), kurtosis (2.89, sens 84%), and IFV mean (4.93e-09 m/s, sens 79%); and Δ IFP kurtosis (−0.469, sens 74%) and IFV mean (9.90e-10 m/s, sens 74%). Conclusions: Objective response was associated with lower post-treatment tumor heterogeneity, as represented by reductions in IFP skewness and kurtosis. These results suggest that early post-treatment assessment of IFP and IFV can be used to predict long-term response of lung cancer brain metastases to SRS, allowing a timelier treatment modification.
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Affiliation(s)
- Nathaniel Swinburne
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Eve LoCastro
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Neil K Taunk
- Department of Radiation Oncology, Hospital of the University of Pennsylvania, Philadelphia, PA, United States
| | - Akash Shah
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Kathryn Beal
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Behroze Vachha
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Robert J Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Andrei I Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
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18
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Qin H, Zhang V, Bok RA, Santos RD, Cunha JA, Hsu IC, Santos Bs JD, Lee JE, Sukumar S, Larson PEZ, Vigneron DB, Wilson DM, Sriram R, Kurhanewicz J. Simultaneous Metabolic and Perfusion Imaging Using Hyperpolarized 13C MRI Can Evaluate Early and Dose-Dependent Response to Radiation Therapy in a Prostate Cancer Mouse Model. Int J Radiat Oncol Biol Phys 2020; 107:887-896. [PMID: 32339646 DOI: 10.1016/j.ijrobp.2020.04.022] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Revised: 04/10/2020] [Accepted: 04/13/2020] [Indexed: 12/21/2022]
Abstract
PURPOSE To investigate use of a novel imaging approach, hyperpolarized (HP) 13C magnetic resonance imaging (MRI) for simultaneous metabolism and perfusion assessment, to evaluate early and dose-dependent response to radiation therapy (RT) in a prostate cancer mouse model. METHODS AND MATERIALS Transgenic Adenocarcinoma of Mouse Prostate (TRAMP) mice (n = 18) underwent single-fraction RT (4-14 Gy steep dose across the tumor) and were imaged serially at pre-RT baseline and 1, 4, and 7 days after RT using HP 13C MRI with combined [1-13C]pyruvate (metabolic active agent) and [13C]urea (perfusion agent), coupled with conventional multiparametric 1H MRI including T2-weighted, dynamic contrast-enhanced, and diffusion-weighted imaging. Tumor tissues were collected 4 and 7 days after RT for biological correlative studies. RESULTS We found a significant decrease in HP pyruvate-to-lactate conversion in tumors responding to RT, with concomitant significant increases in HP pyruvate-to-alanine conversion and HP urea signal; the opposite changes were observed in tumors resistant to RT. Moreover, HP lactate change was dependent on radiation dose; tumor regions treated with higher radiation doses (10-14 Gy) exhibited a greater decrease in HP lactate signal than low-dose regions (4-7 Gy) as early as 1 day post-RT, consistent with lactate dehydrogenase enzyme activity and expression data. We also found that HP [13C]urea MRI provided assessments of tumor perfusion similar to those provided by 1H dynamic contrast-enhanced MRI in this animal model. However, apparent diffusion coefficien , a conventional 1H MRI functional biomarker, did not exhibit statistically significant changes within 7 days after RT. CONCLUSION These results demonstrate the ability of HP 13C MRI to monitor radiation-induced physiologic changes in a timely and dose-dependent manner, providing the basic science premise for further clinical investigation and translation.
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Affiliation(s)
- Hecong Qin
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California; Graduate Program in Bioengineering, University of California, Berkeley and University of California, San Francisco, California
| | - Vickie Zhang
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - Robert A Bok
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - Romelyn Delos Santos
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - J Adam Cunha
- Department of Radiation Oncology, University of California, San Francisco, California
| | - I-Chow Hsu
- Department of Radiation Oncology, University of California, San Francisco, California
| | - Justin Delos Santos Bs
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - Jessie E Lee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - Subramaniam Sukumar
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - Peder E Z Larson
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California; Graduate Program in Bioengineering, University of California, Berkeley and University of California, San Francisco, California
| | - Daniel B Vigneron
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California; Graduate Program in Bioengineering, University of California, Berkeley and University of California, San Francisco, California
| | - David M Wilson
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - Renuka Sriram
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - John Kurhanewicz
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California; Graduate Program in Bioengineering, University of California, Berkeley and University of California, San Francisco, California.
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19
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Mouawad M, Biernaski H, Brackstone M, Lock M, Yaremko B, Shmuilovich O, Kornecki A, Ben Nachum I, Muscedere G, Lynn K, Prato FS, Thompson RT, Gaede S, Gelman N. DCE-MRI assessment of response to neoadjuvant SABR in early stage breast cancer: Comparisons of single versus three fraction schemes and two different imaging time delays post-SABR. Clin Transl Radiat Oncol 2020; 21:25-31. [PMID: 32021911 PMCID: PMC6993055 DOI: 10.1016/j.ctro.2019.12.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 12/22/2019] [Indexed: 12/25/2022] Open
Abstract
PURPOSE To determine the effect of dose fractionation and time delay post-neoadjuvant stereotactic ablative radiotherapy (SABR) on dynamic contrast-enhanced (DCE)-MRI parameters in early stage breast cancer patients. MATERIALS AND METHODS DCE-MRI was acquired in 17 patients pre- and post-SABR. Five patients were imaged 6-7 days post-21 Gy/1fraction (group 1), six 16-19 days post-21 Gy/1fraction (group 2), and six 16-18 days post-30 Gy/3 fractions every other day (group 3). DCE-MRI scans were performed using half the clinical dose of contrast agent. Changes in the surrounding tissue were quantified using a signal-enhancement threshold metric that characterizes changes in signal-enhancement volume (SEV). Tumour response was quantified using Ktrans and ve (Tofts model) pre- and post-SABR. Significance was assessed using a Wilcoxin signed-rank test. RESULTS All group 1 and 4/6 group 2 patients' SEV increased post-SABR. All group 3 patients' SEV decreased. The mean Ktrans increased for group 1 by 76% (p = 0.043) while group 2 and 3 decreased 15% (p = 0.028) and 34% (p = 0.028), respectively. For ve, there was no significant change in Group 1 (p = 0.35). Groups 2 showed an increase of 24% (p = 0.043), and Group 3 trended toward an increase (23%, p = 0.08). CONCLUSION Kinetic parameters measured 2.5 weeks post-SABR in both single fraction and three fraction groups were indicative of response but only the single fraction protocol led to enhancement in the surrounding tissue. Our results also suggest that DCE-MRI one-week post-SABR may be too early for response assessment, at least for single fraction SABR, whereas 2.5 weeks appears sufficiently long to minimize confounding acute effects.
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Affiliation(s)
- Matthew Mouawad
- Medical Biophysics, Western University, London, Ontario, Canada
| | | | - Muriel Brackstone
- Medical Biophysics, Western University, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
- London Health Sciences Centre, London, Ontario, Canada
- St. Joseph’s Health Care, London, Ontario, Canada
| | - Michael Lock
- London Health Sciences Centre, London, Ontario, Canada
- Department of Oncology, Western University, London, Ontario, Canada
| | - Brian Yaremko
- London Health Sciences Centre, London, Ontario, Canada
- Department of Oncology, Western University, London, Ontario, Canada
| | - Olga Shmuilovich
- Lawson Health Research Institute, London, Ontario, Canada
- St. Joseph’s Health Care, London, Ontario, Canada
- Department of Medical Imaging, Western University, London, Ontario, Canada
| | - Anat Kornecki
- Lawson Health Research Institute, London, Ontario, Canada
- St. Joseph’s Health Care, London, Ontario, Canada
- Department of Medical Imaging, Western University, London, Ontario, Canada
| | - Ilanit Ben Nachum
- Lawson Health Research Institute, London, Ontario, Canada
- St. Joseph’s Health Care, London, Ontario, Canada
- Department of Medical Imaging, Western University, London, Ontario, Canada
| | - Giulio Muscedere
- Lawson Health Research Institute, London, Ontario, Canada
- St. Joseph’s Health Care, London, Ontario, Canada
- Department of Medical Imaging, Western University, London, Ontario, Canada
| | - Kalan Lynn
- Lawson Health Research Institute, London, Ontario, Canada
- London Health Sciences Centre, London, Ontario, Canada
- St. Joseph’s Health Care, London, Ontario, Canada
| | - Frank S. Prato
- Medical Biophysics, Western University, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
- St. Joseph’s Health Care, London, Ontario, Canada
- Department of Medical Imaging, Western University, London, Ontario, Canada
| | - R. Terry Thompson
- Medical Biophysics, Western University, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
| | - Stewart Gaede
- Medical Biophysics, Western University, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
- London Health Sciences Centre, London, Ontario, Canada
- Department of Oncology, Western University, London, Ontario, Canada
| | - Neil Gelman
- Medical Biophysics, Western University, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
- Department of Medical Imaging, Western University, London, Ontario, Canada
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20
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Gharzeddine K, Hatzoglou V, Holodny AI, Young RJ. MR Perfusion and MR Spectroscopy of Brain Neoplasms. Radiol Clin North Am 2019; 57:1177-1188. [PMID: 31582043 DOI: 10.1016/j.rcl.2019.07.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Advances in imaging techniques, such as MR perfusion and spectroscopy, are increasingly indispensable in the management and treatment plans of brain neoplasms: from diagnosing, molecular/genetic typing and grading neoplasms, augmenting biopsy results and improving accuracy, to ultimately directing and monitoring treatment and response. New developments in treatment methods have resulted in new diagnostic challenges for conventional MR imaging, such as pseudoprogression, where MR perfusion has the widest current application. MR spectroscopy is showing increasing promise in noninvasively determining genetic subtypes and, potentially, susceptibility to molecular targeted therapies.
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Affiliation(s)
- Karem Gharzeddine
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, 1275 York Avenue, New York, NY 10065, USA
| | - Andrei I Holodny
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, Weill Cornell Graduate School of Medical Sciences, 1275 York Avenue, New York, NY 10065, USA.
| | - Robert J Young
- Brain Imaging, Neuroradiology Research, Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
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21
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Conte GM, Altabella L, Castellano A, Cuccarini V, Bizzi A, Grimaldi M, Costa A, Caulo M, Falini A, Anzalone N. Comparison of T1 mapping and fixed T1 method for dynamic contrast-enhanced MRI perfusion in brain gliomas. Eur Radiol 2019; 29:3467-3479. [PMID: 30972545 DOI: 10.1007/s00330-019-06122-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 01/14/2019] [Accepted: 02/22/2019] [Indexed: 12/30/2022]
Abstract
OBJECTIVES To compare dynamic contrast-enhanced MRI (DCE-MRI) data obtained using different prebolus T1 values in glioma grading and molecular profiling. METHODS We retrospectively reviewed 83 cases of gliomas: 46 lower-grade gliomas (LGG; grades II and III) and 37 high-grade gliomas (HGG; grade IV). DCE-MRI maps of plasma volume fraction (Vp), extravascular-extracellular volume fraction (Ve), and tracer transfer constant from plasma to tissue (Ktrans) were obtained using a fixed T1 value of 1400 ms and a measured T1 obtained with variable flip angle (VFA). Tumour segmentations were performed and first-order histogram parameters were extracted from volumes of interest (VOIs) after co-registration with the perfusion maps. The two methods were compared using Wilcoxon matched-pairs signed-rank test and Bland-Altman analysis. Diagnostic accuracy was obtained and compared using ROC curve analysis and DeLong's test. RESULTS Perfusion parameters obtained with the fixed T1 value were significantly higher than those obtained with the VFA. As regards diagnostic accuracy, there were no significant differences between the two methods both for glioma grading and molecular classification, except for few parameters of both methods. CONCLUSIONS DCE-MRI data obtained with different prebolus T1 are not comparable and the definition of a prebolus T1 by T1 mapping is not mandatory since it does not improve the diagnostic accuracy of DCE-MRI. KEY POINTS • DCE-MRI data obtained with different prebolus T1 are significantly different, thus not comparable. • The definition of a prebolus T1 by T1 mapping is not mandatory since it does not improve the diagnostic accuracy of DCE-MRI for glioma grading. • The use of a fixed T1 value represents a valid alternative to T1 mapping for DCE-MRI analysis.
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Affiliation(s)
- G M Conte
- Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - L Altabella
- Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - A Castellano
- Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - V Cuccarini
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - A Bizzi
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - M Grimaldi
- Department of Radiology, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy
| | - A Costa
- Department of Neuroradiology, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - M Caulo
- Department of Neuroscience and Imaging and ITAB-Institute of Advanced Biomedical Technologies, University G. D'Annunzio, Chieti, Italy
| | - A Falini
- Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - N Anzalone
- Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy.
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22
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Press RH, Zhong J, Gurbani SS, Weinberg BD, Eaton BR, Shim H, Shu HKG. The Role of Standard and Advanced Imaging for the Management of Brain Malignancies From a Radiation Oncology Standpoint. Neurosurgery 2018; 85:165-179. [DOI: 10.1093/neuros/nyy461] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 08/30/2018] [Indexed: 01/20/2023] Open
Abstract
Abstract
Radiation therapy (RT) plays a critical role in the overall management of many central nervous system (CNS) tumors. Advances in RT treatment planning, with techniques such as intensity modulated radiation therapy, volumetric modulated arc therapy, and stereotactic radiosurgery, now allow the delivery of highly conformal dose with great precision. These techniques rely on high-resolution 3-dimensional anatomical imaging modalities such as computed tomography or magnetic resonance imaging (MRI) scans to accurately and reliably define CNS targets and normal tissue avoidance structures. The integration of cross-sectional imaging into radiation oncology has directly translated into improvements in the therapeutic window of RT, and the union between radiation oncology and imaging is only expected to grow stronger. In addition, advanced imaging modalities including diffusion, perfusion, and spectroscopic MRIs as well as positron emission tomography (PET) scans with novel tracers are being utilized to provide additional insight into tumor biology and behavior beyond anatomy. Together, these standard and advanced imaging modalities hold significant potential to improve future RT delivery and response assessment. In this review, we will discuss the current utilization of standard/advanced imaging for CNS tumors from a radiation oncology perspective as well as the implications of novel MRI and PET modalities currently under investigation.
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Affiliation(s)
- Robert H Press
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, Georgia
| | - Jim Zhong
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, Georgia
| | - Saumya S Gurbani
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia
| | - Brent D Weinberg
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia
| | - Bree R Eaton
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, Georgia
| | - Hyunsuk Shim
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, Georgia
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia
| | - Hui-Kuo G Shu
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, Georgia
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23
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Winter JD, Moraes FY, Chung C, Coolens C. Detectability of radiation-induced changes in magnetic resonance biomarkers following stereotactic radiosurgery: A pilot study. PLoS One 2018; 13:e0207933. [PMID: 30475887 PMCID: PMC6258119 DOI: 10.1371/journal.pone.0207933] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 11/05/2018] [Indexed: 11/18/2022] Open
Abstract
Our objective was to investigate direct voxel-wise relationship between dose and early MR biomarker changes both within and in the high-dose region surrounding brain metastases following stereotactic radiosurgery (SRS). Specifically, we examined the apparent diffusion coefficient (ADC) from diffusion-weighted imaging and the contrast transfer coefficient (Ktrans) and volume of extracellular extravascular space (ve) derived from dynamic contrast-enhanced (DCE) MRI data. We investigated 29 brain metastases in 18 patients using 3 T MRI to collect imaging data at day 0, day 3 and day 20 following SRS. The ADC maps were generated by the scanner and Ktrans and ve maps were generated using in-house software for dynamic tracer-kinetic analysis. To enable spatially-correlated voxel-wise analysis, we developed a registration pipeline to register all ADC, Ktrans and ve maps to the planning MRI scan. To interrogate longitudinal changes, we computed absolute ΔADC, ΔKtrans and Δve for day 3 and 20 post-SRS relative to day 0. We performed a Kruskall-Wallice test on each biomarker between time points and investigated dose correlations within the gross tumour volume (GTV) and surrounding high dose region > 12 Gy via Spearman’s rho. Only ve exhibited significant differences between day 0 and 20 (p < 0.005) and day 3 and 20 (p < 0.05) within the GTV following SRS. Strongest dose correlations were observed for ADC within the GTV (rho = 0.17 to 0.20) and weak correlations were observed for ADC and Ktrans in the surrounding > 12 Gy region. Both ΔKtrans and Δve showed a trend with dose at day 20 within the GTV and > 12 Gy region (rho = -0.04 to -0.16). Weak dose-related decreases in Ktrans and ve within the GTV and high dose region at day 20 most likely reflect underlying vascular responses to radiation. Our study also provides a voxel-wise analysis schema for future MR biomarker studies with the goal of elucidating surrogates for radionecrosis.
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Affiliation(s)
- Jeff D. Winter
- Radiation Medicine Program, Princess Margaret Cancer Center and University Health Network, Toronto, Ontario, Canada
| | - Fabio Y. Moraes
- Radiation Medicine Program, Princess Margaret Cancer Center and University Health Network, Toronto, Ontario, Canada
| | - Caroline Chung
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- TECHNA Institute, University Health Network, Toronto, Ontario, Canada
| | - Catherine Coolens
- Radiation Medicine Program, Princess Margaret Cancer Center and University Health Network, Toronto, Ontario, Canada
- TECHNA Institute, University Health Network, Toronto, Ontario, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Ontario, Canada
- * E-mail:
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