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He R, Zhou J, Xu X, Wei X, Wang F, Li Y. Comparing the predictive value of quantitative magnetic resonance imaging parametric response mapping and conventional perfusion magnetic resonance imaging for clinical outcomes in patients with chronic ischemic stroke. Front Neurosci 2023; 17:1177044. [PMID: 37304032 PMCID: PMC10248057 DOI: 10.3389/fnins.2023.1177044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 05/02/2023] [Indexed: 06/13/2023] Open
Abstract
Predicting clinical outcomes after stroke, using magnetic resonance imaging (MRI) measures, remains a challenge. The purpose of this study was to investigate the prediction of long-term clinical outcomes after ischemic stroke using parametric response mapping (PRM) based on perfusion MRI data. Multiparametric perfusion MRI datasets from 30 patients with chronic ischemic stroke were acquired at four-time points ranging from V2 (6 weeks) to V5 (7 months) after stroke onset. All perfusion MR parameters were analyzed using the classic whole-lesion approach and voxel-based PRM at each time point. The imaging biomarkers from each acquired MRI metric that was predictive of both neurological and functional outcomes were prospectively investigated. For predicting clinical outcomes at V5, it was identified that PRMTmax-, PRMrCBV-, and PRMrCBV+ at V3 were superior to the mean values of the corresponding maps at V3. We identified correlations between the clinical prognosis after stroke and MRI parameters, emphasizing the superiority of the PRM over the whole-lesion approach for predicting long-term clinical outcomes. This indicates that complementary information for the predictive assessment of clinical outcomes can be obtained using PRM analysis. Moreover, new insights into the heterogeneity of stroke lesions revealed by PRM can help optimize the accurate stratification of patients with stroke and guide rehabilitation.
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Affiliation(s)
- Rui He
- Department of Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jia Zhou
- Department of Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaoyu Xu
- Department of Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaoer Wei
- Department of Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Feng Wang
- Department of Neurology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuehua Li
- Department of Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Stoecker JB, Eddinger KC, Pouch AM, Vrudhula A, Jackson BM. Local aortic aneurysm wall expansion measured with automated image analysis. JVS Vasc Sci 2022; 3:48-63. [PMID: 35146458 PMCID: PMC8802047 DOI: 10.1016/j.jvssci.2021.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 11/17/2021] [Indexed: 11/29/2022] Open
Abstract
Background Assessment of regional aortic wall deformation (RAWD) might better predict for abdominal aortic aneurysm (AAA) rupture than the maximal aortic diameter or growth rate. Using sequential computed tomography angiograms (CTAs), we developed a streamlined, semiautomated method of computing RAWD using deformable image registration (dirRAWD). Methods Paired sequential CTAs performed 1 to 2 years apart of 15 patients with AAAs of various shapes and sizes were selected. Using each patient’s initial CTA, the luminal and aortic wall surfaces were segmented both manually and semiautomatically. Next, the same patient’s follow-up CTA was aligned with the first using automated rigid image registration. Deformable image registration was then used to calculate the local aneurysm wall expansion between the sequential scans (dirRAWD). To measure technique accuracy, the deformable registration results were compared with the local displacement of anatomic landmarks (fiducial markers), such as the origin of the inferior mesenteric artery and/or aortic wall calcifications. Additionally, for each patient, the maximal RAWD was manually measured for each aneurysm and was compared with the dirRAWD at the same location. Results The technique was successful in all patients. The mean landmark displacement error was 0.59 ± 0.93 mm with no difference between true landmark displacement and deformable registration landmark displacement by Wilcoxon rank sum test (P = .39). The absolute difference between the manually measured maximal RAWD and dirRAWD was 0.27 ± 0.23 mm, with a relative difference of 7.9% and no difference using the Wilcoxon rank sum test (P = .69). No differences were found in the maximal dirRAWD when derived using a purely manual AAA segmentation compared with using semiautomated AAA segmentation (P = .55). Conclusions We found accurate and automated RAWD measurements were feasible with clinically insignificant errors. Using semiautomated AAA segmentations for deformable image registration methods did not alter maximal dirRAWD accuracy compared with using manual AAA segmentations. Future work will compare dirRAWD with finite element analysis–derived regional wall stress and determine whether dirRAWD might serve as an independent predictor of rupture risk. Current abdominal aortic aneurysm (AAA) surveillance methods are limited to assessments of the maximal diameter, which cannot accurately predict for AAA expansion and rupture risk. Automated assessment of AAA expansion across the entire three-dimensional geometry of the aneurysm could better describe aneurysm growth and could substantially inform management decisions, including the indications for repair. We have developed an accurate and streamlined approach to assessing local three-dimensional AAA expansion with submillimeter accuracy using computed tomography imaging obtained during routine aneurysm surveillance. This novel process does not require significant user expertise nor computer processing power and can be performed using open-source software readily accessible to both scientists and clinicians.
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Affiliation(s)
- Jordan B. Stoecker
- Division of Vascular Surgery and Endovascular Therapy, Department of Surgery, Hospital of the University of Pennsylvania, Philadelphia, Pa
- Correspondence: Jordan B. Stoecker, MD, Division of Vascular Surgery and Endovascular Therapy, Department of Surgery, Hospital of the University of Pennsylvania, 3400 Spruce St, 4th FL, Silverstein Bldg, Philadelphia, PA 19146
| | - Kevin C. Eddinger
- Division of Vascular Surgery and Endovascular Therapy, Department of Surgery, Hospital of the University of Pennsylvania, Philadelphia, Pa
| | - Alison M. Pouch
- Division of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pa
| | - Amey Vrudhula
- Icahn School of Medicine at Mount Sinai, New York, NY
| | - Benjamin M. Jackson
- Division of Vascular Surgery and Endovascular Therapy, Department of Surgery, Hospital of the University of Pennsylvania, Philadelphia, Pa
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Hoff BA, Lemasson B, Chenevert TL, Luker GD, Tsien CI, Amouzandeh G, Johnson TD, Ross BD. Parametric Response Mapping of FLAIR MRI Provides an Early Indication of Progression Risk in Glioblastoma. Acad Radiol 2021; 28:1711-1720. [PMID: 32928633 DOI: 10.1016/j.acra.2020.08.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 08/11/2020] [Accepted: 08/13/2020] [Indexed: 12/14/2022]
Abstract
RATIONALE AND OBJECTIVES Glioblastoma image evaluation utilizes Magnetic Resonance Imaging contrast-enhanced, T1-weighted, and noncontrast T2-weighted fluid-attenuated inversion recovery (FLAIR) acquisitions. Disease progression assessment relies on changes in tumor diameter, which correlate poorly with survival. To improve treatment monitoring in glioblastoma, we investigated serial voxel-wise comparison of anatomically-aligned FLAIR signal as an early predictor of GBM progression. MATERIALS AND METHODS We analyzed longitudinal normalized FLAIR images (rFLAIR) from 52 subjects using voxel-wise Parametric Response Mapping (PRM) to monitor volume fractions of increased (PRMrFLAIR+), decreased (PRMrFLAIR-), or unchanged (PRMrFLAIR0) rFLAIR intensity. We determined response by rFLAIR between pretreatment and 10 weeks posttreatment. Risk of disease progression in a subset of subjects (N = 26) with stable disease or partial response as defined by Response Assessment in Neuro-Oncology (RANO) criteria was assessed by PRMrFLAIR between weeks 10 and 20 and continuously until the PRMrFLAIR+ exceeded a defined threshold. RANO defined criteria were compared with PRM-derived outcomes for tumor progression detection. RESULTS Patient stratification for progression-free survival (PFS) and overall survival (OS) was achieved at week 10 using RANO criteria (PFS: p <0.0001; OS: p <0.0001), relative change in FLAIR-hyperintense volume (PFS: p = 0.0011; OS: p <0.0001), and PRMrFLAIR+ (PFS: p <0.01; OS: p <0.001). PRMrFLAIR+ also stratified responding patients' progression between weeks 10 and 20 (PFS: p <0.05; OS: p = 0.01) while changes in FLAIR-volume measurements were not predictive. As a continuous evaluation, PRMrFLAIR+ exceeding 10% stratified patients for PFA after 5.6 months (p<0.0001), while RANO criteria did not stratify patients until 15.4 months (p <0.0001). CONCLUSION PRMrFLAIR may provide an early biomarker of disease progression in glioblastoma.
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Ross BD, Chenevert TL, Meyer CR. Retrospective Registration in Molecular Imaging. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00080-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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Drisis S, El Adoui M, Flamen P, Benjelloun M, Dewind R, Paesmans M, Ignatiadis M, Bali M, Lemort M. Early prediction of neoadjuvant treatment outcome in locally advanced breast cancer using parametric response mapping and radial heterogeneity from breast MRI. J Magn Reson Imaging 2019; 51:1403-1411. [PMID: 31737963 DOI: 10.1002/jmri.26996] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 10/25/2019] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Early prediction of nonresponse is essential in order to avoid inefficient treatments. PURPOSE To evaluate if parametrical response mapping (PRM)-derived biomarkers could predict early morphological response (EMR) and pathological complete response (pCR) 24-72 hours after initiation of chemotherapy treatment and whether concentric analysis of nonresponding PRM regions could better predict response. STUDY TYPE This was a retrospective analysis of prospectively acquired cohort, nonrandomized, monocentric, diagnostic study. POPULATION Sixty patients were initially recruited, with 39 women participating in the final cohort. FIELD STRENGTH/SEQUENCE A 1.5T scanner was used for MRI examinations. ASSESSMENT Dynamic contrast-enhanced (DCE)-MR images were acquired at baseline (timepoint 1, TP1), 24-72 hours after the first chemotherapy (TP2), and after the end of anthracycline treatment (TP3). PRM was performed after fusion of T1 subtraction images from TP1 and TP2 using an affine registration algorithm. Pixels with an increase of more than 10% of their value (PRMdce+) were corresponding nonresponding regions of the tumor. Patients with a decrease of maximum diameter (%dDmax) between TP1 and TP3 of more than 30% were defined as EMR responders. pCR patients achieved a residual cancer burden score of 0. STATISTICAL TESTS T-test, receiver operating characteristic (ROC) curves, and logistic regression were used for the analysis. RESULTS PRM showed a statistical difference between pCR response groups (P < 0.01) and AUC of 0.88 for the prediction of non-pCR. Logistic regression analysis demonstrated that PRMdce+ and Grade II were significant (P < 0.01) for non-pCR prediction (AUC = 0.94). Peripheral tumor region demonstrated higher performance for the prediction of non-pCR (AUC = 0.85) than intermediate and central zones; however, statistical comparison showed no significant difference. DATA CONCLUSION PRM could be predictive of non-pCR 24-72 hours after initiation of chemotherapy treatment. Moreover, the peripheral region showed increased AUC for non-pCR prediction and increased signal intensity during treatment for non-pCR tumors, information that could be used for optimal tissue sampling. LEVEL OF EVIDENCE 1 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2020;51:1403-1411.
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Affiliation(s)
| | - Mohammed El Adoui
- Medical Imaging Department, Polytechnic University of Mons, Mons, Belgium
| | - Patrick Flamen
- Nuclear Department, Institute Jules Bordet, Brussels, Belgium
| | | | - Roland Dewind
- Pathology Department, Institute Jules Bordet, Brussels, Belgium
| | - Mariane Paesmans
- Statistics Department, Institute Jules Bordet, Brussels, Belgium
| | | | - Maria Bali
- Radiology Department, Institute Jules Bordet, Brussels, Belgium
| | - Marc Lemort
- Radiology Department, Institute Jules Bordet, Brussels, Belgium
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Sjöholm T, Ekström S, Strand R, Ahlström H, Lind L, Malmberg F, Kullberg J. A whole-body FDG PET/MR atlas for multiparametric voxel-based analysis. Sci Rep 2019; 9:6158. [PMID: 30992502 PMCID: PMC6467986 DOI: 10.1038/s41598-019-42613-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 04/04/2019] [Indexed: 01/12/2023] Open
Abstract
Quantitative multiparametric imaging is a potential key application for Positron Emission Tomography/Magnetic Resonance (PET/MR) hybrid imaging. To enable objective and automatic voxel-based multiparametric analysis in whole-body applications, the purpose of this study was to develop a multimodality whole-body atlas of functional 18F-fluorodeoxyglucose (FDG) PET and anatomical fat-water MR data of adults. Image registration was used to transform PET/MR images of healthy control subjects into male and female reference spaces, producing a fat-water MR, local tissue volume and FDG PET whole-body normal atlas consisting of 12 male (66.6 ± 6.3 years) and 15 female (69.5 ± 3.6 years) subjects. Manual segmentations of tissues and organs in the male and female reference spaces confirmed that the atlas contained adequate physiological and anatomical values. The atlas was applied in two anomaly detection tasks as proof of concept. The first task automatically detected anomalies in two subjects with suspected malignant disease using FDG data. The second task successfully detected abnormal liver fat infiltration in one subject using fat fraction data.
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Affiliation(s)
- Therese Sjöholm
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
| | - Simon Ekström
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Robin Strand
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Håkan Ahlström
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Antaros Medical AB, Mölndal, Sweden
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Filip Malmberg
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Joel Kullberg
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Antaros Medical AB, Mölndal, Sweden
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A PRM approach for early prediction of breast cancer response to chemotherapy based on registered MR images. Int J Comput Assist Radiol Surg 2018; 13:1233-1243. [PMID: 29790078 DOI: 10.1007/s11548-018-1790-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Accepted: 05/09/2018] [Indexed: 12/17/2022]
Abstract
PURPOSE This study aims to provide and optimize a performing algorithm for predicting the breast cancer response rate to the first round of chemotherapy using Magnetic Resonance Imaging (MRI). This provides an early recognition of breast tumor reaction to chemotherapy by using the Parametric Response Map (PRM) method. METHODS PRM may predict the breast cancer response to chemotherapy by analyzing voxel-by-voxel temporal intra-tumor changes during one round of chemotherapy. Indeed, the tumor recognizes intra-tumor changes concerning its vascularity, which is an important criterion in the present study. This method is mainly based on spatial image affine registration between the breast tumor MRI volumes, acquired before and after the first cycle of chemotherapy, and region growing segmentation of the tumor volume. To evaluate our method, we used a retrospective study of 40 patients provided by a collaborating institute. RESULTS PRM allows a color map to be created with the percentages of positive, negative and stable breast tumor response during the first round of chemotherapy, identifying each region with its response rate. We assessed the accuracy of the proposed method using technical and medical validation methods. The technical validation was based on landmarks-based registration and fully manual segmentation. The medical evaluation was based on the accuracy calculation of the standard reference of anatomic pathology. The p-values and the Area Under the Curve (AUC) of the Receiver Operating Characteristics were calculated to evaluate the proposed PRM method. CONCLUSION We performed and evaluated the proposed PRM method to study and analyze the behavior of a tumor during the first round of chemotherapy, based on the intra-tumor changes of MR breast tumor images. The AUC obtained for the PRM method is considered as relevant in the early prediction of breast tumor response.
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Burris NS, Hoff BA, Kazerooni EA, Ross BD. Vascular Deformation Mapping (VDM) of Thoracic Aortic Enlargement in Aneurysmal Disease and Dissection. ACTA ACUST UNITED AC 2017; 3:163-173. [PMID: 29124128 PMCID: PMC5675573 DOI: 10.18383/j.tom.2017.00015] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Thoracic aortic aneurysm is a common and lethal disease that requires regular imaging surveillance to determine timing of surgical repair and prevent major complications such as rupture. Current cross-sectional imaging surveillance techniques, largely based on computed tomography angiography, are focused on measurement of maximal aortic diameter, although this approach is limited to fixed anatomic positions and is prone to significant measurement error. Here we present preliminary results showing the feasibility of a novel technique for assessing change in aortic dimensions, termed vascular deformation mapping (VDM). This technique allows quantification of 3-dimensional changes in the aortic wall geometry through nonrigid coregistration of computed tomography angiography images and spatial Jacobian analysis of aortic deformation. Through several illustrative cases we demonstrate that this method can be used to measure changes in the aortic wall geometry among patients with stable and enlarging thoracic aortic aneurysm and dissection. Furthermore, VDM results yield observations about the presence, distribution, and rate of aortic wall deformation that are not apparent by routine clinical evaluation. Finally, we show the feasibility of superposing patient-specific VDM results on a 3-dimensional aortic model using color 3D printing and discuss future directions and potential applications for the VDM technique.
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Affiliation(s)
| | - Benjamin A Hoff
- Department of Radiology, University of Michigan, Ann Arbor, MI.,Center for Molecular Imaging, University of Michigan, Ann Arbor, MI
| | | | - Brian D Ross
- Department of Radiology, University of Michigan, Ann Arbor, MI.,Center for Molecular Imaging, University of Michigan, Ann Arbor, MI.,Department of Biological Chemistry, University of Michigan, Ann Arbor, MI
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Volume fractions of DCE-MRI parameter as early predictor of histologic response in soft tissue sarcoma: A feasibility study. Eur J Radiol 2017; 95:228-235. [PMID: 28987672 DOI: 10.1016/j.ejrad.2017.08.021] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Revised: 07/01/2017] [Accepted: 08/22/2017] [Indexed: 12/17/2022]
Abstract
OBJECTIVE To find early predictors of histologic response in soft tissue sarcoma through volume transfer constant (Ktrans) analysis based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS 11 Patients with soft tissue sarcoma of the lower extremity that underwent preoperative chemoradiotherapy followed by limb salvage surgery were included in this retrospective study. For each patient, DCE-MRI data sets were collected before and two weeks after therapy initiation, and histologic tumor cell necrosis rate (TCNR) was reported at surgery. The DCE-MRI volumes were aligned by registration. Then, the aligned volumes were used to obtain the Ktrans variation map. Accordingly, three sub-volumes (with increased, decreased or unchanged Ktrans) were defined and identified, and fractions of the sub-volumes, denoted as F+, F- and F0, respectively, were calculated. The predictive ability of volume fractions was determined by using area under a receiver operating characteristic curve (AUC). Linear regression analysis was performed to investigate the relationship between TCNR and volume fractions. In addition, the Ktrans values of the sub-volumes were compared. RESULTS The AUC for F- (0.896) and F0 (0.833) were larger than that for change of tumor longest diameter ΔD (0.625) and the change of mean KtransΔKtrans¯ (0.792). Moreover, the regression results indicated that TCNR was directly proportional to F0 (R2=0.75, P=0.0003), while it was inversely proportional to F- (R2=0.77, P=0.0002). However, TCNR had relatively weak linear relationship with ΔKtrans¯ (R2=0.64, P=0.0018). Additionally, TCNR did not have linear relationship with DD (R2=0.16, P=0.1246). CONCLUSION The volume fraction F- and F0 have potential as early predictors of soft tissue sarcoma histologic response.
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Choi SJ, Kim J, Kim HS, Park H. Parametric response mapping of dynamic CT: enhanced prediction of survival in hepatocellular carcinoma patients treated with transarterial chemoembolization. Abdom Radiol (NY) 2017; 42:1871-1879. [PMID: 28204855 DOI: 10.1007/s00261-017-1082-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
PURPOSE The aim of this study was to evaluate the prognostic significance of parametric response mapping (PRM) analysis for hepatocellular carcinoma (HCC) patients undergoing transarterial chemoembolization (TACE). METHODS We recruited 65 HCC patients who underwent TACE. These patients underwent longitudinal multiphasic CT before and after TACE. We applied PRM analysis to the baseline CT before TACE and first/second follow-up CTs. The results of PRM analyses were used to stratify patients into responders and non-responders. Overall survival was compared between the two groups. An independent survival analysis using conventional radiological assessments was performed, and the results were compared with PRM results. Univariate and multivariate analyses were performed to identify clinical factors affecting survival. RESULTS The PRM analyses demonstrated that the responding group had a median survival of 529 days, while the non-responding group had a median survival of 263 days [hazard ratio (HR) 12.9, p < 0.05 for differences in survival]. The manual analyses indicated median survivals of 491 and 329 days for the responding and non-responding groups, respectively (HR 2.7, p < 0.05). Tumor size, albumin level, and PRM values were found to be significantly related to overall survival after univariate and multivariate analyses. CONCLUSIONS The PRM analysis could be a better predictor of overall survival for patients with HCC undergoing TACE than conventional radiological assessments.
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Affiliation(s)
- Seung Joon Choi
- Department of Radiology, Gachon University Gil Hospital, Incheon, Korea
| | - Jonghoon Kim
- Department of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea
| | - Hyung Sik Kim
- Department of Radiology, Gachon University Gil Hospital, Incheon, Korea
| | - Hyunjin Park
- School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, 16419, Korea.
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea.
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Fernandes L, Gulati N, Fernandes Y, Mesquita AM, Sardessai M, Lammers JWJ, Mohamed Hoesein FA, ten Hacken NH, van den Berge M, Galbán CJ, Siddiqui S. Small airway imaging phenotypes in biomass- and tobacco smoke-exposed patients with COPD. ERJ Open Res 2017; 3:00124-2016. [PMID: 28828380 PMCID: PMC5555765 DOI: 10.1183/23120541.00124-2016] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2016] [Accepted: 02/19/2017] [Indexed: 01/01/2023] Open
Abstract
Biomass and functional small airway disease http://ow.ly/gXu730abpKu.
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Affiliation(s)
| | - Nandani Gulati
- Dept of Pulmonary Medicine, Goa Medical College, Goa, India
| | | | | | | | - Jan-Willem J. Lammers
- University Medical Center Utrecht, Dept of Pulmonary Diseases, Utrecht, The Netherlands
| | | | - Nick H.T. ten Hacken
- University of Groningen, University Medical Center Groningen, Dept of Pulmonary Disease, Groningen, The Netherlands
| | - Maarten van den Berge
- University of Groningen, University Medical Center Groningen, Dept of Pulmonary Disease, Groningen, The Netherlands
| | - Craig J. Galbán
- Dept of Radiology, Center for Molecular Imaging, University of Michigan, Ann Arbor, MI, USA
| | - Salman Siddiqui
- University of Leicester/NIHR Respiratory Biomedical Research Unit, Leicester, UK
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12
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Keith L, Ross BD, Galbán CJ, Luker GD, Galbán S, Zhao B, Guo X, Chenevert TL, Hoff BA. Semiautomated Workflow for Clinically Streamlined Glioma Parametric Response Mapping. Tomography 2017; 2:267-275. [PMID: 28286871 PMCID: PMC5345939 DOI: 10.18383/j.tom.2016.00181] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Management of glioblastoma multiforme remains a challenging problem despite recent advances in targeted therapies. Timely assessment of therapeutic agents is hindered by the lack of standard quantitative imaging protocols for determining targeted response. Clinical response assessment for brain tumors is determined by volumetric changes assessed at 10 weeks post-treatment initiation. Further, current clinical criteria fail to use advanced quantitative imaging approaches, such as diffusion and perfusion magnetic resonance imaging. Development of the parametric response mapping (PRM) applied to diffusion-weighted magnetic resonance imaging has provided a sensitive and early biomarker of successful cytotoxic therapy in brain tumors while maintaining a spatial context within the tumor. Although PRM provides an earlier readout than volumetry and sometimes greater sensitivity compared with traditional whole-tumor diffusion statistics, it is not routinely used for patient management; an automated and standardized software for performing the analysis and for the generation of a clinical report document is required for this. We present a semiautomated and seamless workflow for image coregistration, segmentation, and PRM classification of glioblastoma multiforme diffusion-weighted magnetic resonance imaging scans. The software solution can be integrated using local hardware or performed remotely in the cloud while providing connectivity to existing picture archive and communication systems. This is an important step toward implementing PRM analysis of solid tumors in routine clinical practice.
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Affiliation(s)
| | - Brian D Ross
- Department of Radiology, Center for Molecular Imaging, University of Michigan, Ann Arbor, Michigan
| | - Craig J Galbán
- Department of Radiology, Center for Molecular Imaging, University of Michigan, Ann Arbor, Michigan
| | - Gary D Luker
- Department of Radiology, Center for Molecular Imaging, University of Michigan, Ann Arbor, Michigan
| | - Stefanie Galbán
- Department of Radiology, Center for Molecular Imaging, University of Michigan, Ann Arbor, Michigan
| | - Binsheng Zhao
- Department of Radiology, Columbia University College of Physicians and Surgeons, New York, New York
| | - Xiaotao Guo
- Department of Radiology, Columbia University College of Physicians and Surgeons, New York, New York
| | - Thomas L Chenevert
- Department of Radiology, Center for Molecular Imaging, University of Michigan, Ann Arbor, Michigan
| | - Benjamin A Hoff
- Department of Radiology, Center for Molecular Imaging, University of Michigan, Ann Arbor, Michigan
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Galbán CJ, Hoff BA, Chenevert TL, Ross BD. Diffusion MRI in early cancer therapeutic response assessment. NMR IN BIOMEDICINE 2017; 30:10.1002/nbm.3458. [PMID: 26773848 PMCID: PMC4947029 DOI: 10.1002/nbm.3458] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Revised: 11/09/2015] [Accepted: 11/12/2015] [Indexed: 05/05/2023]
Abstract
Imaging biomarkers for the predictive assessment of treatment response in patients with cancer earlier than standard tumor volumetric metrics would provide new opportunities to individualize therapy. Diffusion-weighted MRI (DW-MRI), highly sensitive to microenvironmental alterations at the cellular level, has been evaluated extensively as a technique for the generation of quantitative and early imaging biomarkers of therapeutic response and clinical outcome. First demonstrated in a rodent tumor model, subsequent studies have shown that DW-MRI can be applied to many different solid tumors for the detection of changes in cellularity as measured indirectly by an increase in the apparent diffusion coefficient (ADC) of water molecules within the lesion. The introduction of quantitative DW-MRI into the treatment management of patients with cancer may aid physicians to individualize therapy, thereby minimizing unnecessary systemic toxicity associated with ineffective therapies, saving valuable time, reducing patient care costs and ultimately improving clinical outcome. This review covers the theoretical basis behind the application of DW-MRI to monitor therapeutic response in cancer, the analytical techniques used and the results obtained from various clinical studies that have demonstrated the efficacy of DW-MRI for the prediction of cancer treatment response. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
| | | | | | - B. D. Ross
- Correspondence to: B. D. Ross, University of Michigan School of Medicine, Center for Molecular Imaging and Department of Radiology, Biomedical Sciences Research Building, 109 Zina Pitcher Place, Ann Arbor, MI 48109, USA.
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Noro A, Nakamura T, Hirai T, Haga M, Kobayashi T, Hayashi A, Kozuka Y, Nakai T, Ogura T, Ogawa T. Impact of parametric imaging on contrast-enhanced ultrasound of breast cancer. J Med Ultrason (2001) 2016; 43:227-35. [DOI: 10.1007/s10396-015-0692-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2015] [Accepted: 11/10/2015] [Indexed: 10/22/2022]
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Wen Q, Jalilian L, Lupo JM, Li Y, Roy R, Molinaro AM, Chang SM, Prados M, Butowski N, Clarke J, Nelson SJ. Association of Diffusion and Anatomic Imaging Parameters with Survival for Patients with Newly Diagnosed Glioblastoma Participating in Two Different Clinical Trials. Transl Oncol 2015; 8:446-55. [PMID: 26692525 PMCID: PMC4700297 DOI: 10.1016/j.tranon.2015.10.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Revised: 09/14/2015] [Accepted: 10/02/2015] [Indexed: 01/20/2023] Open
Abstract
PURPOSE: To evaluate the time course and association with survival of anatomic lesion volumes and diffusion imaging parameters for patients with newly diagnosed glioblastoma who were treated with radiation and concurrently with either temozolomide and enzastaurin (TMZ+enza cohort) or temozolomide, erlotonib, and bevaciumab (TMZ+erl+bev cohort). MATERIALS AND METHODS: Regions of interest corresponding to the contrast-enhancing and hyperintense lesions on T2-weighted images were generated. Diffusion-weighted images were processed to provide maps of apparent diffusion coefficient, fractional anisotropy, and longitudinal and radial eigenvalues. Histograms of diffusion values were generated and summary statistics calculated. Cox proportional hazards models were employed to assess the association of representative imaging parameters with survival with adjustments for age, Karnofsky performance status, and extent of resection. RESULTS: Although progression-free survival was significantly longer for the TMZ+erl+bev cohort (12.8 vs 7.3 months), there was no significant difference in overall survival between the two populations (17.0 vs 17.8 months). The median contrast-enhancing lesion volumes decreased from 6.3 to 1.9 cm3 from baseline to the postradiotherapy scan for patients in the TMZ+enza cohort and from 2.8 to 0.9cm3 for the TMZ+erl+bev cohort. Changes in the T2 lesion volumes were only significant for the latter cohort (26.5 to 11.9 cm3). The median apparent diffusion coefficient and related diffusion parameters were significantly increased for the TMZ+enza cohort (1054 to 1225 μm2/s). More of the anatomic parameters were associated with survival for the TMZ+enza cohort, whereas more diffusion parameters were associated with survival for the TMZ+erl+bev cohort. CONCLUSION: The early changes in anatomic and diffusion imaging parameters and their association with survival reflected differences in the mechanisms of action of the treatments that were being given. This suggests that integrating diffusion metrics and anatomic lesion volumes into the Response Assessment in Neuro-Oncology criteria would assist in interpreting treatment-induced changes and predicting outcome in patients with newly diagnosed glioblastoma who are receiving such combination treatments.
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Affiliation(s)
- Qiuting Wen
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA United States; UCSF/UCB Joint Graduate Group in Bioengineering, University of California, San Francisco, San Francisco, CA United States
| | - Laleh Jalilian
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA United States
| | - Janine M Lupo
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA United States
| | - Yan Li
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA United States
| | - Ritu Roy
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA United States; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA United States
| | - Annette M Molinaro
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA United States; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA United States
| | - Susan M Chang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA United States
| | - Michael Prados
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA United States
| | - Nicholas Butowski
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA United States
| | - Jennifer Clarke
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA United States
| | - Sarah J Nelson
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA United States; UCSF/UCB Joint Graduate Group in Bioengineering, University of California, San Francisco, San Francisco, CA United States; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA United States.
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Galbán CJ, Ma B, Malyarenko D, Pickles MD, Heist K, Henry NL, Schott AF, Neal CH, Hylton NM, Rehemtulla A, Johnson TD, Meyer CR, Chenevert TL, Turnbull LW, Ross BD. Multi-site clinical evaluation of DW-MRI as a treatment response metric for breast cancer patients undergoing neoadjuvant chemotherapy. PLoS One 2015; 10:e0122151. [PMID: 25816249 PMCID: PMC4376686 DOI: 10.1371/journal.pone.0122151] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2014] [Accepted: 02/18/2015] [Indexed: 01/22/2023] Open
Abstract
PURPOSE To evaluate diffusion weighted MRI (DW-MR) as a response metric for assessment of neoadjuvant chemotherapy (NAC) in patients with primary breast cancer using prospective multi-center trials which provided MR scans along with clinical outcome information. MATERIALS AND METHODS A total of 39 patients with locally advanced breast cancer accrued from three different prospective clinical trials underwent DW-MR examination prior to and at 3-7 days (Hull University), 8-11 days (University of Michigan) and 35 days (NeoCOMICE) post-treatment initiation. Thirteen patients, 12 of which participated in treatment response study, from UM underwent short interval (<1hr) MRI examinations, referred to as "test-retest" for examination of repeatability. To further evaluate stability in ADC measurements, a thermally controlled diffusion phantom was used to assess repeatability of diffusion measurements. MRI sequences included contrast-enhanced T1-weighted, when appropriate, and DW images acquired at b-values of 0 and 800 s/mm2. Histogram analysis and a voxel-based analytical technique, the Parametric Response Map (PRM), were used to derive diffusion response metrics for assessment of treatment response prediction. RESULTS Mean tumor apparent diffusion coefficient (ADC) values generated from patient test-retest examinations were found to be very reproducible (|ΔADC|<0.1x10-3mm2/s). This data was used to calculate the 95% CI from the linear fit of tumor voxel ADC pairs of co-registered examinations (±0.45x10-3mm2/s) for PRM analysis of treatment response. Receiver operating characteristic analysis identified the PRM metric to be predictive of outcome at the 8-11 (AUC = 0.964, p = 0.01) and 35 day (AUC = 0.770, p = 0.05) time points (p<.05) while whole-tumor ADC changes where significant at the later 35 day time interval (AUC = 0.825, p = 0.02). CONCLUSION This study demonstrates the feasibility of performing a prospective analysis of DW-MRI as a predictive biomarker of NAC in breast cancer patients. In addition, we provide experimental evidence supporting the use of sensitive analytical tools, such as PRM, for evaluating ADC measurements.
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Affiliation(s)
- Craig J. Galbán
- Departments of Radiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Bing Ma
- Departments of Radiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Dariya Malyarenko
- Departments of Radiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Martin D. Pickles
- Centre for MR Investigations, Hull York Medical School, University of Hull, Hull, United Kingdom
| | - Kevin Heist
- Departments of Radiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Norah L. Henry
- Departments of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Anne F. Schott
- Departments of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Colleen H. Neal
- Departments of Radiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Nola M. Hylton
- Department of Radiology, University of California San Francisco, San Francisco, California, United States of America
| | - Alnawaz Rehemtulla
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Timothy D. Johnson
- Departments of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Charles R. Meyer
- Departments of Radiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Thomas L. Chenevert
- Departments of Radiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Lindsay W. Turnbull
- Centre for MR Investigations, Hull York Medical School, University of Hull, Hull, United Kingdom
| | - Brian D. Ross
- Departments of Radiology, University of Michigan, Ann Arbor, Michigan, United States of America
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Baikeev RF, Gubanov RA, Sadikov KK, Safina SZ, Muhamadiev FF, Sibgatullin TA. Dynamic properties of water in breast pathology depend on the histological compounds: distinguishing tissue malignancy by water diffusion coefficients. BMC Res Notes 2014; 7:887. [PMID: 25487139 PMCID: PMC4295355 DOI: 10.1186/1756-0500-7-887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2013] [Accepted: 11/18/2014] [Indexed: 11/11/2022] Open
Abstract
Background The parameters that characterize the intricate water diffusion in tumors may also reveal their distinct pathology. Specifically, characterization of breast cancer could be aided by diffusion magnetic resonance. The present in vitro study aimed to discover connections between the NMR biexponential diffusion parameters [fast diffusion phase (DFDP ), slow diffusion phase (DSDP ), and spin population of fast diffusion phase (P1)] and the histological constituents of nonmalignant (control) and malignant human breast tissue. It also investigates whether the diffusion coefficients indicate tissue status. Methods Post-surgical specimens of control (mastopathy and peritumoral tissues) and malignant human breast tissue were placed in an NMR spectrometer and diffusion sequences were applied. The resulting decay curves were analyzed by a biexponential model, and slow and fast diffusion parameters as well as percentage signal were identified. The same samples were also histologically examined and their percentage composition of several tissue constituents were measured: parenchyma (P), stroma (St), adipose tissue (AT), vessels (V) , pericellular edema (PCE), and perivascular edema (PVE). Correlations between the biexponential model parameters and tissue types were evaluated for different specimens. The effects of tissue composition on the biexponential model parameters, and the effects of histological and model parameters on cancer probability, were determined by non-linear regression. Results Meaningful relationships were found among the in vitro data. The dynamic parameters of water in breast tissue are stipulated by the histological constituents of the tissues (P, St, AT, PCE, and V). High coefficients of determination (R2) were obtained in the non-linear regression analysis: DFDP (R2 = 0.92), DSDP (R2 = 0.81), and P1(R2 = 0.93). In the cancer probability analysis, the informative value (R2) of the obtained equations of cancer probability in distinguishing tissue malignancy depended on the parameters input to the model. In order of increasing value, these equations were: cancer probability (P, St, AT, PCE, V) (R2 = 0.66), cancer probability (DFDP, DSDP)(R2 = 0.69), cancer probability (DFDP, DSDP, P1) (R2 = 0.85). Conclusion Histological tissue components are related to the diffusion biexponential model parameters. From these parameters, the relative probability of cancer in a given specimen can be determined with some certainty.
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Affiliation(s)
- Rustem F Baikeev
- Department of Biochemistry, Kazan State Medical University, Butlerova St,, 49, Kazan, Tatarstan, Russia.
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