1
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Evaluation of functional and metabolic tumor volume using voxel-wise analysis in childhood rhabdomyosarcoma. Pediatr Radiol 2023; 53:438-449. [PMID: 36399161 PMCID: PMC9968707 DOI: 10.1007/s00247-022-05540-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/21/2022] [Accepted: 10/14/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND Cross-sectional imaging-based morphological characteristics of pediatric rhabdomyosarcoma have failed to predict outcomes. OBJECTIVE To evaluate the feasibility and possible value of generating tumor sub-volumes using voxel-wise analysis of metabolic and functional data from positron emission tomography/magnetic resonance imaging (PET/MR) or PET/computed tomography (CT) and MRI in rhabdomyosarcoma. MATERIALS AND METHODS Thirty-four examinations in 17 patients who received PET/MRI or PET/CT plus MRI were analyzed. The volume of interest included total tumor volume before and after therapy. Apparent diffusion coefficients (ADC) and standard uptake values (SUV) were determined voxel-wise. Voxels were assigned to three different groups based on ADC and SUV: "viable tumor tissue," "intermediate tissue" or "possible necrosis." In a second approach, data were grouped into three clusters using the Gaussian mixture model. The ratio of these clusters to total tumor volume and changes due to chemotherapy were correlated with clinical and histopathological data. RESULTS After chemotherapy, the proportion of voxels in the different groups changed significantly. A significant reduction of the proportion of voxels assigned to cluster 1 was found, from a mean of 36.4% to 2.5% (P < 0.001). There was a significant increase in the proportion of voxels in cluster 3 following chemotherapy from 24.8% to 81.6% (P = 0.02). The proportion of voxels in cluster 2 differed depending on the presence or absence of tumor recurrence, falling from 48% to 10% post-chemotherapy in the group with no tumor recurrence (P < 0.05) and from 29% to 23% (P > 0.05) in the group with tumor recurrence. CONCLUSION Voxel-wise evaluation of multimodal data in rhabdomyosarcoma is feasible. Our initial results suggest that the different distribution of sub-volumes before and after therapy may have prognostic significance.
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2
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Bi C, Ou MY, Bouhrara M, Spencer RG. Span of regularization for solution of inverse problems with application to magnetic resonance relaxometry of the brain. Sci Rep 2022; 12:20194. [PMID: 36418516 PMCID: PMC9684479 DOI: 10.1038/s41598-022-22739-3] [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: 04/11/2022] [Accepted: 10/19/2022] [Indexed: 11/25/2022] Open
Abstract
We present a new regularization method for the solution of the Fredholm integral equation (FIE) of the first kind, in which we incorporate solutions corresponding to a range of Tikhonov regularizers into the end result. This method identifies solutions within a much larger function space, spanned by this set of regularized solutions, than is available to conventional regularization methods. An additional key development is the use of dictionary functions derived from noise-corrupted inversion of the discretized FIE. In effect, we combine the stability of solutions with greater degrees of regularization with the resolution of those that are less regularized. The span of regularizations (SpanReg) method may be widely applicable throughout the field of inverse problems.
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Affiliation(s)
- Chuan Bi
- grid.411024.20000 0001 2175 4264Department of Psychiatry, University of Maryland, Baltimore, Baltimore, MD 21201 USA
| | - M. Yvonne Ou
- grid.33489.350000 0001 0454 4791Department of Mathematical Sciences, University of Delaware, Newark, DE 19716 USA
| | - Mustapha Bouhrara
- grid.94365.3d0000 0001 2297 5165National Institute on Aging, National Institutes of Health, Baltimore, MD 21224 USA
| | - Richard G. Spencer
- grid.94365.3d0000 0001 2297 5165National Institute on Aging, National Institutes of Health, Baltimore, MD 21224 USA
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3
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Efrima B, Ovadia J, Drukman I, Khoury A, Rath E, Dadia S, Gortzak Y, Albagli A, Sternheim A, Segal O. Cryo-surgery for symptomatic extra-abdominal desmoids. A proof of concept study. J Surg Oncol 2021; 124:627-634. [PMID: 34043245 DOI: 10.1002/jso.26528] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 05/05/2021] [Indexed: 12/21/2022]
Abstract
BACKGROUND AND OBJECTIVE Extra abdominal desmoid tumors are rare, highly aggressive, and invasive benign soft tissue tumors. Current treatment modalities show high levels of recurrence and comorbidities. Cryo-surgery as an alternative was subsequently investigated. METHODS In this retrospective, single center study 11 patients showing symptomatic tumors were treated with individualized cryo-surgery. Treatment protocol included preoperative planning using computer rendered 3D models, intraoperative navigation and execution using cone beam guidance, and postoperative magnetic resonance imaging image analysis using a gaussian mixture model software. Subjective outcomes were reported using Short Form Health Survey (SF-36) questionnaires. RESULTS Sixteen ablations were performed, each demonstrating a complete match with the determined preoperative plan and model. A total of 9/11 (82%) of patients showed improvements in symptoms and a reduction in tumor volume while 2/11 (18%) did not. Average reduction in tumor volume and viable segments were 36.7% (p = 0.0397) and 63.3% (p = 0.0477), respectively. Mild complications according to the SIR Adverse Event Classification Guidelines were experienced in 3/16 (19%) ablations. SF-36 scores showed a statistically significant improvement (p = 0.0194) in the mental health category and a nonsignificant (p = 0.8071) improvement in the physical health category. CONCLUSION Cryo-surgery using the three-phase protocol as described may improve the overall outcome of future ablation procedures.
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Affiliation(s)
- Ben Efrima
- Division of Orthopaedic Surgery, Tel Aviv Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Joshua Ovadia
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ido Drukman
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Division of Radiology, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Amal Khoury
- Division of Orthopaedic Surgery, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Ehud Rath
- Division of Orthopaedic Surgery, Tel Aviv Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Solomon Dadia
- Division of Orthopaedic Surgery, Tel Aviv Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Yair Gortzak
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,National Department of Orthopaedic Oncology, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Assaf Albagli
- Division of Orthopaedic Surgery, Tel Aviv Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Amir Sternheim
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,National Department of Orthopaedic Oncology, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Ortal Segal
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,National Department of Orthopaedic Oncology, Tel Aviv Medical Center, Tel Aviv, Israel
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4
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Kontopodis E, Venianaki M, Manikis GC, Nikiforaki K, Salvetti O, Papadaki E, Papadakis GZ, Karantanas AH, Marias K. Investigating the Role of Model-Based and Model-Free Imaging Biomarkers as Early Predictors of Neoadjuvant Breast Cancer Therapy Outcome. IEEE J Biomed Health Inform 2019; 23:1834-1843. [DOI: 10.1109/jbhi.2019.2895459] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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5
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Jardim-Perassi BV, Huang S, Dominguez-Viqueira W, Poleszczuk J, Budzevich MM, Abdalah MA, Pillai SR, Ruiz E, Bui MM, Zuccari DAPC, Gillies RJ, Martinez GV. Multiparametric MRI and Coregistered Histology Identify Tumor Habitats in Breast Cancer Mouse Models. Cancer Res 2019; 79:3952-3964. [PMID: 31186232 DOI: 10.1158/0008-5472.can-19-0213] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 04/23/2019] [Accepted: 06/05/2019] [Indexed: 12/31/2022]
Abstract
It is well-recognized that solid tumors are genomically, anatomically, and physiologically heterogeneous. In general, more heterogeneous tumors have poorer outcomes, likely due to the increased probability of harboring therapy-resistant cells and regions. It is hypothesized that the genomic and physiologic heterogeneity are related, because physiologically distinct regions will exert variable selection pressures leading to the outgrowth of clones with variable genomic/proteomic profiles. To investigate this, methods must be in place to interrogate and define, at the microscopic scale, the cytotypes that exist within physiologically distinct subregions ("habitats") that are present at mesoscopic scales. MRI provides a noninvasive approach to interrogate physiologically distinct local environments, due to the biophysical principles that govern MRI signal generation. Here, we interrogate different physiologic parameters, such as perfusion, cell density, and edema, using multiparametric MRI (mpMRI). Signals from six different acquisition schema were combined voxel-by-voxel into four clusters identified using a Gaussian mixture model. These were compared with histologic and IHC characterizations of sections that were coregistered using MRI-guided 3D printed tumor molds. Specifically, we identified a specific set of MRI parameters to classify viable-normoxic, viable-hypoxic, nonviable-hypoxic, and nonviable-normoxic tissue types within orthotopic 4T1 and MDA-MB-231 breast tumors. This is the first coregistered study to show that mpMRI can be used to define physiologically distinct tumor habitats within breast tumor models. SIGNIFICANCE: This study demonstrates that noninvasive imaging metrics can be used to distinguish subregions within heterogeneous tumors with histopathologic correlation.
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Affiliation(s)
- Bruna V Jardim-Perassi
- Department of Cancer Physiology, Moffitt Cancer Center, Tampa, Florida.,Faculdade de Medicina de Sao Jose do Rio Preto, Sao Jose do Rio Preto, Brazil
| | - Suning Huang
- Department of Cancer Physiology, Moffitt Cancer Center, Tampa, Florida.,Guangxi Tumor Hospital, Nanning Guangxi, China
| | | | - Jan Poleszczuk
- Department of Integrative Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida
| | | | - Mahmoud A Abdalah
- Image Response Assessment Team, Moffitt Cancer Center, Tampa, Florida
| | - Smitha R Pillai
- Department of Cancer Physiology, Moffitt Cancer Center, Tampa, Florida
| | - Epifanio Ruiz
- Small Animal Imaging Laboratory, Moffitt Cancer Center, Tampa, Florida
| | - Marilyn M Bui
- Department of Anatomic Pathology, Moffitt Cancer Center, Tampa, Florida
| | | | - Robert J Gillies
- Department of Cancer Physiology, Moffitt Cancer Center, Tampa, Florida.
| | - Gary V Martinez
- Small Animal Imaging Laboratory, Moffitt Cancer Center, Tampa, Florida.
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6
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Jalnefjord O, Montelius M, Arvidsson J, Forssell-Aronsson E, Starck G, Ljungberg M. Data-driven identification of tumor subregions based on intravoxel incoherent motion reveals association with proliferative activity. Magn Reson Med 2019; 82:1480-1490. [PMID: 31081969 PMCID: PMC6767386 DOI: 10.1002/mrm.27820] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 04/29/2019] [Accepted: 04/29/2019] [Indexed: 12/16/2022]
Abstract
PURPOSE Intravoxel incoherent motion (IVIM) analysis gives information on tissue diffusion and perfusion and may thus have a potential for e.g. tumor tissue characterization. This work aims to study if clustering based on IVIM parameter maps can identify tumor subregions, and to assess the relevance of obtained subregions by histological analysis. METHODS Fourteen mice with human neuroendocrine tumors were examined with diffusion-weighted imaging to obtain IVIM parameter maps. Gaussian mixture models with IVIM maps from all tumors as input were used to partition voxels into k clusters, where k = 2 was chosen for further analysis based on goodness of fit. Clustering was performed with and without the perfusion-related IVIM parameter D * , and with and without including spatial information. The validity of the clustering was assessed by comparison with corresponding histologically stained tumor sections. A Ki-67-based index quantifying the degree of tumor proliferation was considered appropriate for the comparison based on the obtained cluster characteristics. RESULTS The clustering resulted in one class with low diffusion and high perfusion and another with slightly higher diffusion and low perfusion. Strong agreement was found between tumor subregions identified by clustering and subregions identified by histological analysis, both regarding size and spatial agreement. Neither D * nor spatial information had substantial effects on the clustering results. CONCLUSIONS The results of this study show that IVIM parameter maps can be used to identify tumor subregions using a data-driven framework based on Gaussian mixture models. In the studied tumor model, the obtained subregions showed agreement with proliferative activity.
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Affiliation(s)
- Oscar Jalnefjord
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mikael Montelius
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Jonathan Arvidsson
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Eva Forssell-Aronsson
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Göran Starck
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Maria Ljungberg
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
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7
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Xie Q, Wu J, Du Z, Di N, Yan R, Pang H, Jin T, Zhang H, Wu Y, Zhang Y, Yao Z, Feng X. DCE-MRI in Human Gliomas: A Surrogate for Assessment of Invasive Hypoxia Marker HIF-1Α Based on MRI-Neuronavigation Stereotactic Biopsies. Acad Radiol 2019; 26:179-187. [PMID: 29754996 DOI: 10.1016/j.acra.2018.04.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2018] [Revised: 03/31/2018] [Accepted: 04/12/2018] [Indexed: 12/15/2022]
Abstract
RATIONALE AND OBJECTIVES The purpose of this study was to correlate dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters with data from a specific marker of hypoxia, hypoxia-inducible factor 1α (HIF-1α), in human gliomas on a point-to-point basis by using coregistered magnetic resonance imaging and frameless stereotactic biopsies. MATERIALS AND METHODS Thirty-four patients with treatment-naive gliomas underwent DCE, axial T1-weighted, T2-weighted, T2-weighted fluid acquisition of inversion recovery, and three-dimensional T1-weighted brain volume with gadolinium contrast enhancement sequences on a 3.0-T magnetic resonance scanner before stereotactic surgery. Quantitative perfusion indices such as endothelial transfer constant, fractional extravascular extracellular space volume, fractional plasma volume, and reflux rate were measured at corresponding stereotactic biopsy sites. Each sample was considered an independent measurement, and its histology grade was diagnosed. HIF-1α expression was quantified from the point-to-point biopsy tissues. Analyses of receiver operating characteristic curves were done for HIF-1α to discriminate different grades of glioma. To look for correlations between immunohistochemical parameters and DCE indices, Spearman's correlation coefficient was used. RESULTS Seventy biopsy samples from 34 subjects were included in the analysis. Mean immunoreactivity scores of HIF-1α were 2.75 ± 1.11 for grade II (n = 24), 6.20 ± 2.33 for grade III (n = 20), and 10.46 ± 2.42 for grade IV (n = 26). HIF-1α showed very good-to-excellent accuracy in discriminating grade II from III, III from IV, and II from IV (area under the curve = 0.838, 0.862, and 0.994, respectively). Endothelial transfer constant and fractional extravascular extracellular space volume showed a significantly positive correlation with HIF-1α expression (r = 0.686, P < .001; r = 0.549, P < .001, respectively). CONCLUSION Our study demonstrated HIF-1α to be a significant predictor of different grades of gliomas with high sensitivity and specificity. DCE-MRI is a useful, noninvasive imaging tool for quantitative evaluation of HIF-1α, and its parameters may be used as a surrogate for HIF-1α expression.
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8
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Harvey H, Morgan V, Fromageau J, O'Shea T, Bamber J, deSouza NM. Ultrasound Shear Wave Elastography of the Normal Prostate: Interobserver Reproducibility and Comparison with Functional Magnetic Resonance Tissue Characteristics. ULTRASONIC IMAGING 2018; 40:158-170. [PMID: 29353529 DOI: 10.1177/0161734618754487] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The purpose of this study was to establish interobserver reproducibility of Young's modulus (YM) derived from ultrasound shear wave elastography (US-SWE) in the normal prostate and correlate it with multiparametric magnetic resonance imaging (mpMRI) tissue characteristics. Twenty men being screened for prostate cancer underwent same-day US-SWE (10 done by two blinded, newly-trained observers) and mpMRI followed by 12-core biopsy. Bland-Altman plots established limits of agreement for YM. Quantitative data from the peripheral zone (PZ) and the transitional zone (TZ) for YM, apparent diffusion coefficient (ADC, mm2/s from diffusion-weighted MRI), and Ktrans (volume transfer coefficient, min-1), Ve (extravascular-extracellular space, %), Kep (rate constant, /min), and initial area under the gadolinium concentration curve (IAUGC60, mmol/L/s) from dynamic contrast-enhanced MRI were obtained for slice-matched prostate sextants. Interobserver intraclass correlation coefficients were fair to good for individual regions (PZ = 0.57, TZ = 0.65) and for whole gland 0.67, (increasing to 0.81 when corrected for systematic observer bias). In the PZ, there were weak negative correlations between YM and ADC ( p = 0.008), and Ve ( p = 0.01) and a weak positive correlation with Kep ( p = 0.003). No significant intermodality correlations were seen in the TZ. Transrectal prostate US-SWE done without controlling manually applied probe pressure has fair/good interobserver reproducibility in inexperienced observers with potential to improve this to excellent by standardization of probe contact pressure. Within the PZ, increase in tissue stiffness is associated with reduced extracellular water (decreased ADC) and space (reduced Ve).
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Affiliation(s)
- Hugh Harvey
- 1 Cancer Research UK Centre, The Institute of Cancer Research, Royal Marsden Hospital, Sutton, UK
| | - Veronica Morgan
- 1 Cancer Research UK Centre, The Institute of Cancer Research, Royal Marsden Hospital, Sutton, UK
| | - Jeremie Fromageau
- 2 Joint Department of Physics, The Institute of Cancer Research, Royal Marsden Hospital, Sutton, UK
| | - Tuathan O'Shea
- 2 Joint Department of Physics, The Institute of Cancer Research, Royal Marsden Hospital, Sutton, UK
| | - Jeffrey Bamber
- 1 Cancer Research UK Centre, The Institute of Cancer Research, Royal Marsden Hospital, Sutton, UK
- 2 Joint Department of Physics, The Institute of Cancer Research, Royal Marsden Hospital, Sutton, UK
| | - Nandita M deSouza
- 1 Cancer Research UK Centre, The Institute of Cancer Research, Royal Marsden Hospital, Sutton, UK
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9
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Casares-Magaz O, Raidou RG, Rørvik J, Vilanova A, Muren LP. Uncertainty evaluation of image-based tumour control probability models in radiotherapy of prostate cancer using a visual analytic tool. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2018; 5:5-8. [PMID: 33458361 PMCID: PMC7807664 DOI: 10.1016/j.phro.2017.12.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Revised: 12/15/2017] [Accepted: 12/21/2017] [Indexed: 11/26/2022]
Abstract
Functional imaging techniques provide radiobiological information that can be included into tumour control probability (TCP) models to enable individualized outcome predictions in radiotherapy. However, functional imaging and the derived radiobiological information are influenced by uncertainties, translating into variations in individual TCP predictions. In this study we applied a previously developed analytical tool to quantify dose and TCP uncertainty bands when initial cell density is estimated from MRI-based apparent diffusion coefficient maps of eleven patients. TCP uncertainty bands of 16% were observed at patient level, while dose variations bands up to 8 Gy were found at voxel level for an iso-TCP approach.
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Affiliation(s)
- Oscar Casares-Magaz
- Department of Medical Physics, Aarhus University Hospital/Aarhus University, Aarhus, Denmark
| | - Renata G Raidou
- Institute of Computer Graphics and Algorithms, Vienna University of Technology, Austria
| | - Jarle Rørvik
- Department of Clinical Medicine, University of Bergen, Bergen, Norway.,Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | | | - Ludvig P Muren
- Department of Medical Physics, Aarhus University Hospital/Aarhus University, Aarhus, Denmark
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10
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Chang YCC, Ackerstaff E, Tschudi Y, Jimenez B, Foltz W, Fisher C, Lilge L, Cho H, Carlin S, Gillies RJ, Balagurunathan Y, Yechieli RL, Subhawong T, Turkbey B, Pollack A, Stoyanova R. Delineation of Tumor Habitats based on Dynamic Contrast Enhanced MRI. Sci Rep 2017; 7:9746. [PMID: 28851989 PMCID: PMC5575347 DOI: 10.1038/s41598-017-09932-5] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Accepted: 08/01/2017] [Indexed: 02/03/2023] Open
Abstract
Tumor heterogeneity can be elucidated by mapping subregions of the lesion with differential imaging characteristics, called habitats. Dynamic Contrast Enhanced (DCE-)MRI can depict the tumor microenvironments by identifying areas with variable perfusion and vascular permeability, since individual tumor habitats vary in the rate and magnitude of the contrast uptake and washout. Of particular interest is identifying areas of hypoxia, characterized by inadequate perfusion and hyper-permeable vasculature. An automatic procedure for delineation of tumor habitats from DCE-MRI was developed as a two-part process involving: (1) statistical testing in order to determine the number of the underlying habitats; and (2) an unsupervised pattern recognition technique to recover the temporal contrast patterns and locations of the associated habitats. The technique is examined on simulated data and DCE-MRI, obtained from prostate and brain pre-clinical cancer models, as well as clinical data from sarcoma and prostate cancer patients. The procedure successfully identified habitats previously associated with well-perfused, hypoxic and/or necrotic tumor compartments. Given the association of tumor hypoxia with more aggressive tumor phenotypes, the obtained in vivo information could impact management of cancer patients considerably.
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Affiliation(s)
| | - Ellen Ackerstaff
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Yohann Tschudi
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, 33136, USA
| | - Bryan Jimenez
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, 33136, USA
| | - Warren Foltz
- STTARR Innovation Centre, Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Carl Fisher
- Department of Medical Biophysics, University of Toronto and Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Lothar Lilge
- Department of Medical Biophysics, University of Toronto and Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - HyungJoon Cho
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Sean Carlin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Robert J Gillies
- Cancer Imaging and Metabolism, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | | | - Raphael L Yechieli
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, 33136, USA
| | - Ty Subhawong
- Department of Radiology, University of Miami Miller School of Medicine, Miami, FL, 33136, USA
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Alan Pollack
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, 33136, USA
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, 33136, USA.
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11
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Wildeboer RR, Postema AW, Demi L, Kuenen MPJ, Wijkstra H, Mischi M. Multiparametric dynamic contrast-enhanced ultrasound imaging of prostate cancer. Eur Radiol 2017; 27:3226-3234. [PMID: 28004162 PMCID: PMC5491563 DOI: 10.1007/s00330-016-4693-8] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2016] [Revised: 11/28/2016] [Accepted: 12/01/2016] [Indexed: 12/29/2022]
Abstract
OBJECTIVES The aim of this study is to improve the accuracy of dynamic contrast-enhanced ultrasound (DCE-US) for prostate cancer (PCa) localization by means of a multiparametric approach. MATERIALS AND METHODS Thirteen different parameters related to either perfusion or dispersion were extracted pixel-by-pixel from 45 DCE-US recordings in 19 patients referred for radical prostatectomy. Multiparametric maps were retrospectively produced using a Gaussian mixture model algorithm. These were subsequently evaluated on their pixel-wise performance in classifying 43 benign and 42 malignant histopathologically confirmed regions of interest, using a prostate-based leave-one-out procedure. RESULTS The combination of the spatiotemporal correlation (r), mean transit time (μ), curve skewness (κ), and peak time (PT) yielded an accuracy of 81% ± 11%, which was higher than the best performing single parameters: r (73%), μ (72%), and wash-in time (72%). The negative predictive value increased to 83% ± 16% from 70%, 69% and 67%, respectively. Pixel inclusion based on the confidence level boosted these measures to 90% with half of the pixels excluded, but without disregarding any prostate or region. CONCLUSIONS Our results suggest multiparametric DCE-US analysis might be a useful diagnostic tool for PCa, possibly supporting future targeting of biopsies or therapy. Application in other types of cancer can also be foreseen. KEY POINTS • DCE-US can be used to extract both perfusion and dispersion-related parameters. • Multiparametric DCE-US performs better in detecting PCa than single-parametric DCE-US. • Multiparametric DCE-US might become a useful tool for PCa localization.
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Affiliation(s)
- Rogier R Wildeboer
- Laboratory of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, PO-Box 513, 5600 MB, Eindhoven, The Netherlands.
| | - Arnoud W Postema
- Department of Urology, Academic Medical Center University Hospital, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Libertario Demi
- Laboratory of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, PO-Box 513, 5600 MB, Eindhoven, The Netherlands
| | | | - Hessel Wijkstra
- Laboratory of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, PO-Box 513, 5600 MB, Eindhoven, The Netherlands
- Department of Urology, Academic Medical Center University Hospital, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Massimo Mischi
- Laboratory of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, PO-Box 513, 5600 MB, Eindhoven, The Netherlands
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12
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Han S, Stoyanova R, Lee H, Carlin SD, Koutcher JA, Cho H, Ackerstaff E. Automation of pattern recognition analysis of dynamic contrast-enhanced MRI data to characterize intratumoral vascular heterogeneity. Magn Reson Med 2017; 79:1736-1744. [PMID: 28727185 DOI: 10.1002/mrm.26822] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2016] [Revised: 05/14/2017] [Accepted: 06/12/2017] [Indexed: 12/20/2022]
Abstract
PURPOSE To automate dynamic contrast-enhanced MRI (DCE-MRI) data analysis by unsupervised pattern recognition (PR) to enable spatial mapping of intratumoral vascular heterogeneity. METHODS Three steps were automated. First, the arrival time of the contrast agent at the tumor was determined, including a calculation of the precontrast signal. Second, four criteria-based algorithms for the slice-specific selection of number of patterns (NP) were validated using 109 tumor slices from subcutaneous flank tumors of five different tumor models. The criteria were: half area under the curve, standard deviation thresholding, percent signal enhancement, and signal-to-noise ratio (SNR). The performance of these criteria was assessed by comparing the calculated NP with the visually determined NP. Third, spatial assignment of single patterns and/or pattern mixtures was obtained by way of constrained nonnegative matrix factorization. RESULTS The determination of the contrast agent arrival time at the tumor slice was successfully automated. For the determination of NP, the SNR-based approach outperformed other selection criteria by agreeing >97% with visual assessment. The spatial localization of single patterns and pattern mixtures, the latter inferring tumor vascular heterogeneity at subpixel spatial resolution, was established successfully by automated assignment from DCE-MRI signal-versus-time curves. CONCLUSION The PR-based DCE-MRI analysis was successfully automated to spatially map intratumoral vascular heterogeneity. Magn Reson Med 79:1736-1744, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- SoHyun Han
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea.,Currently at: Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, South Korea
| | - Radka Stoyanova
- Department of Radiation Oncology, Miller School of Medicine, University of Miami, Miami, Florida, USA
| | - Hansol Lee
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Sean D Carlin
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Currently at: Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jason A Koutcher
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Sloan Kettering Institute Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Weill Cornell Medical College, Cornell University, New York, New York, USA
| | - HyungJoon Cho
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Ellen Ackerstaff
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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13
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Schmitz J, Schwab J, Schwenck J, Chen Q, Quintanilla-Martinez L, Hahn M, Wietek B, Schwenzer N, Staebler A, Kohlhofer U, Aina OH, Hubbard NE, Reischl G, Borowsky AD, Brucker S, Nikolaou K, la Fougère C, Cardiff RD, Pichler BJ, Schmid AM. Decoding Intratumoral Heterogeneity of Breast Cancer by Multiparametric In Vivo Imaging: A Translational Study. Cancer Res 2016; 76:5512-22. [PMID: 27466286 PMCID: PMC5414858 DOI: 10.1158/0008-5472.can-15-0642] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2015] [Accepted: 07/06/2016] [Indexed: 01/20/2023]
Abstract
Differential diagnosis and therapy of heterogeneous breast tumors poses a major clinical challenge. To address the need for a comprehensive, noninvasive strategy to define the molecular and functional profiles of tumors in vivo, we investigated a novel combination of metabolic PET and diffusion-weighted (DW)-MRI in the polyoma virus middle T antigen transgenic mouse model of breast cancer. The implementation of a voxelwise analysis for the clustering of intra- and intertumoral heterogeneity in this model resulted in a multiparametric profile based on [(18)F]Fluorodeoxyglucose ([(18)F]FDG)-PET and DW-MRI, which identified three distinct tumor phenotypes in vivo, including solid acinar, and solid nodular malignancies as well as cystic hyperplasia. To evaluate the feasibility of this approach for clinical use, we examined estrogen receptor-positive and progesterone receptor-positive breast tumors from five patient cases using DW-MRI and [(18)F]FDG-PET in a simultaneous PET/MRI system. The postsurgical in vivo PET/MRI data were correlated to whole-slide histology using the latter traditional diagnostic standard to define phenotype. By this approach, we showed how molecular, structural (microscopic, anatomic), and functional information could be simultaneously obtained noninvasively to identify precancerous and malignant subtypes within heterogeneous tumors. Combined with an automatized analysis, our results suggest that multiparametric molecular and functional imaging may be capable of providing comprehensive tumor profiling for noninvasive cancer diagnostics. Cancer Res; 76(18); 5512-22. ©2016 AACR.
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Affiliation(s)
- Jennifer Schmitz
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Julian Schwab
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Johannes Schwenck
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany. Department of Nuclear Medicine and Clinical Molecular Imaging, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Qian Chen
- Center for Comparative Medicine, University of California, Davis, California
| | | | - Markus Hahn
- Department of Women's Health, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Beate Wietek
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen, Germany
| | - Nina Schwenzer
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen, Germany
| | - Annette Staebler
- Department of Pathology, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Ursula Kohlhofer
- Department of Pathology, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Olulanu H Aina
- Center for Comparative Medicine, University of California, Davis, California
| | - Neil E Hubbard
- Center for Comparative Medicine, University of California, Davis, California
| | - Gerald Reischl
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | | | - Sara Brucker
- Department of Women's Health, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen, Germany. German Cancer Consortium, German Cancer Research Center, Tuebingen, Germany
| | - Christian la Fougère
- Department of Nuclear Medicine and Clinical Molecular Imaging, Eberhard Karls University Tuebingen, Tuebingen, Germany. German Cancer Consortium, German Cancer Research Center, Tuebingen, Germany
| | - Robert D Cardiff
- Center for Comparative Medicine, University of California, Davis, California
| | - Bernd J Pichler
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany. German Cancer Consortium, German Cancer Research Center, Tuebingen, Germany
| | - Andreas M Schmid
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany.
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14
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Hormuth DA, Skinner JT, Does MD, Yankeelov TE. A comparison of individual and population-derived vascular input functions for quantitative DCE-MRI in rats. Magn Reson Imaging 2014; 32:397-401. [PMID: 24556502 DOI: 10.1016/j.mri.2013.12.019] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2013] [Accepted: 12/30/2013] [Indexed: 11/26/2022]
Abstract
Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) can quantitatively and qualitatively assess physiological characteristics of tissue. Quantitative DCE-MRI requires an estimate of the time rate of change of the concentration of the contrast agent in the blood plasma, the vascular input function (VIF). Measuring the VIF in small animals is notoriously difficult as it requires high temporal resolution images limiting the achievable number of slices, field-of-view, spatial resolution, and signal-to-noise. Alternatively, a population-averaged VIF could be used to mitigate the acquisition demands in studies aimed to investigate, for example, tumor vascular characteristics. Thus, the overall goal of this manuscript is to determine how the kinetic parameters estimated by a population based VIF differ from those estimated by an individual VIF. Eight rats bearing gliomas were imaged before, during, and after an injection of Gd-DTPA. K(trans), ve, and vp were extracted from signal-time curves of tumor tissue using both individual and population-averaged VIFs. Extended model voxel estimates of K(trans) and ve in all animals had concordance correlation coefficients (CCC) ranging from 0.69 to 0.98 and Pearson correlation coefficients (PCC) ranging from 0.70 to 0.99. Additionally, standard model estimates resulted in CCCs ranging from 0.81 to 0.99 and PCCs ranging from 0.98 to 1.00, supporting the use of a population based VIF if an individual VIF is not available.
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Affiliation(s)
- David A Hormuth
- Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Jack T Skinner
- Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA
| | - Mark D Does
- Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Thomas E Yankeelov
- Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Physics, Vanderbilt University, Nashville, TN, USA; Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA.
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