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Zhang J, Zheng Y, Li L, Wang R, Jiang W, Ai K, Gan T, Wang P. Combination of IVIM with DCE-MRI for diagnostic and prognostic evaluation of breast cancer. Magn Reson Imaging 2024; 113:110204. [PMID: 38971263 DOI: 10.1016/j.mri.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 06/14/2024] [Accepted: 07/03/2024] [Indexed: 07/08/2024]
Abstract
PURPOSE To identify the most effective combination of DCE-MRI (Ktrans,Kep) and IVIM (D,f) and analyze the correlations of these parameters with prognostic indicators (ER, PR, and HER2, Ki-67 index, axillary lymph node (ALN) and tumor size) to improve the diagnostic and prognostic efficiency in breast cancer. METHODS This is a prospective study. We performed T1WI, T2WI, IVIM, DCE-MRI at 3 T MRI examinations on benign and malignant breast lesions that met the inclusion criteria. We also collected pathological results of corresponding lesions, including ER, PR, and HER2, Ki-67 index, axillary lymph node (ALN) and tumor size. The diagnostic efficacy of DCE-MRI, IVIM imaging, and their combination for benign and malignant breast lesions was assessed. Correlations between the DCE-MRI and IVIM parameters and prognostic indicators were assessed. RESULTS Overall,59 female patients with 62 lesions (22 benign lesions and 40 malignant lesions) were included in this study. The malignant group showed significantly lower D values (p < 0.05) and significantly higher Ktrans, Kep, and f values (p < 0.05). The AUC values of DCE, IVIM, DCE + IVIM were 0.828, 0.882, 0.901. Ktrans, Kep, D and f values were correlated with the pathological grade (p < 0.05); Ktrans was negatively correlated with ER expression (r = -0.519, p < 0.05); Kep was correlated with PR expression and the Ki-67 index (r = -0.489, 0.330, p < 0.05); the DCE and IVIM parameters showed no significant correlations with the HER2 and ALN (p > 0.05). Tumor diameter was correlated with the Kep, D and f values (r = 0.246, -0.278, 0.293; p < 0.05). CONCLUSION IVIM and DCE-MRI allowed differential diagnosis of benign and malignant breast lesions, and their combination showed significantly better diagnostic efficiency. DCE- and IVIM-derived parameters showed correlations with some prognostic factors for breast cancer.
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Affiliation(s)
- Jing Zhang
- Department of Magnetic Resonance, LanZhou University Second Hospital, Lanzhou 730030, China.
| | - Yurong Zheng
- Department of Magnetic Resonance, LanZhou University Second Hospital, Lanzhou 730030, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China
| | - Li Li
- Department of Magnetic Resonance, LanZhou University Second Hospital, Lanzhou 730030, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China
| | - Rui Wang
- Department of Magnetic Resonance, LanZhou University Second Hospital, Lanzhou 730030, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China
| | - Weilong Jiang
- Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou, Gansu 730000, China
| | - Kai Ai
- Philips Healthcare, Xi'an, China
| | - Tiejun Gan
- Department of Magnetic Resonance, LanZhou University Second Hospital, Lanzhou 730030, China
| | - Pengfei Wang
- Department of Magnetic Resonance, LanZhou University Second Hospital, Lanzhou 730030, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China
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Jager A, Oddens JR, Postema AW, Miclea RL, Schoots IG, Nooijen PGTA, van der Linden H, Barentsz JO, Heijmink SWTPJ, Wijkstra H, Mischi M, Turco S. Is There an Added Value of Quantitative DCE-MRI by Magnetic Resonance Dispersion Imaging for Prostate Cancer Diagnosis? Cancers (Basel) 2024; 16:2431. [PMID: 39001493 PMCID: PMC11240399 DOI: 10.3390/cancers16132431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 06/25/2024] [Accepted: 06/26/2024] [Indexed: 07/16/2024] Open
Abstract
In this multicenter, retrospective study, we evaluated the added value of magnetic resonance dispersion imaging (MRDI) to standard multiparametric MRI (mpMRI) for PCa detection. The study included 76 patients, including 51 with clinically significant prostate cancer (csPCa), who underwent radical prostatectomy and had an mpMRI including dynamic contrast-enhanced MRI. Two radiologists performed three separate randomized scorings based on mpMRI, MRDI and mpMRI+MRDI. Radical prostatectomy histopathology was used as the reference standard. Imaging and histopathology were both scored according to the Prostate Imaging-Reporting and Data System V2.0 sector map. Sensitivity and specificity for PCa detection were evaluated for mpMRI, MRDI and mpMRI+MRDI. Inter- and intra-observer variability for both radiologists was evaluated using Cohen's Kappa. On a per-patient level, sensitivity for csPCa for radiologist 1 (R1) for mpMRI, MRDI and mpMRI+MRDI was 0.94, 0.82 and 0.94, respectively. For the second radiologist (R2), these were 0.78, 0.94 and 0.96. R1 detected 4% additional csPCa cases using MRDI compared to mpMRI, and R2 detected 20% extra csPCa cases using MRDI. Inter-observer agreement was significant only for MRDI (Cohen's Kappa = 0.4250, p = 0.004). The results of this study show the potential of MRDI to improve inter-observer variability and the detection of csPCa.
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Affiliation(s)
- Auke Jager
- Department of Urology, Amsterdam UMC, University of Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Jorg R Oddens
- Department of Urology, Amsterdam UMC, University of Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands
| | - Arnoud W Postema
- Leiden University Medical Center, Department of Urology, 2333 ZA Leiden, The Netherlands
| | - Razvan L Miclea
- Department of Radiology and Nuclear Imaging, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands
| | - Ivo G Schoots
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
- Department of Radiology, Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Peet G T A Nooijen
- Department of Pathology, Jeroen Bosch Hospital, 5223 GZ 's-Hertogenbosch, The Netherlands
| | - Hans van der Linden
- Department of Pathology, Jeroen Bosch Hospital, 5223 GZ 's-Hertogenbosch, The Netherlands
| | - Jelle O Barentsz
- Department of Radiology, Radboud University Nijmegen Medical Center, 6525 GA Nijmegenfi, The Netherlands
| | - Stijn W T P J Heijmink
- Department of Radiology, Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Hessel Wijkstra
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands
| | - Massimo Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands
| | - Simona Turco
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands
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Li X, Huang W, Holmes JH. Dynamic Contrast-Enhanced (DCE) MRI. Magn Reson Imaging Clin N Am 2024; 32:47-61. [PMID: 38007282 DOI: 10.1016/j.mric.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2023]
Abstract
The non-invasive dynamic contrast-enhanced MRI (DCE-MRI) method provides valuable insights into tissue perfusion and vascularity. Primarily used in oncology, DCE-MRI is typically utilized to assess morphology and contrast agent (CA) kinetics in the tissue of interest. Interpretation of the temporal signatures of DCE-MRI data includes qualitative, semi-quantitative, and quantitative approaches. Recent advances in MRI technology allow simultaneous high spatial and temporal resolutions in DCE-MRI data acquisition on most vendor platforms, enabling the more desirable approach of quantitative data analysis using pharmacokinetic (PK) modeling. Many technical factors, including signal-to-noise ratio, temporal resolution, quantifications of arterial input function and native tissue T1, and PK model selection, need to be carefully considered when performing quantitative DCE-MRI. Standardization in data acquisition and analysis is especially important in multi-center studies.
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Affiliation(s)
- Xin Li
- Advanced Imaging Research Center, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA
| | - Wei Huang
- Advanced Imaging Research Center, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA
| | - James H Holmes
- Radiology, Biomedical Engineering, and Holden Cancer Center, University of Iowa, 169 Newton Road, Iowa City, IA 52242, USA.
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Kåstad Høiskar M, Sæther O, Delange Alsaker M, Røe Redalen K, Winter RM. Quantitative dynamic contrast-enhanced magnetic resonance imaging in head and neck cancer: A systematic comparison of different modelling approaches. Phys Imaging Radiat Oncol 2024; 29:100548. [PMID: 38380153 PMCID: PMC10876686 DOI: 10.1016/j.phro.2024.100548] [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: 10/27/2023] [Revised: 01/24/2024] [Accepted: 02/05/2024] [Indexed: 02/22/2024] Open
Abstract
Background and purpose Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) describes tissue microvasculature and has prognostic and predictive potential in radiotherapy for head and neck cancer (HNC). However, lack in standardization of DCE-MRI hinders comparison of studies and clinical implementation. This study investigated the accuracy and robustness of the population arterial input function (AIF), correlations between pharmacokinetic parameters and their association to T stage and human papillomavirus (HPV) status for HNC. Materials and methods DCE-MRI was acquired for 44 HNC patients. Population AIFs were calculated with six different approaches. DCE-MRI was analysed in primary and lymph node tumours using Tofts model (TM) with population AIFs and individual AIFs, extended TM (ETM) with individual AIFs, Brix model (BM), and areas under the curve (AUCs). Intraclass correlation, concordance correlation, Pearson correlation and Whitney Mann U test helped examining the robustness and accuracy of population AIF, correlations between DCE-MRI parameters and their association to T stage and HPV status, respectively. Results The population AIF was robust but differed from individual AIFs. There was significant correlation between KtransTM/ETM and ve, TM/ETM, and KtransTM/ETM and Kep, TM/ETM. ABrix and AUCs correlated for lymph nodes. Kep, Brix correlated with ABrix, KtransTM/ETM and Kep, TM/ETM for primary tumours. Kep, TM significantly decreased with increasing T stage. Both the correlations and the parameters' association to T stage were stronger for HPV negative lesions. Conclusions Individual AIF was preferred for accurate pharmacokinetic modelling of DCE-MRI. DCE-MRI parameters and their correlations were affected by the lesion type, HPV status and T staging.
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Affiliation(s)
- Marte Kåstad Høiskar
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
| | - Oddbjørn Sæther
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | | | - Kathrine Røe Redalen
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
| | - René M. Winter
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
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Guljaš S, Dupan Krivdić Z, Drežnjak Madunić M, Šambić Penc M, Pavlović O, Krajina V, Pavoković D, Šmit Takač P, Štefančić M, Salha T. Dynamic Contrast-Enhanced Study in the mpMRI of the Prostate-Unnecessary or Underutilised? A Narrative Review. Diagnostics (Basel) 2023; 13:3488. [PMID: 37998624 PMCID: PMC10670922 DOI: 10.3390/diagnostics13223488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 10/30/2023] [Accepted: 11/16/2023] [Indexed: 11/25/2023] Open
Abstract
The aim of this review is to summarise recent scientific literature regarding the clinical use of DCE-MRI as a component of multiparametric resonance imaging of the prostate. This review presents the principles of DCE-MRI acquisition and analysis, the current role of DCE-MRI in clinical practice with special regard to its role in presently available categorisation systems, and an overview of the advantages and disadvantages of DCE-MRI described in the current literature. DCE-MRI is an important functional sequence that requires intravenous administration of a gadolinium-based contrast agent and gives information regarding the vascularity and capillary permeability of the lesion. Although numerous studies have confirmed that DCE-MRI has great potential in the diagnosis and monitoring of prostate cancer, its role is still inadequate in the PI-RADS categorisation. Moreover, there have been numerous scientific discussions about abandoning the intravenous application of gadolinium-based contrast as a routine part of MRI examination of the prostate. In this review, we summarised the recent literature on the advantages and disadvantages of DCE-MRI, focusing on an overview of currently available data on bpMRI and mpMRI, as well as on studies providing information on the potential better usability of DCE-MRI in improving the sensitivity and specificity of mpMRI examinations of the prostate.
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Affiliation(s)
- Silva Guljaš
- Clinical Department of Radiology, University Hospital Centre, 31000 Osijek, Croatia; (S.G.); (Z.D.K.)
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.D.M.); (M.Š.P.); (O.P.); (V.K.); (D.P.)
| | - Zdravka Dupan Krivdić
- Clinical Department of Radiology, University Hospital Centre, 31000 Osijek, Croatia; (S.G.); (Z.D.K.)
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.D.M.); (M.Š.P.); (O.P.); (V.K.); (D.P.)
| | - Maja Drežnjak Madunić
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.D.M.); (M.Š.P.); (O.P.); (V.K.); (D.P.)
- Department of Oncology, University Hospital Centre, 31000 Osijek, Croatia
| | - Mirela Šambić Penc
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.D.M.); (M.Š.P.); (O.P.); (V.K.); (D.P.)
- Department of Oncology, University Hospital Centre, 31000 Osijek, Croatia
| | - Oliver Pavlović
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.D.M.); (M.Š.P.); (O.P.); (V.K.); (D.P.)
- Department of Urology, University Hospital Centre, 31000 Osijek, Croatia
| | - Vinko Krajina
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.D.M.); (M.Š.P.); (O.P.); (V.K.); (D.P.)
- Department of Urology, University Hospital Centre, 31000 Osijek, Croatia
| | - Deni Pavoković
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.D.M.); (M.Š.P.); (O.P.); (V.K.); (D.P.)
- Department of Urology, University Hospital Centre, 31000 Osijek, Croatia
| | - Petra Šmit Takač
- Clinical Department of Surgery, Osijek University Hospital Centre, 31000 Osijek, Croatia;
| | - Marin Štefančić
- Department of Radiology, National Memorial Hospital Vukovar, 32000 Vukovar, Croatia;
| | - Tamer Salha
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.D.M.); (M.Š.P.); (O.P.); (V.K.); (D.P.)
- Department of Teleradiology and Artificial Intelligence, Health Centre Osijek-Baranja County, 31000 Osijek, Croatia
- Faculty of Dental Medicine and Health, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
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Stoyanova R, Zavala-Romero O, Kwon D, Breto AL, Xu IR, Algohary A, Alhusseini M, Gaston SM, Castillo P, Kryvenko ON, Davicioni E, Nahar B, Spieler B, Abramowitz MC, Dal Pra A, Parekh DJ, Punnen S, Pollack A. Clinical-Genomic Risk Group Classification of Suspicious Lesions on Prostate Multiparametric-MRI. Cancers (Basel) 2023; 15:5240. [PMID: 37958414 PMCID: PMC10647832 DOI: 10.3390/cancers15215240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/12/2023] [Accepted: 10/24/2023] [Indexed: 11/15/2023] Open
Abstract
The utilization of multi-parametric MRI (mpMRI) in clinical decisions regarding prostate cancer patients' management has recently increased. After biopsy, clinicians can assess risk using National Comprehensive Cancer Network (NCCN) risk stratification schema and commercially available genomic classifiers, such as Decipher. We built radiomics-based models to predict lesions/patients at low risk prior to biopsy based on an established three-tier clinical-genomic classification system. Radiomic features were extracted from regions of positive biopsies and Normally Appearing Tissues (NAT) on T2-weighted and Diffusion-weighted Imaging. Using only clinical information available prior to biopsy, five models for predicting low-risk lesions/patients were evaluated, based on: 1: Clinical variables; 2: Lesion-based radiomic features; 3: Lesion and NAT radiomics; 4: Clinical and lesion-based radiomics; and 5: Clinical, lesion and NAT radiomic features. Eighty-three mpMRI exams from 78 men were analyzed. Models 1 and 2 performed similarly (Area under the receiver operating characteristic curve were 0.835 and 0.838, respectively), but radiomics significantly improved the lesion-based performance of the model in a subset analysis of patients with a negative Digital Rectal Exam (DRE). Adding normal tissue radiomics significantly improved the performance in all cases. Similar patterns were observed on patient-level models. To the best of our knowledge, this is the first study to demonstrate that machine learning radiomics-based models can predict patients' risk using combined clinical-genomic classification.
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Affiliation(s)
- Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
| | - Olmo Zavala-Romero
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Deukwoo Kwon
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Adrian L. Breto
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Isaac R. Xu
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Ahmad Algohary
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Mohammad Alhusseini
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Sandra M. Gaston
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
| | - Patricia Castillo
- Department of Radiology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Oleksandr N. Kryvenko
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
- Department of Pathology and Laboratory Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Desai Sethi Urology Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Elai Davicioni
- Research and Development, Veracyte Inc., San Francisco, CA 94080, USA
| | - Bruno Nahar
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
- Desai Sethi Urology Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Benjamin Spieler
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
| | - Matthew C. Abramowitz
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
| | - Alan Dal Pra
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
| | - Dipen J. Parekh
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
- Desai Sethi Urology Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Sanoj Punnen
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
- Desai Sethi Urology Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Alan Pollack
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
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Roberts J, Kim SE, Kholmovski EG, Hitchcock Y, Richards TJ, Anzai Y. The arterial input function: Spatial dependence within the imaging volume and its influence on 3D quantitative dynamic contrast-enhanced MRI for head and neck cancer. Magn Reson Imaging 2023; 101:40-46. [PMID: 37030177 PMCID: PMC10194023 DOI: 10.1016/j.mri.2023.03.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 03/21/2023] [Indexed: 04/10/2023]
Abstract
PURPOSE To evaluate the dependence of the arterial input function (AIF) on the imaging z-axis and its effect on 3D DCE MRI pharmacokinetic parameters as mediated by the SPGR signal equation and Extended Tofts-Kermode model. THEORY For SPGR-based 3D DCE MRI acquisition of the head and neck, inflow effects within vessels violate the assumptions underlying the SPGR signal model. Errors in the SPGR-based AIF estimate propagate through the Extended Tofts-Kermode model to affect the output pharmacokinetic parameters. MATERIALS AND METHODS 3D DCE-MRI data were acquired for six newly diagnosed HNC patients in a prospective single arm cohort study. AIF were selected within the carotid arteries at each z-axis location. A region of interest (ROI) was placed in normal paravertebral muscle and the Extended Tofts-Kermode model solved for each pixel within the ROI for each AIF. Results were compared to those obtained with a published population average AIF. RESULTS Due to inflow effect, the AIF showed extreme variation in their temporal shapes. Ktrans was most sensitive to the initial bolus concentration and showed more variation over the muscle ROI with AIF taken from the upstream portion of the carotid. kep was less sensitive to the peak bolus concentration and showed less variation for AIF taken from the upstream portion of the carotid. CONCLUSION Inflow effects may introduce an unknown bias to SPGR-based 3D DCE pharmacokinetic parameters. Variation in the computed parameters depends on the selected AIF location. In the context of high flow, measurements may be limited to relative rather than absolute quantitative parameters.
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Affiliation(s)
- John Roberts
- Dept. Radiology & Imaging Sciences, University of Utah, SLC, UT, USA..
| | - Seong-Eun Kim
- Dept. Radiology & Imaging Sciences, University of Utah, SLC, UT, USA
| | - Eugene G Kholmovski
- Dept. Radiology & Imaging Sciences, University of Utah, SLC, UT, USA.; Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Ying Hitchcock
- Radiation Oncology, Huntsman Cancer Institute, SLC, UT, USA
| | | | - Yoshimi Anzai
- Dept. Radiology & Imaging Sciences, University of Utah, SLC, UT, USA
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8
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Bae J, Li C, Masurkar A, Ge Y, Kim SG. Improving measurement of blood-brain barrier permeability with reduced scan time using deep-learning-derived capillary input function. Neuroimage 2023; 278:120284. [PMID: 37507078 PMCID: PMC10475161 DOI: 10.1016/j.neuroimage.2023.120284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 07/13/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
PURPOSE In Dynamic contrast-enhanced MRI (DCE-MRI), Arterial Input Function (AIF) has been shown to be a significant contributor to uncertainty in the estimation of kinetic parameters. This study is to assess the feasibility of using a deep learning network to estimate local Capillary Input Function (CIF) to estimate blood-brain barrier (BBB) permeability, while reducing the required scan time. MATERIALS AND METHOD A total of 13 healthy subjects (younger (<40 y/o): 8, older (> 67 y/o): 5) were recruited and underwent 25-min DCE-MRI scans. The 25 min data were retrospectively truncated to 10 min to simulate a reduced scan time of 10 min. A deep learning network was trained to predict the CIF using simulated tissue contrast dynamics with two vascular transport models. The BBB permeability (PS) was measured using 3 methods: (i) Ca-25min, using DCE-MRI data of 25 min with individually sampled AIF (Ca); (ii) Ca-10min, using truncated 10min data with AIF (Ca); and (iii) Cp-10min, using truncated 10 min data with CIF (Cp). The PS estimates from the Ca-25min method were used as reference standard values to assess the accuracy of the Ca-10min and Cp-10min methods in estimating the PS values. RESULTS When compared to the reference method(Ca-25min), the Ca-10min and Cp-10min methods resulted in an overestimation of PS by 217 ± 241 % and 48.0 ± 30.2 %, respectively. The Bland Altman analysis showed that the mean difference from the reference was 8.85 ± 1.78 (x10-4 min-1) with the Ca-10min, while it was reduced to 1.63 ± 2.25 (x10-4 min-1) with the Cp-10min, resulting in an average reduction of 81%. The limits of agreement also reduced by up to 39.2% with the Cp-10min. We found a 75% increase of BBB permeability in the gray matter and a 35% increase in the white matter, when comparing the older group to the younger group. CONCLUSIONS We demonstrated the feasibility of estimating the capillary-level input functions using a deep learning network. We also showed that this method can be used to estimate subtle age-related changes in BBB permeability with reduced scan time, without compromising accuracy. Moreover, the trained deep learning network can automatically select CIF, reducing the potential uncertainty resulting from manual user-intervention.
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Affiliation(s)
- Jonghyun Bae
- Vilcek Institute of Graduate Biomedical Science, New York University School of Medicine; Center for Biomedical Imaging, Radiology, New York University School of Medicine; Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine; Department of Radiology, Weill Cornell Medical College.
| | - Chenyang Li
- Vilcek Institute of Graduate Biomedical Science, New York University School of Medicine; Center for Biomedical Imaging, Radiology, New York University School of Medicine; Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine.
| | - Arjun Masurkar
- Center for Cognitive Neurology, Department of Neurology, New York University School of Medicine; Department of Neuroscience & Physiology, New York University School of Medicine; Neuroscience Institute, New York University School of Medicine.
| | - Yulin Ge
- Center for Biomedical Imaging, Radiology, New York University School of Medicine; Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine.
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Thulasi Seetha S, Garanzini E, Tenconi C, Marenghi C, Avuzzi B, Catanzaro M, Stagni S, Villa S, Chiorda BN, Badenchini F, Bertocchi E, Sanduleanu S, Pignoli E, Procopio G, Valdagni R, Rancati T, Nicolai N, Messina A. Stability of Multi-Parametric Prostate MRI Radiomic Features to Variations in Segmentation. J Pers Med 2023; 13:1172. [PMID: 37511785 PMCID: PMC10381192 DOI: 10.3390/jpm13071172] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/13/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
Stability analysis remains a fundamental step in developing a successful imaging biomarker to personalize oncological strategies. This study proposes an in silico contour generation method for simulating segmentation variations to identify stable radiomic features. Ground-truth annotation provided for the whole prostate gland on the multi-parametric MRI sequences (T2w, ADC, and SUB-DCE) were perturbed to mimic segmentation differences observed among human annotators. In total, we generated 15 synthetic contours for a given image-segmentation pair. One thousand two hundred twenty-four unfiltered/filtered radiomic features were extracted applying Pyradiomics, followed by stability assessment using ICC(1,1). Stable features identified in the internal population were then compared with an external population to discover and report robust features. Finally, we also investigated the impact of a wide range of filtering strategies on the stability of features. The percentage of unfiltered (filtered) features that remained robust subjected to segmentation variations were T2w-36% (81%), ADC-36% (94%), and SUB-43% (93%). Our findings suggest that segmentation variations can significantly impact radiomic feature stability but can be mitigated by including pre-filtering strategies as part of the feature extraction pipeline.
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Affiliation(s)
- Sithin Thulasi Seetha
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (S.T.S.); (R.V.)
- Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6211 LK Maastricht, The Netherlands
| | - Enrico Garanzini
- Department of Radiology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (E.G.); (A.M.)
| | - Chiara Tenconi
- Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy;
- Department of Oncology and Hematooncology, Università degli Studi di Milano, 20133 Milan, Italy
| | - Cristina Marenghi
- Unit of Genito-Urinary Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (C.M.); (F.B.); (E.B.); (G.P.)
| | - Barbara Avuzzi
- Department of Radiation Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (B.A.); (S.V.); (B.N.C.)
| | - Mario Catanzaro
- Department of Urology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (M.C.); (S.S.); (N.N.)
| | - Silvia Stagni
- Department of Urology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (M.C.); (S.S.); (N.N.)
| | - Sergio Villa
- Department of Radiation Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (B.A.); (S.V.); (B.N.C.)
| | - Barbara Noris Chiorda
- Department of Radiation Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (B.A.); (S.V.); (B.N.C.)
| | - Fabio Badenchini
- Unit of Genito-Urinary Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (C.M.); (F.B.); (E.B.); (G.P.)
| | - Elena Bertocchi
- Unit of Genito-Urinary Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (C.M.); (F.B.); (E.B.); (G.P.)
| | - Sebastian Sanduleanu
- Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6211 LK Maastricht, The Netherlands
| | - Emanuele Pignoli
- Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy;
| | - Giuseppe Procopio
- Unit of Genito-Urinary Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (C.M.); (F.B.); (E.B.); (G.P.)
| | - Riccardo Valdagni
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (S.T.S.); (R.V.)
- Department of Oncology and Hematooncology, Università degli Studi di Milano, 20133 Milan, Italy
| | - Tiziana Rancati
- Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy
| | - Nicola Nicolai
- Department of Urology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (M.C.); (S.S.); (N.N.)
| | - Antonella Messina
- Department of Radiology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (E.G.); (A.M.)
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10
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Cao J, Pickup S, Rosen M, Zhou R. Impact of Arterial Input Function and Pharmacokinetic Models on DCE-MRI Biomarkers for Detection of Vascular Effect Induced by Stroma-Directed Drug in an Orthotopic Mouse Model of Pancreatic Cancer. Mol Imaging Biol 2023:10.1007/s11307-023-01824-7. [PMID: 37166575 DOI: 10.1007/s11307-023-01824-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 04/28/2023] [Accepted: 05/01/2023] [Indexed: 05/12/2023]
Abstract
PURPOSE We demonstrated earlier in mouse models of pancreatic ductal adenocarcinoma (PDA) that Ktrans derived from dynamic contrast-enhanced (DCE) MRI detected microvascular effect induced by PEGPH20, a hyaluronidase which removes stromal hyaluronan, leading to reduced interstitial fluid pressure in the tumor (Clinical Cancer Res (2019) 25: 2314-2322). How the choice of pharmacokinetic (PK) model and arterial input function (AIF) may impact DCE-derived markers for detecting such an effect is not known. PROCEDURES Retrospective analyses of the DCE-MRI of the orthotopic PDA model are performed to examine the impact of individual versus group AIF combined with Tofts model (TM), extended-Tofts model (ETM), or shutter-speed model (SSM) on the ability to detect the microvascular changes induced by PEGPH20 treatment. RESULTS Individual AIF exhibit a marked difference in peak gadolinium concentration. However, across all three PK models, kep values show a significant correlation between individual versus group-AIF (p < 0.01). Regardless individual or group AIF, when kep is obtained from fitting the DCE-MRI data using the SSM, kep shows a significant increase after PEGPH20 treatment (p < 0.05 compared to the baseline); %change of kep from baseline to post-treatment is also significantly different between PEGPH20 versus vehicle group (p < 0.05). In comparison, when kep is derived from the TM, only the use of individual AIF leads to a significant increase of kep after PEGPH20 treatment, whereas the %change of kep is not different between PEGPH20 versus vehicle group. Group AIF but not individual AIF allows detection of a significant increase of Vp (derived from the ETM) in PEGPH20 versus vehicle group (p < 0.05). Increase of Vp is consistent with a large increase of mean capillary lumen area estimated from immunostaining. CONCLUSION Our results suggest that kep derived from SSM and Vp from ETM, both using group AIF, are optimal for the detection of microvascular changes induced by stroma-directed drug PEGPH20. These analyses provide insights in the choice of PK model and AIF for optimal DCE protocol design in mouse pancreatic cancer models.
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Affiliation(s)
- Jianbo Cao
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Current address: Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
| | - Stephen Pickup
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Mark Rosen
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Pancreatic Cancer Research Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Rong Zhou
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Pancreatic Cancer Research Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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11
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Reynolds HM, Tadimalla S, Wang YF, Montazerolghaem M, Sun Y, Williams S, Mitchell C, Finnegan ME, Murphy DG, Haworth A. Semi-quantitative and quantitative dynamic contrast-enhanced (DCE) MRI parameters as prostate cancer imaging biomarkers for biologically targeted radiation therapy. Cancer Imaging 2022; 22:71. [PMID: 36536464 PMCID: PMC9762110 DOI: 10.1186/s40644-022-00508-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 12/07/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Biologically targeted radiation therapy treatment planning requires voxel-wise characterisation of tumours. Dynamic contrast enhanced (DCE) DCE MRI has shown promise in defining voxel-level biological characteristics. In this study we consider the relative value of qualitative, semi-quantitative and quantitative assessment of DCE MRI compared with diffusion weighted imaging (DWI) and T2-weighted (T2w) imaging to detect prostate cancer at the voxel level. METHODS Seventy prostate cancer patients had multiparametric MRI prior to radical prostatectomy, including T2w, DWI and DCE MRI. Apparent Diffusion Coefficient (ADC) maps were computed from DWI, and semi-quantitative and quantitative parameters computed from DCE MRI. Tumour location and grade were validated with co-registered whole mount histology. Kolmogorov-Smirnov tests were applied to determine whether MRI parameters in tumour and benign voxels were significantly different. Cohen's d was computed to quantify the most promising biomarkers. The Parker and Weinmann Arterial Input Functions (AIF) were compared for their ability to best discriminate between tumour and benign tissue. Classifier models were used to determine whether DCE MRI parameters improved tumour detection versus ADC and T2w alone. RESULTS All MRI parameters had significantly different data distributions in tumour and benign voxels. For low grade tumours, semi-quantitative DCE MRI parameter time-to-peak (TTP) was the most discriminating and outperformed ADC. For high grade tumours, ADC was the most discriminating followed by DCE MRI parameters Ktrans, the initial rate of enhancement (IRE), then TTP. Quantitative parameters utilising the Parker AIF better distinguished tumour and benign voxel values than the Weinmann AIF. Classifier models including DCE parameters versus T2w and ADC alone, gave detection accuracies of 78% versus 58% for low grade tumours and 85% versus 72% for high grade tumours. CONCLUSIONS Incorporating DCE MRI parameters with DWI and T2w gives improved accuracy for tumour detection at a voxel level. DCE MRI parameters should be used to spatially characterise tumour biology for biologically targeted radiation therapy treatment planning.
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Affiliation(s)
- Hayley M Reynolds
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
| | | | - Yu-Feng Wang
- School of Physics, The University of Sydney, Sydney, NSW, Australia
| | | | - Yu Sun
- School of Physics, The University of Sydney, Sydney, NSW, Australia
| | - Scott Williams
- Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Catherine Mitchell
- Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Mary E Finnegan
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
- Department of Bioengineering, Imperial College London, London, UK
| | - Declan G Murphy
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Annette Haworth
- School of Physics, The University of Sydney, Sydney, NSW, Australia
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12
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Fan X, Chatterjee A, Pittman JM, Yousuf A, Antic T, Karczmar GS, Oto A. Effectiveness of Dynamic Contrast Enhanced MRI with a Split Dose of Gadoterate Meglumine for Detection of Prostate Cancer. Acad Radiol 2022; 29:796-803. [PMID: 34583866 DOI: 10.1016/j.acra.2021.07.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 07/19/2021] [Accepted: 07/31/2021] [Indexed: 01/08/2023]
Abstract
RATIONALE AND OBJECTIVES To evaluate whether dynamic contrast enhanced (DCE) MRI with a split injection of 30% followed by 70% of a standard dose (30PSD and 70PSD) of gadoterate meglumine (DOTAREM) can improve diagnosis of prostate cancer (PCa). MATERIALS AND METHODS MRI for twenty patients was performed on a Philips Ingenia 3T scanner without an endorectal coil followed by subsequent radical prostatectomy. DCE 3D T1-FFE data were acquired with injection of 0.03 mmol/kg followed after 2 minutes by 0.07 mmol/kg of DOTAREM. Regions-of-interest on histologically verified PCa and normal tissue in different prostate zones and the iliac artery were drawn. Average signal intensity as function of time was calculated for each ROI and fitted by using the signal intensity form of the Tofts (SI-Tofts) model to extract physiological parameters (Ktrans and ve). In addition, the scaled arterial input function (AIF) obtained from 30PSD data was used to analyze 70PSD data. RESULTS The AIF obtained from 30PSD data showed both first and second passes clearly and had much higher peak magnitude than AIFs from 70PSD data. Ktrans was significantly (p < 0.05) larger in PCa than in normal tissue in peripheral zone (PZ) and central zone (CZ) for both 70PSD and 70PSD data analyzed with a scaled AIF. Ktrans in cancer overlapped with that of normal tissue in the transition zone (TZ). There was no statistical difference in ve between cancer and normal tissue. Receiver operating characteristic analysis showed that use of the AIF from 30PSD data to analyze 70PSD data increased the diagnostic efficacy of Ktrans in the PZ and CZ. CONCLUSION The split dose protocol for injection of Dotarem increased diagnostic accuracy of quantitative analysis with the SI-Tofts model.
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13
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Amini Farsani Z, Schmid VJ. Maximum Entropy Technique and Regularization Functional for Determining the Pharmacokinetic Parameters in DCE-MRI. J Digit Imaging 2022; 35:1176-1188. [PMID: 35618849 PMCID: PMC9582183 DOI: 10.1007/s10278-022-00646-3] [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: 01/04/2021] [Revised: 04/15/2022] [Accepted: 04/21/2022] [Indexed: 10/31/2022] Open
Abstract
This paper aims to solve the arterial input function (AIF) determination in dynamic contrast-enhanced MRI (DCE-MRI), an important linear ill-posed inverse problem, using the maximum entropy technique (MET) and regularization functionals. In addition, estimating the pharmacokinetic parameters from a DCE-MR image investigations is an urgent need to obtain the precise information about the AIF-the concentration of the contrast agent on the left ventricular blood pool measured over time. For this reason, the main idea is to show how to find a unique solution of linear system of equations generally in the form of [Formula: see text] named an ill-conditioned linear system of equations after discretization of the integral equations, which appear in different tomographic image restoration and reconstruction issues. Here, a new algorithm is described to estimate an appropriate probability distribution function for AIF according to the MET and regularization functionals for the contrast agent concentration when applying Bayesian estimation approach to estimate two different pharmacokinetic parameters. Moreover, by using the proposed approach when analyzing simulated and real datasets of the breast tumors according to pharmacokinetic factors, it indicates that using Bayesian inference-that infer the uncertainties of the computed solutions, and specific knowledge of the noise and errors-combined with the regularization functional of the maximum entropy problem, improved the convergence behavior and led to more consistent morphological and functional statistics and results. Finally, in comparison to the proposed exponential distribution based on MET and Newton's method, or Weibull distribution via the MET and teaching-learning-based optimization (MET/TLBO) in the previous studies, the family of Gamma and Erlang distributions estimated by the new algorithm are more appropriate and robust AIFs.
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Affiliation(s)
- Zahra Amini Farsani
- Bayesian Imaging and Spatial Statistics Group, Institute of Statistics, Ludwig-Maximilian-Universität München, Ludwigstraße 33, 80539, Munich, Germany. .,Statistics Department, School of Science, Lorestan University, 68151-44316, Khorramabad, Iran.
| | - Volker J Schmid
- Bayesian Imaging and Spatial Statistics Group, Institute of Statistics, Ludwig-Maximilian-Universität München, Ludwigstraße 33, 80539, Munich, Germany
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14
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Bae J, Huang Z, Knoll F, Geras K, Sood TP, Feng L, Heacock L, Moy L, Kim SG. Estimation of the capillary level input function for dynamic contrast-enhanced MRI of the breast using a deep learning approach. Magn Reson Med 2022; 87:2536-2550. [PMID: 35001423 PMCID: PMC8852816 DOI: 10.1002/mrm.29148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 12/09/2021] [Accepted: 12/16/2021] [Indexed: 11/06/2022]
Abstract
PURPOSE To develop a deep learning approach to estimate the local capillary-level input function (CIF) for pharmacokinetic model analysis of DCE-MRI. METHODS A deep convolutional network was trained with numerically simulated data to estimate the CIF. The trained network was tested using simulated lesion data and used to estimate voxel-wise CIF for pharmacokinetic model analysis of breast DCE-MRI data using an abbreviated protocol from women with malignant (n = 25) and benign (n = 28) lesions. The estimated parameters were used to build a logistic regression model to detect the malignancy. RESULT The pharmacokinetic parameters estimated using the network-predicted CIF from our breast DCE data showed significant differences between the malignant and benign groups for all parameters. Testing the diagnostic performance with the estimated parameters, the conventional approach with arterial input function (AIF) showed an area under the curve (AUC) between 0.76 and 0.87, and the proposed approach with CIF demonstrated similar performance with an AUC between 0.79 and 0.81. CONCLUSION This study shows the feasibility of estimating voxel-wise CIF using a deep neural network. The proposed approach could eliminate the need to measure AIF manually without compromising the diagnostic performance to detect the malignancy in the clinical setting.
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Affiliation(s)
- Jonghyun Bae
- Vilcek Institute of Graduate Biomedical Science, New York University School of Medicine
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
- Department of Radiology, Weill Cornell Medical College
| | - Zhengnan Huang
- Vilcek Institute of Graduate Biomedical Science, New York University School of Medicine
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
| | - Florian Knoll
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
| | - Krzysztof Geras
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
- Center for Data Science, New York University
| | - Terlika Pandit Sood
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
| | - Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai
| | - Laura Heacock
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
| | - Linda Moy
- Vilcek Institute of Graduate Biomedical Science, New York University School of Medicine
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
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15
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Das AB, Tranos JA, Zhang J, Wadghiri YZ, Kim SG. Estimation of Contrast Agent Concentration in DCE-MRI Using 2 Flip Angles. Invest Radiol 2022; 57:343-351. [PMID: 35025833 PMCID: PMC8986601 DOI: 10.1097/rli.0000000000000845] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
PURPOSE The aim of this study was to investigate the feasibility of using 2 flip angles (FAs) with an ultrashort echo time during dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) for estimation of plasma gadolinium (Gd) concentration without using a precontrast longitudinal relaxation time T1 (T10) measurement. METHODS T1-weighted DCE-MRI experiments were carried out with C57BL/6J mice using the scan protocol with 2 FAs over 3 sequential segments during 1 scan. The data with 2 FAs were used to estimate T10 (T1T) during conversion of a time-intensity curve to the time-concentration curve. Three dosages of gadolinium-based contrast agent were used to achieve a wide range of variability in Gd concentrations when measured at 10 minutes postinjection: 0.05 mmol/kg (n = 6), 0.1 mmol/kg (n = 11), and 0.15 mmol/kg (n = 7). For comparison, the signal-to-concentration conversion was also conducted using the T10 measured from the precontrast scan (T1M) as well as a constant T10 (2.1 seconds) from the literature (T1C). The Gd concentrations ([Gd]) estimated using DCE-MRI data for the time of retro-orbital blood collection ([Gd]T1T, [Gd]T1M, and [Gd]T1C, respectively) were compared against the [Gd] of the blood samples measured by inductively coupled plasma mass spectrometry ([Gd]MS). In addition, contrast kinetic model analysis was conducted on mice with GL261 brain tumors (n = 5) using the 3 different methods for T10. RESULTS T1T strongly correlated with T1M (r = 0.81). [Gd]T1M and [Gd]T1T were significantly different from [Gd]T1C. [Gd]T1M and [Gd]T1T were in good agreement with [Gd]MS with strong correlations (mean percentage error ± standard deviation) of r = 0.70 (16% ± 56%) and r = 0.85 (15% ± 44%), respectively. In contrast, [Gd]T1C had a weak correlation of r = 0.52 with larger errors of 33% ± 24%. The contrast kinetic model parameters of GL261 brain tumors using T1T were not significantly different from those using T1M. CONCLUSIONS This study substantiates the feasibility of using the 2-FA approach during DCE-MRI scan to estimate [Gd] in the plasma without using an extra scan to perform precontrast T1 measurements.
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Affiliation(s)
| | - James A. Tranos
- Bernard & Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, United States
| | - Jin Zhang
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Youssef Zaim Wadghiri
- Bernard & Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, United States
| | - S. Gene Kim
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
- Bernard & Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, United States
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16
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Guo C, Zheng K, Ye Q, Lu Z, Xie Z, Li X, Zhao Y. Intravoxel Incoherent Motion Imaging on Sacroiliitis in Patients With Axial Spondyloarthritis: Correlation With Perfusion Characteristics Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging. Front Med (Lausanne) 2022; 8:798845. [PMID: 35155474 PMCID: PMC8826054 DOI: 10.3389/fmed.2021.798845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 12/22/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND To prospectively explore the relationship between intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) parameters of sacroiliitis in patients with axial spondyloarthritis (axSpA). METHODS Patients with initially diagnosed axSpA prospectively underwent on 3.0 T MRI of sacroiliac joint (SIJ). The IVIM parameters (D, f, D *) were calculated using biexponential analysis. K trans, K ep, V e, and V p from DCE-MRI were obtained in SIJ. The uni-variable and multi-variable linear regression analyses were used to evaluate the correlation between the parameters from these two imaging methods after controlling confounders, such as bone marrow edema (BME), age, agenda, scopes, and localization of lesions, and course of the disease. Then, their correlations were measured by calculating the Pearson's correlation coefficient (r). RESULTS The study eventually enrolled 234 patients (178 men, 56 women; mean age, 28.51 ± 9.50 years) with axSpA. With controlling confounders, D was independently related to K trans (regression coefficient [b] = 27.593, p < 0.001), K ep (b = -6.707, p = 0.021), and V e (b = 131.074, p = 0.003), whereas f and D * had no independent correlation with the parameters from DCE MRI. The correlations above were exhibited with Pearson's correlation coefficients (r) (r = 0.662, -0.408, and 0.396, respectively, all p < 0.001). CONCLUSION There were independent correlations between D derived from IVIM DWI and K trans, K ep, and V e derived from DCE-MRI. The factors which affect their correlations mainly included BME, gender, and scopes of lesions.
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Affiliation(s)
- Chang Guo
- Department of Radiology, Academy of Orthopedics, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China
| | - Kai Zheng
- Department of Radiology, Academy of Orthopedics, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China
| | - Qiang Ye
- Department of Radiology, Academy of Orthopedics, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China
| | - Zixiao Lu
- Department of Radiology, Academy of Orthopedics, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China
| | - Zhuoyao Xie
- Department of Radiology, Academy of Orthopedics, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China
| | - Xin Li
- Department of Radiology, Academy of Orthopedics, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China
| | - Yinghua Zhao
- Department of Radiology, Academy of Orthopedics, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China
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17
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Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis. ENTROPY 2022; 24:e24020155. [PMID: 35205451 PMCID: PMC8871336 DOI: 10.3390/e24020155] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/16/2022] [Accepted: 01/17/2022] [Indexed: 02/06/2023]
Abstract
Background: For the kinetic models used in contrast-based medical imaging, the assignment of the arterial input function named AIF is essential for the estimation of the physiological parameters of the tissue via solving an optimization problem. Objective: In the current study, we estimate the AIF relayed on the modified maximum entropy method. The effectiveness of several numerical methods to determine kinetic parameters and the AIF is evaluated—in situations where enough information about the AIF is not available. The purpose of this study is to identify an appropriate method for estimating this function. Materials and Methods: The modified algorithm is a mixture of the maximum entropy approach with an optimization method, named the teaching-learning method. In here, we applied this algorithm in a Bayesian framework to estimate the kinetic parameters when specifying the unique form of the AIF by the maximum entropy method. We assessed the proficiency of the proposed method for assigning the kinetic parameters in the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), when determining AIF with some other parameter-estimation methods and a standard fixed AIF method. A previously analyzed dataset consisting of contrast agent concentrations in tissue and plasma was used. Results and Conclusions: We compared the accuracy of the results for the estimated parameters obtained from the MMEM with those of the empirical method, maximum likelihood method, moment matching (“method of moments”), the least-square method, the modified maximum likelihood approach, and our previous work. Since the current algorithm does not have the problem of starting point in the parameter estimation phase, it could find the best and nearest model to the empirical model of data, and therefore, the results indicated the Weibull distribution as an appropriate and robust AIF and also illustrated the power and effectiveness of the proposed method to estimate the kinetic parameters.
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18
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Anderson VC, Tagge IJ, Doud A, Li X, Springer CS, Quinn JF, Kaye JA, Wild KV, Rooney WD. DCE-MRI of Brain Fluid Barriers: In Vivo Water Cycling at the Human Choroid Plexus. Tissue Barriers 2021; 10:1963143. [PMID: 34542012 DOI: 10.1080/21688370.2021.1963143] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
Abstract
Metabolic deficits at brain-fluid barriers are an increasingly recognized feature of cognitive decline in older adults. At the blood-cerebrospinal fluid barrier, water is transported across the choroid plexus (CP) epithelium against large osmotic gradients via processes tightly coupled to activity of the sodium/potassium pump. Here, we quantify CP homeostatic water exchange using dynamic contrast-enhanced MRI and investigate the association of the water efflux rate constant (kco) with cognitive dysfunction in older individuals. Temporal changes in the longitudinal relaxation rate constant (R1) after contrast agent bolus injection were measured in a CP region of interest in 11 participants with mild cognitive dysfunction [CI; 73 ± 6 years] and 28 healthy controls [CN; 72 ± 7 years]. kco was determined from a modified two-site pharmacokinetic exchange analysis of the R1 time-course. Ktrans, a measure of contrast agent extravasation to the interstitial space was also determined. Cognitive function was assessed by neuropsychological test performance. kco averages 5.8 ± 2.7 s-1 in CN individuals and is reduced by 2.4 s-1 [ca. 40%] in CI subjects. Significant associations of kco with global cognition and multiple cognitive domains are observed. Ktrans averages 0.13 ± 0.07 min-1 and declines with age [-0.006 ± 0.002 min-1 yr-1], but shows no difference between CI and CN individuals or association with cognitive performance. Our findings suggest that the CP water efflux rate constant is associated with cognitive dysfunction and shows an age-related decline in later life, consistent with the metabolic disturbances that characterize brain aging.
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Affiliation(s)
- Valerie C Anderson
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA
| | - Ian J Tagge
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA
| | - Aaron Doud
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA
| | - Xin Li
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA
| | - Charles S Springer
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA
| | - Joseph F Quinn
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - Jeffrey A Kaye
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - Katherine V Wild
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - William D Rooney
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA
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19
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Wang C, Padgett KR, Su MY, Mellon EA, Maziero D, Chang Z. Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy. Med Phys 2021; 49:2794-2819. [PMID: 34374098 DOI: 10.1002/mp.15130] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 06/23/2021] [Accepted: 06/28/2021] [Indexed: 11/11/2022] Open
Abstract
Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft-tissue contrast for radiographic evaluation. Based on quantitative models, MRI can be used to assess tissue functional and physiological information. With the developments of scanner design, acquisition strategy, advanced data analysis, and modeling, multiparametric MRI (mpMRI), a combination of morphologic and functional imaging modalities, has been increasingly adopted for disease detection, localization, and characterization. Integration of mpMRI techniques into RT enriches the opportunities to individualize RT. In particular, RT response assessment using mpMRI allows for accurate characterization of both tissue anatomical and biochemical changes to support decision-making in monotherapy of radiation treatment and/or systematic cancer management. In recent years, accumulating evidence have, indeed, demonstrated the potentials of mpMRI in RT response assessment regarding patient stratification, trial benchmarking, early treatment intervention, and outcome modeling. Clinical application of mpMRI for treatment response assessment in routine radiation oncology workflow, however, is more complex than implementing an additional imaging protocol; mpMRI requires additional focus on optimal study design, practice standardization, and unified statistical reporting strategy to realize its full potential in the context of RT. In this article, the mpMRI theories, including image mechanism, protocol design, and data analysis, will be reviewed with a focus on the radiation oncology field. Representative works will be discussed to demonstrate how mpMRI can be used for RT response assessment. Additionally, issues and limits of current works, as well as challenges and potential future research directions, will also be discussed.
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Affiliation(s)
- Chunhao Wang
- Department of Radiation Oncology, Duke University, Durham, North Carolina, USA
| | - Kyle R Padgett
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA.,Department of Radiology, University of Miami, Miami, Florida, USA
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, California, USA.,Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Eric A Mellon
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA
| | - Danilo Maziero
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA
| | - Zheng Chang
- Department of Radiation Oncology, Duke University, Durham, North Carolina, USA
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20
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Operator dependency of arterial input function in dynamic contrast-enhanced MRI. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2021; 35:105-112. [PMID: 34213687 PMCID: PMC8901481 DOI: 10.1007/s10334-021-00926-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 04/20/2021] [Accepted: 04/23/2021] [Indexed: 11/09/2022]
Abstract
Objective To investigate the effect of inter-operator variability in arterial input function (AIF) definition on kinetic parameter estimates (KPEs) from dynamic contrast-enhanced (DCE) MRI in patients with high-grade gliomas. Methods The study included 118 DCE series from 23 patients. AIFs were measured by three domain experts (DEs), and a population AIF (pop-AIF) was constructed from the measured AIFs. The DE-AIFs, pop-AIF and AUC-normalized DE-AIFs were used for pharmacokinetic analysis with the extended Tofts model. AIF-dependence of KPEs was assessed by intraclass correlation coefficient (ICC) analysis, and the impact on relative longitudinal change in Ktrans was assessed by Fleiss’ kappa (κ). Results There was a moderate to substantial agreement (ICC 0.51–0.76) between KPEs when using DE-AIFs, while AUC-normalized AIFs yielded ICC 0.77–0.95 for Ktrans, kep and ve and ICC 0.70 for vp. Inclusion of the pop-AIF did not reduce agreement. Agreement in relative longitudinal change in Ktrans was moderate (κ = 0.591) using DE-AIFs, while AUC-normalized AIFs gave substantial (κ = 0.809) agreement. Discussion AUC-normalized AIFs can reduce the variation in kinetic parameter results originating from operator input. The pop-AIF presented in this work may be applied in absence of a satisfactory measurement.
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21
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Zhou L, Wang Y, Pinho MC, Pan E, Xi Y, Maldjian JA, Madhuranthakam AJ. Intrasession Reliability of Arterial Spin-Labeled MRI-Measured Noncontrast Perfusion in Glioblastoma at 3 T. ACTA ACUST UNITED AC 2021; 6:139-147. [PMID: 32548290 PMCID: PMC7289238 DOI: 10.18383/j.tom.2020.00010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Arterial spin-labeled magnetic resonance imaging can provide quantitative perfusion measurements in the brain and can be potentially used to evaluate therapy response assessment in glioblastoma (GBM). The reliability and reproducibility of this method to measure noncontrast perfusion in GBM, however, are lacking. We evaluated the intrasession reliability of brain and tumor perfusion in both healthy volunteers and patients with GBM at 3 T using pseudocontinuous labeling (pCASL) and 3D turbo spin echo (TSE) using Cartesian acquisition with spiral profile reordering (CASPR). Two healthy volunteers at a single time point and 6 newly diagnosed patients with GBM at multiple time points (before, during, and after chemoradiation) underwent scanning (total, 14 sessions). Compared with 3D GraSE, 3D TSE-CASPR generated cerebral blood flow maps with better tumor-to-normal background tissue contrast and reduced image distortions. The intraclass correlation coefficient between the 2 runs of 3D pCASL with TSE-CASPR was consistently high (≥0.90) across all normal-appearing gray matter (NAGM) regions of interest (ROIs), and was particularly high in tumors (0.98 with 95% confidence interval [CI]: 0.97-0.99). The within-subject coefficients of variation were relatively low in all normal-appearing gray matter regions of interest (3.40%-7.12%), and in tumors (4.91%). Noncontrast perfusion measured using 3D pCASL with TSE-CASPR provided robust cerebral blood flow maps in both healthy volunteers and patients with GBM with high intrasession repeatability at 3 T. This approach can be an appropriate noncontrast and noninvasive quantitative perfusion imaging method for longitudinal assessment of therapy response and management of patients with GBM.
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Affiliation(s)
| | | | - Marco C Pinho
- Department of Radiology.,Advanced Imaging Research Center
| | - Edward Pan
- Department of Neurology and Neurotherapeutics.,Department of Neurological Surgery.,Harold C. Simmons Cancer Center; and
| | - Yin Xi
- Department of Radiology.,Department of Population and Data Sciences, University of Texas Southwestern Medical Center at Dallas, Dallas, TX
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22
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Castiglioni I, Rundo L, Codari M, Di Leo G, Salvatore C, Interlenghi M, Gallivanone F, Cozzi A, D'Amico NC, Sardanelli F. AI applications to medical images: From machine learning to deep learning. Phys Med 2021; 83:9-24. [PMID: 33662856 DOI: 10.1016/j.ejmp.2021.02.006] [Citation(s) in RCA: 152] [Impact Index Per Article: 50.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/09/2021] [Accepted: 02/13/2021] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Artificial intelligence (AI) models are playing an increasing role in biomedical research and healthcare services. This review focuses on challenges points to be clarified about how to develop AI applications as clinical decision support systems in the real-world context. METHODS A narrative review has been performed including a critical assessment of articles published between 1989 and 2021 that guided challenging sections. RESULTS We first illustrate the architectural characteristics of machine learning (ML)/radiomics and deep learning (DL) approaches. For ML/radiomics, the phases of feature selection and of training, validation, and testing are described. DL models are presented as multi-layered artificial/convolutional neural networks, allowing us to directly process images. The data curation section includes technical steps such as image labelling, image annotation (with segmentation as a crucial step in radiomics), data harmonization (enabling compensation for differences in imaging protocols that typically generate noise in non-AI imaging studies) and federated learning. Thereafter, we dedicate specific sections to: sample size calculation, considering multiple testing in AI approaches; procedures for data augmentation to work with limited and unbalanced datasets; and the interpretability of AI models (the so-called black box issue). Pros and cons for choosing ML versus DL to implement AI applications to medical imaging are finally presented in a synoptic way. CONCLUSIONS Biomedicine and healthcare systems are one of the most important fields for AI applications and medical imaging is probably the most suitable and promising domain. Clarification of specific challenging points facilitates the development of such systems and their translation to clinical practice.
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Affiliation(s)
- Isabella Castiglioni
- Department of Physics, Università degli Studi di Milano-Bicocca, Piazza della Scienza 3, 20126 Milano, Italy; Institute of Biomedical Imaging and Physiology, National Research Council, Via Fratelli Cervi 93, 20090 Segrate, Italy.
| | - Leonardo Rundo
- Department of Radiology, Box 218, Cambridge Biomedical Campus, Cambridge CB2 0QQ, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom.
| | - Marina Codari
- Department of Radiology, Stanford University School of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, USA.
| | - Giovanni Di Leo
- Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097 San Donato Milanese, Italy.
| | - Christian Salvatore
- Scuola Universitaria Superiore IUSS Pavia, Piazza della Vittoria 15, 27100 Pavia, Italy; DeepTrace Technologies S.r.l., Via Conservatorio 17, 20122 Milano, Italy.
| | - Matteo Interlenghi
- DeepTrace Technologies S.r.l., Via Conservatorio 17, 20122 Milano, Italy.
| | - Francesca Gallivanone
- Institute of Biomedical Imaging and Physiology, National Research Council, Via Fratelli Cervi 93, 20090 Segrate, Italy.
| | - Andrea Cozzi
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Luigi Mangiagalli 31, 20133 Milano, Italy.
| | - Natascha Claudia D'Amico
- Department of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano S.p.A., Via Saint Bon 20, 20147 Milano, Italy; Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy.
| | - Francesco Sardanelli
- Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097 San Donato Milanese, Italy; Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Luigi Mangiagalli 31, 20133 Milano, Italy.
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23
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Liu YJ, Yang HT, Yao MMS, Lin SC, Cho DY, Shen WC, Juan CJ, Chan WP. Quantifying lumbar vertebral perfusion by a Tofts model on DCE-MRI using segmental versus aortic arterial input function. Sci Rep 2021; 11:2920. [PMID: 33536471 PMCID: PMC7859214 DOI: 10.1038/s41598-021-82300-6] [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: 06/27/2020] [Accepted: 01/19/2021] [Indexed: 11/09/2022] Open
Abstract
The purpose of this study was to investigate the influence of arterial input function (AIF) selection on the quantification of vertebral perfusion using axial dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In this study, axial DCE-MRI was performed on 2 vertebrae in each of eight healthy volunteers (mean age, 36.9 years; 5 men) using a 1.5-T scanner. The pharmacokinetic parameters Ktrans, ve, and vp, derived using a Tofts model on axial DCE-MRI of the lumbar vertebrae, were evaluated using various AIFs: the population-based aortic AIF (AIF_PA), a patient-specific aortic AIF (AIF_A) and a patient-specific segmental arterial AIF (AIF_SA). Additionally, peaks and delay times were changed to simulate the effects of various AIFs on the calculation of perfusion parameters. Nonparametric analyses including the Wilcoxon signed rank test and the Kruskal–Wallis test with a Dunn–Bonferroni post hoc analysis were performed. In simulation, Ktrans and ve increased as the peak in the AIF decreased, but vp increased when delay time in the AIF increased. In humans, the estimated Ktrans and ve were significantly smaller using AIF_A compared to AIF_SA no matter the computation style (pixel-wise or region-of-interest based). Both these perfusion parameters were significantly greater using AIF_SA compared to AIF_A.
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Affiliation(s)
- Yi-Jui Liu
- Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan.,Master's Program of Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung, Taiwan
| | - Hou-Ting Yang
- Ph.D. Program in Electrical and Communication Engineering in Feng Chia University, Taichung, Taiwan.,Department of Nuclear Medicine, Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Melissa Min-Szu Yao
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, 111 Hsing-Long Road, Section 3, Taipei, 116, Taiwan.,Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Shao-Chieh Lin
- Ph.D. Program in Electrical and Communication Engineering in Feng Chia University, Taichung, Taiwan
| | - Der-Yang Cho
- Department of Neurosurgery, China Medical University Hospital, Taichung, Taiwan
| | - Wu-Chung Shen
- Department of Radiology, School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.,Department of Medical Imaging, China Medical University Hospital, Taichung, Taiwan
| | - Chun-Jung Juan
- Department of Radiology, School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan. .,Department of Medical Imaging, China Medical University Hospital, Taichung, Taiwan. .,Department of Medical Imaging, China Medical University Hsinchu Hospital, Hsinchu, 199, Sec. 1, Xinglong Rd., Zhubei City, Hsinchu County, 302, Taiwan.
| | - Wing P Chan
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, 111 Hsing-Long Road, Section 3, Taipei, 116, Taiwan. .,Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
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24
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Liu B, Sun Z, Ma WL, Ren J, Zhang GW, Wei MQ, Hou WH, Hou BX, Wei LC, Huan Y, Zheng MW. DCE-MRI Quantitative Parameters as Predictors of Treatment Response in Patients With Locally Advanced Cervical Squamous Cell Carcinoma Underwent CCRT. Front Oncol 2020; 10:585738. [PMID: 33194734 PMCID: PMC7658627 DOI: 10.3389/fonc.2020.585738] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 09/22/2020] [Indexed: 12/21/2022] Open
Abstract
Purpose To evaluate the predictive value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative parameters in treatment response to concurrent chemoradiotherapy (CCRT) for locally advanced cervical squamous cell carcinoma (LACSC). Methods and materials LACSC patients underwent CCRT had DCE-MRI before (e0) and after 3 days of treatment (e3). Extended Tofts Linear model with a user arterial input function was adopted to generate quantitative measurements. Endothelial transfer constant (Ktrans), reflux rate (Kep), fractional extravascular extracellular space volume (Ve), and fractional plasma volume (Vp) were calculated, and percentage changes ΔKtrans, ΔKep, ΔVe, and ΔVp were computed. The correlations of these measurements with the tumor regression rate were analyzed. The predictive value of these parameters on treatment outcome was generated by the receiver operating characteristic (ROC) curve. Univariate and multivariate logistic regression analyses were conducted to find the independent variables. Results Ktrans-e0, Kep -e0, ΔKtrans, and ΔVe were positively correlated with the tumor regression rate. Mean values of Ktrans-e0, Ktrans-e3, ΔKtrans, and ΔVe were higher in the non-residual tumor group than residual tumor group and were independent prognostic factors for predicting residual tumor occurrence. Ktrans-e3 showed the highest area under the curve (AUC) for treatment response prediction. Conclusions Quantitative parameters at e0 and e3 from DCE-MRI could be used as potential indicators for predicting treatment response of LACSC.
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Affiliation(s)
- Bing Liu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Zhen Sun
- Department of Orthopedic, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Wan-Ling Ma
- Department of Radiology, Longgang District People's Hospital, Shenzhen, China
| | - Jing Ren
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Guang-Wen Zhang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Meng-Qi Wei
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Wei-Huan Hou
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Bing-Xin Hou
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Li-Chun Wei
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Yi Huan
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Min-Wen Zheng
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
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25
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Lorza AMA, Ravi H, Philip RC, Galons JP, Trouard TP, Parra NA, Von Hoff DD, Read WL, Tibes R, Korn RL, Raghunand N. Dose-response assessment by quantitative MRI in a phase 1 clinical study of the anti-cancer vascular disrupting agent crolibulin. Sci Rep 2020; 10:14449. [PMID: 32879326 PMCID: PMC7468301 DOI: 10.1038/s41598-020-71246-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 08/10/2020] [Indexed: 02/08/2023] Open
Abstract
The vascular disrupting agent crolibulin binds to the colchicine binding site and produces anti-vascular and apoptotic effects. In a multisite phase 1 clinical study of crolibulin (NCT00423410), we measured treatment-induced changes in tumor perfusion and water diffusivity (ADC) using dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DW-MRI), and computed correlates of crolibulin pharmacokinetics. 11 subjects with advanced solid tumors were imaged by MRI at baseline and 2–3 days post-crolibulin (13–24 mg/m2). ADC maps were computed from DW-MRI. Pre-contrast T1 maps were computed, co-registered with the DCE-MRI series, and maps of area-under-the-gadolinium-concentration-curve-at-90 s (AUC90s) and the Extended Tofts Model parameters ktrans, ve, and vp were calculated. There was a strong correlation between higher plasma drug \documentclass[12pt]{minimal}
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\begin{document}$${AUC}_{90s}>15.8$$\end{document}AUC90s>15.8 mM s, and, (2) increase in tumor fraction with \documentclass[12pt]{minimal}
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\begin{document}$${v}_{e}<0.3$$\end{document}ve<0.3. A higher plasma drug AUC was correlated with a linear combination of (1) increase in tumor fraction with \documentclass[12pt]{minimal}
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\begin{document}$${\text{ADC}} < 1.1 \times 10^{ - 3} \;{\text{mm}}^{2} /{\text{s}}$$\end{document}ADC<1.1×10-3mm2/s, and, (2) increase in tumor fraction with \documentclass[12pt]{minimal}
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\begin{document}$$v_{e}<0.3$$\end{document}ve<0.3. These findings are suggestive of cell swelling and decreased tumor perfusion 2–3 days post-treatment with crolibulin. The multivariable linear regression models reported here can inform crolibulin dosing in future clinical studies of crolibulin combined with cytotoxic or immune-oncology agents.
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Affiliation(s)
- Andres M Arias Lorza
- Department of Cancer Physiology, Moffitt Cancer Center, SRB-4, Tampa, FL, 33612, USA
| | - Harshan Ravi
- Department of Cancer Physiology, Moffitt Cancer Center, SRB-4, Tampa, FL, 33612, USA
| | - Rohit C Philip
- Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, 85721, USA
| | | | - Theodore P Trouard
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ, 85724, USA
| | - Nestor A Parra
- Department of Cancer Physiology, Moffitt Cancer Center, SRB-4, Tampa, FL, 33612, USA
| | - Daniel D Von Hoff
- Translational Genomics Research Institute (TGen), Phoenix, AZ, USA.,HonorHealth Clinical Research Institute, Scottsdale, AZ, USA
| | - William L Read
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA, USA
| | - Raoul Tibes
- Department of Internal Medicine II, Julius Maximilians University and Medical Center, Würzburg, Germany
| | | | - Natarajan Raghunand
- Department of Cancer Physiology, Moffitt Cancer Center, SRB-4, Tampa, FL, 33612, USA. .,Department of Oncologic Sciences, University of South Florida, Tampa, FL, USA.
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26
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Bliesener Y, Acharya J, Nayak KS. Efficient DCE-MRI Parameter and Uncertainty Estimation Using a Neural Network. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1712-1723. [PMID: 31794389 PMCID: PMC8887912 DOI: 10.1109/tmi.2019.2953901] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Quantitative DCE-MRI provides voxel-wise estimates of tracer-kinetic parameters that are valuable in the assessment of health and disease. These maps suffer from many known sources of variability. This variability is expensive to compute using current methods, and is typically not reported. Here, we demonstrate a novel approach for simultaneous estimation of tracer-kinetic parameters and their uncertainty due to intrinsic characteristics of the tracer-kinetic model, with very low computation time. We train and use a neural network to estimate the approximate joint posterior distribution of tracer-kinetic parameters. Uncertainties are estimated for each voxel and are specific to the patient, exam, and lesion. We demonstrate the methods' ability to produce accurate tracer-kinetic maps. We compare predicted parameter ranges with uncertainties introduced by noise and by differences in post-processing in a digital reference object. The predicted parameter ranges correlate well with tracer-kinetic parameter ranges observed across different noise realizations and regression algorithms. We also demonstrate the value of this approach to differentiate significant from insignificant changes in brain tumor pharmacokinetics over time. This is achieved by enforcing consistency in resolving model singularities in the applied tracer-kinetic model.
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27
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Kim MM, Parmar HA, Schipper M, Devasia T, Aryal MP, Kesari S, O'Day S, Morikawa A, Spratt DE, Junck L, Mammoser A, Hayman JA, Lawrence TS, Tsien CI, Aiken R, Goyal S, Abrouk N, Trimble M, Cao Y, Lao CD. BRAINSTORM: A Multi-Institutional Phase 1/2 Study of RRx-001 in Combination With Whole Brain Radiation Therapy for Patients With Brain Metastases. Int J Radiat Oncol Biol Phys 2020; 107:478-486. [PMID: 32169409 DOI: 10.1016/j.ijrobp.2020.02.639] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 02/19/2020] [Accepted: 02/29/2020] [Indexed: 01/05/2023]
Abstract
PURPOSE To determine the recommended phase 2 dose of RRx-001, a radiosensitizer with vascular normalizing properties, when used with whole-brain radiation therapy (WBRT) for brain metastases and to assess whether quantitative changes in perfusion magnetic resonance imaging (MRI) after RRx-001 correlate with response. METHODS AND MATERIALS Five centers participated in this phase 1/2 trial of RRx-001 given once pre-WBRT and then twice weekly during WBRT. Four dose levels were planned (5 mg/m2, 8.4 mg/m2, 16.5 mg/m2, 27.5 mg/m2). Dose escalation was managed by the time-to-event continual reassessment method algorithm. Linear mixed models were used to correlate change in 24-hour T1, Ktrans (capillary permeability), and fractional plasma volume with change in tumor volume. RESULTS Between 2015 and 2017, 31 patients were enrolled. Two patients dropped out before any therapy. Median age was 60 years (range, 30-76), and 12 were male. The most common tumor types were melanoma (59%) and non-small cell lung cancer (18%). No dose limiting toxicities were observed. The most common severe adverse event was grade 3 asthenia (6.9%, 2 of 29). The median intracranial response rate was 46% (95% confidence interval, 24-68) and median overall survival was 5.2 months (95% confidence interval, 4.5-9.4). No neurologic deaths occurred. Among 10 patients undergoing dynamic contrast-enhanced MRI, a reduction in Vp 24 hours after RRx-001 was associated with reduced tumor volume at 1 and 4 months (P ≤ .01). CONCLUSIONS The addition of RRx-001 to WBRT is well tolerated with favorable intracranial response rates. Because activity was observed across all dose levels, the recommended phase 2 dose is 10 mg twice weekly. A reduction in fractional plasma volume on dynamic contrast-enhanced MRI 24 hours after RRx-001 suggests antiangiogenic activity associated with longer-term tumor response.
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Affiliation(s)
- Michelle M Kim
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
| | - Hemant A Parmar
- Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Matthew Schipper
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Theresa Devasia
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Madhava P Aryal
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Santosh Kesari
- Providence Saint John's Health Center, John Wayne Cancer Institute, Santa Monica, California
| | - Steven O'Day
- Providence Saint John's Health Center, John Wayne Cancer Institute, Santa Monica, California
| | - Aki Morikawa
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Daniel E Spratt
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Larry Junck
- Department of Neurology, University of Michigan, Ann Arbor, Michigan
| | - Aaron Mammoser
- Department of Neurosurgery, Louisiana State University, New Orleans, Louisiana
| | - James A Hayman
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Theodore S Lawrence
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Christina I Tsien
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri
| | - Robert Aiken
- The Cancer Institute of New Jersey/Rutgers University, New Brunswick, New Jersey
| | - Sharad Goyal
- Department of Radiation Oncology, George Washington University, Washington, DC
| | - Nacer Abrouk
- Clinical Trials Innovations, Mountain View, California
| | | | - Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Christopher D Lao
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
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Use of monoexponential diffusion-weighted imaging and diffusion kurtosis imaging and dynamic contrast-enhanced-MRI for the differentiation of spinal tumors. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2020; 29:1112-1120. [DOI: 10.1007/s00586-020-06330-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 11/22/2019] [Accepted: 02/01/2020] [Indexed: 12/26/2022]
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29
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Stringfield O, Arrington JA, Johnston SK, Rognin NG, Peeri NC, Balagurunathan Y, Jackson PR, Clark-Swanson KR, Swanson KR, Egan KM, Gatenby RA, Raghunand N. Multiparameter MRI Predictors of Long-Term Survival in Glioblastoma Multiforme. ACTA ACUST UNITED AC 2020; 5:135-144. [PMID: 30854451 PMCID: PMC6403044 DOI: 10.18383/j.tom.2018.00052] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Standard-of-care multiparameter magnetic resonance imaging (MRI) scans of the brain were used to objectively subdivide glioblastoma multiforme (GBM) tumors into regions that correspond to variations in blood flow, interstitial edema, and cellular density. We hypothesized that the distribution of these distinct tumor ecological "habitats" at the time of presentation will impact the course of the disease. We retrospectively analyzed initial MRI scans in 2 groups of patients diagnosed with GBM, a long-term survival group comprising subjects who survived >36 month postdiagnosis, and a short-term survival group comprising subjects who survived ≤19 month postdiagnosis. The single-institution discovery cohort contained 22 subjects in each group, while the multi-institution validation cohort contained 15 subjects per group. MRI voxel intensities were calibrated, and tumor voxels clustered on contrast-enhanced T1-weighted and fluid-attenuated inversion-recovery (FLAIR) images into 6 distinct "habitats" based on low- to medium- to high-contrast enhancement and low-high signal on FLAIR scans. Habitat 6 (high signal on calibrated contrast-enhanced T1-weighted and FLAIR sequences) comprised a significantly higher volume fraction of tumors in the long-term survival group (discovery cohort, 35% ± 6.5%; validation cohort, 34% ± 4.8%) compared with tumors in the short-term survival group (discovery cohort, 17% ± 4.5%, P < .03; validation cohort, 16 ± 4.0%, P < .007). Of the 6 distinct MRI-defined habitats, the fractional tumor volume of habitat 6 at diagnosis was significantly predictive of long- or short-term survival. We discuss a possible mechanistic basis for this association and implications for habitat-driven adaptive therapy of GBM.
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Affiliation(s)
| | - John A Arrington
- Departments of Diagnostic & Interventional Radiology.,Department of Oncologic Sciences, University of S Florida, Tampa, FL
| | - Sandra K Johnston
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ.,Department of Radiology, University of Washington, Seattle, WA; and
| | | | - Noah C Peeri
- Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL
| | | | - Pamela R Jackson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Kamala R Clark-Swanson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Kristin R Swanson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Kathleen M Egan
- Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL.,Department of Oncologic Sciences, University of S Florida, Tampa, FL
| | - Robert A Gatenby
- Departments of Diagnostic & Interventional Radiology.,Department of Oncologic Sciences, University of S Florida, Tampa, FL
| | - Natarajan Raghunand
- Cancer Physiology, and.,Department of Oncologic Sciences, University of S Florida, Tampa, FL
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30
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Aryal MP, Lee C, Hawkins PG, Chapman C, Eisbruch A, Mierzwa M, Cao Y. Real-Time Quantitative Assessment of Accuracy and Precision of Blood Volume Derived from DCE-MRI in Individual Patients During a Clinical Trial. ACTA ACUST UNITED AC 2020; 5:61-67. [PMID: 30854443 PMCID: PMC6403042 DOI: 10.18383/j.tom.2018.00029] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Accuracy and precision of quantitative imaging (QI) metrics should be assessed in real time in each patient during a clinical trial to support QI-based decision-making. We developed a framework for real-time quantitative assessment of QI metrics and evaluated accuracy and precision of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI)–derived blood volume (BV) in a clinical trial for head and neck cancers. Patients underwent DCE-MRI before and after 2 weeks of radiation therapy (2wkRT). A mean as a reference value and a repeatability coefficient (RC) of BV values established from n patients in cerebellum volumes of interest (VOIs), which were normal and affected little by therapy, served as accuracy and precision measurements. The BV maps of a new patient were called accurate and precise if the values in cerebellum VOIs and the difference between the 2 scans agreed with the respective mean and RC with 95% confidence. The new data could be used to update reference values. Otherwise, the data were flagged for further evaluation before use in the trial. BV maps from 62 patients enrolled on the trial were evaluated. Mean BV values were 2.21 (±0.14) mL/100 g pre-RT and 2.22 (±0.17) mL/100 g at 2wkRT; relative RC was 15.9%. The BV maps from 3 patients were identified to be inaccurate and imprecise before use in the clinical trial. Our framework of real-time quantitative assessment of QI metrics during a clinical trial can be translated to different QI metrics and organ-sites for supporting QI-based decision-making that warrants success of a clinical trial.
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Affiliation(s)
| | | | | | | | | | | | - Yue Cao
- Departments of Radiation Oncology.,Radiology; and.,Biomedical Engineering, University of Michigan, Ann Arbor, MI
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31
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Kim MM, Parmar HA, Aryal MP, Mayo CS, Balter JM, Lawrence TS, Cao Y. Developing a Pipeline for Multiparametric MRI-Guided Radiation Therapy: Initial Results from a Phase II Clinical Trial in Newly Diagnosed Glioblastoma. ACTA ACUST UNITED AC 2020; 5:118-126. [PMID: 30854449 PMCID: PMC6403045 DOI: 10.18383/j.tom.2018.00035] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Quantitative mapping of hyperperfused and hypercellular regions of glioblastoma has been proposed to improve definition of tumor regions at risk for local recurrence following conventional radiation therapy. As the processing of the multiparametric dynamic contrast-enhanced (DCE-) and diffusion-weighted (DW-) magnetic resonance imaging (MRI) data for delineation of these subvolumes requires additional steps that go beyond the standard practices of target definition, we sought to devise a workflow to support the timely planning and treatment of patients. A phase II study implementing a multiparametric imaging biomarker for tumor hyperperfusion and hypercellularity consisting of DCE-MRI and high b-value DW-MRI to guide intensified (75 Gy/30 fractions) radiation therapy (RT) in patients with newly diagnosed glioblastoma was launched. In this report, the workflow and the initial imaging outcomes of the first 12 patients are described. Among all the first 12 patients, treatment was initiated within 6 weeks of surgery and within 2 weeks of simulation. On average, the combined hypercellular volume and high cerebral blood volume/tumor perfusion volume were 1.8 times smaller than the T1 gadolinium abnormality and 10 times smaller than the FLAIR abnormality. Hypercellular volume and high cerebral blood volume/tumor perfusion volume each identified largely distinct regions and showed 57% overlap with the enhancing abnormality, and minimal-to-no extension outside of the FLAIR. These results show the feasibility of implementing a workflow for multiparametric magnetic resonance-guided radiation therapy into clinical trials with a coordinated multidisciplinary team, and the unique and complementary tumor subregions identified by the combination of high b-value DW-MRI and DCE-MRI.
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Affiliation(s)
| | | | | | | | | | | | - Yue Cao
- Departments of Radiation Oncology and
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Abstract
The Quantitative Imaging Network of the National Cancer Institute is in its 10th year of operation, and research teams within the network are developing and validating clinical decision support software tools to measure or predict the response of cancers to various therapies. As projects progress from development activities to validation of quantitative imaging tools and methods, it is important to evaluate the performance and clinical readiness of the tools before committing them to prospective clinical trials. A variety of tests, including special challenges and tool benchmarking, have been instituted within the network to prepare the quantitative imaging tools for service in clinical trials. This article highlights the benchmarking process and provides a current evaluation of several tools in their transition from development to validation.
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Affiliation(s)
- Keyvan Farahani
- Cancer Imaging Program, National Cancer Institute of NIH, Bethesda, MD
| | - Darrell Tata
- Cancer Imaging Program, National Cancer Institute of NIH, Bethesda, MD
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33
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Baboli M, Zhang J, Kim SG. Advances in Diffusion and Perfusion MRI for Quantitative Cancer Imaging. CURRENT PATHOBIOLOGY REPORTS 2019; 7:129-141. [PMID: 33344067 PMCID: PMC7747414 DOI: 10.1007/s40139-019-00204-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
PURPOSE OF REVIEW This article is to review recent technical developments and their clinical applications in cancer imaging quantitative measurement of cellular and vascular properties of the tumors. RECENT FINDINGS Rapid development of fast Magnetic Resonance Imaging (MRI) technologies over last decade brought new opportunities in quantitative MRI methods to measure both cellular and vascular properties of tumors simultaneously. SUMMARY Diffusion MRI (dMRI) and dynamic contrast enhanced (DCE)-MRI have become widely used to assess the tissue structural and vascular properties, respectively. However, the ultimate potential of these advanced imaging modalities has not been fully exploited. The dependency of dMRI on the diffusion weighting gradient strength and diffusion time can be utilized to measure tumor perfusion, cellular structure, and cellular membrane permeability. Similarly, DCE-MRI can be used to measure vascular and cellular membrane permeability along with cellular compartment volume fractions. To facilitate the understanding of these potentially important methods for quantitative cancer imaging, we discuss the basic concepts and recent developments, as well as future directions for further development.
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Affiliation(s)
- Mehran Baboli
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
| | - Jin Zhang
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
| | - Sungheon Gene Kim
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
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34
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Bliesener Y, Lingala SG, Haldar JP, Nayak KS. Impact of (k,t) sampling on DCE MRI tracer kinetic parameter estimation in digital reference objects. Magn Reson Med 2019; 83:1625-1639. [PMID: 31605556 PMCID: PMC6982604 DOI: 10.1002/mrm.28024] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 09/06/2019] [Accepted: 09/09/2019] [Indexed: 12/12/2022]
Abstract
Purpose To evaluate the impact of (k,t) data sampling on the variance of tracer‐kinetic parameter (TK) estimation in high‐resolution whole‐brain dynamic contrast enhanced magnetic resonance imaging (DCE‐MRI) using digital reference objects. We study this in the context of TK model constraints, and in the absence of other constraints. Methods Three anatomically and physiologically realistic brain‐tumor digital reference objects were generated. Data sampling strategies included uniform and variable density; zone‐based, lattice, pseudo‐random, and pseudo‐radial; with 50‐time frames and 4‐fold to 25‐fold undersampling. In all cases, we assume a fully sampled first time frame, and prior knowledge of the arterial input function. TK parameters were estimated by indirect estimation (i.e., image‐time‐series reconstruction followed by model fitting), and direct estimation from the under‐sampled data. We evaluated methods based on the Cramér‐Rao bound and Monte‐Carlo simulations, over the range of signal‐to‐noise ratio (SNR) seen in clinical brain DCE‐MRI. Results Lattice‐based sampling provided the lowest SDs, followed by pseudo‐random, pseudo‐radial, and zone‐based. This ranking was consistent for the Patlak and extended Tofts model. Pseudo‐random sampling resulted in 19% higher averaged SD compared to lattice‐based sampling. Zone‐based sampling resulted in substantially higher SD at undersampling factors above 10. CRB analysis showed only a small difference between uniform and variable density for both lattice‐based and pseudo‐random sampling up to undersampling factors of 25. Conclusion Lattice sampling provided the lowest SDs, although the differences between sampling schemes were not substantial at low undersampling factors. The differences between lattice‐based and pseudo‐random sampling strategies with both uniform and variable density were within the range of error induced by other sources, at up to 25‐fold undersampling.
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Affiliation(s)
- Yannick Bliesener
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California
| | - Sajan G Lingala
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California
| | - Justin P Haldar
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California
| | - Krishna S Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California
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35
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Peled S, Vangel M, Kikinis R, Tempany CM, Fennessy FM, Fedorov A. Selection of Fitting Model and Arterial Input Function for Repeatability in Dynamic Contrast-Enhanced Prostate MRI. Acad Radiol 2019; 26:e241-e251. [PMID: 30467073 DOI: 10.1016/j.acra.2018.10.018] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Revised: 10/19/2018] [Accepted: 10/21/2018] [Indexed: 12/18/2022]
Abstract
RATIONALE AND OBJECTIVES Analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging is notable for the variability of calculated parameters. The purpose of this study was to evaluate the level of measurement variability and error/variability due to modeling in DCE magnetic resonance imaging parameters. MATERIALS AND METHODS Two prostate DCE scans were performed on 11 treatment-naïve patients with suspected or confirmed prostate peripheral zone cancer within an interval of less than two weeks. Tumor-suspicious and normal-appearing regions of interest (ROI) in the prostate peripheral zone were segmented. Different Tofts-Kety based models and different arterial input functions, with and without bolus arrival time (BAT) correction, were used to extract pharmacokinetic parameters. The percent repeatability coefficient (%RC) of fitted model parameters Ktrans, ve, and kep was calculated. Paired t-tests comparing parameters in tumor-suspicious ROIs and in normal-appearing tissue evaluated each parameter's sensitivity to pathology. RESULTS Although goodness-of-fit criteria favored the four-parameter extended Tofts-Kety model with the BAT correction included, the simplest two-parameter Tofts-Kety model overall yielded the best repeatability scores. The best %RC in the tumor-suspicious ROI was 63% for kep, 28% for ve, and 83% for Ktrans . The best p values for discrimination between tissues were p <10-5 for kep and Ktrans, and p = 0.11 for ve. Addition of the BAT correction to the models did not improve repeatability. CONCLUSION The parameter kep, using an arterial input functions directly measured from blood signals, was more repeatable than Ktrans. Both Ktrans and kep values were highly discriminatory between healthy and diseased tissues in all cases. The parameter ve had high repeatability but could not distinguish the two tissue types.
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36
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Ahmed Z, Levesque IR. Pharmacokinetic modeling of dynamic contrast-enhanced MRI using a reference region and input function tail. Magn Reson Med 2019; 83:286-298. [PMID: 31393033 DOI: 10.1002/mrm.27913] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 06/18/2019] [Accepted: 06/18/2019] [Indexed: 12/20/2022]
Abstract
PURPOSE Quantitative analysis of dynamic contrast-enhanced MRI (DCE-MRI) requires an arterial input function (AIF) which is difficult to measure. We propose the reference region and input function tail (RRIFT) approach which uses a reference tissue and the washout portion of the AIF. METHODS RRIFT was evaluated in simulations with 100 parameter combinations at various temporal resolutions (5-30 s) and noise levels (σ = 0.01-0.05 mM). RRIFT was compared against the extended Tofts model (ETM) in 8 studies from patients with glioblastoma multiforme. Two versions of RRIFT were evaluated: one using measured patient-specific AIF tails, and another assuming a literature-based AIF tail. RESULTS RRIFT estimated the transfer constant K trans and interstitial volume v e with median errors within 20% across all simulations. RRIFT was more accurate and precise than the ETM at temporal resolutions slower than 10 s. The percentage error of K trans had a median and interquartile range of -9 ± 45% with the ETM and -2 ± 17% with RRIFT at a temporal resolution of 30 s under noiseless conditions. RRIFT was in excellent agreement with the ETM in vivo, with concordance correlation coefficients (CCC) of 0.95 for K trans , 0.96 for v e , and 0.73 for the plasma volume v p using a measured AIF tail. With the literature-based AIF tail, the CCC was 0.89 for K trans , 0.93 for v e and 0.78 for v p . CONCLUSIONS Quantitative DCE-MRI analysis using the input function tail and a reference tissue yields absolute kinetic parameters with the RRIFT method. This approach was viable in simulation and in vivo for temporal resolutions as low as 30 s.
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Affiliation(s)
- Zaki Ahmed
- Medical Physics Unit, McGill University, Montreal, Canada.,Department of Physics, McGill University, Montreal, Canada
| | - Ives R Levesque
- Medical Physics Unit, McGill University, Montreal, Canada.,Department of Physics, McGill University, Montreal, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montreal, Canada.,Cancer Research Program, Research Institute of the McGill University Health Centre, Montreal, Canada
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37
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Knight SP, Meaney JF, Fagan AJ. DCE‐MRI protocol for constraining absolute pharmacokinetic modeling errors within specific accuracy limits. Med Phys 2019; 46:3592-3602. [DOI: 10.1002/mp.13635] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Revised: 04/30/2019] [Accepted: 05/21/2019] [Indexed: 01/01/2023] Open
Affiliation(s)
- Silvin P. Knight
- School of Medicine Trinity College University of Dublin Dublin Ireland
- National Centre for Advanced Medical Imaging (CAMI) St James's Hospital Dublin Ireland
| | - James F. Meaney
- School of Medicine Trinity College University of Dublin Dublin Ireland
- National Centre for Advanced Medical Imaging (CAMI) St James's Hospital Dublin Ireland
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38
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Elkin R, Nadeem S, LoCastro E, Paudyal R, Hatzoglou V, Lee NY, Shukla-Dave A, Deasy JO, Tannenbaum A. Optimal mass transport kinetic modeling for head and neck DCE-MRI: Initial analysis. Magn Reson Med 2019; 82:2314-2325. [PMID: 31273818 DOI: 10.1002/mrm.27897] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 06/13/2019] [Accepted: 06/14/2019] [Indexed: 12/21/2022]
Abstract
PURPOSE Current state-of-the-art models for estimating the pharmacokinetic parameters do not account for intervoxel movement of the contrast agent (CA). We introduce an optimal mass transport (OMT) formulation that naturally handles intervoxel CA movement and distinguishes between advective and diffusive flows. METHOD Ten patients with head and neck squamous cell carcinoma (HNSCC) were enrolled in the study between June 2014 and October 2015 and underwent DCE MRI imaging prior to beginning treatment. The CA tissue concentration information was taken as the input in the data-driven OMT model. The OMT approach was tested on HNSCC DCE data that provides quantitative information for forward flux ( Φ F ) and backward flux ( Φ B ). OMT-derived Φ F was compared with the volume transfer constant for CA, K trans , derived from the Extended Tofts Model (ETM). RESULTS The OMT-derived flows showed a consistent jump in the CA diffusive behavior across the images in accordance with the known CA dynamics. The mean forward flux was 0.0082 ± 0.0091 ( min - 1 ) whereas the mean advective component was 0.0052 ± 0.0086 ( min - 1 ) in the HNSCC patients. The diffusive percentages in forward and backward flux ranged from 8.67% to 18.76% and 12.76% to 30.36%, respectively. The OMT model accounts for intervoxel CA movement and results show that the forward flux ( Φ F ) is comparable with the ETM-derived K trans . CONCLUSIONS This is a novel data-driven study based on optimal mass transport principles applied to patient DCE imaging to analyze CA flow in HNSCC.
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Affiliation(s)
- Rena Elkin
- Applied Mathematics & Statistics, Stony Brook University, Stony Brook, New York
| | - Saad Nadeem
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Eve LoCastro
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Nancy Y Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.,Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Allen Tannenbaum
- Computer Science and Applied Mathematics & Statistics, Stony Brook University, Stony Brook, New York
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Schwier M, van Griethuysen J, Vangel MG, Pieper S, Peled S, Tempany C, Aerts HJWL, Kikinis R, Fennessy FM, Fedorov A. Repeatability of Multiparametric Prostate MRI Radiomics Features. Sci Rep 2019; 9:9441. [PMID: 31263116 PMCID: PMC6602944 DOI: 10.1038/s41598-019-45766-z] [Citation(s) in RCA: 148] [Impact Index Per Article: 29.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 06/12/2019] [Indexed: 12/17/2022] Open
Abstract
In this study we assessed the repeatability of radiomics features on small prostate tumors using test-retest Multiparametric Magnetic Resonance Imaging (mpMRI). The premise of radiomics is that quantitative image-based features can serve as biomarkers for detecting and characterizing disease. For such biomarkers to be useful, repeatability is a basic requirement, meaning its value must remain stable between two scans, if the conditions remain stable. We investigated repeatability of radiomics features under various preprocessing and extraction configurations including various image normalization schemes, different image pre-filtering, and different bin widths for image discretization. Although we found many radiomics features and preprocessing combinations with high repeatability (Intraclass Correlation Coefficient > 0.85), our results indicate that overall the repeatability is highly sensitive to the processing parameters. Neither image normalization, using a variety of approaches, nor the use of pre-filtering options resulted in consistent improvements in repeatability. We urge caution when interpreting radiomics features and advise paying close attention to the processing configuration details of reported results. Furthermore, we advocate reporting all processing details in radiomics studies and strongly recommend the use of open source implementations.
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Affiliation(s)
- Michael Schwier
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | - Mark G Vangel
- Harvard Medical School, Boston, MA, USA
- Massachusetts General Hospital, Charlestown, MA, USA
| | | | - Sharon Peled
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Clare Tempany
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Hugo J W L Aerts
- Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ron Kikinis
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Fraunhofer MEVIS, Bremen, Germany
- Mathematics/Computer Science Faculty, University of Bremen, Bremen, Germany
| | - Fiona M Fennessy
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Andriy Fedorov
- Brigham and Women's Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
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Wang S, Fan X, Zhang Y, Medved M, He D, Yousuf A, Jamison E, Oto A, Karczmar GS. Use of Indicator Dilution Principle to Evaluate Accuracy of Arterial Input Function Measured With Low-Dose Ultrafast Prostate Dynamic Contrast-Enhanced MRI. ACTA ACUST UNITED AC 2019; 5:260-265. [PMID: 31245547 PMCID: PMC6588202 DOI: 10.18383/j.tom.2019.00004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Accurately measuring arterial input function (AIF) is essential for quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). We used the indicator dilution principle to evaluate the accuracy of AIF measured directly from an artery following a low-dose contrast media ultrafast DCE-MRI. In total, 15 patients with biopsy-confirmed localized prostate cancers were recruited. Cardiac MRI (CMRI) and ultrafast DCE-MRI were acquired on a Philips 3 T Ingenia scanner. The AIF was measured at iliac arties following injection of a low-dose (0.015 mmol/kg) gadolinium (Gd) contrast media. The cardiac output (CO) from CMRI (COCMRI) was calculated from the difference in ventricular volume at diastole and systole measured on the short axis of heart. The CO from DCE-MRI (CODCE) was also calculated from the AIF and dose of the contrast media used. A correlation test and Bland–Altman plot were used to compare COCMRI and CODCE. The average (±standard deviation [SD]) area under the curve measured directly from local AIF was 0.219 ± 0.07 mM·min. The average (±SD) COCMRI and CODCE were 6.52 ± 1.47 L/min and 6.88 ± 1.64 L/min, respectively. There was a strong positive correlation (r = 0.82, P < .01) and good agreement between COCMRI and CODCE. The CODCE is consistent with the reference standard COCMRI. This indicates that the AIF can be measured accurately from an artery with ultrafast DCE-MRI following injection of a low-dose contrast media.
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Affiliation(s)
- Shiyang Wang
- Department of Radiology, University of Chicago, Chicago, IL and
| | - Xiaobing Fan
- Department of Radiology, University of Chicago, Chicago, IL and
| | - Yue Zhang
- Department of Radiology, University of Chicago, Chicago, IL and
| | - Milica Medved
- Department of Radiology, University of Chicago, Chicago, IL and
| | - Dianning He
- Department of Radiology, University of Chicago, Chicago, IL and.,Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China
| | - Ambereen Yousuf
- Department of Radiology, University of Chicago, Chicago, IL and
| | - Ernest Jamison
- Department of Radiology, University of Chicago, Chicago, IL and
| | - Aytekin Oto
- Department of Radiology, University of Chicago, Chicago, IL and
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41
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Crombé A, Saut O, Guigui J, Italiano A, Buy X, Kind M. Influence of temporal parameters of DCE‐MRI on the quantification of heterogeneity in tumor vascularization. J Magn Reson Imaging 2019; 50:1773-1788. [DOI: 10.1002/jmri.26753] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 03/31/2019] [Accepted: 04/02/2019] [Indexed: 12/20/2022] Open
Affiliation(s)
- Amandine Crombé
- Department of RadiologyInstitut Bergonié, Comprehensive Cancer Center Bordeaux France
- University of BordeauxIMB, UMR CNRS 5251, INRIA Project Team Monc Talence France
| | - Olivier Saut
- University of BordeauxIMB, UMR CNRS 5251, INRIA Project Team Monc Talence France
| | - Jerome Guigui
- Department of RadiologyInstitut Bergonié, Comprehensive Cancer Center Bordeaux France
| | - Antoine Italiano
- Department of Medical OncologyInstitut Bergonié, Comprehensive Cancer Center Bordeaux France
| | - Xavier Buy
- Department of RadiologyInstitut Bergonié, Comprehensive Cancer Center Bordeaux France
| | - Michèle Kind
- Department of RadiologyInstitut Bergonié, Comprehensive Cancer Center Bordeaux France
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42
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Cao J, Pickup S, Clendenin C, Blouw B, Choi H, Kang D, Rosen M, O'Dwyer PJ, Zhou R. Dynamic Contrast-enhanced MRI Detects Responses to Stroma-directed Therapy in Mouse Models of Pancreatic Ductal Adenocarcinoma. Clin Cancer Res 2019; 25:2314-2322. [PMID: 30587546 PMCID: PMC6445712 DOI: 10.1158/1078-0432.ccr-18-2276] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Revised: 11/20/2018] [Accepted: 12/19/2018] [Indexed: 12/18/2022]
Abstract
PURPOSE The dense stroma underlies the drug resistance of pancreatic ductal adenocarcinoma (PDA) and has motivated the development of stroma-directed drugs. Our objective is to test the concept that dynamic contrast-enhanced (DCE) MRI using FDA-approved contrast media, an imaging method sensitive to the tumor microenvironment, can detect early responses to stroma-directed drug. EXPERIMENTAL DESIGN Imaging studies were performed in three mouse models exhibiting high desmoplastic reactions: the autochthonous PDA in genetically engineered mice (KPC), an orthotopic model in syngeneic mice, and a xenograft model of human PDA in athymic mice. An investigational drug, PEGPH20 (pegvorhyaluronidase alfa), which degrades hyaluronan (HA) in the stroma of PDA, was injected alone or in combination with gemcitabine. RESULTS At 24 hours after a single injection of PEGPH20, Ktrans , a DCE-MRI-derived marker that measures how fast a unit volume of contrast media is transferred from capillaries to interstitial space, increased 56% and 50% from baseline in the orthotopic and xenograft tumors, respectively, compared with a 4% and 6% decrease in vehicle groups (both P < 0.05). Similarly, after three combined treatments, Ktrans in KPC mice increased 54%, whereas it decreased 4% in controls treated with gemcitabine alone (P < 0.05). Consistently, after a single injection of PEGPH20, tumor HA content assessed by IHC was reduced substantially in all three models while drug delivery (measured by paclitaxel accumulation in tumor) was increased by 2.6-fold. CONCLUSIONS These data demonstrated a DCE-MRI marker, Ktrans , can detect early responses to stroma-directed drug and reveal the sustained effect of combination treatment (PEGPH20+ gemcitabine).
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Affiliation(s)
- Jianbo Cao
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
- Medical College, Xiamen University, Xiamen, Fujian, P.R. China
| | - Stephen Pickup
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Cynthia Clendenin
- Pancreatic Cancer Research Center, University of Pennsylvania, Philadelphia, Pennsylvania
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania
| | | | - Hoon Choi
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - David Kang
- Halozyme Therapeutics, San Diego, California
| | - Mark Rosen
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Peter J O'Dwyer
- Pancreatic Cancer Research Center, University of Pennsylvania, Philadelphia, Pennsylvania.
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Rong Zhou
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania.
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania
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43
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Svistoun I, Driscoll B, Coolens C. Accuracy and Performance of Functional Parameter Estimation Using a Novel Numerical Optimization Approach for GPU-Based Kinetic Compartmental Modeling. ACTA ACUST UNITED AC 2019; 5:209-219. [PMID: 30854459 PMCID: PMC6403032 DOI: 10.18383/j.tom.2018.00048] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Quantitative kinetic parameters derived from dynamic contrast-enhanced (DCE) data are dependent on signal measurement quality and choice of pharmacokinetic model. However, the fundamental optimization analysis method is equally important and its impact on pharmacokinetic parameters has been mostly overlooked. We examine the effects of those choices on accuracy and performance of parameter estimation using both computer processing unit and graphical processing unit (GPU) numerical optimization implementations and evaluate the improvements offered by a novel optimization approach. A test framework was developed where experimentally derived population-average arterial input function and randomly sampled parameter sets {Ktrans, Kep, Vb, τ} were used to generate known tissue curves. Five numerical optimization algorithms were evaluated: sequential quadratic programming, downhill simplex (Nelder–Mead), pattern search, simulated annealing, and differential evolution. This was combined with various objective function implementation details: delay approximation, discretization and varying sampling rates. Then, impact of noise and CPU/GPU implementation was tested for speed and accuracy. Finally, the optimal method was compared to conventional implementation as applied to clinical DCE computed tomography. Nelder–Mead, differential evolution and sequential quadratic programming produced good results on clean and noisy input data outperforming simulated annealing and pattern search in terms of speed and accuracy in the respective order of 10−8%, 10−7%, and ×10−6%). A novel approach for DCE numerical optimization (infinite impulse response with fractional delay approximation) was implemented on GPU for speed increase of at least 2 orders of magnitude. Applied to clinical data, the magnitude of overall parameter error was <10%.
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Affiliation(s)
- Igor Svistoun
- Department of Medical Physics, Princess Margaret Cancer Centre and University Health Network, Toronto, Canada
| | - Brandon Driscoll
- Department of Medical Physics, Princess Margaret Cancer Centre and University Health Network, Toronto, Canada
| | - Catherine Coolens
- Department of Medical Physics, Princess Margaret Cancer Centre and University Health Network, Toronto, Canada.,Departments of Radiation Oncology and IBBME, University of Toronto, Toronto, Canada; and.,TECHNA Institute, University Health Network, Toronto, Canada
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44
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Huang W, Chen Y, Fedorov A, Li X, Jajamovich GH, Malyarenko DI, Aryal MP, LaViolette PS, Oborski MJ, O'Sullivan F, Abramson RG, Jafari-Khouzani K, Afzal A, Tudorica A, Moloney B, Gupta SN, Besa C, Kalpathy-Cramer J, Mountz JM, Laymon CM, Muzi M, Kinahan PE, Schmainda K, Cao Y, Chenevert TL, Taouli B, Yankeelov TE, Fennessy F, Li X. The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II. Tomography 2019; 5:99-109. [PMID: 30854447 PMCID: PMC6403046 DOI: 10.18383/j.tom.2018.00027] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate Ktrans (volume transfer rate constant), ve (extravascular, extracellular volume fraction), kep (efflux rate constant), and τi (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for Ktrans, ve, kep, and τi, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in Ktrans and ve (wCV = 0.50 and 0.10, respectively), but had smaller effects on kep and τi (wCV = 0.39 and 0.22, respectively). kep is less sensitive to AIF variation than Ktrans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τi parameter may have advantages over the conventional PK parameters in a longitudinal study.
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Affiliation(s)
- Wei Huang
- Oregon Health and Science University, Portland, OR
| | - Yiyi Chen
- Oregon Health and Science University, Portland, OR
| | - Andriy Fedorov
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Xia Li
- General Electric Global Research, Niskayuna, NY
| | | | | | | | | | | | | | | | | | - Aneela Afzal
- Oregon Health and Science University, Portland, OR
| | | | | | | | - Cecilia Besa
- Icahn School of Medicine at Mt Sinai, New York, NY
| | | | | | | | - Mark Muzi
- University of Washington, Seattle, WA; and
| | | | | | - Yue Cao
- University of Michigan, Ann Arbor, MI
| | | | | | | | - Fiona Fennessy
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Xin Li
- Oregon Health and Science University, Portland, OR
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45
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Sung K. Modified MR dispersion imaging in prostate dynamic contrast-enhanced MRI. J Magn Reson Imaging 2019; 50:1307-1317. [PMID: 30773769 DOI: 10.1002/jmri.26685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 02/05/2019] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND An estimation of an intravascular dispersion parameter was previously proposed to improve the overall accuracy and precision of the model parameters, but the high computation complexity can limit its practical usability in prostate dynamic contrast-enhanced MRI (DCE-MRI). PURPOSE To compare and evaluate the model fitting uncertainty and error in the model parameter estimation using different DCE-MRI analysis models and to evaluate the ability of the intravascular dispersion parameter to delineate between noncancerous and cancerous prostate tissue in the transition and peripheral zones. STUDY TYPE Retrospective. POPULATION Fifty-three patients who underwent radical prostatectomy. FIELD STRENGTH/SEQUENCE 3 T/3D RF-spoiled gradient echo sequence. ASSESSMENT The coefficient of variation was used to assess the model fitting uncertainty by adding random noise to the time-concentration curves, and the Akaike information criterion was used to assess the model fitting error. The parametric maps derived from four DCE-MRI analysis models were evaluated by evaluating the delineation between noncancerous tissue and prostate cancer or clinically significant prostate cancer. STATISTICAL TESTS The receiver operating curve analysis was performed to compare the ability to delineate between noncancerous and prostate cancer tissue in the transition and peripheral zones. RESULTS Both MR dispersion imaging (MRDI) and Weinmann analysis models had the maximum coefficient of variation in different tissue types, while the model fitting uncertainty of modified (m)MRDI was similar to the standard Toft model. In mMRDI, the model fitting error was minimum, and the delineation between noncancerous and clinically significant prostate cancer tissue was improved in both transition (area under the curve [AUC] = 0.92) and peripheral zones (AUC = 0.92), in comparison with MRDI (AUC = 0.89 and AUC = 0.85, respectively). DATA CONCLUSION The mMRDI showed promising results in detecting prostate cancer while maintaining a similar model fitting uncertainty. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1307-1317.
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Affiliation(s)
- Kyunghyun Sung
- Department of Radiological Sciences, UCLA, Los Angeles, California, USA
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46
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Zhong X, Shakeri S, Liu D, Sayre J, Raman SS, Wu HH, Sung K. Repeatability and reproducibility of variable flip angle T
1
quantification in the prostate at 3 T. J Magn Reson Imaging 2018; 49:1730-1735. [DOI: 10.1002/jmri.26596] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Revised: 11/18/2018] [Accepted: 11/20/2018] [Indexed: 11/09/2022] Open
Affiliation(s)
- Xinran Zhong
- Department of Radiological Sciences, David Geffen School of Medicine University of California California Los Angeles USA
- Physics and Biology in Medicine IDP, David Geffen School of Medicine University of California California Los Angeles USA
| | - Sepideh Shakeri
- Physics and Biology in Medicine IDP, David Geffen School of Medicine University of California California Los Angeles USA
| | - Dapeng Liu
- Department of Radiological Sciences, David Geffen School of Medicine University of California California Los Angeles USA
| | - James Sayre
- Department of Radiological Sciences, David Geffen School of Medicine University of California California Los Angeles USA
| | - Steven S. Raman
- Physics and Biology in Medicine IDP, David Geffen School of Medicine University of California California Los Angeles USA
| | - Holden H. Wu
- Department of Radiological Sciences, David Geffen School of Medicine University of California California Los Angeles USA
- Physics and Biology in Medicine IDP, David Geffen School of Medicine University of California California Los Angeles USA
| | - Kyunghyun Sung
- Department of Radiological Sciences, David Geffen School of Medicine University of California California Los Angeles USA
- Physics and Biology in Medicine IDP, David Geffen School of Medicine University of California California Los Angeles USA
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47
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Press RH, Shu HKG, Shim H, Mountz JM, Kurland BF, Wahl RL, Jones EF, Hylton NM, Gerstner ER, Nordstrom RJ, Henderson L, Kurdziel KA, Vikram B, Jacobs MA, Holdhoff M, Taylor E, Jaffray DA, Schwartz LH, Mankoff DA, Kinahan PE, Linden HM, Lambin P, Dilling TJ, Rubin DL, Hadjiiski L, Buatti JM. The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective. Int J Radiat Oncol Biol Phys 2018; 102:1219-1235. [PMID: 29966725 PMCID: PMC6348006 DOI: 10.1016/j.ijrobp.2018.06.023] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2018] [Revised: 05/25/2018] [Accepted: 06/14/2018] [Indexed: 02/07/2023]
Abstract
Modern radiation therapy is delivered with great precision, in part by relying on high-resolution multidimensional anatomic imaging to define targets in space and time. The development of quantitative imaging (QI) modalities capable of monitoring biologic parameters could provide deeper insight into tumor biology and facilitate more personalized clinical decision-making. The Quantitative Imaging Network (QIN) was established by the National Cancer Institute to advance and validate these QI modalities in the context of oncology clinical trials. In particular, the QIN has significant interest in the application of QI to widen the therapeutic window of radiation therapy. QI modalities have great promise in radiation oncology and will help address significant clinical needs, including finer prognostication, more specific target delineation, reduction of normal tissue toxicity, identification of radioresistant disease, and clearer interpretation of treatment response. Patient-specific QI is being incorporated into radiation treatment design in ways such as dose escalation and adaptive replanning, with the intent of improving outcomes while lessening treatment morbidities. This review discusses the current vision of the QIN, current areas of investigation, and how the QIN hopes to enhance the integration of QI into the practice of radiation oncology.
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Affiliation(s)
- Robert H. Press
- Dept. of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - Hui-Kuo G. Shu
- Dept. of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - Hyunsuk Shim
- Dept. of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - James M. Mountz
- Dept. of Radiology, University of Pittsburgh, Pittsburgh, PA
| | | | | | - Ella F. Jones
- Dept. of Radiology, University of California, San Francisco, San Francisco, CA
| | - Nola M. Hylton
- Dept. of Radiology, University of California, San Francisco, San Francisco, CA
| | - Elizabeth R. Gerstner
- Dept. of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | | | - Lori Henderson
- Cancer Imaging Program, National Cancer Institute, Bethesda, MD
| | | | - Bhadrasain Vikram
- Radiation Research Program/Division of Cancer Treatment & Diagnosis, National Cancer Institute, Bethesda, MD
| | - Michael A. Jacobs
- Dept. of Radiology and Radiological Science, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore MD
| | - Matthias Holdhoff
- Brain Cancer Program, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore MD
| | - Edward Taylor
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - David A. Jaffray
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | | | - David A. Mankoff
- Dept. of Radiology, University of Pennsylvania, Philadelphia, PA
| | | | | | - Philippe Lambin
- Dept. of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Thomas J. Dilling
- Dept. of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | | | | | - John M. Buatti
- Dept. of Radiation Oncology, University of Iowa, Iowa City, IA
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48
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Influence of arterial input function (AIF) on quantitative prostate dynamic contrast-enhanced (DCE) MRI and zonal prostate anatomy. Magn Reson Imaging 2018; 53:28-33. [DOI: 10.1016/j.mri.2018.06.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 06/07/2018] [Accepted: 06/10/2018] [Indexed: 11/23/2022]
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49
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Quarles CC, Bell LC, Stokes AM. Imaging vascular and hemodynamic features of the brain using dynamic susceptibility contrast and dynamic contrast enhanced MRI. Neuroimage 2018; 187:32-55. [PMID: 29729392 DOI: 10.1016/j.neuroimage.2018.04.069] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2017] [Revised: 04/27/2018] [Accepted: 04/29/2018] [Indexed: 12/22/2022] Open
Abstract
In the context of neurologic disorders, dynamic susceptibility contrast (DSC) and dynamic contrast enhanced (DCE) MRI provide valuable insights into cerebral vascular function, integrity, and architecture. Even after two decades of use, these modalities continue to evolve as their biophysical and kinetic basis is better understood, with improvements in pulse sequences and accelerated imaging techniques and through application of more robust and automated data analysis strategies. Here, we systematically review each of these elements, with a focus on how their integration improves kinetic parameter accuracy and the development of new hemodynamic biomarkers that provide sub-voxel sensitivity (e.g., capillary transit time and flow heterogeneity). Regarding contrast mechanisms, we discuss the dipole-dipole interactions and susceptibility effects that give rise to simultaneous T1, T2 and T2∗ relaxation effects, including their quantification, influence on pulse sequence parameter optimization, and use in methods such as vessel size and vessel architectural imaging. The application of technologic advancements, such as parallel imaging, simultaneous multi-slice, undersampled k-space acquisitions, and sliding window strategies, enables improved spatial and/or temporal resolution of DSC and DCE acquisitions. Such acceleration techniques have also enabled the implementation of, clinically feasible, simultaneous multi-echo spin- and gradient echo acquisitions, providing more comprehensive and quantitative interrogation of T1, T2 and T2∗ changes. Characterizing these relaxation rate changes through different post-processing options allows for the quantification of hemodynamics and vascular permeability. The application of different biophysical models provides insight into traditional hemodynamic parameters (e.g., cerebral blood volume) and more advanced parameters (e.g., capillary transit time heterogeneity). We provide insight into the appropriate selection of biophysical models and the necessary post-processing steps to ensure reliable measurements while minimizing potential sources of error. We show representative examples of advanced DSC- and DCE-MRI methods applied to pathologic conditions affecting the cerebral microcirculation, including brain tumors, stroke, aging, and multiple sclerosis. The maturation and standardization of conventional DSC- and DCE-MRI techniques has enabled their increased integration into clinical practice and use in clinical trials, which has, in turn, spurred renewed interest in their technological and biophysical development, paving the way towards a more comprehensive assessment of cerebral hemodynamics.
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Affiliation(s)
- C Chad Quarles
- Division of Neuro imaging Research, Barrow Neurological Institute, 350 W. Thomas Rd, Phoenix, AZ, USA.
| | - Laura C Bell
- Division of Neuro imaging Research, Barrow Neurological Institute, 350 W. Thomas Rd, Phoenix, AZ, USA
| | - Ashley M Stokes
- Division of Neuro imaging Research, Barrow Neurological Institute, 350 W. Thomas Rd, Phoenix, AZ, USA
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50
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Variability induced by the MR imager in dynamic contrast-enhanced imaging of the prostate. Diagn Interv Imaging 2018; 99:255-264. [DOI: 10.1016/j.diii.2017.12.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2017] [Revised: 12/03/2017] [Accepted: 12/07/2017] [Indexed: 12/22/2022]
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