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Kallis K, Conlin CC, Zhong AY, Hussain TS, Chatterjee A, Karczmar GS, Rakow-Penner R, Dale AM, Seibert TM. Comparison of synthesized and acquired high b-value diffusion-weighted MRI for detection of prostate cancer. Cancer Imaging 2024; 24:89. [PMID: 38972972 PMCID: PMC11229343 DOI: 10.1186/s40644-024-00723-6] [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: 12/19/2023] [Accepted: 06/10/2024] [Indexed: 07/09/2024] Open
Abstract
BACKGROUND High b-value diffusion-weighted images (DWI) are used for detection of clinically significant prostate cancer (csPCa). This study qualitatively and quantitatively compares synthesized DWI (sDWI) to acquired (aDWI) for detection of csPCa. METHODS One hundred fifty-one consecutive patients who underwent prostate MRI and biopsy were included in the study. Axial DWI with b = 0, 500, 1000, and 2000 s/mm2 using a 3T clinical scanner using a 32-channel phased-array body coil were acquired. We retrospectively synthesized DWI for b = 2000 s/mm2 via extrapolation based on mono-exponential decay, using b = 0 and b = 500 s/mm2 (sDWI500) and b = 0, b = 500 s/mm2, and b = 1000 s/mm2 (sDWI1000). Differences in signal intensity between sDWI and aDWI were evaluated within different regions of interest (prostate alone, prostate plus 5 mm, 30 mm and 70 mm margin and full field of view). The maximum DWI value within each ROI was evaluated for prediction of csPCa. Classification accuracy was compared to Restriction Spectrum Imaging restriction score (RSIrs), a previously validated biomarker based on multi-exponential DWI. Discrimination of csPCa was evaluated via area under the receiver operating characteristic curve (AUC). RESULTS Within the prostate, mean ± standard deviation of percent mean differences between sDWI and aDWI signal were -46 ± 35% for sDWI1000 and -67 ± 24% for sDWI500. AUC for aDWI, sDWI500, sDWI1000, and RSIrs within the prostate 0.62[95% confidence interval: 0.53, 0.71], 0.63[0.54, 0.72], 0.65[0.56, 0.73] and 0.78[0.71, 0.86], respectively. CONCLUSION sDWI is qualitatively comparable to aDWI within the prostate. However, hyperintense artifacts are introduced with sDWI in the surrounding pelvic tissue that interfere with quantitative cancer detection and might mask metastases. In the prostate, RSIrs yields superior quantitative csPCa detection than sDWI or aDWI.
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Affiliation(s)
- Karoline Kallis
- Department of Radiation Medicine and Applied Sciences, University of California San Diego Health, La Jolla, CA, USA
| | - Christopher C Conlin
- Department of Radiology, University of California San Diego Health, La Jolla, San Diego, CA, USA
| | - Allison Y Zhong
- Department of Radiation Medicine and Applied Sciences, University of California San Diego Health, La Jolla, CA, USA
| | - Troy S Hussain
- Department of Radiation Medicine and Applied Sciences, University of California San Diego Health, La Jolla, CA, USA
| | - Aritrick Chatterjee
- Department of Radiology, University of Chicago, Chicago, IL, USA
- Sanford J. Grossmann Center of Excellence in Prostate Imaging and Image Guided Therapy, University of Chicago, Chicago, IL, USA
| | - Gregory S Karczmar
- Department of Radiology, University of Chicago, Chicago, IL, USA
- Sanford J. Grossmann Center of Excellence in Prostate Imaging and Image Guided Therapy, University of Chicago, Chicago, IL, USA
| | - Rebecca Rakow-Penner
- Department of Radiology, University of California San Diego Health, La Jolla, San Diego, CA, USA
| | - Anders M Dale
- Department of Radiology, University of California San Diego Health, La Jolla, San Diego, CA, USA
- Department of Neurosciences, University of California San Diego Health, La Jolla, San Diego, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, San Diego, CA, USA
| | - Tyler M Seibert
- Department of Radiology, University of California San Diego Health, La Jolla, San Diego, CA, USA.
- Department of Radiation Medicine and Applied Sciences, University of California San Diego Health, La Jolla, CA, USA.
- Department of Bioengineering, University of California San Diego Jacobs School of Engineering, La Jolla, San Diego, CA, USA.
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2
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Chatterjee A, Dwivedi DK. MRI-based virtual pathology of the prostate. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01163-w. [PMID: 38856839 DOI: 10.1007/s10334-024-01163-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 06/11/2024]
Abstract
Prostate cancer poses significant diagnostic challenges, with conventional methods like prostate-specific antigen (PSA) screening and transrectal ultrasound (TRUS)-guided biopsies often leading to overdiagnosis or miss clinically significant cancers. Multiparametric MRI (mpMRI) has emerged as a more reliable tool. However, it is limited by high inter-observer variability and radiologists missing up to 30% of clinically significant cancers. This article summarizes a few of these recent advancements in quantitative MRI techniques that look at the "Virtual Pathology" of the prostate with an aim to enhance prostate cancer detection and characterization. These techniques include T2 relaxation-based techniques such as luminal water imaging, diffusion based such as vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) and restriction spectrum imaging or combined relaxation-diffusion techniques such as hybrid multi-dimensional MRI (HM-MRI), time-dependent diffusion imaging, and diffusion-relaxation correlation spectrum imaging. These methods provide detailed insights into underlying prostate microstructure and tissue composition and have shown improved diagnostic accuracy over conventional MRI. These innovative MRI methods hold potential for augmenting mpMRI, reducing variability in diagnosis, and paving the way for MRI as a 'virtual histology' tool in prostate cancer diagnosis. However, they require further validation in larger multi-center clinical settings and rigorous in-depth radiological-pathology correlation are needed for broader implementation.
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Affiliation(s)
- Aritrick Chatterjee
- Department of Radiology, University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, IL, 60637, USA.
- Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy, University of Chicago, Chicago, IL, USA.
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Lorenzo G, Heiselman JS, Liss MA, Miga MI, Gomez H, Yankeelov TE, Reali A, Hughes TJ. A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model. CANCER RESEARCH COMMUNICATIONS 2024; 4:617-633. [PMID: 38426815 PMCID: PMC10906139 DOI: 10.1158/2767-9764.crc-23-0449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/15/2023] [Accepted: 02/09/2024] [Indexed: 03/02/2024]
Abstract
Active surveillance (AS) is a suitable management option for newly diagnosed prostate cancer, which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor monitored via longitudinal multiparametric MRI (mpMRI), PSA tests, and biopsies. Hence, treatment is prescribed when these tests identify progression to higher-risk prostate cancer. However, current AS protocols rely on detecting tumor progression through direct observation according to population-based monitoring strategies. This approach limits the design of patient-specific AS plans and may delay the detection of tumor progression. Here, we present a pilot study to address these issues by leveraging personalized computational predictions of prostate cancer growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data (T2-weighted MRI and apparent diffusion coefficient maps from diffusion-weighted MRI). Our results show that our technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients from 0.93 to 0.99 across our cohort (n = 7). In addition, we identify a model-based biomarker of higher-risk prostate cancer: the mean proliferation activity of the tumor (P = 0.041). Using logistic regression, we construct a prostate cancer risk classifier based on this biomarker that achieves an area under the ROC curve of 0.83. We further show that coupling our tumor forecasts with this prostate cancer risk classifier enables the early identification of prostate cancer progression to higher-risk disease by more than 1 year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for patients with prostate cancer. SIGNIFICANCE Personalization of a biomechanistic model of prostate cancer with mpMRI data enables the prediction of tumor progression, thereby showing promise to guide clinical decision-making during AS for each individual patient.
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Affiliation(s)
- Guillermo Lorenzo
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
| | - Jon S. Heiselman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Michael A. Liss
- Department of Urology, The University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | - Michael I. Miga
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Neurological Surgery, Radiology, and Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hector Gomez
- School of Mechanical Engineering, Weldon School of Biomedical Engineering, and Purdue Institute for Cancer Research, Purdue University, West Lafayette, Indiana
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
- Livestrong Cancer Institutes and Departments of Biomedical Engineering, Diagnostic Medicine, and Oncology, The University of Texas at Austin, Austin, Texas
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Alessandro Reali
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Thomas J.R. Hughes
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
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Baris MM, Quarterman P, Shin J, Fung MM, Jambawalikar SR, Moonis G. Diagnostic Utility of Restriction Spectrum Imaging in Head and Neck Tumors: A Pilot Study. J Comput Assist Tomogr 2024; 48:150-155. [PMID: 37551157 DOI: 10.1097/rct.0000000000001513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
OBJECTIVE Imaging is crucial in the assessment of head and neck cancers for site, extension, and enlarged lymph nodes. Restriction spectrum imaging (RSI) is a new diffusion-weighted magnetic resonance imaging (MRI) technique that enhances the ability to differentiate aggressive cancer from low-grade or benign tumors and helps guide treatment and biopsy. Its contribution to imaging of brain and prostate tumors has been previously published. However, there are no prior studies using RSI sequence in head and neck tumors. The purpose of this study was to evaluate the feasibility of performing RSI in head and neck cancer. METHODS An additional RSI sequence was added in the routine MRI neck protocol for 13 patients diagnosed with head and neck cancer between November 2018 and April 2019. Restriction spectrum imaging sequence was performed with b values of 0, 500, 1500, and 3000 s/mm 2 and 29 directions on 1.5T magnetic resonance scanners.Diffusion-weighted imaging (DWI) images and RSI images were compared according to their ability to detect the primary malignancy and possible metastatic lymph nodes. RESULTS In 71% of the patients, RSI outperformed DWI in detecting the primary malignancy and possible metastatic lymph nodes, whereas in the remaining cases, the 2 were comparable. In 66% of the patients, RSI detected malignant lymph nodes that DWI/apparent diffusion coefficient failed to detect. CONCLUSIONS This is the first study of RSI in head and neck imaging and showed its superiority over the conventional DWI sequence. Because of its ability to differentiate benign and malignant lymph nodes in some cases, the addition of RSI to routine head and neck MRI should be considered.
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Lui AJ, Kallis K, Zhong AY, Hussain TS, Conlin C, Digma LA, Phan N, Mathews IT, Do DD, Domingo MR, Karunamuni R, Kuperman J, Dale AM, Shabaik A, Rakow-Penner R, Hahn ME, Seibert TM. ReIGNITE Radiation Therapy Boost: A Prospective, International Study of Radiation Oncologists' Accuracy in Contouring Prostate Tumors for Focal Radiation Therapy Boost on Conventional Magnetic Resonance Imaging Alone or With Assistance of Restriction Spectrum Imaging. Int J Radiat Oncol Biol Phys 2023; 117:1145-1152. [PMID: 37453559 PMCID: PMC11088932 DOI: 10.1016/j.ijrobp.2023.07.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 06/27/2023] [Accepted: 07/02/2023] [Indexed: 07/18/2023]
Abstract
PURPOSE In a phase III randomized trial, adding a radiation boost to tumor(s) visible on MRI improved prostate cancer (PCa) disease-free and metastasis-free survival without additional toxicity. Radiation oncologists' ability to identify prostate tumors is critical to widely adopting intraprostatic tumor radiotherapy boost for patients. A diffusion MRI biomarker, called the Restriction Spectrum Imaging restriction score (RSIrs), has been shown to improve radiologists' identification of clinically significant PCa. We hypothesized that (1) radiation oncologists would find accurately delineating PCa tumors on conventional MRI challenging and (2) using RSIrs maps would improve radiation oncologists' accuracy for PCa tumor delineation. METHODS AND MATERIALS In this multi-institutional, international, prospective study, 44 radiation oncologists (participants) and 2 expert radiologists (experts) contoured prostate tumors on 39 total patient cases using conventional MRI with or without RSIrs maps. Participant volumes were compared to the consensus expert volumes. Contouring accuracy metrics included percent overlap with expert volume, Dice coefficient, conformal number, and maximum distance beyond expert volume. RESULTS 1604 participant volumes were produced. 40 of 44 participants (91%) completely missed ≥1 expert-defined target lesion without RSIrs, compared to 13 of 44 (30%) with RSIrs maps. On conventional MRI alone, 134 of 762 contour attempts (18%) completely missed the target, compared to 18 of 842 (2%) with RSIrs maps. Use of RSIrs maps improved all contour accuracy metrics by approximately 50% or more. Mixed effects modeling confirmed that RSIrs maps were the main variable driving improvement in all metrics. System Usability Scores indicated RSIrs maps significantly improved the contouring experience (72 vs. 58, p < 0.001). CONCLUSIONS Radiation oncologists struggle with accurately delineating visible PCa tumors on conventional MRI. RSIrs maps improve radiation oncologists' ability to target MRI-visible tumors for prostate tumor boost.
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Affiliation(s)
- Asona J Lui
- Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California
| | - Karoline Kallis
- Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California
| | - Allison Y Zhong
- Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California; UC San Diego School of Medicine, La Jolla, California
| | - Troy S Hussain
- Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California
| | - Christopher Conlin
- Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California
| | - Leonardino A Digma
- Department of Neurosciences, UC San Diego School of Medicine, La Jolla, California
| | - Nikki Phan
- Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California
| | - Ian T Mathews
- Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California; UC San Diego School of Medicine, La Jolla, California
| | - Deondre D Do
- Department of Bioengineering, UC San Diego Jacobs School of Engineering, La Jolla, California
| | - Mariluz Rojo Domingo
- Department of Bioengineering, UC San Diego Jacobs School of Engineering, La Jolla, California
| | - Roshan Karunamuni
- Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California
| | - Joshua Kuperman
- Department of Radiology, UC San Diego School of Medicine, La Jolla, California
| | - Anders M Dale
- Department of Radiology, UC San Diego School of Medicine, La Jolla, California; Department of Neurosciences, UC San Diego School of Medicine, La Jolla, California; Halıcıoğlu Data Science Institute, UC San Diego School of Medicine, La Jolla, California
| | - Ahmed Shabaik
- Department of Pathology, UC San Diego School of Medicine, La Jolla, California
| | - Rebecca Rakow-Penner
- Department of Bioengineering, UC San Diego Jacobs School of Engineering, La Jolla, California; Department of Radiology, UC San Diego School of Medicine, La Jolla, California
| | - Michael E Hahn
- Department of Radiology, UC San Diego School of Medicine, La Jolla, California
| | - Tyler M Seibert
- Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California; Department of Bioengineering, UC San Diego Jacobs School of Engineering, La Jolla, California; Department of Radiology, UC San Diego School of Medicine, La Jolla, California.
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Fokkinga E, Hernandez-Tamames JA, Ianus A, Nilsson M, Tax CMW, Perez-Lopez R, Grussu F. Advanced Diffusion-Weighted MRI for Cancer Microstructure Assessment in Body Imaging, and Its Relationship With Histology. J Magn Reson Imaging 2023. [PMID: 38032021 DOI: 10.1002/jmri.29144] [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: 08/11/2023] [Revised: 10/30/2023] [Accepted: 10/31/2023] [Indexed: 12/01/2023] Open
Abstract
Diffusion-weighted magnetic resonance imaging (DW-MRI) aims to disentangle multiple biological signal sources in each imaging voxel, enabling the computation of innovative maps of tissue microstructure. DW-MRI model development has been dominated by brain applications. More recently, advanced methods with high fidelity to histology are gaining momentum in other contexts, for example, in oncological applications of body imaging, where new biomarkers are urgently needed. The objective of this article is to review the state-of-the-art of DW-MRI in body imaging (ie, not including the nervous system) in oncology, and to analyze its value as compared to reference colocalized histology measurements, given that demonstrating the histological validity of any new DW-MRI method is essential. In this article, we review the current landscape of DW-MRI techniques that extend standard apparent diffusion coefficient (ADC), describing their acquisition protocols, signal models, fitting settings, microstructural parameters, and relationship with histology. Preclinical, clinical, and in/ex vivo studies were included. The most used techniques were intravoxel incoherent motion (IVIM; 36.3% of used techniques), diffusion kurtosis imaging (DKI; 16.7%), vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT; 13.3%), and imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED; 11.7%). Another notable category of techniques relates to innovative b-tensor diffusion encoding or joint diffusion-relaxometry. The reviewed approaches provide histologically meaningful indices of cancer microstructure (eg, vascularization/cellularity) which, while not necessarily accurate numerically, may still provide useful sensitivity to microscopic pathological processes. Future work of the community should focus on improving the inter-/intra-scanner robustness, and on assessing histological validity in broader contexts. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Ella Fokkinga
- Biomedical Engineering, Track Medical Physics, Delft University of Technology, Delft, The Netherlands
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Juan A Hernandez-Tamames
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Andrada Ianus
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Markus Nilsson
- Department of Diagnostic Radiology, Clinical Sciences Lund, Lund, Sweden
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Center (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Francesco Grussu
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
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7
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Andreassen MMS, Loubrie S, Tong MW, Fang L, Seibert TM, Wallace AM, Zare S, Ojeda-Fournier H, Kuperman J, Hahn M, Jerome NP, Bathen TF, Rodríguez-Soto AE, Dale AM, Rakow-Penner R. Restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancer. Front Oncol 2023; 13:1237720. [PMID: 37781199 PMCID: PMC10541212 DOI: 10.3389/fonc.2023.1237720] [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: 06/09/2023] [Accepted: 08/08/2023] [Indexed: 10/03/2023] Open
Abstract
Purpose Dynamic contrast-enhanced MRI (DCE) and apparent diffusion coefficient (ADC) are currently used to evaluate treatment response of breast cancer. The purpose of the current study was to evaluate the three-component Restriction Spectrum Imaging model (RSI3C), a recent diffusion-weighted MRI (DWI)-based tumor classification method, combined with elastic image registration, to automatically monitor breast tumor size throughout neoadjuvant therapy. Experimental design Breast cancer patients (n=27) underwent multi-parametric 3T MRI at four time points during treatment. Elastically-registered DWI images were used to generate an automatic RSI3C response classifier, assessed against manual DCE tumor size measurements and mean ADC values. Predictions of therapy response during treatment and residual tumor post-treatment were assessed using non-pathological complete response (non-pCR) as an endpoint. Results Ten patients experienced pCR. Prediction of non-pCR using ROC AUC (95% CI) for change in measured tumor size from pre-treatment time point to early-treatment time point was 0.65 (0.38-0.92) for the RSI3C classifier, 0.64 (0.36-0.91) for DCE, and 0.45 (0.16-0.75) for change in mean ADC. Sensitivity for detection of residual disease post-treatment was 0.71 (0.44-0.90) for the RSI3C classifier, compared to 0.88 (0.64-0.99) for DCE and 0.76 (0.50-0.93) for ADC. Specificity was 0.90 (0.56-1.00) for the RSI3C classifier, 0.70 (0.35-0.93) for DCE, and 0.50 (0.19-0.81) for ADC. Conclusion The automatic RSI3C classifier with elastic image registration suggested prediction of response to treatment after only three weeks, and showed performance comparable to DCE for assessment of residual tumor post-therapy. RSI3C may guide clinical decision-making and enable tailored treatment regimens and cost-efficient evaluation of neoadjuvant therapy of breast cancer.
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Affiliation(s)
- Maren M. Sjaastad Andreassen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Oncology, Vestre Viken, Drammen, Norway
| | - Stephane Loubrie
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Michelle W. Tong
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Lauren Fang
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Tyler M. Seibert
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA, United States
| | - Anne M. Wallace
- Department of Surgery, University of California, San Diego, La Jolla, CA, United States
| | - Somaye Zare
- Department of Pathology, University of California, San Diego, La Jolla, CA, United States
| | - Haydee Ojeda-Fournier
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Joshua Kuperman
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Michael Hahn
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Neil P. Jerome
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
| | - Tone F. Bathen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olav’s University Hospital, Trondheim, Norway
| | - Ana E. Rodríguez-Soto
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Anders M. Dale
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA, United States
| | - Rebecca Rakow-Penner
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
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8
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Zhang Y, Yang C, Sheng R, Dai Y, Zeng M. Preoperatively Identify the Microvascular Invasion of Hepatocellular Carcinoma with the Restricted Spectrum Imaging. Acad Radiol 2023; 30 Suppl 1:S30-S39. [PMID: 37442719 DOI: 10.1016/j.acra.2023.06.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/07/2023] [Accepted: 06/14/2023] [Indexed: 07/15/2023]
Abstract
RATIONALE AND OBJECTIVES To noninvasively and preoperatively identify the microvascular invasion (MVI) of hepatocellular carcinoma (HCC) with the restricted spectrum imaging (RSI). MATERIALS AND METHODS 62 patients were included into this prospective study and underwent the RSI examination with a 3.0-T scanner. Mono-exponential diffusion-weighted imaging-derived apparent diffusion coefficient (ADC) and RSI-derived metrics including f1 (fraction of restricted diffusion), f2 (fraction of hindered diffusion), f3 (fraction of free diffusion), and f1f2 (the multiply of f1 and f2) were calculated. Univariate and multivariate logistic regression were used to select the independent risk factors. Nomogram-based model was constructed with the selected indexes. Receiver operative characteristics analysis and calibration curve were used to evaluate the diagnostic accuracy. RESULTS MVI-positive HCC showed significantly higher f1 and lower ADC values (ADC: 1.549 ± 0.228 ×10-3 vs 1.365 ± 0.239 ×10-3 mm2/s, P = .003; f1: 0.1633 ± 0.0341 vs 0.2221 ± 0.0491, P < .001). Tumor size and f1 were selected as independent risk factors for MVI. The nomogram-based model was then constructed with tumor size and f1. Nomogram-based model (area under ROC curve [AUC]= 0.856) yielded the best diagnostic accuracy followed by f1 (AUC=0.842) and ADC (AUC=0.708). The AUC of both the f1 and nomogram model were significantly higher than that of ADC. CONCLUSION RSI-derived metrics can be utilized to noninvasively and efficiently identify the MVI of HCC. Considering the importance of MVI as a significant prognostic factor for HCC, the utilization of RSI has the potential to assist in prognostic prediction and clinical management.
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Affiliation(s)
- Yunfei Zhang
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China (Y.Z., R.S., M.Z.); Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China (Y.Z., C.Y., R.S., M.Z.)
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China (Y.Z., C.Y., R.S., M.Z.)
| | - Ruofan Sheng
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China (Y.Z., R.S., M.Z.); Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China (Y.Z., C.Y., R.S., M.Z.)
| | - Yongming Dai
- School of Biomedical Engineering, ShanghaiTech Univerisity, Shanghai, China (Y.D.)
| | - Mengsu Zeng
- Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China (Y.Z., R.S., M.Z.); Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China (Y.Z., C.Y., R.S., M.Z.).
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9
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Qin Y, Tang C, Hu Q, Zhang Y, Yi J, Dai Y, Ai T. Quantitative Assessment of Restriction Spectrum MR Imaging for the Diagnosis of Breast Cancer and Association With Prognostic Factors. J Magn Reson Imaging 2022; 57:1832-1841. [PMID: 36205354 DOI: 10.1002/jmri.28468] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 09/23/2022] [Accepted: 09/23/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Restriction spectrum imaging (RSI) is an advanced quantitative diffusion-weighted magnetic resonance imaging (DWI) technique to assess breast cancer. PURPOSE To investigate the ability of RSI to differentiate the benign and malignant breast lesions and the association with prognostic factors of breast cancer. STUDY TYPE Retrospective. POPULATION Seventy women (mean age, 49.6 ± 12.3 years) with 56 malignant and 19 benign breast lesions. FIELD STRENGTH/SEQUENCE 3-T; RSI-based DWI sequence with echo-planar imaging technique. ASSESSMENT The apparent diffusion coefficient (ADC) and RSI parameters (restricted diffusion f1 , hindered diffusion f2 , free diffusion f3 , and signal fractions f1 f2 ) were calculated by two readers for the whole lesion volume and compared between the benign and malignant groups and the subgroups with different statuses of prognostic factors in breast cancer. STATISTICAL TESTS Mann-Whitney U test or Student's t-test was applied to compare the quantitative parameters between the different groups. Intraclass correlation coefficient (ICC) was used to assess readers' reproducibility. Binary logistic regression was used to combine parameters. Area under the curve (AUC) of receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of parameters to distinguish benign from malignant breast lesions. A P-value <0.05 was considered statistically significant. RESULTS Malignant breast lesions showed significantly lower ADC and f3 values, and significantly higher f1 and f1 f2 values than the benign lesions, with AUC of 0.951, 0.877, 0.868, and 0.860, respectively. When RSI-derived parameters and ADC were combined, the diagnostic performance was superior to either single parameter (AUC = 0.973). The f3 value was significantly differed between estrogen receptor (ER)-positive and ER-negative tumors. The ADC, f1 , f3 , and f1 f2 values were significantly different progesterone receptor (PR)-positive and PR-negative status. DATA CONCLUSION The RSI-derived parameters (f1 , f3 , and f1 f2 ) may facilitate the differential diagnosis between benign and malignant breast lesions. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Yanjin Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Caili Tang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qilan Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yunfei Zhang
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Jingru Yi
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yongming Dai
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Tao Ai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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10
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Xiong Z, Geng Z, Lian S, Yin S, Xu G, Zhang Y, Dai Y, Zhao J, Ma L, Liu X, Zheng H, Zou C, Xie C. Discriminating rectal cancer grades using restriction spectrum imaging. Abdom Radiol (NY) 2022; 47:2014-2022. [PMID: 35368206 DOI: 10.1007/s00261-022-03500-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 03/14/2022] [Accepted: 03/15/2022] [Indexed: 12/25/2022]
Abstract
PURPOSE Restriction spectrum imaging (RSI) is a novel diffusion MRI model that separates water diffusion into several microscopic compartments. The restricted compartment correlating to the tumor cellularity is expected to be a potential indicator of rectal cancer aggressiveness. Our aim was to assess the ability of RSI model for rectal tumor grading. METHODS Fifty-eight patients with different rectal cancer grading confirmed by biopsy were involved in this study. DWI acquisitions were performed using single-shot echo-planar imaging (SS-EPI) with multi-b-values at 3 T. We applied a three-compartment RSI model, along with ADC model and diffusion kurtosis imaging (DKI) model, to DWI images of 58 patients. ROC and AUC were used to compare the performance of the three models in differentiating the low grade (G1 + G2) and high grade (G3). Mean ± standard deviation, ANOVA, ROC analysis, and correlation analysis were used in this study. RESULTS The volume fraction of restricted compartment C1 from RSI was significantly correlated with grades (r = 0.403, P = 0.002). It showed significant difference between G1 and G3 (P = 0.008) and between G2 and G3 (P = 0.01). As for the low-grade and high-grade discrimination, significant difference was found in C1 (P < 0.001). The AUC of C1 for differentiation between low-grade and high-grade groups was 0.753 with a sensitivity of 72.0% and a specificity of 70.0%. CONCLUSION The three-compartment RSI model was able to discriminate the rectal cancer of low and high grades. The results outperform the traditional ADC model and DKI model in rectal cancer grading.
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Affiliation(s)
- Zhongyan Xiong
- Paul C. Lauterbur Centre for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Zhijun Geng
- State Key Laboratory of Oncology in Southern China, Department of Radiology, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou, 510060, China
| | - Shanshan Lian
- State Key Laboratory of Oncology in Southern China, Department of Radiology, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou, 510060, China
| | - Shaohan Yin
- State Key Laboratory of Oncology in Southern China, Department of Radiology, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou, 510060, China
| | - Guixiao Xu
- State Key Laboratory of Oncology in Southern China, Department of Radiology, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou, 510060, China
| | - Yunfei Zhang
- Central Research Institute, United Imaging Healthcare, Shanghai, 201807, China
| | - Yongming Dai
- Central Research Institute, United Imaging Healthcare, Shanghai, 201807, China
| | - Jing Zhao
- State Key Laboratory of Oncology in Southern China, Department of Radiology, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou, 510060, China
| | - Lidi Ma
- State Key Laboratory of Oncology in Southern China, Department of Radiology, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou, 510060, China
| | - Xin Liu
- Paul C. Lauterbur Centre for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen, 518000, China
| | - Hairong Zheng
- Paul C. Lauterbur Centre for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Chao Zou
- Paul C. Lauterbur Centre for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen, 518000, China.
| | - Chuanmiao Xie
- State Key Laboratory of Oncology in Southern China, Department of Radiology, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou, 510060, China.
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11
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Chatterjee A, Antic T, Gallan AJ, Paner GP, Lin LIK, Karczmar GS, Oto A. Histological validation of prostate tissue composition measurement using hybrid multi-dimensional MRI: agreement with pathologists' measures. Abdom Radiol (NY) 2022; 47:801-813. [PMID: 34878579 PMCID: PMC8916544 DOI: 10.1007/s00261-021-03371-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/24/2021] [Accepted: 11/27/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE To validate prostate tissue composition measured using hybrid multi-dimensional MRI (HM-MRI) by comparing with reference standard (ground truth) results from pathologists' interpretation of clinical histopathology slides following whole mount prostatectomy. MATERIALS AND METHODS 36 prospective participants with biopsy-confirmed prostate cancer underwent 3 T MRI prior to radical prostatectomy. Axial HM-MRI was acquired with all combinations of echo times of 57, 70, 150, 200 ms and b-values of 0, 150, 750, 1500 s/mm2 and data were fitted using a 3-compartment signal model using custom software to generate volumes for each tissue component (stroma, epithelium, lumen). Three experienced genitourinary pathologists independently as well as in consensus reviewed each histology image and provide an estimate of percentage of epithelium and lumen for regions-of-interest corresponding to MRI (n = 165; 64 prostate cancers and 101 benign tissue). Agreement statistics using total deviation index (TDI0.9) was performed for tissue composition measured using HM-MRI and reference standard results from pathologists' consensus. RESULTS Based on the initial results showing typical variation among pathologists TDI0.9 = 25%, we determined we will declare acceptable agreement if the 95% one-sided upper confident limit of TDI0.9 is less than 30%. The results of tissue composition measurement from HM-MRI compared to ground truth results from the consensus of 3 pathologists, reveal that ninety percent of absolute paired differences (TDI0.9) were within 18.8% and 22.4% in measuring epithelium and lumen, respectively. We are 95% confident that 90% of absolute paired differences were within 20.6% and 24.2% in measuring epithelium and lumen, respectively. These were less than our criterion of 30% and inter-pathologists' agreement (22.3% for epithelium and 24.2% for lumen) and therefore we accept the agreement performance of HM-MRI. The results revealed excellent area under the ROC curve for differentiating cancer from benign tissue based on epithelium (HM-MRI: 0.87, pathologists: 0.97) and lumen volume (HM-MRI: 0.85, pathologists: 0.77). CONCLUSION The agreement in tissue composition measurement using hybrid multidimensional MRI and consensus of pathologists is on par with the inter-raters (pathologists) agreement.
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Affiliation(s)
- Aritrick Chatterjee
- Department of Radiology, University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, IL, 60637, USA.
- Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy, University of Chicago, Chicago, IL, USA.
| | - Tatjana Antic
- Department of Pathology, University of Chicago, Chicago, IL, USA
| | - Alexander J Gallan
- Department of Pathology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Gladell P Paner
- Department of Pathology, University of Chicago, Chicago, IL, USA
| | | | - Gregory S Karczmar
- Department of Radiology, University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, IL, 60637, USA
- Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy, University of Chicago, Chicago, IL, USA
| | - Aytekin Oto
- Department of Radiology, University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, IL, 60637, USA
- Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy, University of Chicago, Chicago, IL, USA
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12
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Rodríguez-Soto AE, Andreassen MMS, Fang LK, Conlin CC, Park HH, Ahn GS, Bartsch H, Kuperman J, Vidić I, Ojeda-Fournier H, Wallace AM, Hahn M, Seibert TM, Jerome NP, Østlie A, Bathen TF, Goa PE, Rakow-Penner R, Dale AM. Characterization of the diffusion signal of breast tissues using multi-exponential models. Magn Reson Med 2021; 87:1938-1951. [PMID: 34904726 DOI: 10.1002/mrm.29090] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 10/12/2021] [Accepted: 11/01/2021] [Indexed: 12/15/2022]
Abstract
PURPOSE Restriction spectrum imaging (RSI) decomposes the diffusion-weighted MRI signal into separate components of known apparent diffusion coefficients (ADCs). The number of diffusion components and optimal ADCs for RSI are organ-specific and determined empirically. The purpose of this work was to determine the RSI model for breast tissues. METHODS The diffusion-weighted MRI signal was described using a linear combination of multiple exponential components. A set of ADC values was estimated to fit voxels in cancer and control ROIs. Later, the signal contributions of each diffusion component were estimated using these fixed ADC values. Relative-fitting residuals and Bayesian information criterion were assessed. Contrast-to-noise ratio between cancer and fibroglandular tissue in RSI-derived signal contribution maps was compared to DCE imaging. RESULTS A total of 74 women with breast cancer were scanned at 3.0 Tesla MRI. The fitting residuals of conventional ADC and Bayesian information criterion suggest that a 3-component model improves the characterization of the diffusion signal over a biexponential model. Estimated ADCs of triexponential model were D1,3 = 0, D2,3 = 1.5 × 10-3 , and D3,3 = 10.8 × 10-3 mm2 /s. The RSI-derived signal contributions of the slower diffusion components were larger in tumors than in fibroglandular tissues. Further, the contrast-to-noise and specificity at 80% sensitivity of DCE and a subset of RSI-derived maps were equivalent. CONCLUSION Breast diffusion-weighted MRI signal was best described using a triexponential model. Tumor conspicuity in breast RSI model is comparable to that of DCE without the use of exogenous contrast. These data may be used as differential features between healthy and malignant breast tissues.
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Affiliation(s)
- Ana E Rodríguez-Soto
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Maren M Sjaastad Andreassen
- Department of Circulation and Medical Imaging, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Lauren K Fang
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Christopher C Conlin
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Helen H Park
- School of Medicine, University of California San Diego, La Jolla, California, USA
| | - Grace S Ahn
- School of Medicine, University of California San Diego, La Jolla, California, USA
| | - Hauke Bartsch
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Joshua Kuperman
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Igor Vidić
- Department of Physics, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Haydee Ojeda-Fournier
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Anne M Wallace
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Michael Hahn
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Tyler M Seibert
- Department of Radiation Oncology, University of California San Diego, La Jolla, California, USA.,Department of Bioengineering, University of California San Diego, La Jolla, California, USA
| | - Neil Peter Jerome
- Department of Circulation and Medical Imaging, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway
| | - Agnes Østlie
- Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway
| | - Tone Frost Bathen
- Department of Circulation and Medical Imaging, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway
| | - Pål Erik Goa
- Department of Physics, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway
| | - Rebecca Rakow-Penner
- Department of Radiology, University of California San Diego, La Jolla, California, USA.,Department of Bioengineering, University of California San Diego, La Jolla, California, USA
| | - Anders M Dale
- Department of Radiology, University of California San Diego, La Jolla, California, USA
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13
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Chatterjee A, Mercado C, Bourne RM, Yousuf A, Hess B, Antic T, Eggener S, Oto A, Karczmar GS. Validation of Prostate Tissue Composition by Using Hybrid Multidimensional MRI: Correlation with Histologic Findings. Radiology 2021; 302:368-377. [PMID: 34751615 PMCID: PMC8805656 DOI: 10.1148/radiol.2021204459] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Background Tissue estimates obtained by using microstructure imaging techniques, such as hybrid multidimensional (HM) MRI, may improve prostate cancer diagnosis but require histologic validation. Purpose To validate prostate tissue composition measured by using HM MRI, with quantitative histologic evaluation from whole-mount prostatectomy as the reference standard. Materials and Methods In this HIPAA-compliant study, from December 2016 to July 2018, prospective participants with biopsy-confirmed prostate cancer underwent 3-T MRI before radical prostatectomy. Axial HM MRI was performed with all combinations of echo times (57, 70, 150, and 200 msec) and b values (0, 150, 750, and 1500 sec/mm2). Data were fitted by using a three-compartment signal model to generate volumes for each tissue component (stroma, epithelium, lumen). Quantitative histologic evaluation was performed to calculate volume fractions for each tissue component for regions of interest corresponding to MRI. Tissue composition measured by using HM MRI and quantitative histologic evaluation were compared (paired t test) and correlated (Pearson correlation coefficient), and agreement (concordance correlation) was assessed. Receiver operating characteristic curve analysis for cancer diagnosis was performed. Results Twenty-five participants (mean age, 60 years ± 7 [standard deviation]; 30 cancers and 45 benign regions of interest) were included. Prostate tissue composition measured with HM MRI and quantitative histologic evaluation did not differ (stroma, 45% ± 11 vs 44% ± 11 [P = .23]; epithelium, 31% ± 15 vs 34% ± 15 [P = .08]; and lumen, 24% ± 13 vs 22% ± 11 [P = .80]). Between HM MRI and histologic evaluation, there was excellent correlation (Pearson r: overall, 0.91; stroma, 0.82; epithelium, 0.93; lumen, 0.90 [all P < .05]) and agreement (concordance correlation coefficient: overall, 0.91; stroma, 0.81; epithelium, 0.90; and lumen, 0.87). High areas under the receiver operating characteristic curve obtained with HM MRI (0.96 for epithelium and 0.94 for lumen, P < .001) and histologic evaluation (0.94 for epithelium and 0.88 for lumen, P < .001) were found for differentiation between benign tissue and prostate cancer. Conclusion Tissue composition measured by using hybrid multidimensional MRI had excellent correlation with quantitative histologic evaluation as the reference standard. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Muglia in this issue.
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14
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Abstract
Prostate MRI has seen increasing interest in recent years and has led to the development of new MRI techniques and sequences to improve prostate cancer (PCa) diagnosis which are reviewed in this article. Numerous studies have focused on improving image quality (segmented DWI) and faster acquisition (compressed sensing, k-t-SENSE, PROPELLER). An increasing number of studies have developed new quantitative and computer-aided diagnosis methods including artificial intelligence (PROSTATEx challenge) that mitigate the subjective nature of mpMRI interpretation. MR fingerprinting allows rapid, simultaneous generation of quantitative maps of multiple physical properties (T1, T2), where PCa are characterized by lower T1 and T2 values. New techniques like luminal water imaging (LWI), restriction spectrum imaging (RSI), VERDICT and hybrid multi-dimensional MRI (HM-MRI) have been developed for microstructure imaging, which provide information similar to histology. The distinct MR properties of tissue components and their change with the presence of cancer is used to diagnose prostate cancer. LWI is a T2-based imaging technique where long T2-component corresponding to luminal water is reduced in PCa. RSI and VERDICT are diffusion-based techniques where PCa is characterized by increased signal from intra-cellular restricted water and increased intracellular volume fraction, respectively, due to increased cellularity. VERDICT also reveal loss of extracellular-extravascular space in PCa due to loss of glandular structure. HM-MRI measures volumes of prostate tissue components, where PCa has reduced lumen and stromal and increased epithelium volume similar to results shown in histology. Similarly, molecular imaging using hyperpolarized 13C imaging has been utilized.
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15
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Her EJ, Haworth A, Reynolds HM, Sun Y, Kennedy A, Panettieri V, Bangert M, Williams S, Ebert MA. Voxel-level biological optimisation of prostate IMRT using patient-specific tumour location and clonogen density derived from mpMRI. Radiat Oncol 2020; 15:172. [PMID: 32660504 PMCID: PMC7805066 DOI: 10.1186/s13014-020-01568-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 05/13/2020] [Indexed: 12/24/2022] Open
Abstract
AIMS This study aimed to develop a framework for optimising prostate intensity-modulated radiotherapy (IMRT) based on patient-specific tumour biology, derived from multiparametric MRI (mpMRI). The framework included a probabilistic treatment planning technique in the effort to yield dose distributions with an improved expected treatment outcome compared with uniform-dose planning approaches. METHODS IMRT plans were generated for five prostate cancer patients using two inverse planning methods: uniform-dose to the planning target volume and probabilistic biological optimisation for clinical target volume tumour control probability (TCP) maximisation. Patient-specific tumour location and clonogen density information were derived from mpMRI and geometric uncertainties were incorporated in the TCP calculation. Potential reduction in dose to sensitive structures was assessed by comparing dose metrics of uniform-dose plans with biologically-optimised plans of an equivalent level of expected tumour control. RESULTS The planning study demonstrated biological optimisation has the potential to reduce expected normal tissue toxicity without sacrificing local control by shaping the dose distribution to the spatial distribution of tumour characteristics. On average, biologically-optimised plans achieved 38.6% (p-value: < 0.01) and 51.2% (p-value: < 0.01) reduction in expected rectum and bladder equivalent uniform dose, respectively, when compared with uniform-dose planning. CONCLUSIONS It was concluded that varying the dose distribution within the prostate to take account for each patient's clonogen distribution was feasible. Lower doses to normal structures compared to uniform-dose plans was possible whilst providing robust plans against geometric uncertainties. Further validation in a larger cohort is warranted along with considerations for adaptive therapy and limiting urethral dose.
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Affiliation(s)
- E J Her
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, Australia.
| | - A Haworth
- Institute of Medical Physics, University of Sydney, Sydney, Australia
| | - H M Reynolds
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia.,Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Y Sun
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia.,Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - A Kennedy
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, Australia
| | - V Panettieri
- Alfred Health Radiation Oncology, Melbourne, Australia
| | - M Bangert
- Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Medical Physics in Radiation Oncology, Heidelberg Institute for Radiation Oncology, Heidelberg, Germany
| | - S Williams
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia.,Division of Radiation Oncology and Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - M A Ebert
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, Australia.,Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, Australia.,5D Clinics, Perth, Australia
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16
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Qi Y, Zhang S, Wei J, Zhang G, Lei J, Yan W, Xiao Y, Yan S, Xue H, Feng F, Sun H, Tian J, Jin Z. Multiparametric MRI-Based Radiomics for Prostate Cancer Screening With PSA in 4-10 ng/mL to Reduce Unnecessary Biopsies. J Magn Reson Imaging 2019; 51:1890-1899. [PMID: 31808980 DOI: 10.1002/jmri.27008] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 11/14/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Whether men with a prostate-specific antigen (PSA) level of 4-10 ng/mL should be recommended for a biopsy is clinically challenging. PURPOSE To develop and validate a radiomics model based on multiparametric MRI (mp-MRI) in patients with PSA levels of 4-10 ng/mL to predict prostate cancer (PCa) preoperatively and reduce unnecessary biopsies. STUDY TYPE Retrospective. SUBJECTS In all, 199 patients with PSA levels of 4-10 ng/mL. FIELD STRENGTH/SEQUENCE 3T, T2 -weighted, diffusion-weighted, and dynamic contrast-enhanced MRI. ASSESSMENT Lesion regions of interest (ROIs) from T2 -weighted, diffusion-weighted, and dynamic contrast-enhanced MRI were annotated by two radiologists. A total of 2104 radiomic features were extracted from the ROI of each patient. A random forest classifier was used to build the radiomics model for PCa in the primary cohort. A combined model was constructed using multivariate logistic regression by incorporating the radiomics signature and clinical-radiological risk factors. STATISTICAL TESTS For continuous variables, variance equality was assessed by Levene's test and Student's t-test, and Welch's t-test was used to assess between-group differences. For categorical variables, Pearson's chi-square test, Fisher's exact test, or the approximate chi-square test was used to assess between-group differences. P < 0.05 was considered statistically significant. RESULTS The combined model incorporating the multi-imaging fusion model, age, PSA density (PSAD), and the PI-RADS v2 score yielded area under the curve (AUC) values of 0.956 and 0.933 on the primary (n = 133) and validation (n = 66) cohorts, respectively. Compared with the clinical-radiological model, the combined model performed better on both the primary and validation cohorts (P < 0.05). Furthermore, the use of the combined model to predict PCa could identify more negative PCa patients than the use of the clinical-radiological model by 18.4%. DATA CONCLUSION The combined model was developed and validated to provide potential preoperative prediction of PCa in men with PSA levels of 4-10 ng/mL and might aid in treatment decision-making and reduce unnecessary biopsies. LEVEL OF EVIDENCE 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2020;51:1890-1899.
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Affiliation(s)
- Yafei Qi
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Shuaitong Zhang
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Science, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Jingwei Wei
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Science, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Gumuyang Zhang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Jing Lei
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Weigang Yan
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Yu Xiao
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Shuang Yan
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Huadan Xue
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Hao Sun
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Jie Tian
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Science, Beijing, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China
| | - Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
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Rourke E, Sunnapwar A, Mais D, Kukkar V, DiGiovanni J, Kaushik D, Liss MA. Inflammation appears as high Prostate Imaging-Reporting and Data System scores on prostate magnetic resonance imaging (MRI) leading to false positive MRI fusion biopsy. Investig Clin Urol 2019; 60:388-395. [PMID: 31501802 PMCID: PMC6722401 DOI: 10.4111/icu.2019.60.5.388] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2018] [Accepted: 05/07/2019] [Indexed: 12/04/2022] Open
Abstract
Purpose To investigate if inflammation as a potential cause of false-positive lesions from recent UroNav magnetic resonance imaging (MRI) fusion prostate biopsy patients. Materials and Methods We retrospectively identified 43 men with 61 MRI lesions noted on prostate MRI before MRI ultrasound-guided fusion prostate biopsy. Men underwent MRI with 3T Siemens TIM Trio MRI system (Siemens AG, Germany), and lesions were identified and marked in DynaCAD system (Invivo Corporation, USA) with subsequent biopsy with MRI fusion with UroNav. We obtained targeted and standard 12-core needle biopsies. We retrospectively reviewed pathology reports for inflammation. Results We noted a total of 43 (70.5%) false-positive lesions with 28 having no cancer on any cores, and 15 lesions with cancer noted on systematic biopsy but not in the target region. Of the men with cancer, 6 of the false positive lesions had inflammation in the location of the targeted region of interest (40.0%, 6/15). However, when we examine the 21/28 lesions with an identified lesion on MRI with no cancer in all cores, 54.5% had inflammation on prostate biopsy pathology (12/22, p=0.024). We noted the highest proportion of inflammation. Conclusions Inflammation can confound the interpretation of MRI by mimicking prostate cancer. We suggested focused efforts to differentiate inflammation and cancer on prostate MRI.
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Affiliation(s)
- Elizabeth Rourke
- Department of Urology, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Abhijit Sunnapwar
- Department of Radiology, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Daniel Mais
- Department of Pathology, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Vishal Kukkar
- Department of Radiology, University of Texas Health San Antonio, San Antonio, TX, USA
| | - John DiGiovanni
- University of Texas Austin, College of Pharmacy, Austin, TX, USA
| | - Dharam Kaushik
- Department of Urology, University of Texas Health San Antonio, San Antonio, TX, USA.,Mays Cancer Center UT Health San Antonio MD Anderson, San Antonio, TX, USA
| | - Michael A Liss
- Department of Urology, University of Texas Health San Antonio, San Antonio, TX, USA.,University of Texas Austin, College of Pharmacy, Austin, TX, USA.,Mays Cancer Center UT Health San Antonio MD Anderson, San Antonio, TX, USA
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18
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Sun Y, Reynolds HM, Parameswaran B, Wraith D, Finnegan ME, Williams S, Haworth A. Multiparametric MRI and radiomics in prostate cancer: a review. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2019; 42:3-25. [PMID: 30762223 DOI: 10.1007/s13246-019-00730-z] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 01/22/2019] [Indexed: 12/30/2022]
Abstract
Multiparametric MRI (mpMRI) is an imaging modality that combines anatomical MR imaging with one or more functional MRI sequences. It has become a versatile tool for detecting and characterising prostate cancer (PCa). The traditional role of mpMRI was confined to PCa staging, but due to the advanced imaging techniques, its role has expanded to various stages in clinical practises including tumour detection, disease monitor during active surveillance and sequential imaging for patient follow-up. Meanwhile, with the growing speed of data generation and the increasing volume of imaging data, it is highly demanded to apply computerised methods to process mpMRI data and extract useful information. Hence quantitative analysis for imaging data using radiomics has become an emerging paradigm. The application of radiomics approaches in prostate cancer has not only enabled automatic localisation of the disease but also provided a non-invasive solution to assess tumour biology (e.g. aggressiveness and the presence of hypoxia). This article reviews mpMRI and its expanding role in PCa detection, staging and patient management. Following that, an overview of prostate radiomics will be provided, with a special focus on its current applications as well as its future directions.
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Affiliation(s)
- Yu Sun
- University of Sydney, Sydney, Australia. .,Peter MacCallum Cancer Centre, Melbourne, Australia.
| | | | | | - Darren Wraith
- Queensland University of Technology, Brisbane, Australia
| | - Mary E Finnegan
- Imperial College Healthcare NHS Trust, London, UK.,Imperial College London, London, UK
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19
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Brunsing RL, Schenker-Ahmed NM, White NS, Parsons JK, Kane C, Kuperman J, Bartsch H, Kader AK, Rakow-Penner R, Seibert TM, Margolis D, Raman SS, McDonald CR, Farid N, Kesari S, Hansel D, Shabaik A, Dale AM, Karow DS. Restriction spectrum imaging: An evolving imaging biomarker in prostate MRI. J Magn Reson Imaging 2016; 45:323-336. [PMID: 27527500 DOI: 10.1002/jmri.25419] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2016] [Accepted: 07/25/2016] [Indexed: 12/28/2022] Open
Abstract
Restriction spectrum imaging (RSI) is a novel diffusion-weighted MRI technique that uses the mathematically distinct behavior of water diffusion in separable microscopic tissue compartments to highlight key aspects of the tissue microarchitecture with high conspicuity. RSI can be acquired in less than 5 min on modern scanners using a surface coil. Multiple field gradients and high b-values in combination with postprocessing techniques allow the simultaneous resolution of length-scale and geometric information, as well as compartmental and nuclear volume fraction filtering. RSI also uses a distortion correction technique and can thus be fused to high resolution T2-weighted images for detailed localization, which improves delineation of disease extension into critical anatomic structures. In this review, we discuss the acquisition, postprocessing, and interpretation of RSI for prostate MRI. We also summarize existing data demonstrating the applicability of RSI for prostate cancer detection, in vivo characterization, localization, and targeting. LEVEL OF EVIDENCE 5 J. Magn. Reson. Imaging 2017;45:323-336.
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Affiliation(s)
- Ryan L Brunsing
- Department of Radiology, University of California San Diego, San Diego, California, USA
| | | | - Nathan S White
- Department of Radiology, University of California San Diego, San Diego, California, USA
| | - J Kellogg Parsons
- Department of Surgery, University of California San Diego, San Diego, California, USA
| | - Christopher Kane
- Department of Surgery, University of California San Diego, San Diego, California, USA
| | - Joshua Kuperman
- Department of Radiology, University of California San Diego, San Diego, California, USA
| | - Hauke Bartsch
- Department of Radiology, University of California San Diego, San Diego, California, USA
| | - Andrew Karim Kader
- Department of Surgery, University of California San Diego, San Diego, California, USA
| | - Rebecca Rakow-Penner
- Department of Radiology, University of California San Diego, San Diego, California, USA
| | - Tyler M Seibert
- Department of Radiation Medicine, University of California San Diego, San Diego, California, USA
| | - Daniel Margolis
- Department of Radiology, University of California Los Angeles, Los Angeles, California, USA
| | - Steven S Raman
- Department of Radiology, University of California Los Angeles, Los Angeles, California, USA
| | - Carrie R McDonald
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA
| | - Nikdokht Farid
- Department of Radiology, University of California San Diego, San Diego, California, USA
| | - Santosh Kesari
- Department of Translational Neuro-Oncology and Neurotherapeutics, Pacific Neuroscience Institute and John Wayne Cancer Institute at Providence Saint John's Health Center, Los Angeles, California, USA
| | - Donna Hansel
- Department of Pathology, University of California San Diego, San Diego, California, USA
| | - Ahmed Shabaik
- Department of Pathology, University of California San Diego, San Diego, California, USA
| | - Anders M Dale
- Department of Radiology, University of California San Diego, San Diego, California, USA.,Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - David S Karow
- Department of Radiology, University of California San Diego, San Diego, California, USA
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