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Pei H, Shepherd TM, Wang Y, Liu F, Sodickson DK, Ben-Eliezer N, Feng L. DeepEMC-T 2 mapping: Deep learning-enabled T 2 mapping based on echo modulation curve modeling. Magn Reson Med 2024; 92:2707-2722. [PMID: 39129209 PMCID: PMC11436299 DOI: 10.1002/mrm.30239] [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: 02/26/2024] [Revised: 07/14/2024] [Accepted: 07/15/2024] [Indexed: 08/13/2024]
Abstract
PURPOSE Echo modulation curve (EMC) modeling enables accurate quantification of T2 relaxation times in multi-echo spin-echo (MESE) imaging. The standard EMC-T2 mapping framework, however, requires sufficient echoes and cumbersome pixel-wise dictionary-matching steps. This work proposes a deep learning version of EMC-T2 mapping, called DeepEMC-T2 mapping, to efficiently estimate accurate T2 maps from fewer echoes. METHODS DeepEMC-T2 mapping was developed using a modified U-Net to estimate both T2 and proton density (PD) maps directly from MESE images. The network implements several new features to improve the accuracy of T2/PD estimation. A total of 67 MESE datasets acquired in axial orientation were used for network training and evaluation. An additional 57 datasets acquired in coronal orientation with different scan parameters were used to evaluate the generalizability of the framework. The performance of DeepEMC-T2 mapping was evaluated in seven experiments. RESULTS Compared to the reference, DeepEMC-T2 mapping achieved T2 estimation errors from 1% to 11% and PD estimation errors from 0.4% to 1.5% with ten/seven/five/three echoes, which are more accurate than standard EMC-T2 mapping. By incorporating datasets acquired with different scan parameters and orientations for joint training, DeepEMC-T2 exhibits robust generalizability across varying imaging protocols. Increasing the echo spacing and including longer echoes improve the accuracy of parameter estimation. The new features proposed in DeepEMC-T2 mapping all enabled more accurate T2 estimation. CONCLUSIONS DeepEMC-T2 mapping enables simplified, efficient, and accurate T2 quantification directly from MESE images without dictionary matching. Accurate T2 estimation from fewer echoes allows for increased volumetric coverage and/or higher slice resolution without prolonging total scan times.
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Affiliation(s)
- Haoyang Pei
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, USA
- Department of Electrical and Computer Engineering and Department of Biomedical Engineering, NYU Tandon School of Engineering, New York, NY, USA
| | - Timothy M. Shepherd
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, USA
| | - Yao Wang
- Department of Electrical and Computer Engineering and Department of Biomedical Engineering, NYU Tandon School of Engineering, New York, NY, USA
| | - Fang Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Daniel K Sodickson
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, USA
| | - Noam Ben-Eliezer
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, USA
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel-Aviv, Israel
| | - Li Feng
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, USA
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Li S, Gao M, He K, Yuan G, Yin T, Hu D, Li Z. Feasibility and Reproducibility of T2 Mapping Compared with Diffusion-Weighted Imaging in Solid Renal Masses. Bioengineering (Basel) 2024; 11:901. [PMID: 39329643 PMCID: PMC11428221 DOI: 10.3390/bioengineering11090901] [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: 07/06/2024] [Revised: 08/28/2024] [Accepted: 09/05/2024] [Indexed: 09/28/2024] Open
Abstract
Accurate prediction of renal mass subtypes, along with the WHO/ISUP grade and pathological T (pT) stage of clear cell renal cell carcinoma (ccRCC), is crucial for optimal decision making. Our study aimed to investigate the feasibility and reproducibility of motion-robust radial T2 mapping in differentiating lipid-poor angiomyolipoma (MFAML) from RCC and characterizing the WHO/ISUP grade and pT stage of ccRCC. Finally, 92 patients undergoing renal radial T2 mapping and ZOOMit DWI were recruited. The T2 values and apparent diffusion coefficient (ADC) were analyzed. Correlation coefficients were calculated between ADC and T2 values. Notably, ccRCC exhibited higher T2 and ADC values than MFAML (p < 0.05). T2 values were lower in the higher WHO/ISUP grade and pT stage of ccRCC (all p < 0.05). ADC showed no significant difference for pT stage (p = 0.056). T2 values revealed a higher area under the curve (AUC) in evaluating the WHO/ISUP grade compared to ADC (0.936 vs. 0.817, p = 0.027). T2 values moderately positively correlated with ADC (r = 0.675, p < 0.001). In conclusion, quantitative motion-robust radial T2 mapping is feasible for characterizing solid renal masses and could provide additional value for multiparametric imaging in predicting WHO/ISUP grade and pT stage of ccRCC.
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Affiliation(s)
- Shichao Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (S.L.); (M.G.); (K.H.); (G.Y.); (D.H.)
| | - Mengmeng Gao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (S.L.); (M.G.); (K.H.); (G.Y.); (D.H.)
| | - Kangwen He
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (S.L.); (M.G.); (K.H.); (G.Y.); (D.H.)
| | - Guanjie Yuan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (S.L.); (M.G.); (K.H.); (G.Y.); (D.H.)
| | - Ting Yin
- MR Collaborations, Siemens Healthineers Ltd., Chengdu 610041, China;
| | - Daoyu Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (S.L.); (M.G.); (K.H.); (G.Y.); (D.H.)
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (S.L.); (M.G.); (K.H.); (G.Y.); (D.H.)
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Bolan PJ, Saunders SL, Kay K, Gross M, Akcakaya M, Metzger GJ. Improved quantitative parameter estimation for prostate T 2 relaxometry using convolutional neural networks. MAGMA (NEW YORK, N.Y.) 2024; 37:721-735. [PMID: 39042205 PMCID: PMC11417079 DOI: 10.1007/s10334-024-01186-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/01/2024] [Accepted: 07/02/2024] [Indexed: 07/24/2024]
Abstract
OBJECTIVE Quantitative parameter mapping conventionally relies on curve fitting techniques to estimate parameters from magnetic resonance image series. This study compares conventional curve fitting techniques to methods using neural networks (NN) for measuring T2 in the prostate. MATERIALS AND METHODS Large physics-based synthetic datasets simulating T2 mapping acquisitions were generated for training NNs and for quantitative performance comparisons. Four combinations of different NN architectures and training corpora were implemented and compared with four different curve fitting strategies. All methods were compared quantitatively using synthetic data with known ground truth, and further compared on in vivo test data, with and without noise augmentation, to evaluate feasibility and noise robustness. RESULTS In the evaluation on synthetic data, a convolutional neural network (CNN), trained in a supervised fashion using synthetic data generated from naturalistic images, showed the highest overall accuracy and precision amongst the methods. On in vivo data, this best performing method produced low-noise T2 maps and showed the least deterioration with increasing input noise levels. DISCUSSION This study showed that a CNN, trained with synthetic data in a supervised manner, may provide superior T2 estimation performance compared to conventional curve fitting, especially in low signal-to-noise regions.
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Affiliation(s)
- Patrick J Bolan
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th Street SE, Minneapolis, MN, 55455, USA.
- Department of Radiology, University of Minnesota, Minneapolis, MN, USA.
| | - Sara L Saunders
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Kendrick Kay
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th Street SE, Minneapolis, MN, 55455, USA
- Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Mitchell Gross
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Mehmet Akcakaya
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th Street SE, Minneapolis, MN, 55455, USA
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Gregory J Metzger
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th Street SE, Minneapolis, MN, 55455, USA
- Department of Radiology, University of Minnesota, Minneapolis, MN, USA
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Riederer SJ, Borisch EA, Froemming AT, Kawashima A, Takahashi N. Comparison of model-based versus deep learning-based image reconstruction for thin-slice T2-weighted spin-echo prostate MRI. Abdom Radiol (NY) 2024; 49:2921-2931. [PMID: 38520510 PMCID: PMC11300170 DOI: 10.1007/s00261-024-04256-1] [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: 01/02/2024] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 03/25/2024]
Abstract
PURPOSE To compare a previous model-based image reconstruction (MBIR) with a newly developed deep learning (DL)-based image reconstruction for providing improved signal-to-noise ratio (SNR) in high through-plane resolution (1 mm) T2-weighted spin-echo (T2SE) prostate MRI. METHODS Large-area contrast and high-contrast spatial resolution of the reconstruction methods were assessed quantitatively in experimental phantom studies. The methods were next evaluated radiologically in 17 subjects at 3.0 Tesla for whom prostate MRI was clinically indicated. For each subject, the axial T2SE raw data were directed to MBIR and to the DL reconstruction at three vendor-provided levels: (L)ow, (M)edium, and (H)igh. Thin-slice images from the four reconstructions were compared using evaluation criteria related to SNR, sharpness, contrast fidelity, and reviewer preference. Results were compared using the Wilcoxon signed-rank test using Bonferroni correction, and inter-reader comparisons were done using the Cohen and Krippendorf tests. RESULTS Baseline contrast and resolution in phantom studies were equivalent for all four reconstruction pathways as desired. In vivo, all three DL levels (L, M, H) provided improved SNR versus MBIR. For virtually, all other evaluation criteria DL L and M were superior to MBIR. DL L and M were evaluated as superior to DL H in fidelity of contrast. For 44 of the 51 evaluations, the DL M reconstruction was preferred. CONCLUSION The deep learning reconstruction method provides significant SNR improvement in thin-slice (1 mm) T2SE images of the prostate while retaining image contrast. However, if taken to too high a level (DL High), both radiological sharpness and fidelity of contrast diminish.
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Affiliation(s)
| | - Eric A Borisch
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | | | | | - Naoki Takahashi
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
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5
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Sørland KI, Trimble CG, Wu CY, Bathen TF, Elschot M, Cloos MA. Reducing femoral flow artefacts in radial magnetic resonance fingerprinting of the prostate using region-optimised virtual coils. NMR IN BIOMEDICINE 2024; 37:e5136. [PMID: 38514929 DOI: 10.1002/nbm.5136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 01/19/2024] [Accepted: 02/07/2024] [Indexed: 03/23/2024]
Abstract
High acceleration factors in radial magnetic resonance fingerprinting (MRF) of the prostate lead to strong streak-like artefacts from flow in the femoral blood vessels, possibly concealing important anatomical information. Region-optimised virtual (ROVir) coils is a beamforming-based framework to create virtual coils that maximise signal in a region of interest while minimising signal in a region of interference. In this study, the potential of removing femoral flow streak artefacts in prostate MRF using ROVir coils is demonstrated in silico and in vivo. The ROVir framework was applied to radial MRF k-space data in an automated pipeline designed to maximise prostate signal while minimising signal from the femoral vessels. The method was tested in 15 asymptomatic volunteers at 3 T. The presence of streaks was visually assessed and measurements of whole prostate T1, T2 and signal-to-noise ratio (SNR) with and without streak correction were examined. In addition, a purpose-built simulation framework in which blood flow through the femoral vessels can be turned on and off was used to quantitatively evaluate ROVir's ability to suppress streaks in radial prostate MRF. In vivo it was shown that removing selected ROVir coils visibly reduces streak-like artefacts from the femoral blood flow, without increasing the reconstruction time. On average, 80% of the prostate SNR was retained. A similar reduction of streaks was also observed in silico, while the quantitative accuracy of T1 and T2 mapping was retained. In conclusion, ROVir coils efficiently suppress streaking artefacts from blood flow in radial MRF of the prostate, thereby improving the visual clarity of the images, without significant sacrifices to acquisition time, reconstruction time and accuracy of quantitative values. This is expected to help enable T1 and T2 mapping of prostate cancer in clinically viable times, aiding differentiation between prostate cancer from noncancer and healthy prostate tissue.
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Affiliation(s)
- Kaia I Sørland
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Christopher G Trimble
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs hospital, Trondheim University Hostpital, Trondheim, Norway
| | - Chia-Yin Wu
- Centre for Advanced Imaging, The University of Queensland, St Lucia, Queensland, Australia
- ARC Training Centre for Innovation on Biomedical Imaging Technology (CIBIT), The University of Queensland, St Lucia, Queensland, Australia
- School of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, Queensland, Australia
| | - Tone F Bathen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs hospital, Trondheim University Hostpital, Trondheim, Norway
| | - Mattijs Elschot
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs hospital, Trondheim University Hostpital, Trondheim, Norway
| | - Martijn A Cloos
- Centre for Advanced Imaging, The University of Queensland, St Lucia, Queensland, Australia
- ARC Training Centre for Innovation on Biomedical Imaging Technology (CIBIT), The University of Queensland, St Lucia, Queensland, Australia
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6
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Bucher AM, Egger J, Dietz J, Strecker R, Hilbert T, Frodl E, Wenzel M, Penzkofer T, Hamm B, Chun FK, Vogl T, Kleesiek J, Beeres M. Value of MRI - T2 Mapping to Differentiate Clinically Significant Prostate Cancer. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01150-6. [PMID: 38926263 DOI: 10.1007/s10278-024-01150-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 05/21/2024] [Accepted: 05/23/2024] [Indexed: 06/28/2024]
Abstract
Standardized reporting of multiparametric prostate MRI (mpMRI) is widespread and follows international standards (Pi-RADS). However, quantitative measurements from mpMRI are not widely comparable. Although T2 mapping sequences can provide repeatable quantitative image measurements and extract reliable imaging biomarkers from mpMRI, they are often time-consuming. We therefore investigated the value of quantitative measurements on a highly accelerated T2 mapping sequence, in order to establish a threshold to differentiate benign from malignant lesions. For this purpose, we evaluated a novel, highly accelerated T2 mapping research sequence that enables high-resolution image acquisition with short acquisition times in everyday clinical practice. In this retrospective single-center study, we included 54 patients with clinically indicated MRI of the prostate and biopsy-confirmed carcinoma (n = 37) or exclusion of carcinoma (n = 17). All patients had received a standard of care biopsy of the prostate, results of which were used to confirm or exclude presence of malignant lesions. We used the linear mixed-effects model-fit by REML to determine the difference between mean values of cancerous tissue and healthy tissue. We found good differentiation between malignant lesions and normal appearing tissue in the peripheral zone based on the mean T2 value. Specifically, the mean T2 value for tissue without malignant lesions was (151.7 ms [95% CI: 146.9-156.5 ms] compared to 80.9 ms for malignant lesions [95% CI: 67.9-79.1 ms]; p < 0.001). Based on this assessment, a limit of 109.2 ms is suggested. Aditionally, a significant correlation was observed between T2 values of the peripheral zone and PI-RADS scores (p = 0.0194). However, no correlation was found between the Gleason Score and the T2 relaxation time. Using REML, we found a difference of -82.7 ms in mean values between cancerous tissue and healthy tissue. We established a cut-off-value of 109.2 ms to accurately differentiate between malignant and non-malignant prostate regions. The addition of T2 mapping sequences to routine imaging could benefit automated lesion detection and facilitate contrast-free multiparametric MRI of the prostate.
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Affiliation(s)
- Andreas Michael Bucher
- Institute for Diagnostic and Interventional Radiology, Goethe University Frankfurt, University Hospital Frankfurt, Theodor-Stern Kai 7, 60590, Frankfurt, Germany
| | - Jan Egger
- Institute for AI in Medicine, University Hospital Essen, Girardetstraße 2, 45131, Essen, Germany.
| | - Julia Dietz
- Institute for Diagnostic and Interventional Radiology, Goethe University Frankfurt, University Hospital Frankfurt, Theodor-Stern Kai 7, 60590, Frankfurt, Germany
| | - Ralph Strecker
- Siemens Healthineers AG, (EMEA Scientific Partnerships), Henkestraße 127, 91052, Erlangen, Germany
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, EPFL, QI E, 1015, Lausanne, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Eric Frodl
- Institute for Diagnostic and Interventional Radiology, Goethe University Frankfurt, University Hospital Frankfurt, Theodor-Stern Kai 7, 60590, Frankfurt, Germany
| | - Mike Wenzel
- Department of Urology, Goethe University Hospital, Goethe University Frankfurt, Frankfurt, Germany, Theodor-Stern Kai 7, 60590, Frankfurt, Germany
| | - Tobias Penzkofer
- Department of Radiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Bernd Hamm
- Department of Radiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Felix Kh Chun
- Department of Urology, Goethe University Hospital, Goethe University Frankfurt, Frankfurt, Germany, Theodor-Stern Kai 7, 60590, Frankfurt, Germany
| | - Thomas Vogl
- Institute for Diagnostic and Interventional Radiology, Goethe University Frankfurt, University Hospital Frankfurt, Theodor-Stern Kai 7, 60590, Frankfurt, Germany
| | - Jens Kleesiek
- Institute for AI in Medicine, University Hospital Essen, Girardetstraße 2, 45131, Essen, Germany
- Department of Physics, TU Dortmund University, Otto-Hahn-Straße 4, 44227, Dortmund, Germany
- Cancer Research Center Cologne Essen (CCCE), West German Cancer Center Essen (WTZ), 45122, Essen, Germany
- German Cancer Research Center (DKFZ), Partner site University Hospital Essen, German Cancer Consortium (DKTK), 45122, Essen, Germany
- Medical Faculty, University of Duisburg-Essen, 45122, Essen, Germany
| | - Martin Beeres
- Institute for Diagnostic and Interventional Radiology, Goethe University Frankfurt, University Hospital Frankfurt, Theodor-Stern Kai 7, 60590, Frankfurt, Germany
- Departement of Neuroradiology, University-Hospital of Giessen and Marburg Campus Marburg, Baldingerstraße 1, 35043, Marburg, Germany
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7
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Algohary A, Zacharaki EI, Breto AL, Alhusseini M, Wallaengen V, Xu IR, Gaston SM, Punnen S, Castillo P, Pattany PM, Kryvenko ON, Spieler B, Abramowitz MC, Pra AD, Ford JC, Pollack A, Stoyanova R. Uncovering prostate cancer aggressiveness signal in T2-weighted MRI through a three-reference tissues normalization technique. NMR IN BIOMEDICINE 2024; 37:e5069. [PMID: 37990759 DOI: 10.1002/nbm.5069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/27/2023] [Accepted: 10/16/2023] [Indexed: 11/23/2023]
Abstract
Quantitative T2-weighted MRI (T2W) interpretation is impeded by the variability of acquisition-related features, such as field strength, coil type, signal amplification, and pulse sequence parameters. The main purpose of this work is to develop an automated method for prostate T2W intensity normalization. The procedure includes the following: (i) a deep learning-based network utilizing MASK R-CNN for automatic segmentation of three reference tissues: gluteus maximus muscle, femur, and bladder; (ii) fitting a spline function between average intensities in these structures and reference values; and (iii) using the function to transform all T2W intensities. The T2W distributions in the prostate cancer regions of interest (ROIs) and normal appearing prostate tissue (NAT) were compared before and after normalization using Student's t-test. The ROIs' T2W associations with the Gleason Score (GS), Decipher genomic score, and a three-tier prostate cancer risk were evaluated with Spearman's correlation coefficient (rS ). T2W differences in indolent and aggressive prostate cancer lesions were also assessed. The MASK R-CNN was trained with manual contours from 32 patients. The normalization procedure was applied to an independent MRI dataset from 83 patients. T2W differences between ROIs and NAT significantly increased after normalization. T2W intensities in 231 biopsy ROIs were significantly negatively correlated with GS (rS = -0.21, p = 0.001), Decipher (rS = -0.193, p = 0.003), and three-tier risk (rS = -0.235, p < 0.001). The average T2W intensities in the aggressive ROIs were significantly lower than in the indolent ROIs after normalization. In conclusion, the automated triple-reference tissue normalization method significantly improved the discrimination between prostate cancer and normal prostate tissue. In addition, the normalized T2W intensities of cancer exhibited a significant association with tumor aggressiveness. By improving the quantitative utilization of the T2W in the assessment of prostate cancer on MRI, the new normalization method represents an important advance over clinical protocols that do not include sequences for the measurement of T2 relaxation times.
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Affiliation(s)
- Ahmad Algohary
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Evangelia I Zacharaki
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Adrian L Breto
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Mohammad Alhusseini
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Veronica Wallaengen
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Isaac R Xu
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Sandra M Gaston
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Sanoj Punnen
- Desai Sethi Urology Institute, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Patricia Castillo
- Department of Radiology, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Pradip M Pattany
- Department of Radiology, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Oleksandr N Kryvenko
- Department of Pathology and Laboratory Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Benjamin Spieler
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Matthew C Abramowitz
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Alan Dal Pra
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - John C Ford
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Alan Pollack
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA
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8
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Hanzlikova P, Vilimek D, Vilimkova Kahankova R, Ladrova M, Skopelidou V, Ruzickova Z, Martinek R, Cvek J. Longitudinal analysis of T2 relaxation time variations following radiotherapy for prostate cancer. Heliyon 2024; 10:e24557. [PMID: 38298676 PMCID: PMC10828070 DOI: 10.1016/j.heliyon.2024.e24557] [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: 07/07/2023] [Revised: 12/02/2023] [Accepted: 01/10/2024] [Indexed: 02/02/2024] Open
Abstract
Aim of this paper is to evaluate short and long-term changes in T 2 relaxation times after radiotherapy in patients with low and intermediate risk localized prostate cancer. A total of 24 patients were selected for this retrospective study. Each participant underwent 1.5T magnetic resonance imaging on seven separate occasions: initially after the implantation of gold fiducials, the required step for Cyberknife therapy guidance, followed by MRI scans two weeks post-therapy and monthly thereafter. As part of each MRI scan, the prostate region was manually delineated, and the T 2 relaxation times were calculated for quantitative analysis. The T 2 relaxation times between individual follow-ups were analyzed using Repeated Measures Analysis of Variance that revealed a significant difference across all measurements (F (6, 120) = 0.611, p << 0.001). A Bonferroni post hoc test revealed significant differences in median T 2 values between the baseline and subsequent measurements, particularly between pre-therapy (M 0 ) and two weeks post-therapy (M 1 ), as well as during the monthly interval checks (M 2 - M 6 ). Some cases showed a delayed decrease in relaxation times, indicating the prolonged effects of therapy. The changes in T 2 values during the course of radiotherapy can help in monitoring radiotherapy response in unconfirmed patients, quantifying the scarring process, and recognizing the therapy failure.
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Affiliation(s)
- Pavla Hanzlikova
- Department of Radiology, University Hospital Ostrava, Czech Republic
- Department of Imaging Methods, Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic
| | - Dominik Vilimek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, 17. listopadu 15, Ostrava – Poruba, 708 00, Czech Republic
| | - Radana Vilimkova Kahankova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, 17. listopadu 15, Ostrava – Poruba, 708 00, Czech Republic
| | - Martina Ladrova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, 17. listopadu 15, Ostrava – Poruba, 708 00, Czech Republic
| | - Valeria Skopelidou
- Institute of Molecular and Clinical Pathology and Medical Genetics, University Hospital Ostrava, 70852, Ostrava, Czech Republic
- Institute of Molecular and Clinical Pathology and Medical Genetics, Faculty of Medicine, University of Ostrava, 70300, Ostrava, Czech Republic
| | - Zuzana Ruzickova
- Faculty of Medicine, University of Ostrava, 70300 Ostrava, Czech Republic
- Department of Oncology, University Hospital Ostrava, 70852 Ostrava, Czech Republic
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, 17. listopadu 15, Ostrava – Poruba, 708 00, Czech Republic
| | - Jakub Cvek
- Faculty of Medicine, University of Ostrava, 70300 Ostrava, Czech Republic
- Department of Oncology, University Hospital Ostrava, 70852 Ostrava, Czech Republic
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Qu J, Pan B, Su T, Chen Y, Zhang T, Chen X, Zhu X, Xu Z, Wang T, Zhu J, Zhang Z, Feng F, Jin Z. T1 and T2 mapping for identifying malignant lymph nodes in head and neck squamous cell carcinoma. Cancer Imaging 2023; 23:125. [PMID: 38105217 PMCID: PMC10726506 DOI: 10.1186/s40644-023-00648-6] [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: 09/05/2023] [Accepted: 12/04/2023] [Indexed: 12/19/2023] Open
Abstract
BACKGROUND This study seeks to assess the utility of T1 and T2 mapping in distinguishing metastatic lymph nodes from reactive lymphadenopathy in patients with head and neck squamous cell carcinoma (HNSCC), using diffusion-weighted imaging (DWI) as a comparison. METHODS Between July 2017 and November 2019, 46 HNSCC patients underwent neck MRI inclusive of T1 and T2 mapping and DWI. Quantitative measurements derived from preoperative T1 and T2 mapping and DWI of metastatic and non-metastatic lymph nodes were compared using independent samples t-test or Mann-Whitney U test. Receiver operating characteristic curves and the DeLong test were employed to determine the most effective diagnostic methodology. RESULTS We examined a total of 122 lymph nodes, 45 (36.9%) of which were metastatic proven by pathology. Mean T2 values for metastatic lymph nodes were significantly lower than those for benign lymph nodes (p < 0.001). Conversely, metastatic lymph nodes exhibited significantly higher apparent diffusion coefficient (ADC) and standard deviation of T1 values (T1SD) (p < 0.001). T2 generated a significantly higher area under the curve (AUC) of 0.890 (0.826-0.954) compared to T1SD (0.711 [0.613-0.809]) and ADC (0.660 [0.562-0.758]) (p = 0.007 and p < 0.001). Combining T2, T1SD, ADC, and lymph node size achieved an AUC of 0.929 (0.875-0.983), which did not significantly enhance diagnostic performance over using T2 alone (p = 0.089). CONCLUSIONS The application of T1 and T2 mapping is feasible in differentiating metastatic from non-metastatic lymph nodes in HNSCC and can improve diagnostic efficacy compared to DWI.
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Affiliation(s)
- Jiangming Qu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical Union Medical College, No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China
| | - Boju Pan
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical Union Medical College, No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China
| | - Tong Su
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical Union Medical College, No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China
| | - Yu Chen
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical Union Medical College, No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.
| | - Tao Zhang
- Department of Stomatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical Union Medical College, No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China
| | - Xingming Chen
- Department of Otolaryngology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical Union Medical College, No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China
| | - Xiaoli Zhu
- Department of Otolaryngology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical Union Medical College, No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China
| | - Zhentan Xu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical Union Medical College, No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China
| | - Tianjiao Wang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical Union Medical College, No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China
| | - Jinxia Zhu
- MR Research Collaboration, Siemens Healthineers Ltd, Beijing, China
| | - Zhuhua Zhang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical Union Medical College, No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.
| | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical Union Medical College, No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China
| | - Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical Union Medical College, No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.
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Hellström M, Löfstedt T, Garpebring A. Denoising and uncertainty estimation in parameter mapping with approximate Bayesian deep image priors. Magn Reson Med 2023; 90:2557-2571. [PMID: 37582257 DOI: 10.1002/mrm.29823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 06/26/2023] [Accepted: 07/18/2023] [Indexed: 08/17/2023]
Abstract
PURPOSE To mitigate the problem of noisy parameter maps with high uncertainties by casting parameter mapping as a denoising task based on Deep Image Priors. METHODS We extend the concept of denoising with Deep Image Prior (DIP) into parameter mapping by treating the output of an image-generating network as a parametrization of tissue parameter maps. The method implicitly denoises the parameter mapping process by filtering low-level image features with an untrained convolutional neural network (CNN). Our implementation includes uncertainty estimation from Bernoulli approximate variational inference, implemented with MC dropout, which provides model uncertainty in each voxel of the denoised parameter maps. The method is modular, so the specifics of different applications (e.g., T1 mapping) separate into application-specific signal equation blocks. We evaluate the method on variable flip angle T1 mapping, multi-echo T2 mapping, and apparent diffusion coefficient mapping. RESULTS We found that deep image prior adapts successfully to several applications in parameter mapping. In all evaluations, the method produces noise-reduced parameter maps with decreased uncertainty compared to conventional methods. The downsides of the proposed method are the long computational time and the introduction of some bias from the denoising prior. CONCLUSION DIP successfully denoise the parameter mapping process and applies to several applications with limited hyperparameter tuning. Further, it is easy to implement since DIP methods do not use network training data. Although time-consuming, uncertainty information from MC dropout makes the method more robust and provides useful information when properly calibrated.
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Affiliation(s)
- Max Hellström
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Tommy Löfstedt
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
- Department of Computing Science, Umeå University, Umeå, Sweden
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11
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Sun H, Wang L, Daskivich T, Qiu S, Han F, D'Agnolo A, Saouaf R, Christodoulou AG, Kim H, Li D, Xie Y. Retrospective T2 quantification from conventional weighted MRI of the prostate based on deep learning. FRONTIERS IN RADIOLOGY 2023; 3:1223377. [PMID: 37886239 PMCID: PMC10598780 DOI: 10.3389/fradi.2023.1223377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023]
Abstract
Purpose To develop a deep learning-based method to retrospectively quantify T2 from conventional T1- and T2-weighted images. Methods Twenty-five subjects were imaged using a multi-echo spin-echo sequence to estimate reference prostate T2 maps. Conventional T1- and T2-weighted images were acquired as the input images. A U-Net based neural network was developed to directly estimate T2 maps from the weighted images using a four-fold cross-validation training strategy. The structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean percentage error (MPE), and Pearson correlation coefficient were calculated to evaluate the quality of network-estimated T2 maps. To explore the potential of this approach in clinical practice, a retrospective T2 quantification was performed on a high-risk prostate cancer cohort (Group 1) and a low-risk active surveillance cohort (Group 2). Tumor and non-tumor T2 values were evaluated by an experienced radiologist based on region of interest (ROI) analysis. Results The T2 maps generated by the trained network were consistent with the corresponding reference. Prostate tissue structures and contrast were well preserved, with a PSNR of 26.41 ± 1.17 dB, an SSIM of 0.85 ± 0.02, and a Pearson correlation coefficient of 0.86. Quantitative ROI analyses performed on 38 prostate cancer patients revealed estimated T2 values of 80.4 ± 14.4 ms and 106.8 ± 16.3 ms for tumor and non-tumor regions, respectively. ROI measurements showed a significant difference between tumor and non-tumor regions of the estimated T2 maps (P < 0.001). In the two-timepoints active surveillance cohort, patients defined as progressors exhibited lower estimated T2 values of the tumor ROIs at the second time point compared to the first time point. Additionally, the T2 difference between two time points for progressors was significantly greater than that for non-progressors (P = 0.010). Conclusion A deep learning method was developed to estimate prostate T2 maps retrospectively from clinically acquired T1- and T2-weighted images, which has the potential to improve prostate cancer diagnosis and characterization without requiring extra scans.
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Affiliation(s)
- Haoran Sun
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States
| | - Lixia Wang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Timothy Daskivich
- Minimal Invasive Urology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Shihan Qiu
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States
| | - Fei Han
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Alessandro D'Agnolo
- Imaging/Nuclear Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Rola Saouaf
- Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Anthony G. Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States
| | - Hyung Kim
- Minimal Invasive Urology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
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Zhang KS, Neelsen CJO, Wennmann M, Glemser PA, Hielscher T, Weru V, Görtz M, Schütz V, Stenzinger A, Hohenfellner M, Schlemmer HP, Bonekamp D. Same-day repeatability and Between-Sequence reproducibility of Mean ADC in PI-RADS lesions. Eur J Radiol 2023; 165:110898. [PMID: 37331287 DOI: 10.1016/j.ejrad.2023.110898] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/02/2023] [Accepted: 05/26/2023] [Indexed: 06/20/2023]
Abstract
PURPOSE This study aimed to assess repeatability after repositioning (inter-scan), intra-rater, inter-rater and inter-sequence variability of mean apparent diffusion coefficient (ADC) measurements in MRI-detected prostate lesions. METHOD Forty-three patients with suspicion for prostate cancer were included and received a clinical prostate bi-/multiparametric MRI examination with repeat scans of the T2-weighted and two DWI-weighted sequences (ssEPI and rsEPI). Two raters (R1 and R2) performed single-slice, 2D regions of interest (2D-ROIs) and 3D-segmentation-ROIs (3D-ROIs). Mean bias, corresponding limits of agreement (LoA), mean absolute difference, within-subject coefficient of variation (CoV) and repeatability/reproducibility coefficient (RC/RDC) were calculated. Bradley & Blackwood test was used for variance comparison. Linear mixed models (LMM) were used to account for multiple lesions per patient. RESULTS Inter-scan repeatability, intra-rater and inter-sequence reproducibility analysis of ADC showed no significant bias. 3D-ROIs demonstrated significantly less variability than 2D-ROIs (p < 0.01). Inter-rater comparison demonstrated small significant systematic bias of 57 × 10-6 mm2/s for 3D-ROIs (p < 0.001). Intra-rater RC, with the lowest variation, was 145 and 189 × 10-6 mm2/s for 3D- and 2D-ROIs, respectively. For 3D-ROIs of ssEPI, RCs and RDCs were 190-198 × 10-6 mm2/s for inter-scan, inter-rater and inter-sequence variation. No significant differences were found for inter-scan, inter-rater and inter-sequence variability. CONCLUSIONS In a single-scanner setting, single-slice ADC measurements showed considerable variation, which may be lowered using 3D-ROIs. For 3D-ROIs, we propose a cut-off of ∼ 200 × 10-6 mm2/s for differences introduced by repositioning, rater or sequence effects. The results suggest that follow-up measurements should be possible by different raters or sequences.
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Affiliation(s)
- Kevin Sun Zhang
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Markus Wennmann
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Thomas Hielscher
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Vivienn Weru
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Magdalena Görtz
- Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany; Junior clinical cooperation unit 'Multiparametric Methods for Early Detection of Prostate Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Viktoria Schütz
- Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Albrecht Stenzinger
- Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Markus Hohenfellner
- Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Heinz-Peter Schlemmer
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany; German Cancer Consortium (DKTK), Germany
| | - David Bonekamp
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany; German Cancer Consortium (DKTK), Germany; Heidelberg University Medical School, Heidelberg, Germany.
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Zhang Z, Liu J, Wang W, Zhang Y, Qu F, Hilbert T, Kober T, Cheng J, Li S, Zhu J. Feasibility of accelerated T2 mapping for the preoperative assessment of endometrial carcinoma. Front Oncol 2023; 13:1117148. [PMID: 37564932 PMCID: PMC10411727 DOI: 10.3389/fonc.2023.1117148] [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: 12/08/2022] [Accepted: 07/07/2023] [Indexed: 08/12/2023] Open
Abstract
Objective The application value of T2 mapping in evaluating endometrial carcinoma (EMC) features remains unclear. The aim of the study was to determine the quantitative T2 values in EMC using a novel accelerated T2 mapping, and evaluate them for detection, classification,and grading of EMC. Materials and methods Fifty-six patients with pathologically confirmed EMC and 17 healthy volunteers were prospectively enrolled in this study. All participants underwent pelvic magnetic resonance imaging, including DWI and accelerated T2 mapping, before treatment. The T2 and apparent diffusion coefficient (ADC) values of different pathologic EMC features were extracted and compared. Receiver operating characteristic (ROC) curve analysis was performed to analyze the diagnostic efficacy of the T2 and ADC values in distinguishing different pathological features of EMC. Results The T2 values and ADC values were significantly lower in EMC than in normal endometrium (bothl p < 0.05). The T2 and ADC values were significantly different between endometrioid adenocarcinoma (EA) and non-EA (both p < 0.05) and EMC tumor grades (all p < 0.05) but not for EMC clinical types (both p > 0.05) and depth of myometrial invasion (both p > 0.05). The area under the ROC curve (AUC) was higher for T2 values than for ADC values in predicting grade 3 EA (0.939 vs. 0.764, p = 0.048). When combined T2 and ADC values, the AUC for predicting grade 3 EA showed a significant increase to 0.947 (p = 0.03) compared with those of ADC values. The T2 and ADC values were negatively correlated with the tumor grades (r = -0.706 and r = -0.537, respectively). Conclusion Quantitative T2 values demonstrate potential suitability in discriminating between EMC and normal endometrium, EA and non-EA, grade 3 EA and grade 1/2 EA. Combining T2 and ADC values performs better in predicting the histological grades of EA in comparison with ADC values alone.
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Affiliation(s)
- Zanxia Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jie Liu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Weijian Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Feifei Qu
- Magnetic Resonance Collaboration, Siemens Healthcare Ltd., Beijing, China
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Signal Processing Lab 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Signal Processing Lab 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shujian Li
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jinxia Zhu
- Magnetic Resonance Collaboration, Siemens Healthcare Ltd., Beijing, China
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Cao H, Xu W, Xu Y, Rong X, Xiao X, Feng H, Wang X, Wang L, Qi T, Zhang L. Value of synthetic MRI quantitative parameters in preprocedural evaluation for TRUS/MRI fusion-guided biopsy of the prostate. Prostate 2023. [PMID: 37157155 DOI: 10.1002/pros.24550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 03/17/2023] [Accepted: 04/24/2023] [Indexed: 05/10/2023]
Abstract
BACKGROUND Transrectal ultrasonography (TRUS)/magnetic resonance imaging (MRI) fusion-guided biopsy has a high clinical application value. However, this technique has some limitations, which limit its use in routine clinical practice. Therefore, the selection of suitable proatate lesions for this technique is worthy of our attention. Synthetic MRI (SyMRI) is capable of quantifying multiple relaxation parameters, which might have potential value in preprocedural evaluation for TRUS/MRI fusion-guided biopsy of the prostate. The aim of our study is to examine the value of SyMRI quantitative parameters in preprocedural evaluation for TRUS/MRI fusion-guided biopsy of the prostate. METHODS We prospectively selected 148 lesions in 137 patients who underwent prostate biopsy in our hospital. Next, 2-4 needles of TRUS/MRI fusion-guided biopsy combined with 10 needles of system biopsy (SB) were used as the protocol for prostate biopsy. Before biopsy, the MAGiC sequences of the MRI images of the enrolled patients underwent post-processing, and the longitudinal relaxation time (T1), transverse relaxation time (T2), and proton density (PD) were extracted. The biopsy pathology results were used as a gold standard to compare the differences in SyMRI quantitative parameters between benign and malignant prostate lesions in the peripheral and transitional zones. The receiver operating characteristic (ROC) curves were plotted to confirm the optimal SyMRI quantitative parameter for prostate lesion benignancy/malignancy performance, and the cutoff values of these parameters were used for grouping the lesions. The single-needle biopsy prostate cancer (PCa)-positivity rates (number of positive biopsy needles/total biopsy needles) and PCa overall detection rates by TRUS/MRI fusion-guided biopsy and SB were compared in different subgroups. RESULTS The T1 and T2 values can determine the benignancy/malignancy of prostate transition lesions(p < 0.01), and the T2 value has a greater diagnostic performance (p = 0.0376). The T2 value can determine the benignancy/malignancy of prostate peripheral lesions. The optimal diagnostic cutoff values for T2 were 77 and 81 ms, respectively. The single-needle PCa positivity rate of TRUS/MRI fusion-guided biopsy was higher than SB for any prostate lesions in different subgroups (p < 0.01). However, only in the subgroup of transition zone lesions with T2 ≤ 77 ms, the PCa overall detection rate of TRUS/MRI fusion-guided biopsy was significantly higher than that of SB (p = 0.031). CONCLUSION SyMRI-T2 value can provide a theoretical basis for the selection of suitable lesions for TRUS/MRI fusion-guided biopsy.
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Affiliation(s)
- Haiyan Cao
- Department of Ultrasound, Affiliated Hospital of Yangzhou University, Medical Imaging Center, Yangzhou University, Yangzhou, China
- Department of Ultrasound, Yancheng First Hospital, Affiliated Hospital of Nanjing University Medical school (The First people's Hospital of Yancheng), Yancheng, China
| | - Wenjuan Xu
- Department of Radiology, Affiliated Hospital of Yangzhou University, Medical Imaging Center, Yangzhou University, Yangzhou, China
| | - Yan Xu
- Department of Ultrasound, Affiliated Hospital of Yangzhou University, Medical Imaging Center, Yangzhou University, Yangzhou, China
| | - Xin Rong
- Department of Ultrasound, Affiliated Hospital of Yangzhou University, Medical Imaging Center, Yangzhou University, Yangzhou, China
| | - Xiao Xiao
- Department of Ultrasound, Affiliated Hospital of Yangzhou University, Medical Imaging Center, Yangzhou University, Yangzhou, China
| | - Hao Feng
- Department of Ultrasound, Affiliated Hospital of Yangzhou University, Medical Imaging Center, Yangzhou University, Yangzhou, China
| | - Xiaoxiang Wang
- Department of Urology, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, China
| | - Lei Wang
- Department of Pathology, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, China
| | - Tingyue Qi
- Department of Ultrasound, Affiliated Hospital of Yangzhou University, Medical Imaging Center, Yangzhou University, Yangzhou, China
| | - Li Zhang
- Department of Interventional Radiology, Affiliated Hospital of Yangzhou University, Medical Imaging Center, Yangzhou University, Yangzhou, China
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Gruenebach N, Abello Mercado MA, Grauhan NF, Sanner A, Kronfeld A, Groppa S, Schoeffling VI, Hilbert T, Brockmann MA, Othman AE. Clinical feasibility and validation of the accelerated T2 mapping sequence GRAPPATINI in brain imaging. Heliyon 2023; 9:e15064. [PMID: 37096006 PMCID: PMC10121777 DOI: 10.1016/j.heliyon.2023.e15064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/23/2023] [Accepted: 03/27/2023] [Indexed: 04/05/2023] Open
Abstract
Rationale and objectives To prospectively evaluate feasibility and robustness of an accelerated T2 mapping sequence (GRAPPATINI) in brain imaging and to assess its synthetic T2-weighted images (sT2w) in comparison with a standard T2-weighted sequence (T2 TSE). Material and methods Volunteers were included to evaluate the robustness and consecutive patients for morphological evaluation. They were scanned on a 3 T MR-scanner. Healthy volunteers underwent GRAPPATINI of the brain three times (day 1: scan/rescan; day 2: follow-up). Patients between the ages of 18 and 85 years who were able to provide written informed consent and who had no MRI contraindications were included. For morphological comparison two radiologists with 5 and 7 years of experience in brain MRI evaluated image quality using a Likert scale (1 being poor, 4 being excellent) in a blinded and randomized fashion. Results Images were successfully acquired in ten volunteers with a mean age of 25 years (ranging from 22 to 31 years) and 52 patients (23 men/29 women) with a mean age of 55 years (range of 22-83 years). Most brain regions showed repeatable and reproducible T2 values (rescan: CoV 0.75%-2.06%, ICC 69%-92.3%; follow-up: CoV 0.41%-1.59%, ICC 79.4%-95.8%), except for the caudate nucleus (rescan: CoV 7.25%, ICC 66.3%; follow-up: CoV 4.78%, ICC 80.9%). Image quality of sT2w was rated inferior to T2 TSE (median for T2 TSE: 3; sT2w: 1-2), but measurements revealed good interrater reliability of sT2w (lesion counting: ICC 0.85; diameter measure: ICC 0.68 and 0.67). Conclusion GRAPPATINI is a feasible and robust T2 mapping sequence of the brain on intra- and intersubject level. The resulting sT2w depict brain lesions comparable to T2 TSE despite its inferior image quality.
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Bolan PJ, Saunders SL, Kay K, Gross M, Akcakaya M, Metzger GJ. Improved Quantitative Parameter Estimation for Prostate T2 Relaxometry using Convolutional Neural Networks. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.11.23284194. [PMID: 36711813 PMCID: PMC9882442 DOI: 10.1101/2023.01.11.23284194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
This work seeks to evaluate multiple methods for quantitative parameter estimation from standard T2 mapping acquisitions in the prostate. The T2 estimation performance of methods based on neural networks (NN) was quantitatively compared to that of conventional curve fitting techniques. Large physics-based synthetic datasets simulating T2 mapping acquisitions were generated for training NNs and for quantitative performance comparisons. Ten combinations of different NN architectures, training strategies, and training corpora were implemented and compared with four different curve fitting strategies. All methods were compared quantitatively using synthetic data with known ground truth, and further compared on in vivo test data, with and without noise augmentation, to evaluate feasibility and noise robustness. In the evaluation on synthetic data, a convolutional neural network (CNN), trained in a supervised fashion using synthetic data generated from naturalistic images, showed the highest overall accuracy and precision amongst all the methods. On in vivo data, this best-performing method produced low-noise T2 maps and showed the least deterioration with increasing input noise levels. This study showed that a CNN, trained with synthetic data in a supervised manner, may provide superior T2 estimation performance compared to conventional curve fitting, especially in low signal-to-noise regions.
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Affiliation(s)
- Patrick J Bolan
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis MN
- Department of Radiology, University of Minnesota, Minneapolis MN
| | - Sara L Saunders
- Department of Biomedical Engineering, University of Minnesota, Minneapolis MN
| | - Kendrick Kay
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis MN
- Department of Radiology, University of Minnesota, Minneapolis MN
| | - Mitchell Gross
- Department of Biomedical Engineering, University of Minnesota, Minneapolis MN
| | - Mehmet Akcakaya
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis MN
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis MN
| | - Gregory J Metzger
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis MN
- Department of Radiology, University of Minnesota, Minneapolis MN
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Gouel P, Decazes P, Vera P, Gardin I, Thureau S, Bohn P. Advances in PET and MRI imaging of tumor hypoxia. Front Med (Lausanne) 2023; 10:1055062. [PMID: 36844199 PMCID: PMC9947663 DOI: 10.3389/fmed.2023.1055062] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 01/30/2023] [Indexed: 02/11/2023] Open
Abstract
Tumor hypoxia is a complex and evolving phenomenon both in time and space. Molecular imaging allows to approach these variations, but the tracers used have their own limitations. PET imaging has the disadvantage of low resolution and must take into account molecular biodistribution, but has the advantage of high targeting accuracy. The relationship between the signal in MRI imaging and oxygen is complex but hopefully it would lead to the detection of truly oxygen-depleted tissue. Different ways of imaging hypoxia are discussed in this review, with nuclear medicine tracers such as [18F]-FMISO, [18F]-FAZA, or [64Cu]-ATSM but also with MRI techniques such as perfusion imaging, diffusion MRI or oxygen-enhanced MRI. Hypoxia is a pejorative factor regarding aggressiveness, tumor dissemination and resistance to treatments. Therefore, having accurate tools is particularly important.
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Affiliation(s)
- Pierrick Gouel
- Département d’Imagerie, Centre Henri Becquerel, Rouen, France,QuantIF-LITIS, EA 4108, IRIB, Université de Rouen, Rouen, France
| | - Pierre Decazes
- Département d’Imagerie, Centre Henri Becquerel, Rouen, France,QuantIF-LITIS, EA 4108, IRIB, Université de Rouen, Rouen, France
| | - Pierre Vera
- Département d’Imagerie, Centre Henri Becquerel, Rouen, France,QuantIF-LITIS, EA 4108, IRIB, Université de Rouen, Rouen, France
| | - Isabelle Gardin
- Département d’Imagerie, Centre Henri Becquerel, Rouen, France,QuantIF-LITIS, EA 4108, IRIB, Université de Rouen, Rouen, France
| | - Sébastien Thureau
- QuantIF-LITIS, EA 4108, IRIB, Université de Rouen, Rouen, France,Département de Radiothérapie, Centre Henri Becquerel, Rouen, France
| | - Pierre Bohn
- Département d’Imagerie, Centre Henri Becquerel, Rouen, France,QuantIF-LITIS, EA 4108, IRIB, Université de Rouen, Rouen, France,*Correspondence: Pierre Bohn,
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Snyder J, Seres P, Stobbe RW, Grenier JG, Smyth P, Blevins G, Wilman AH. Inline dual-echo T2 quantification in brain using a fast mapping reconstruction technique. NMR IN BIOMEDICINE 2023; 36:e4811. [PMID: 35934839 DOI: 10.1002/nbm.4811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 07/06/2022] [Accepted: 07/17/2022] [Indexed: 06/15/2023]
Abstract
T2 mapping from 2D proton density and T2-weighted images (PD-T2) using Bloch equation simulations can be time consuming and introduces a latency between image acquisition and T2 map production. A fast T2 mapping reconstruction method is investigated and compared with a previous modeling approach to reduce computation time and allow inline T2 maps on the MRI console. Brain PD-T2 images from five multiple sclerosis patients were used to compare T2 map reconstruction times between the new subtraction method and the Euclidean norm minimization technique. Bloch equation simulations were used to create the lookup table for decay curve matching in both cases. Agreement of the two techniques used Bland-Altman analysis for investigating individual subsets of data and all image points in the five volumes (meta-analysis). The subtraction method resulted in an average reduction of computation time for single slices from 134 s (minimization method) to 0.44 s. Comparing T2 values between the subtraction and minimization methods resulted in a confidence interval ranging from -0.06 to 0.06 ms (95% of values were within ± 0.06 ms between the techniques). Using identical reconstruction code based on the subtraction method, inline T2 maps were produced from PD-T2 images directly on the scanner console. The excellent agreement between the two methods permits the subtraction technique to be interchanged with the previous method, reducing computation time and allowing inline T2 map reconstruction based on Bloch simulations directly on the scanner.
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Affiliation(s)
- Jeff Snyder
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
| | - Peter Seres
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
| | - Robert W Stobbe
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
| | - Justin G Grenier
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
| | - Penelope Smyth
- Department of Medicine, Division of Neurology, University of Alberta, Edmonton, Canada
| | - Gregg Blevins
- Department of Medicine, Division of Neurology, University of Alberta, Edmonton, Canada
| | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
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Tissue Characteristics of Endometrial Carcinoma Analyzed by Quantitative Synthetic MRI and Diffusion-Weighted Imaging. Diagnostics (Basel) 2022; 12:diagnostics12122956. [PMID: 36552962 PMCID: PMC9776551 DOI: 10.3390/diagnostics12122956] [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: 10/05/2022] [Revised: 11/08/2022] [Accepted: 11/24/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND This study investigates the association of T1, T2, proton density (PD) and the apparent diffusion coefficient (ADC) with histopathologic features of endometrial carcinoma (EC). METHODS One hundred and nine EC patients were prospectively enrolled from August 2019 to December 2020. Synthetic magnetic resonance imaging (MRI) was acquired through one acquisition, in addition to diffusion-weighted imaging (DWI) and other conventional sequences using 1.5T MRI. T1, T2, PD derived from synthetic MRI and ADC derived from DWI were compared among different histopathologic features, namely the depth of myometrial invasion (MI), tumor grade, cervical stromal invasion (CSI) and lymphovascular invasion (LVSI) of EC by the Mann-Whitney U test. Classification models based on the significant MRI metrics were constructed with their respective receiver operating characteristic (ROC) curves, and their micro-averaged ROC was used to evaluate the overall performance of these significant MRI metrics in determining aggressive histopathologic features of EC. RESULTS EC with MI had significantly lower T2, PD and ADC than those without MI (p = 0.007, 0.006 and 0.043, respectively). Grade 2-3 EC and EC with LVSI had significantly lower ADC than grade 1 EC and EC without LVSI, respectively (p = 0.005, p = 0.020). There were no differences in the MRI metrics in EC with or without CSI. Micro-averaged ROC of the three models had an area under the curve of 0.83. CONCLUSIONS Synthetic MRI provided quantitative metrics to characterize EC with one single acquisition. Low T2, PD and ADC were associated with aggressive histopathologic features of EC, offering excellent performance in determining aggressive histopathologic features of EC.
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20
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Zhang KS, Schelb P, Netzer N, Tavakoli AA, Keymling M, Wehrse E, Hog R, Rotkopf LT, Wennmann M, Glemser PA, Thierjung H, von Knebel Doeberitz N, Kleesiek J, Görtz M, Schütz V, Hielscher T, Stenzinger A, Hohenfellner M, Schlemmer HP, Maier-Hein K, Bonekamp D. Pseudoprospective Paraclinical Interaction of Radiology Residents With a Deep Learning System for Prostate Cancer Detection: Experience, Performance, and Identification of the Need for Intermittent Recalibration. Invest Radiol 2022; 57:601-612. [PMID: 35467572 DOI: 10.1097/rli.0000000000000878] [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: 11/26/2022]
Abstract
OBJECTIVES The aim of this study was to estimate the prospective utility of a previously retrospectively validated convolutional neural network (CNN) for prostate cancer (PC) detection on prostate magnetic resonance imaging (MRI). MATERIALS AND METHODS The biparametric (T2-weighted and diffusion-weighted) portion of clinical multiparametric prostate MRI from consecutive men included between November 2019 and September 2020 was fully automatically and individually analyzed by a CNN briefly after image acquisition (pseudoprospective design). Radiology residents performed 2 research Prostate Imaging Reporting and Data System (PI-RADS) assessments of the multiparametric dataset independent from clinical reporting (paraclinical design) before and after review of the CNN results and completed a survey. Presence of clinically significant PC was determined by the presence of an International Society of Urological Pathology grade 2 or higher PC on combined targeted and extended systematic transperineal MRI/transrectal ultrasound fusion biopsy. Sensitivities and specificities on a patient and prostate sextant basis were compared using the McNemar test and compared with the receiver operating characteristic (ROC) curve of CNN. Survey results were summarized as absolute counts and percentages. RESULTS A total of 201 men were included. The CNN achieved an ROC area under the curve of 0.77 on a patient basis. Using PI-RADS ≥3-emulating probability threshold (c3), CNN had a patient-based sensitivity of 81.8% and specificity of 54.8%, not statistically different from the current clinical routine PI-RADS ≥4 assessment at 90.9% and 54.8%, respectively ( P = 0.30/ P = 1.0). In general, residents achieved similar sensitivity and specificity before and after CNN review. On a prostate sextant basis, clinical assessment possessed the highest ROC area under the curve of 0.82, higher than CNN (AUC = 0.76, P = 0.21) and significantly higher than resident performance before and after CNN review (AUC = 0.76 / 0.76, P ≤ 0.03). The resident survey indicated CNN to be helpful and clinically useful. CONCLUSIONS Pseudoprospective paraclinical integration of fully automated CNN-based detection of suspicious lesions on prostate multiparametric MRI was demonstrated and showed good acceptance among residents, whereas no significant improvement in resident performance was found. General CNN performance was preserved despite an observed shift in CNN calibration, identifying the requirement for continuous quality control and recalibration.
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Affiliation(s)
- Kevin Sun Zhang
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | | | | | | | - Myriam Keymling
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | - Eckhard Wehrse
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | - Robert Hog
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | | | - Markus Wennmann
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | | | - Heidi Thierjung
- From the Division of Radiology, German Cancer Research Center (DKFZ)
| | | | | | | | - Viktoria Schütz
- Department of Urology, University of Heidelberg Medical Center
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Magnetic Resonance Image Compilation Was Used in Conjunction with Prostate PI-RADS v2.1 Score Has Diagnostic Relevance for Benign and Malignant Prostate Lesions. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:3613540. [PMID: 36072774 PMCID: PMC9444436 DOI: 10.1155/2022/3613540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 07/22/2022] [Accepted: 07/27/2022] [Indexed: 12/24/2022]
Abstract
Objective To assess the diagnostic usefulness of magic in conjunction with PI-RADS v2.1 for prostate cancer malignant foci. Methods A total of 202 lesions (97 transitional zone lesions and 105 peripheral zone lesions) from 198 people were investigated retrospectively using traditional MRI and magic images. Each lesion has a unique pathological consequence. Lesions T1, T2, and PD values were employed as magic observation markers. The locations of the lesions were aggregated, and the paired t-test and receiver operating characteristic curve (ROC) were employed to find the indices with statistical significance in separating benign from malignant prostatic nodules (+1 point) and (−1 point) respectively. Draw a ROC curve and compare it to the PI-RADS v2.1 score using the magic positive and negative indices as well as the PI-RADS v2.1 score. By comparing the ROC curves scored separately, the diagnostic efficiency of the two scoring approaches for benign and malignant prostate lesions was investigated. Results T2 value has the highest diagnostic efficiency among the magic observation indices. T2 value of 77 ms for transitional zone lesions and T2 value of 89 ms for peripheral zone lesions are positive indices, whereas T2 value >77 ms and T2 value >89 ms are negative indexes. PI-RADS v2.1 combines one score and magic. In the transitional zone, the sensitivity, specificity, positive predictive value, and negative predictive value of the two scoring methods were 57.52, 87.70, 76.70, and 74.6 percent and 82.50, 73.68, 95.5, and 74.7 percent, respectively, and the AUC values were 0.735 and 0.846, respectively (P = 0.004); in the peripheral zone, the AUC values were 86.15 percent, 68.42 percent, 82.4. Conclusions Magic T2 value is a favorable sign for diagnosing benign and malignant prostate cancers when used in conjunction with PI-RADS v2.1. The end product exceeds PI-RADS v2.1 on its own, which is more useful in identifying benign and malignant prostate lesions, decreasing unnecessary puncture and alleviating patient pain.
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22
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Ju S, Chen C, Zhang J, Xu L, Zhang X, Li Z, Chen Y, Zhou J, Ji F, Wang L. Detection of circulating tumor cells: opportunities and challenges. Biomark Res 2022; 10:58. [PMID: 35962400 PMCID: PMC9375360 DOI: 10.1186/s40364-022-00403-2] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 07/26/2022] [Indexed: 11/25/2022] Open
Abstract
Circulating tumor cells (CTCs) are cells that shed from a primary tumor and travel through the bloodstream. Studying the functional and molecular characteristics of CTCs may provide in-depth knowledge regarding highly lethal tumor diseases. Researchers are working to design devices and develop analytical methods that can capture and detect CTCs in whole blood from cancer patients with improved sensitivity and specificity. Techniques using whole blood samples utilize physical prosperity, immunoaffinity or a combination of the above methods and positive and negative enrichment during separation. Further analysis of CTCs is helpful in cancer monitoring, efficacy evaluation and designing of targeted cancer treatment methods. Although many advances have been achieved in the detection and molecular characterization of CTCs, several challenges still exist that limit the current use of this burgeoning diagnostic approach. In this review, a brief summary of the biological characterization of CTCs is presented. We focus on the current existing CTC detection methods and the potential clinical implications and challenges of CTCs. We also put forward our own views regarding the future development direction of CTCs.
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Affiliation(s)
- Siwei Ju
- Department of Surgical Oncology, The Sir Run Run Shaw Hospital Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China
- Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, Zhejiang, Hangzhou, China
| | - Cong Chen
- Department of Surgical Oncology, The Sir Run Run Shaw Hospital Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China
- Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, Zhejiang, Hangzhou, China
| | - Jiahang Zhang
- Department of Surgical Oncology, The Sir Run Run Shaw Hospital Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China
- Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, Zhejiang, Hangzhou, China
| | - Lin Xu
- Department of Surgical Oncology, The Sir Run Run Shaw Hospital Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China
- Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, Zhejiang, Hangzhou, China
| | - Xun Zhang
- Department of Surgical Oncology, The Sir Run Run Shaw Hospital Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China
- Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, Zhejiang, Hangzhou, China
| | - Zhaoqing Li
- Department of Surgical Oncology, The Sir Run Run Shaw Hospital Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China
- Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, Zhejiang, Hangzhou, China
| | - Yongxia Chen
- Department of Surgical Oncology, The Sir Run Run Shaw Hospital Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China
- Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, Zhejiang, Hangzhou, China
| | - Jichun Zhou
- Department of Surgical Oncology, The Sir Run Run Shaw Hospital Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China
- Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, Zhejiang, Hangzhou, China
| | - Feiyang Ji
- Department of Surgical Oncology, The Sir Run Run Shaw Hospital Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China.
- Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, Zhejiang, Hangzhou, China.
| | - Linbo Wang
- Department of Surgical Oncology, The Sir Run Run Shaw Hospital Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China.
- Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, Zhejiang, Hangzhou, China.
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Dwivedi DK, Jagannathan NR. Emerging MR methods for improved diagnosis of prostate cancer by multiparametric MRI. MAGMA (NEW YORK, N.Y.) 2022; 35:587-608. [PMID: 35867236 DOI: 10.1007/s10334-022-01031-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 06/28/2022] [Accepted: 07/08/2022] [Indexed: 06/15/2023]
Abstract
Current challenges of using serum prostate-specific antigen (PSA) level-based screening, such as the increased false positive rate, inability to detect clinically significant prostate cancer (PCa) with random biopsy, multifocality in PCa, and the molecular heterogeneity of PCa, can be addressed by integrating advanced multiparametric MR imaging (mpMRI) approaches into the diagnostic workup of PCa. The standard method for diagnosing PCa is a transrectal ultrasonography (TRUS)-guided systematic prostate biopsy, but it suffers from sampling errors and frequently fails to detect clinically significant PCa. mpMRI not only increases the detection of clinically significant PCa, but it also helps to reduce unnecessary biopsies because of its high negative predictive value. Furthermore, non-Cartesian image acquisition and compressed sensing have resulted in faster MR acquisition with improved signal-to-noise ratio, which can be used in quantitative MRI methods such as dynamic contrast-enhanced (DCE)-MRI. With the growing emphasis on the role of pre-biopsy mpMRI in the evaluation of PCa, there is an increased demand for innovative MRI methods that can improve PCa grading, detect clinically significant PCa, and biopsy guidance. To meet these demands, in addition to routine T1-weighted, T2-weighted, DCE-MRI, diffusion MRI, and MR spectroscopy, several new MR methods such as restriction spectrum imaging, vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) method, hybrid multi-dimensional MRI, luminal water imaging, and MR fingerprinting have been developed for a better characterization of the disease. Further, with the increasing interest in combining MR data with clinical and genomic data, there is a growing interest in utilizing radiomics and radiogenomics approaches. These big data can also be utilized in the development of computer-aided diagnostic tools, including automatic segmentation and the detection of clinically significant PCa using machine learning methods.
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Affiliation(s)
- Durgesh Kumar Dwivedi
- Department of Radiodiagnosis, King George Medical University, Lucknow, UP, 226 003, India.
| | - Naranamangalam R Jagannathan
- Department of Radiology, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam, TN, 603 103, India.
- Department of Radiology, Sri Ramachandra Institute of Higher Education and Research, Chennai, TN, 600 116, India.
- Department of Electrical Engineering, Indian Institute Technology Madras, Chennai, TN, 600 036, India.
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Pseudo-T2 mapping for normalization of T2-weighted prostate MRI. MAGMA (NEW YORK, N.Y.) 2022; 35:573-585. [PMID: 35150363 PMCID: PMC9363383 DOI: 10.1007/s10334-022-01003-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 12/22/2021] [Accepted: 01/23/2022] [Indexed: 01/04/2023]
Abstract
Objective Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle (AutoRefF), femoral head/muscle (AutoRefFH) and pelvic bone/muscle (AutoRefPB). Materials and methods T2s measured by multi-echo spin echo (MESE) were compared to AutoRef pseudo-T2s in the whole prostate (WP) and zones (PZ and TZ/CZ/AFS) for seven asymptomatic volunteers with a paired Wilcoxon signed-rank test. AutoRef normalization was assessed on T2W images from a multicenter evaluation set of 1186 prostate cancer patients. Performance was measured by inter-patient histogram intersections of voxel intensities in the WP before and after normalization in a selected subset of 80 cases. Results AutoRefFH pseudo-T2s best approached MESE T2s in the volunteer study, with no significant difference shown (WP: p = 0.30, TZ/CZ/AFS: p = 0.22, PZ: p = 0.69). All three AutoRef versions increased inter-patient histogram intersections in the multicenter dataset, with median histogram intersections of 0.505 (original data), 0.738 (AutoRefFH), 0.739 (AutoRefF) and 0.726 (AutoRefPB). Discussion All AutoRef versions reduced variation in the multicenter data. AutoRefFH pseudo-T2s were closest to experimentally measured T2s. Supplementary Information The online version contains supplementary material available at 10.1007/s10334-022-01003-9.
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Lo WC, Panda A, Jiang Y, Ahad J, Gulani V, Seiberlich N. MR fingerprinting of the prostate. MAGMA (NEW YORK, N.Y.) 2022; 35:557-571. [PMID: 35419668 PMCID: PMC10288492 DOI: 10.1007/s10334-022-01012-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 03/21/2022] [Accepted: 03/24/2022] [Indexed: 06/03/2023]
Abstract
Multiparametric magnetic resonance imaging (mpMRI) has been adopted as the key tool for detection, localization, characterization, and risk stratification of patients suspected to have prostate cancer. Despite advantages over systematic biopsy, the interpretation of prostate mpMRI has limitations including a steep learning curve, leading to considerable interobserver variation. There is growing interest in clinical translation of quantitative imaging techniques for more objective lesion assessment. However, traditional mapping techniques are slow, precluding their use in the clinic. Magnetic resonance fingerprinting (MRF) is an efficient approach for quantitative maps of multiple tissue properties simultaneously. The T1 and T2 values obtained with MRF have been validated with phantom studies as well as in normal volunteers and patients. Studies have shown that MRF-derived T1 and T2 along with ADC values are all significant independent predictors in the differentiation between normal prostate tissue and prostate cancer, and hold promise in differentiating low and intermediate/high-grade cancers. This review seeks to introduce the basics of the prostate MRF technique, discuss the potential applications of prostate MRF for the characterization of prostate cancer, and describes ongoing areas of research.
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Affiliation(s)
- Wei-Ching Lo
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Siemens Medical Solutions USA, Boston, Massachusetts, USA
| | - Ananya Panda
- Department of Radiology, Mayo Clinic, 200 1st Street SW, Rochester, MN, 55905, USA
| | - Yun Jiang
- Department of Radiology, University of Michigan, University of Michigan Health System, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109-5030, USA
| | - James Ahad
- Case Western Reserve University, Cleveland, OH, USA
| | - Vikas Gulani
- Department of Radiology, University of Michigan, University of Michigan Health System, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109-5030, USA
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, University of Michigan Health System, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109-5030, USA.
- Case Western Reserve University, Cleveland, OH, USA.
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Ge Y, Jia Y, Li X, Dou W, Chen Z, Yan G. T2 relaxation time for the early prediction of treatment response to chemoradiation in locally advanced rectal cancer. Insights Imaging 2022; 13:113. [PMID: 35796881 PMCID: PMC9263013 DOI: 10.1186/s13244-022-01254-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/19/2022] [Indexed: 12/02/2022] Open
Abstract
Objectives Poor responders to chemoradiotherapy (CRT) for locally advanced rectal cancer (LARC) can still have a good prognosis if the treatment strategy is changed in time. However, no reliable predictor of early-treatment response has been identified. The purpose of this study was to investigate the role of T2 relaxation time in magnetic resonance imaging (MRI) for the early prediction of a pathological response to CRT in LARC. Methods A total of 123 MRIs were performed on 41 LARC patients immediately before, during, and after CRT. The corresponding tumor volume, T2 relaxation time, and apparent diffusion coefficient (ADC) values at different scan time points were obtained. The Mann–Whitney U test was used to compare the T2 relaxation time between pathological good responders (GR) and non-good responders (non-GR). The area under the curve (AUC) value was used to quantify the diagnostic ability of each parameter in predicting tumor response to CRT. Results Twenty-one (51%) and 20 (49%) were GRs and non-GRs, respectively. T2 relaxation time showed an excellent intraclass correlation coefficient (ICC) of > 0.85 at three-time points. It was significantly lower in the GR group than in the non-GR group during and after CRT. The early T2 decrease had a high AUC of 0.91 in differentiating non-GRs and GRs, similar to 0.90 of the T2 value after CRT. Conclusions T2 relaxation time may help predict treatment response to CRT for LARC earlier, rather than having to wait until the end of CRT, thereby alleviating the physical burden for patients with no good response. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01254-z.
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Affiliation(s)
- Yuxi Ge
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Yanlong Jia
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Xiaohong Li
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Weiqiang Dou
- GE Healthcare, MR Research China, Beijing, China
| | - Zhong Chen
- School of Electronic Science and Engineering, Xiamen University, Xiamen, Fujian, China
| | - Gen Yan
- Department of Radiology, The Second Affiliated Hospital of Xiamen University, Xiamen, 361021, Fujian, China.
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Park SB. Quantitative relaxation maps from synthetic MRI for prostate cancer. Acta Radiol 2022; 63:982-983. [PMID: 35200049 DOI: 10.1177/02841851221077405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Sung Bin Park
- Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Republic of Korea
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28
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Salvi M, De Santi B, Pop B, Bosco M, Giannini V, Regge D, Molinari F, Meiburger KM. Integration of Deep Learning and Active Shape Models for More Accurate Prostate Segmentation in 3D MR Images. J Imaging 2022; 8:133. [PMID: 35621897 PMCID: PMC9146644 DOI: 10.3390/jimaging8050133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/06/2022] [Accepted: 05/09/2022] [Indexed: 01/27/2023] Open
Abstract
Magnetic resonance imaging (MRI) has a growing role in the clinical workup of prostate cancer. However, manual three-dimensional (3D) segmentation of the prostate is a laborious and time-consuming task. In this scenario, the use of automated algorithms for prostate segmentation allows us to bypass the huge workload of physicians. In this work, we propose a fully automated hybrid approach for prostate gland segmentation in MR images using an initial segmentation of prostate volumes using a custom-made 3D deep network (VNet-T2), followed by refinement using an Active Shape Model (ASM). While the deep network focuses on three-dimensional spatial coherence of the shape, the ASM relies on local image information and this joint effort allows for improved segmentation of the organ contours. Our method is developed and tested on a dataset composed of T2-weighted (T2w) MRI prostatic volumes of 60 male patients. In the test set, the proposed method shows excellent segmentation performance, achieving a mean dice score and Hausdorff distance of 0.851 and 7.55 mm, respectively. In the future, this algorithm could serve as an enabling technology for the development of computer-aided systems for prostate cancer characterization in MR imaging.
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Affiliation(s)
- Massimo Salvi
- Biolab, PolitoBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy; (M.S.); (B.P.); (F.M.)
| | - Bruno De Santi
- Multi-Modality Medical Imaging (M3I), Technical Medical Centre, University of Twente, PB217, 7500 AE Enschede, The Netherlands;
| | - Bianca Pop
- Biolab, PolitoBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy; (M.S.); (B.P.); (F.M.)
| | - Martino Bosco
- Department of Pathology, Ospedale Michele e Pietro Ferrero, 12060 Verduno, Italy;
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, 10126 Turin, Italy; (V.G.); (D.R.)
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy
| | - Daniele Regge
- Department of Surgical Sciences, University of Turin, 10126 Turin, Italy; (V.G.); (D.R.)
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy
| | - Filippo Molinari
- Biolab, PolitoBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy; (M.S.); (B.P.); (F.M.)
| | - Kristen M. Meiburger
- Biolab, PolitoBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy; (M.S.); (B.P.); (F.M.)
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Gouel P, Hapdey S, Dumouchel A, Gardin I, Torfeh E, Hinault P, Vera P, Thureau S, Gensanne D. Synthetic MRI for Radiotherapy Planning for Brain and Prostate Cancers: Phantom Validation and Patient Evaluation. Front Oncol 2022; 12:841761. [PMID: 35515105 PMCID: PMC9065558 DOI: 10.3389/fonc.2022.841761] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 03/15/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose We aimed to evaluate the accuracy of T1 and T2 mappings derived from a multispectral pulse sequence (magnetic resonance image compilation, MAGiC®) on 1.5-T MRI and with conventional sequences [gradient echo with variable flip angle (GRE-VFA) and multi-echo spin echo (ME-SE)] compared to the reference values for the purpose of radiotherapy treatment planning. Methods The accuracy of T1 and T2 measurements was evaluated with 2 coils [head and neck unit (HNU) and BODY coils] on phantoms using descriptive statistics and Bland–Altman analysis. The reproducibility and repeatability of T1 and T2 measurements were performed on 15 sessions with the HNU coil. The T1 and T2 synthetic sequences obtained by both methods were evaluated according to quality assurance (QA) requirements for radiotherapy. T1 and T2in vivo measurements of the brain or prostate tissues of two groups of five subjects were also compared. Results The phantom results showed good agreement (mean bias, 8.4%) between the two measurement methods for T1 values between 490 and 2,385 ms and T2 values between 25 and 400 ms. MAGiC® gave discordant results for T1 values below 220 ms (bias with the reference values, from 38% to 1,620%). T2 measurements were accurately estimated below 400 ms (mean bias, 8.5%) by both methods. The QA assessments are in agreement with the recommendations of imaging for contouring purposes for radiotherapy planning. On patient data of the brain and prostate, the measurements of T1 and T2 by the two quantitative MRI (qMRI) methods were comparable (max difference, <7%). Conclusion This study shows that the accuracy, reproducibility, and repeatability of the multispectral pulse sequence (MAGiC®) were compatible with its use for radiotherapy treatment planning in a range of values corresponding to soft tissues. Even validated for brain imaging, MAGiC® could potentially be used for prostate qMRI.
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Affiliation(s)
- Pierrick Gouel
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France
| | - Sebastien Hapdey
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France
| | - Arthur Dumouchel
- Imaging Department, Henri Becquerel Cancer Center, Rouen, France
| | - Isabelle Gardin
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France.,Radiotherapy Department, Henri Becquerel Cancer Center, Rouen, France
| | - Eva Torfeh
- Radiotherapy Department, Henri Becquerel Cancer Center, Rouen, France
| | - Pauline Hinault
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France
| | - Pierre Vera
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France
| | - Sebastien Thureau
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France.,Radiotherapy Department, Henri Becquerel Cancer Center, Rouen, France
| | - David Gensanne
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France.,Radiotherapy Department, Henri Becquerel Cancer Center, Rouen, France
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[Differential diagnosis of benign and malignant breast lesions using quantitative synthetic magnetic resonance imaging]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2022; 42:457-462. [PMID: 35527481 PMCID: PMC9085598 DOI: 10.12122/j.issn.1673-4254.2022.04.01] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
OBJECTIVE To investigate the value of quantitative synthetic magnetic resonance imaging (SyMRI) in distinguishing between benign and malignant breast lesions. METHODS We retrospectively collected data of preoperative conventional MRI and multi-dynamic multi-echo sequences from 95 patients with breast lesions showing mass-type enhancement on DCE-MRI, including 27 patients with benign lesions and 68 with malignant lesions. The MRI features of the lesions (shape, margin, internal enhancement pattern, time-signal intensity curve, and T2WI signal) were analyzed, and for each lesion, SyMRI-generated quantitative parameters including T1 and T2 relaxation time and proton density (PD) were measured before and after enhancement and recorded as T1p, T2p, PDp and T1e, T2e, and PDe, respectively. The relative change rate of each parameter was calculated. Logistic regression and all-subset regression analyses were performed for variable selection to construct diagnostic models of the breast lesions, and receiver-operating characteristic (ROC) analysis was used to assess the performance of each model for differentiation of benign and malignant lesions. RESULTS There were significant differences in the MRI features between benign and malignant lesions (P < 0.05). All the SyMRI-generated quantitative parameters, with the exception of T2e and Pdp, showed significant differences between benign and malignant lesions (P < 0.05). Among the constructed diagnostic models, the model based on all the DCE-MRI features combined with SyMRI parameters T2p and T1e (DCE-MRI+T2p+T1e) showed the best performance in the differential diagnosis malignant breast masses with an AUC of 0.995 (95% CI: 0.983-1.000). CONCLUSION Quantitative SyMRI can be used for differential diagnosis of benign and malignant breast lesions.
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Herrmann J, Kaufmann S, Zhang C, Rausch S, Bedke J, Stenzl A, Nikolaou K, Kruck S, Seith F. [Multiparametric MRI of the prostate]. Urologe A 2022; 61:428-440. [PMID: 35389061 DOI: 10.1007/s00120-022-01806-7] [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] [Accepted: 02/18/2022] [Indexed: 11/27/2022]
Abstract
Multiparametric magnetic resonance imaging (mpMRI) is an integral component of prostate cancer diagnostics. According to the S3 guidelines on prostate cancer, mpMRI should be used for the primary diagnostics of prostate cancer as well as in active surveillance (AS). Basically, mpMRI consists of high-resolution T2-weighted (T2w) sequences, diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) sequences, which in turn are the basis for structured reporting according to the prostate imaging reporting and data system (PI-RADS) classification.
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Affiliation(s)
- Judith Herrmann
- Institut für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Tübingen, Tübingen, Deutschland
| | - Sascha Kaufmann
- Institut für Diagnostische und Interventionelle Radiologie, Siloah St. Trudpert Klinikum, Wilferdinger Str. 67, 75179, Pforzheim, Deutschland.
| | - Cecilia Zhang
- Institut für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Tübingen, Tübingen, Deutschland
| | - Steffen Rausch
- Klinik für Urologie, Universitätsklinikum Tübingen, Tübingen, Deutschland
| | - Jens Bedke
- Klinik für Urologie, Universitätsklinikum Tübingen, Tübingen, Deutschland
| | - Arnulf Stenzl
- Klinik für Urologie, Universitätsklinikum Tübingen, Tübingen, Deutschland
| | - Konstantin Nikolaou
- Institut für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Tübingen, Tübingen, Deutschland
| | - Stephan Kruck
- Klinik für Urologie, Siloah St. Trudpert Klinikum, Pforzheim, Deutschland
| | - Ferdinand Seith
- Institut für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Tübingen, Tübingen, Deutschland
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Feng L, Ma D, Liu F. Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends. NMR IN BIOMEDICINE 2022; 35:e4416. [PMID: 33063400 PMCID: PMC8046845 DOI: 10.1002/nbm.4416] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 08/25/2020] [Accepted: 09/09/2020] [Indexed: 05/08/2023]
Abstract
Quantitative mapping of MR tissue parameters such as the spin-lattice relaxation time (T1 ), the spin-spin relaxation time (T2 ), and the spin-lattice relaxation in the rotating frame (T1ρ ), referred to as MR relaxometry in general, has demonstrated improved assessment in a wide range of clinical applications. Compared with conventional contrast-weighted (eg T1 -, T2 -, or T1ρ -weighted) MRI, MR relaxometry provides increased sensitivity to pathologies and delivers important information that can be more specific to tissue composition and microenvironment. The rise of deep learning in the past several years has been revolutionizing many aspects of MRI research, including image reconstruction, image analysis, and disease diagnosis and prognosis. Although deep learning has also shown great potential for MR relaxometry and quantitative MRI in general, this research direction has been much less explored to date. The goal of this paper is to discuss the applications of deep learning for rapid MR relaxometry and to review emerging deep-learning-based techniques that can be applied to improve MR relaxometry in terms of imaging speed, image quality, and quantification robustness. The paper is comprised of an introduction and four more sections. Section 2 describes a summary of the imaging models of quantitative MR relaxometry. In Section 3, we review existing "classical" methods for accelerating MR relaxometry, including state-of-the-art spatiotemporal acceleration techniques, model-based reconstruction methods, and efficient parameter generation approaches. Section 4 then presents how deep learning can be used to improve MR relaxometry and how it is linked to conventional techniques. The final section concludes the review by discussing the promise and existing challenges of deep learning for rapid MR relaxometry and potential solutions to address these challenges.
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Affiliation(s)
- Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Fang Liu
- Department of Radiology, Massachusetts General Hospital, Harvard University, Boston, Massachusetts
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T2 mapping for the characterization of prostate lesions. World J Urol 2022; 40:1455-1461. [PMID: 35357510 PMCID: PMC9166840 DOI: 10.1007/s00345-022-03991-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 03/11/2022] [Indexed: 11/26/2022] Open
Abstract
Purpose Purpose of this study is to evaluate the diagnostic accuracy of quantitative T2/ADC values in differentiating between PCa and lesions showing non-specific inflammatory infiltrates and atrophy, features of chronic prostatitis, as the most common histologically proven differential diagnosis. Methods In this retrospective, single-center cohort study, we analyzed 55 patients suspected of PCa, who underwent mpMRI (3T) including quantitative T2 maps before robot-assisted mpMRI-TRUS fusion prostate biopsy. All prostate lesions were scored according to PI-RADS v2.1. Regions of interest (ROIs) were annotated in focal lesions and normal prostate tissue. Quantitative mpMRI values from T2 mapping and ADC were compared using two-tailed t tests. Receiver operating characteristic curves (ROCs) and cutoff were calculated to differentiate between PCa and chronic prostatitis. Results Focal lesions showed significantly lower ADC and T2 mapping values than normal prostate tissue (p < 0.001). PCa showed significantly lower ADC and T2 values than chronic prostatitis (p < 0.001). ROC analysis revealed areas under the receiver operating characteristic curves (AUCs) of 0.85 (95% CI 0.74–0.97) for quantitative ADC values and 0.84 (95% CI 0.73–0.96) for T2 mapping. A significant correlation between ADC and T2 values was observed (r = 0.70; p < 0.001). Conclusion T2 mapping showed high diagnostic accuracy for differentiating between PCa and chronic prostatitis, comparable to the performance of ADC values. Supplementary Information The online version contains supplementary material available at 10.1007/s00345-022-03991-8.
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Lee YS, Choi MH, Lee YJ, Han D, Kim DH. Magnetic resonance fingerprinting in prostate cancer before and after contrast enhancement. Br J Radiol 2022; 95:20210479. [PMID: 34415785 PMCID: PMC8978224 DOI: 10.1259/bjr.20210479] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVES To assess the apparent diffusion coefficient (ADC) values and the T1 and T2 values derived from nonenhanced (NE) and contrast-enhanced (CE) magnetic resonance fingerprinting (MRF) in the prostate gland and to evaluate differences in values among prostate cancer, the normal peripheral zone (PZ) and the normal transition zone (TZ). METHODS Fifty-seven patients (median age, 73 years; range, 48-86) with prostate cancer who underwent multiparametric MRI including NE and CE MRF were included in this study. T1 and T2 values were extracted from NE and CE MRF, respectively. Five quantitative values (the ADC, NE T1, NE T2, CE T1 and CE T2 values) were measured in three areas: prostate cancer, PZ and TZ. We compared the values among the three areas and evaluated the differences between NE MRF and CE MRF values. RESULTS ADC values and MRF-derived values were significantly higher in PZ than prostate cancer or TZ (p < 0.001). TZ had a significantly lower CE T1 but significantly higher values of the other variables than prostate cancer (p < 0.001). The T1 values in all three areas and the T2 values in prostate cancer and TZ were significantly lower on CE MRF than on NE MRF (p < 0.001). CONCLUSIONS Quantitative analysis of NE and CE MRI can be conducted by using the MRF technique. The ADC value and the T1 and T2 values from CE MRF and NE MRF were found to be significantly different between prostate cancer and normal prostate tissue. ADVANCES IN KNOWLEDGE The T1 and T2 values from contrast-enhanced MR fingerprinting are significantly different between prostate cancer and normal prostate tissue.
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Affiliation(s)
- Young Sub Lee
- Department of Hospital Pathology, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Moon Hyung Choi
- Department of Radiology, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Young Joon Lee
- Department of Radiology, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Dongyeob Han
- Siemens Healthineers Ltd., Seoul, Republic of Korea
| | - Dong-Hyun Kim
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea
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Baohong W, Jing Z, Zanxia Z, kun F, Liang L, eryuan G, Yong Z, Fei H, Jingliang C, Jinxia Z. T2 mapping and readout segmentation of long variable echo-train diffusion-weighted imaging for the differentiation of parotid gland tumors. Eur J Radiol 2022; 151:110265. [DOI: 10.1016/j.ejrad.2022.110265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 02/27/2022] [Accepted: 03/16/2022] [Indexed: 11/03/2022]
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Klingebiel M, Schimmöller L, Weiland E, Franiel T, Jannusch K, Kirchner J, Hilbert T, Strecker R, Arsov C, Wittsack HJ, Albers P, Antoch G, Ullrich T. Value of T 2 Mapping MRI for Prostate Cancer Detection and Classification. J Magn Reson Imaging 2022; 56:413-422. [PMID: 35038203 DOI: 10.1002/jmri.28061] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 12/21/2021] [Accepted: 12/22/2021] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Currently, multi-parametric prostate MRI (mpMRI) consists of a qualitative T2 , diffusion weighted, and dynamic contrast enhanced imaging. Quantification of T2 imaging might further standardize PCa detection and support artificial intelligence solutions. PURPOSE To evaluate the value of T2 mapping to detect prostate cancer (PCa) and to differentiate PCa aggressiveness. STUDY TYPE Retrospective single center cohort study. POPULATION Forty-four consecutive patients (mean age 67 years; median PSA 7.9 ng/mL) with mpMRI and verified PCa by subsequent targeted plus systematic MR/ultrasound (US)-fusion biopsy from February 2019 to December 2019. FIELD STRENGTH/SEQUENCE Standardized mpMRI at 3 T with an additionally acquired T2 mapping sequence. ASSESSMENT Primary endpoint was the analysis of quantitative T2 values and contrast differences/ratios (CD/CR) between PCa and benign tissue. Secondary objectives were the correlation between T2 values, ISUP grade, apparent diffusion coefficient (ADC) value, and PI-RADS, and the evaluation of thresholds for differentiating PCa and clinically significant PCa (csPCa). STATISTICAL TESTS Mann-Whitney test, Spearman's rank (rs ) correlation, receiver operating curves, Youden's index (J), and AUC were performed. Statistical significance was defined as P < 0.05. RESULTS Median quantitative T2 values were significantly lower for PCa in PZ (85 msec) and PCa in TZ (75 msec) compared to benign PZ (141 msec) or TZ (97 msec) (P < 0.001). CD/CR between PCa and benign PZ (51.2/1.77), respectively TZ (19.8/1.29), differed significantly (P < 0.001). The best T2 -mapping threshold for PCa/csPCa detection was for TZ 81/86 msec (J = 0.929/1.0), and for PZ 110 msec (J = 0.834/0.905). Quantitative T2 values of PCa did not correlate significantly with the ISUP grade (rs = 0.186; P = 0.226), ADC value (rs = 0.138; P = 0.372), or PI-RADS (rs = 0.132; P = 0.392). DATA CONCLUSION Quantitative T2 values could differentiate PCa in TZ and PZ and might support standardization of mpMRI of the prostate. Different thresholds seem to apply for PZ and TZ lesions. However, in the present study quantitative T2 values were not able to indicate PCa aggressiveness. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Maximilian Klingebiel
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Dusseldorf, Germany
| | - Lars Schimmöller
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Dusseldorf, Germany
| | - Elisabeth Weiland
- MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Tobias Franiel
- Department of Diagnostic and Interventional Radiology, University Hospital Jena, Jena, Germany
| | - Kai Jannusch
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Dusseldorf, Germany
| | - Julian Kirchner
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Dusseldorf, Germany
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Ralph Strecker
- SHS EMEA ST&BD SP PS&O, Siemens Healthcare GmbH, Eschborn, Germany
| | - Christian Arsov
- Department of Urology, University Dusseldorf, Medical Faculty, Dusseldorf, Germany
| | - Hans-Jörg Wittsack
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Dusseldorf, Germany
| | - Peter Albers
- Department of Urology, University Dusseldorf, Medical Faculty, Dusseldorf, Germany
| | - Gerald Antoch
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Dusseldorf, Germany
| | - Tim Ullrich
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Dusseldorf, Germany
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Comparison of T2 Quantification Strategies in the Abdominal-Pelvic Region for Clinical Use. Invest Radiol 2022; 57:412-421. [PMID: 34999669 DOI: 10.1097/rli.0000000000000852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVES The aim of the study was to compare different magnetic resonance imaging (MRI) acquisition strategies appropriate for T2 quantification in the abdominal-pelvic area. The different techniques targeted in the study were chosen according to 2 main considerations: performing T2 measurement in an acceptable time for clinical use and preventing/correcting respiratory motion. MATERIALS AND METHODS Acquisitions were performed at 3 T. To select sequences for in vivo measurements, a phantom experiment was conducted, for which the T2 values obtained with the different techniques of interest were compared with the criterion standard (single-echo SE sequence, multiple acquisitions with varying echo time). Repeatability and temporal reproducibility studies for the different techniques were also conducted on the phantom. Finally, an in vivo study was conducted on 12 volunteers to compare the techniques that offer acceptable acquisition time for clinical use and either address or correct respiratory motion. RESULTS For the phantom study, the DESS and T2-preparation techniques presented the lowest precision (ρ2 = 0.9504 and ρ2 = 0.9849 respectively), and showed a poor repeatability/reproducibility compared with the other techniques. The strategy relying on SE-EPI showed the best precision and accuracy (ρ2 = 0.9994 and Cb = 0.9995). GRAPPATINI exhibited a very good precision (ρ2 = 0.9984). For the technique relying on radial TSE, the precision was not as good as GRAPPATINI (ρ2 = 0.9872). The in vivo study demonstrated good respiratory motion management for all of the selected techniques. It also showed that T2 estimate ranges were different from one method to another. For GRAPPATINI and radial TSE techniques, there were significant differences between all the different types of organs of interest. CONCLUSIONS To perform T2 measurement in the abdominal-pelvic region, one should favor a technique with acceptable acquisition time for clinical use, with proper respiratory motion management, with good repeatability, reproducibility, and precision. In this study, the techniques relying respectively on SE-EPI, radial TSE, and GRAPPATINI appeared as good candidates.
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Netzer N, Weißer C, Schelb P, Wang X, Qin X, Görtz M, Schütz V, Radtke JP, Hielscher T, Schwab C, Stenzinger A, Kuder TA, Gnirs R, Hohenfellner M, Schlemmer HP, Maier-Hein KH, Bonekamp D. Fully Automatic Deep Learning in Bi-institutional Prostate Magnetic Resonance Imaging: Effects of Cohort Size and Heterogeneity. Invest Radiol 2021; 56:799-808. [PMID: 34049336 DOI: 10.1097/rli.0000000000000791] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
BACKGROUND The potential of deep learning to support radiologist prostate magnetic resonance imaging (MRI) interpretation has been demonstrated. PURPOSE The aim of this study was to evaluate the effects of increased and diversified training data (TD) on deep learning performance for detection and segmentation of clinically significant prostate cancer-suspicious lesions. MATERIALS AND METHODS In this retrospective study, biparametric (T2-weighted and diffusion-weighted) prostate MRI acquired with multiple 1.5-T and 3.0-T MRI scanners in consecutive men was used for training and testing of prostate segmentation and lesion detection networks. Ground truth was the combination of targeted and extended systematic MRI-transrectal ultrasound fusion biopsies, with significant prostate cancer defined as International Society of Urological Pathology grade group greater than or equal to 2. U-Nets were internally validated on full, reduced, and PROSTATEx-enhanced training sets and subsequently externally validated on the institutional test set and the PROSTATEx test set. U-Net segmentation was calibrated to clinically desired levels in cross-validation, and test performance was subsequently compared using sensitivities, specificities, predictive values, and Dice coefficient. RESULTS One thousand four hundred eighty-eight institutional examinations (median age, 64 years; interquartile range, 58-70 years) were temporally split into training (2014-2017, 806 examinations, supplemented by 204 PROSTATEx examinations) and test (2018-2020, 682 examinations) sets. In the test set, Prostate Imaging-Reporting and Data System (PI-RADS) cutoffs greater than or equal to 3 and greater than or equal to 4 on a per-patient basis had sensitivity of 97% (241/249) and 90% (223/249) at specificity of 19% (82/433) and 56% (242/433), respectively. The full U-Net had corresponding sensitivity of 97% (241/249) and 88% (219/249) with specificity of 20% (86/433) and 59% (254/433), not statistically different from PI-RADS (P > 0.3 for all comparisons). U-Net trained using a reduced set of 171 consecutive examinations achieved inferior performance (P < 0.001). PROSTATEx training enhancement did not improve performance. Dice coefficients were 0.90 for prostate and 0.42/0.53 for MRI lesion segmentation at PI-RADS category 3/4 equivalents. CONCLUSIONS In a large institutional test set, U-Net confirms similar performance to clinical PI-RADS assessment and benefits from more TD, with neither institutional nor PROSTATEx performance improved by adding multiscanner or bi-institutional TD.
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Affiliation(s)
| | | | | | | | | | - Magdalena Görtz
- Department of Urology, University of Heidelberg Medical Center
| | - Viktoria Schütz
- Department of Urology, University of Heidelberg Medical Center
| | | | | | | | | | | | - Regula Gnirs
- From the Division of Radiology, German Cancer Research Center
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Makowski MR, Bressem KK, Franz L, Kader A, Niehues SM, Keller S, Rueckert D, Adams LC. De Novo Radiomics Approach Using Image Augmentation and Features From T1 Mapping to Predict Gleason Scores in Prostate Cancer. Invest Radiol 2021; 56:661-668. [PMID: 34047538 DOI: 10.1097/rli.0000000000000788] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVES The aims of this study were to discriminate among prostate cancers (PCa's) with Gleason scores 6, 7, and ≥8 on biparametric magnetic resonance imaging (bpMRI) of the prostate using radiomics and to evaluate the added value of image augmentation and quantitative T1 mapping. MATERIALS AND METHODS Eighty-five patients with subsequently histologically proven PCa underwent bpMRI at 3 T (T2-weighted imaging, diffusion-weighted imaging) with 66 patients undergoing additional T1 mapping at 3 T. The PCa lesions as well as the peripheral and transition zones were segmented pixel by pixel in multiple slices of the 3D MRI data sets (T2-weighted images, apparent diffusion coefficient, and T1 maps). To increase the size of the data set, images were augmented for contrast, brightness, noise, and perspective multiple times, effectively increasing the sample size 10-fold, and 322 different radiomics features were extracted before and after augmentation. Four different machine learning algorithms, including a random forest (RF), stochastic gradient boosting (SGB), support vector machine (SVM), and k-nearest neighbor, were trained with and without features from T1 maps to differentiate among 3 different Gleason groups (6, 7, and ≥8). RESULTS Support vector machine showed the highest accuracy of 0.92 (95% confidence interval [CI], 0.62-1.00) for classifying the different Gleason scores, followed by RF (0.83; 95% CI, 0.52-0.98), SGB (0.75; 95% CI, 0.43-0.95), and k-nearest neighbor (0.50; 95% CI, 0.21-0.79). Image augmentation resulted in an average increase in accuracy between 0.08 (SGB) and 0.48 (SVM). Removing T1 mapping features led to a decline in accuracy for RF (-0.16) and SGB (-0.25) and a higher generalization error. CONCLUSIONS When data are limited, image augmentations and features from quantitative T1 mapping sequences might help to achieve higher accuracy and lower generalization error for classification among different Gleason groups in bpMRI by using radiomics.
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Affiliation(s)
- Marcus R Makowski
- From the Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Munich
| | - Keno K Bressem
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health
| | - Luise Franz
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health
| | | | - Stefan M Niehues
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health
| | - Sarah Keller
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health
| | - Daniel Rueckert
- Institute for Artificial Intelligence and Informatics in Medicine, Klinik Rechts der Isar, Technische Universität München, Munich, Germany
| | - Lisa C Adams
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health
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Breit HC, Block TK, Winkel DJ, Gehweiler JE, Glessgen CG, Seifert H, Wetterauer C, Boll DT, Heye TJ. Revisiting DCE-MRI: Classification of Prostate Tissue Using Descriptive Signal Enhancement Features Derived From DCE-MRI Acquisition With High Spatiotemporal Resolution. Invest Radiol 2021; 56:553-562. [PMID: 33660631 PMCID: PMC8373655 DOI: 10.1097/rli.0000000000000772] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
METHODS A retrospective study (from January 2016 to July 2019) including 75 subjects (mean, 65 years; 46-80 years) with 2.5-second temporal resolution DCE-MRI and PIRADS 4 or 5 lesions was performed. Fifty-four subjects had biopsy-proven prostate cancer (Gleason 6, 15; Gleason 7, 20; Gleason 8, 13; Gleason 9, 6), whereas 21 subjects had negative MRI/ultrasound fusion-guided biopsies. Voxel-wise analysis of contrast signal enhancement was performed for all time points using custom-developed software, including automatic arterial input function detection. Seven descriptive parameter maps were calculated: normalized maximum signal intensity, time to start, time to maximum, time-to-maximum slope, and maximum slope with normalization on maximum signal and the arterial input function (SMN1, SMN2). The parameters were compared with ADC using multiparametric machine-learning models to determine classification accuracy. A Wilcoxon test was used for the hypothesis test and the Spearman coefficient for correlation. RESULTS There were significant differences (P < 0.05) for all 7 DCE-derived parameters between the normal peripheral zone versus PIRADS 4 or 5 lesions and the biopsy-positive versus biopsy-negative lesions. Multiparametric analysis showed better performance when combining ADC + DCE as input (accuracy/sensitivity/specificity, 97%/93%/100%) relative to ADC alone (accuracy/sensitivity/specificity, 94%/95%/95%) and to DCE alone (accuracy/sensitivity/specificity, 78%/79%/77%) in differentiating the normal peripheral zone from PIRADS lesions, biopsy-positive versus biopsy-negative lesions (accuracy/sensitivity/specificity, 68%/33%/81%), and Gleason 6 versus ≥7 prostate cancer (accuracy/sensitivity/specificity, 69%/60%/72%). CONCLUSIONS Descriptive perfusion characteristics derived from high-resolution DCE-MRI using model-free computations show significant differences between normal and cancerous tissue but do not reach the accuracy achieved with solely ADC-based classification. Combining ADC with DCE-based input features improved classification accuracy for PIRADS lesions, discrimination of biopsy-positive versus biopsy-negative lesions, and differentiation between Gleason 6 versus Gleason ≥7 lesions.
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Affiliation(s)
- Hanns C. Breit
- Department of Radiology, University Hospital Basel, Basel, Switzerland
| | | | - David J. Winkel
- Department of Radiology, University Hospital Basel, Basel, Switzerland
| | | | - Carl G. Glessgen
- Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Helge Seifert
- Department of Radiology, University Hospital Basel, Basel, Switzerland
| | | | - Daniel T. Boll
- Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Tobias J. Heye
- Department of Radiology, University Hospital Basel, Basel, Switzerland
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Zhang J, Ge Y, Zhang H, Wang Z, Dou W, Hu S. Quantitative T2 Mapping to Discriminate Mucinous from Nonmucinous Adenocarcinoma in Rectal Cancer: Comparison with Diffusion-weighted Imaging. Magn Reson Med Sci 2021; 21:593-598. [PMID: 34421090 PMCID: PMC9618932 DOI: 10.2463/mrms.mp.2021-0067] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
Purpose: Mucinous adenocarcinoma (MA) is associated with worse clinicopathological characteristics and a poorer prognosis than non-MA. Moreover, MA is related to worse tumor regression grade and tumor downstaging than non-MA. This study investigated whether lesions in MA and non-MA can be quantitatively assessed by T2 mapping technique and compared with the diffusion-weighted imaging (DWI). Methods: High-resolution MRI, DWI, and T2 mapping were performed on 81 patients diagnosed with rectal cancer via biopsy. Afterward, T2 and apparent diffusion coefficient (ADC) values were manually measured by a senior and a junior radiologist independently. By examining surgical specimens, the patients with MA and non-MA were identified. Inter-observer reproducibility was tested, and T2 and ADC values were compared using Mann–Whitney U test. Finally, receiver operating characteristic (ROC) curves were drawn to determine the cut-off value. Results: Of the 81 patients, 11 patients with MA were confirmed by pathology. The inter-observer reproducibility of T2 and ADC values showed an excellent intraclass correlation coefficient (ICC) of 0.993 and 0.913, respectively. MA had higher T2 (87.9 ± 5.11 ms) (P = 0.000) and ADC (2.03 × 10−3 mm2/s) (P = 0.000) values than non-MA (66.6 ± 6.86 ms and 1.17 × 10−3 mm2/s, respectively). The area under the ROC curves (AUC) of the T2 and ADC values were 0.999 (95% confidence interval [CI]: 0.953–1) and 0.979 (95% CI: 0.920–0.998), respectively. When the cutoff value in T2 mapping was 80 ms, the Youden index was the largest, sensitivity was 100%, and specificity was 97%. Conclusion: As a stable quantitative sequence, T2 mapping of MRI is useful in differentiating MA from non-MA. Compared to ADC values, T2 values are also diagnostically effective and non-inferior to ADC values.
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Affiliation(s)
- Junqin Zhang
- Department of Radiology, The First People's Hospital of Yuhang District
| | - Yuxi Ge
- Department of Radiology, Affiliated Hospital of Jiangnan University
| | - Heng Zhang
- Department of Radiology, Affiliated Hospital of Jiangnan University
| | - Zi Wang
- Department of Radiology, Affiliated Hospital of Jiangnan University
| | | | - Shudong Hu
- Department of Radiology, Affiliated Hospital of Jiangnan University
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Sathiadoss P, Schieda N, Haroon M, Osman H, Alrasheed S, Flood TA, Melkus G. Utility of Quantitative T2-Mapping Compared to Conventional and Advanced Diffusion Weighted Imaging Techniques for Multiparametric Prostate MRI in Men with Hip Prosthesis. J Magn Reson Imaging 2021; 55:265-274. [PMID: 34223675 DOI: 10.1002/jmri.27803] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 06/11/2021] [Accepted: 06/11/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Diffusion weighted imaging (DWI) is fundamental for prostate cancer (PCa) detection with MRI; however, limited by susceptibility artifact from hip prosthesis. PURPOSE To evaluate image quality and ability to detect PCa with quantitative T2-mapping and DWI in men with hip prosthesis undergoing prostate MRI. STUDY TYPE Prospective, cross-sectional study. POPULATION Thirty consecutive men with hip replacement (18 unilateral, 12 bilateral) undergoing prostate MRI from 2019 to 2021. FIELD STRENGTH/SEQUENCE 3-T; multiparametric MRI (T2W, DCE-MRI, echo-planar [EPI]-DWI), T2-mapping (Carr-Purcell-Meiboom-Gill), FOCUS-EPI-DWI, PROPELLER-DWI. ASSESSMENT Five blinded radiologists independently evaluated MRI image quality using a 5-point Likert scale. PI-RADS v2.1 scores were applied in four interpretation strategies: 1) T2W-FSE+DCE-MRI+EPI-DWI, 2) T2W-FSE+DCE-MRI+EPI-DWI+FOCUS-EPI-DWI, 3) T2W-FSE+DCE-MRI+EPI-DWI+PROPELLER-DWI, 4) T2W-FSE+DCE-MRI+EPI-DWI+T2-maps. Five-point confidence scores were recorded. STATISTICAL ANALYSIS ANOVA, Kruskal-Wallis with pair-wise comparisons by Wilcoxon sign-rank, and paired t-tests, P < 0.05 was considered significant. Cohen's Kappa (k) for PI-RADSv2.1 scoring and proportion of correctly classified lesions tabulated for pathology-confirmed cases with 95% confidence intervals (CIs). RESULTS For all radiologists, T2-map image quality was significantly higher than EPI-DWI, FOCUS-EPI-DWI, and PROPELLER-DWI and similar (P = 0.146-0.706) or significantly better (for two readers) than T2W-FSE and DCE-MRI. PI-RADS v2.1 agreement improved comparing strategy A (k = 0.46) to strategy B (k = 0.58) to strategy C (k = 0.58) and was highest with strategy D which included T2-maps (k = 1.00). Radiologists' confidence was significantly highest with strategy D. Strategies B and C had similar confidence (P = 0.051-0.063) both significantly outperforming strategy A. Twelve men with 17 lesions had pathology confirmed diagnoses (13 PCa, 4 benign). Strategy D had the highest proportion of correctly classified lesions (76.5-82.4%) with overlapping 95% confidence intervals. DATA CONCLUSION T2-mapping may be a valuable adjunct to prostate MRI in men with hip replacement resulting in improved image quality, higher reader confidence, interobserver agreement, and accuracy in PI-RADS scoring. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Paul Sathiadoss
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada
| | - Nicola Schieda
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada
| | - Mohammad Haroon
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada
| | - Heba Osman
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada
| | - Sumaya Alrasheed
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada
| | - Trevor A Flood
- Department of Anatomical Pathology, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada
| | - Gerd Melkus
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada
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Wang YF, Tadimalla S, Hayden AJ, Holloway L, Haworth A. Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment. J Med Imaging Radiat Oncol 2021; 65:612-626. [PMID: 34060219 DOI: 10.1111/1754-9485.13242] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 04/18/2021] [Accepted: 05/02/2021] [Indexed: 12/15/2022]
Abstract
Magnetic resonance imaging (MRI) is increasingly used in the management of prostate cancer (PCa). Quantitative MRI (qMRI) parameters, derived from multi-parametric MRI, provide indirect measures of tumour characteristics such as cellularity, angiogenesis and hypoxia. Using Artificial Intelligence (AI), relevant information and patterns can be efficiently identified in these complex data to develop quantitative imaging biomarkers (QIBs) of tumour function and biology. Such QIBs have already demonstrated potential in the diagnosis and staging of PCa. In this review, we explore the role of these QIBs in monitoring treatment response during and after PCa radiotherapy (RT). Recurrence of PCa after RT is not uncommon, and early detection prior to development of metastases provides an opportunity for salvage treatments with curative intent. However, the current method of monitoring treatment response using prostate-specific antigen levels lacks specificity. QIBs, derived from qMRI and developed using AI techniques, can be used to monitor biological changes post-RT providing the potential for accurate and early diagnosis of recurrent disease.
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Affiliation(s)
- Yu-Feng Wang
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Sirisha Tadimalla
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
| | - Amy J Hayden
- Sydney West Radiation Oncology, Westmead Hospital, Wentworthville, New South Wales, Australia
- Faculty of Medicine, Western Sydney University, Sydney, New South Wales, Australia
- Faculty of Medicine, Health & Human Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Lois Holloway
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
- Liverpool and Macarthur Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia
| | - Annette Haworth
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
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Han D, Choi MH, Lee YJ, Kim DH. Feasibility of Novel Three-Dimensional Magnetic Resonance Fingerprinting of the Prostate Gland: Phantom and Clinical Studies. Korean J Radiol 2021; 22:1332-1340. [PMID: 34047506 PMCID: PMC8316768 DOI: 10.3348/kjr.2020.1362] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 02/08/2021] [Accepted: 03/17/2021] [Indexed: 01/24/2023] Open
Abstract
Objective To evaluate the feasibility of a new three-dimensional (3D) MR fingerprinting (MRF) technique for the prostate gland by conducting phantom and clinical studies. Materials and Methods The new 3D MRF technique used in this study enables quick data acquisition and has a high resolution. For the phantom study, the MRF T1 and T2 values in an in-house phantom were compared with those of gold-standard mapping methods using linear regression analysis. For the clinical study, we evaluated 90 patients who underwent prostate imaging with MRF for suspected prostate cancer between September 2019 and February 2020. The mean T1 and T2 values were compared in the peripheral zone, transition zone, and focal lesions using paired t tests. The differences in the T1 and T2 values according to cancer aggressiveness were evaluated using one-way analysis of variance. Results In the phantom study, the MRF T1 and T2 values showed a perfect correlation with the gold-standard T1 and T2 values (R > 0.99). In the clinical study, the T1 and T2 values in the peripheral zone were significantly higher than those in the transitional zone (p < 0.001, both). The T1 and T2 values in prostate cancer were significantly lower than those in the peripheral and transitional zones. The higher the grade of cancer, the lower the T2 values. Conclusion The T1 and T2 values obtained from the 3D MRF showed a perfect correlation with the gold standard values in the phantom study. Differences in the T1 and T2 values among the different zones of the prostate gland were identified using 3D MRF in patients.
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Affiliation(s)
| | - Moon Hyung Choi
- Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
| | - Young Joon Lee
- Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Dong Hyun Kim
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea
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Zhao L, Liang M, Shi Z, Xie L, Zhang H, Zhao X. Preoperative volumetric synthetic magnetic resonance imaging of the primary tumor for a more accurate prediction of lymph node metastasis in rectal cancer. Quant Imaging Med Surg 2021; 11:1805-1816. [PMID: 33936966 DOI: 10.21037/qims-20-659] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background An accurate assessment of lymph node (LN) status in patients with rectal cancer is important for treatment planning and an essential factor for predicting local recurrence and overall survival. In this study, we explored the potential value of histogram parameters of synthetic magnetic resonance imaging (SyMRI) in predicting LN metastasis in rectal cancer and compared their predictive performance with traditional morphological characteristics and chemical shift effect (CSE). Methods A total of 70 patients with pathologically proven rectal adenocarcinoma who received direct surgical resection were enrolled in this prospective study. Preoperative rectal MRI, including SyMRI, were performed, and morphological characteristics and CSE of LN were assessed. Histogram parameters were extracted on a T1 map, T2 map, and proton density (PD) map, including mean, variance, maximum, minimum, 10th percentile, median, 90th percentile, energy, kurtosis, entropy, and skewness. Receiver operating characteristic (ROC) curves were used to explore their predictive performance for assessing LN status. Results Significant differences in the energy of the T1, T2, and PD maps were observed between LN-negative and LN-positive groups [all P<0.001; the area under the ROC curve (AUC) was 0.838, 0.858, and 0.823, respectively]. The maximum and kurtosis of the T2 map, maximum, and variance of PD map could also predict LN metastasis with moderate diagnostic power (P=0.032, 0.045, 0.016, and 0.047, respectively). Energy of the T1 map [odds ratio (OR) =1.683, 95% confidence interval (CI): 1.207-2.346, P=0.002] and extramural venous invasion on MRI (mrEMVI) (OR =10.853, 95% CI: 2.339-50.364, P=0.002) were significant predictors of LN metastasis. Moreover, the T1 map energy significantly improved the predictive performance compared to morphological features and CSE (P=0.0002 and 0.0485). Conclusions The histogram parameters derived from SyMRI of the primary tumor were associated with LN metastasis in rectal cancer and could significantly improve the predictive performance compared with morphological features and CSE.
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Affiliation(s)
- Li Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Meng Liang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhuo Shi
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lizhi Xie
- GE Healthcare, Magnetic Resonance Research China, Beijing, China
| | - Hongmei Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Li S, Zhang Z, Liu J, Zhang F, Yang M, Lu H, Zhang Y, Han F, Cheng J, Zhu J. The feasibility of a radial turbo-spin-echo T2 mapping for preoperative prediction of the histological grade and lymphovascular space invasion of cervical squamous cell carcinoma. Eur J Radiol 2021; 139:109684. [PMID: 33836336 DOI: 10.1016/j.ejrad.2021.109684] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 03/05/2021] [Accepted: 03/25/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE The study aimed to analyze the feasibility of a radial turbo-spin-echo (TSE) T2 mapping to differentiate the histological grades and lymphovascular space invasion (LVSI) of cervical squamous cell carcinoma (CSCC) in comparison with diffusion-weighted imaging (DWI). METHODS A total of 58 patients with CSCC and 40 healthy volunteers underwent T2 mapping and DWI before therapy. The T2 and apparent diffusion coefficient (ADC) values were calculated using different tumor characteristics. The differences, efficacies and correlations between parameters were determined. RESULTS The T2 and ADC values were significantly different between CSCC and normal cervical stroma (both p < 0.05). Poorly differentiated (G3) tumor showed lower T2 and ADC values than well differentiated (G1) and moderately differentiated (G2) tumor (all p < 0.05). The T2 values were significantly lower in LVSI-positive CSCC than LVSI-negative CSCC (p < 0.05). No significant difference was found in ADC values for LVSI status (p = 0.561). The area under the ROC (AUC) for T2 and ADC values to distinguish G1/G2 and G3 tumor were 0.741 and 0.763, respectively. The AUC for T2 and ADC values to distinguish LVSI-positive and LVSI-negative CSCC were 0.877 and 0.537, respectively. The T2 and ADC values were negatively correlated with the tumor grades (r = -0.402 and r = -0.339, respectively). CONCLUSIONS Radial TSE T2 mapping is feasible for CSCC. Similar to ADC values, quantitative T2 values could serve as a noninvasive biomarker to predict histological grades preoperatively. Moreover, T2 values could determine the presence of LVSI better than ADC values.
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Affiliation(s)
- Shujian Li
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zanxia Zhang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jie Liu
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Feifei Zhang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Meng Yang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Huifang Lu
- Department of Gynecology and Obstetrics, Huaihe Hospital of Henan University, Kaifeng, China
| | - Yong Zhang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Fei Han
- MR R&D Collaboration, Siemens Healthineers, Los Angeles, CA, USA
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Jinxia Zhu
- MR Collaboration, Siemens Healthcare Ltd., Beijing, China
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Shridhar Konar A, Qian E, Geethanath S, Buonincontri G, Obuchowski NA, Fung M, Gomez P, Schulte R, Cencini M, Tosetti M, Schwartz LH, Shukla-Dave A. Quantitative imaging metrics derived from magnetic resonance fingerprinting using ISMRM/NIST MRI system phantom: An international multicenter repeatability and reproducibility study. Med Phys 2021; 48:2438-2447. [PMID: 33690905 DOI: 10.1002/mp.14833] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 02/04/2021] [Accepted: 02/23/2021] [Indexed: 11/10/2022] Open
Abstract
PURPOSE To compare the bias and inherent reliability of the quantitative (T1 and T2 ) imaging metrics generated from the magnetic resonance fingerprinting (MRF) technique using the ISMRM/NIST system phantom in an international multicenter setting. METHOD ISMRM/NIST MRI system phantom provides standard reference T1 and T2 relaxation values (vendor-provided) for each of the 14 vials in T1 and T2 arrays. MRF-SSFP scans repeated over 30 days on GE 1.5 and 3.0 T scanners at three collaborative centers. MRF estimated T1, and T2 values averaged over 30 days were compared with the phantom vendor-provided and spin-echo (SE) based convention gold standard (GS) method. Repeatability and reproducibility were characterized by the within-case coefficient of variation (wCV) of the MRF data acquired over 30 days, along with the biases. RESULT For the wide ranges of MRF estimated T1 values, vials #1-8 (T1 relaxation time between 2033 and 184 ms) exhibited a wCV less than 3% and 4%, respectively, on 3.0 and 1.5 T scanners. T2 values in vials #1-8 (T2 relaxation, 1044-45 ms) have shown wCV to be <7% on both 3.0 and 1.5 T scanners. A stronger linear correlation overall for T1 (R2 = 0.9960 and 0.9963 at center-1 and center-2 on 3.0 T scanner, and R2 = 0.9951 and 0.9988 at center-1 and center-3 on 1.5 T scanner) compared to T2 (R2 = 0.9971 and 0.9972 at center-1 and center-2 on 3.0 T scanner, and R2 = 0.9815 and 0.9754 at center-1 and center-3 on 1.5 T scanner). Bland-Altman (BA) analysis showed MRF based T1 and T2 values were within the limit of agreement (LOA) except for one data point. The mean difference or bias and 95% lower bound (LB) and upper bound (UB) LOA are reported in the format; mean bias: 95% LB LOA: 95% UB LOA. The biases for T1 values were 21.34: -50.00: 92.69, 21.32: -47.29: 89.94 ms, and for T2 values were -19.88: -42.37: 2.61, -19.06: -43.58: 5.45 ms on 3.0 T scanner at center-1 and center-2, respectively. Similarly, on 1.5 T scanner biases for T1 values were 26.54: -53.41: 106.50, 9.997: -51.94: 71.94 ms, and for T2 values were -23.84: -135.40: 87.76, -37.30: 134.30: 59.73 ms at center-1 and center-3, respectively. Additionally, the correlation between the SE based GS and MRF estimated T1 and T2 values (R2 = 0.9969 and 0.9977) showed a similar trend as we observed between vendor-provided and MRF estimated T1 and T2 values (R2 = 0.9963 and 0.9972). In addition to correlation, BA analysis showed that all the vials are within the LOA between the GS and vendor-provided for the T1 values and except one vial for T2 . All the vials are within the LOA between GS and MRF except one vial in T1 and T2 array. The wCV for reproducibility was <3% for both T1 and T2 values in vials #1-8, for all the 14 vials, wCV calculated for reproducibility was <4% for T1 values and <5% for T2 . CONCLUSION This study shows that MRF is highly repeatable (wCV <4% for T1 and <7% for T2 ) and reproducible (wCV < 3% for both T1 and T2 ) in certain vials (vials #1-8).
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Affiliation(s)
- Amaresha Shridhar Konar
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Enlin Qian
- Columbia Magnetic Resonance Research Center, Columbia University in the City of New York, New York, NY, 10027, USA
| | - Sairam Geethanath
- Columbia Magnetic Resonance Research Center, Columbia University in the City of New York, New York, NY, 10027, USA
| | - Guido Buonincontri
- Imago7 Foundation and IRCCS Stella Maris Foundation, Pisa, PI, 56128, Italy
| | - Nancy A Obuchowski
- Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, OH, 44106, USA
| | | | - Pedro Gomez
- Munich School of Bioengineering, Technical University of Munich, Munich, BY, 85748, Germany
| | | | - Matteo Cencini
- Imago7 Foundation and IRCCS Stella Maris Foundation, Pisa, PI, 56128, Italy
| | - Michela Tosetti
- Imago7 Foundation and IRCCS Stella Maris Foundation, Pisa, PI, 56128, Italy
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University Irving Medical Center and New York Presbyterian Hospital, New York, NY, 10032, USA
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.,Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
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Franiel T, Asbach P, Beyersdorff D, Blondin D, Kaufmann S, Mueller-Lisse UG, Quentin M, Rödel S, Röthke M, Schlemmer HP, Schimmöller L. mpMRI of the Prostate (MR-Prostatography): Updated Recommendations of the DRG and BDR on Patient Preparation and Scanning Protocol. ROFO-FORTSCHR RONTG 2021; 193:763-777. [PMID: 33735931 DOI: 10.1055/a-1406-8477] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
The Working Group Uroradiology and Urogenital Diagnosis of the German Roentgen Society (DRG) revised and updated the recommendations for preparation and scanning protocol of the multiparametric MRI of the Prostate in a consensus process and harmonized it with the managing board of German Roentgen Society and Professional Association of the German Radiologist (BDR e. V.). These detailed recommendation define the referenced "validated quality standards" of the German S3-Guideline Prostate Cancer and describe in detail the topic 1. anamnestic datas, 2. termination of examinations and preparation of examinations, 3. examination protocol and 4. MRI-(in-bore)-biopsy. KEY POINTS:: · The recommendations for preparation and scanning protocol of the multiparametric MRI of the Prostate were revised and updated in a consensus process and harmonized with the managing board of German Roentgen Society (DRG) and Professional Asssociation of the German Radiologist (BDR).. · Detailed recommendations are given for topic 1. anamnestic datas, 2. termination and preparation of examinations, 3. examination protocoll and 4. MRI-(in-bore)-biopsy.. · These recommendations define the referenced "validated quality standards" of the German S3-Guideline Prostate Cancer.. CITATION FORMAT: · Franiel T, Asbach P, Beyersdorff D et al. mpMRI of the Prostate (MR-Prostatography): Updated Recommendations of the DRG and BDR on Patient Preparation and Examination Protocol. Fortschr Röntgenstr 2021; 193: 763 - 776.
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Affiliation(s)
- Tobias Franiel
- Institut für diagnostische und interventionelle Radiologie, Universitätsklinikum Jena, Deutschland
| | - Patrick Asbach
- Klinik für Radiologie, Charité Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, Deutschland
| | - Dirk Beyersdorff
- Klinik und Poliklinik für Diagnostische und Interventionelle Radiologie und Nuklearmedizin, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Dirk Blondin
- Klinik für Radiologie, Gefäßradiologie und Nuklearmedizin, Städtische Kliniken Mönchengladbach GmbH Elisabeth-Krankenhaus Rheydt, Mönchengladbach, Germany.,Klinik für Radiologie, Gefäßradiologie und Nuklearmedizin, Städtische Kliniken Mönchengladbach, Germany
| | - Sascha Kaufmann
- Institut für Diagnostische und Interventionelle Radiologie, Siloah St. Trudpert Klinikum, Pforzheim, Deutschland
| | | | - Michael Quentin
- Centrum für Diagnostik und Therapie GmbH, Medizinisches Versorgungszentrum CDT Strahleninstitut GmbH, Köln, Germany
| | - Stefan Rödel
- Radiologische Klinik, Städtisches Klinikum Dresden, Germany
| | - Matthias Röthke
- Conradia Radiologie und Nuklearmedizin, Conradia Hamburg MVZ GmbH, Hamburg, Germany
| | | | - Lars Schimmöller
- Institut für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Düsseldorf, Düsseldorf, Germany
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Crombé A, Buy X, Han F, Toupin S, Kind M. Assessment of Repeatability, Reproducibility, and Performances of T2 Mapping-Based Radiomics Features: A Comparative Study. J Magn Reson Imaging 2021; 54:537-548. [PMID: 33594768 DOI: 10.1002/jmri.27558] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 01/26/2021] [Accepted: 01/26/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Magnetic resonance imaging (MRI)-based radiomics features (RFs) quantify tumors radiological phenotypes but are sensitive to postprocessing parameters, including the intensity harmonization technique (IHT), while mappings enable objective quantitative assessment. PURPOSE To investigate whether T2 mapping could improve repeatability, reproducibility, and performances of radiomics compared to conventional T2-weighted imaging (T2WI). STUDY TYPE Prospective. SUBJECTS Twenty-six healthy adults. FIELD STRENGTH/SEQUENCE Respiratory-trigged radial turbo spin echo (TSE) multiecho T2 mapping (prototype) and conventional TSE T2WI of the abdomen were acquired twice at 1.5 T. ASSESSMENT T2 maps were reconstructed using a two-parameter exponential fitting model. Volumes-of-interest (VOIs) were manually drawn in six tissues: liver, kidney, pancreas, muscle, bone, and spleen. After co-registration, conventional T2WIs were processed with two IHTs (standardization [std] and histogram-matching [HM]) resulting in four paired input image types: initial T2WI, T2WIstd , T2WIHM , and T2-map. VOIs were propagated to extract 45 RFs from MRI-1 and MRI-2 of each image type (LIFEx, v5.10). STATISTICAL TESTS Influence of the input data type on RF values was evaluated with analysis of variance. RFs test-retest repeatability and reproducibility over multiple segmentations were evaluated with intra-class correlation coefficient (ICC). Correlations between k-means clusters and the six tissues depending on the RFs dataset were investigated with adjusted-Rand-index (ARI). RESULTS About 41 of 45 (91.1%) RFs were significantly influenced by the input image type (P values < 0.05), which was the most influential factor on repeatability of RFs (P-value < 0.05). Repeatability ICCs from T2-map displayed intermediate values between the initial T2WI (range: 0.407-0.736) and the T2WIHM (range: 0.724-0.817). The number of RFs with interobserver and intraobserver reproducibility ICCs ≥ 0.90 was 37/45 (82.2%) for T2WIHM , 33/45 (73.3%) for T2WIstd , 31/45 (68.9%) for T2 map, and 25/45 (55.6%) for the initial T2WI. T2 map provided the best tissue discrimination (ARI = 0.414 vs. 0.157 with T2WIHM ). DATA CONCLUSION T2 mapping provided RFs with moderate to substantial repeatability and reproducibility ICCs, along with the most preserved discriminative information. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY: 1.
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Affiliation(s)
- Amandine Crombé
- Department of Oncologic Imaging, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, Bordeaux, France.,Bordeaux University, Bordeaux, France.,Modelisation in Oncology (MOnc) Team, INRIA Bordeaux-Sud-Ouest, CNRS UMR 5251, Talence, France
| | - Xavier Buy
- Department of Oncologic Imaging, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, Bordeaux, France
| | - Fei Han
- Siemens Medical Solutions USA, Los Angeles, California, USA
| | | | - Michèle Kind
- Department of Oncologic Imaging, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, Bordeaux, France
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50
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Tavakoli AA, Kuder TA, Tichy D, Radtke JP, Görtz M, Schütz V, Stenzinger A, Hohenfellner M, Schlemmer HP, Bonekamp D. Measured Multipoint Ultra-High b-Value Diffusion MRI in the Assessment of MRI-Detected Prostate Lesions. Invest Radiol 2021; 56:94-102. [PMID: 32930560 DOI: 10.1097/rli.0000000000000712] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
OBJECTIVES The aim of this study was to assess quantitative ultra-high b-value (UHB) diffusion magnetic resonance imaging (MRI)-derived parameters in comparison to standard clinical apparent diffusion coefficient (SD-ADC-2b-1000, SD-ADC-2b-1500) for the prediction of clinically significant prostate cancer, defined as Gleason Grade Group greater than or equal to 2. MATERIALS AND METHODS Seventy-three patients who underwent 3-T prostate MRI with diffusion-weighted imaging acquired at b = 50/500/1000/1500s/mm2 and b = 100/500/1000/1500/2250/3000/4000 s/mm2 were included. Magnetic resonance lesions were segmented manually on individual sequences, then matched to targeted transrectal ultrasonography/MRI fusion biopsies. Monoexponential 2-point and multipoint fits of standard diffusion and of UHB diffusion were calculated with incremental b-values. Furthermore, a kurtosis fit with parameters Dapp and Kapp with incremental b-values was obtained. Each parameter was examined for prediction of clinically significant prostate cancer using bootstrapped receiver operating characteristics and decision curve analysis. Parameter models were compared using Vuong test. RESULTS Fifty of 73 men (age, 66 years [interquartile range, 61-72]; prostate-specific antigen, 6.6 ng/mL [interquartile range, 5-9.7]) had 64 MRI-detected lesions. The performance of SD-ADC-2b-1000 (area under the curve, 0.82) and SD-ADC-2b-1500 (area under the curve, 0.82) was not statistically different (P = 0.99), with SD-ADC-2b-1500 selected as reference. Compared with the reference model, none of the 19 tested logistic regression parameter models including multipoint and 2-point UHB-ADC, Dapp, and Kapp with incremental b-values of up to 4000 s/mm2 outperformed SD-ADC-2b-1500 (all P's > 0.05). Decision curve analysis confirmed these results indicating no higher net benefit for UHB parameters in comparison to SD-ADC-2b-1500 in the clinically important range from 3% to 20% of cancer threshold probability. Net reduction analysis showed no reduction of MR lesions requiring biopsy. CONCLUSIONS Despite evaluation of a large b-value range and inclusion of 2-point, multipoint, and kurtosis models, none of the parameters provided better predictive performance than standard 2-point ADC measurements using b-values 50/1000 or 50/1500. Our results suggest that most of the diagnostic benefits available in diffusion MRI are already represented in an ADC composed of one low and one 1000 to 1500 s/mm2 b-value.
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Affiliation(s)
| | | | - Diana Tichy
- Division of Biostatistics, German Cancer Research Center (DKFZ)
| | | | - Magdalena Görtz
- Department of Urology, University of Heidelberg Medical Center
| | - Viktoria Schütz
- Department of Urology, University of Heidelberg Medical Center
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