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Zhang C, Ma L, Zhao Y, Zhang Z, Zhang Q, Li X, Qin J, Ren Y, Hu Z, Zhao Q, Shen W, Cheng Y. Estimating pathological prognostic factors in epithelial ovarian cancers using apparent diffusion coefficients of functional tumor volume. Eur J Radiol 2024; 176:111514. [PMID: 38776804 DOI: 10.1016/j.ejrad.2024.111514] [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: 01/23/2024] [Revised: 04/26/2024] [Accepted: 05/17/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE To assess the utility of apparent diffusion coefficients (ADCs) of whole tumor volume (WTV) and functional tumor volume (FTV) in determining the pathologicalprognostic factors in epithelial ovarian cancers (EOCs). METHODS A total of 155 consecutive patients who were diagnosed with EOC between January 2017 and August 2022 and underwent both conventional magnetic resonance imaging and diffusion-weighted imaging were assessed in this study. The maximum, minimum, and mean ADC values of the whole tumor (ADCwmax, ADCwmin, and ADCwmean, respectively) and functional tumor (ADCfmax, ADCfmin, and ADCfmean, respectively) as well as the WTV and FTV were derived from the ADC maps. The univariate and multivariate logistic regression analyses and receiver operating characteristic curve (ROC) analysis were used to assess the correlation between these ADC values and the pathological prognostic factors, namely subtypes, lymph node metastasis (LNM), Ki-67 index, and p53 expression. RESULTS The ADCfmean value was significantly lower in type II EOC, LNM-positive, and high-Ki-67 index groups compared to the type I EOC, LNM-negative, and low-Ki-67 index groups (p ≤ 0.001). Similarly, the ADCwmean and ADCfmean values were lower in the mutant-p53 group compared to the wild-type-p53 group (p ≤ 0.001). Additionally, the ADCfmean showed the highest area under the ROC curve (AUC) for evaluating type II EOC (0.725), LNM-positive (0.782), and high-Ki-67 index (0.688) samples among the given ROC curves, while both ADCwmean and ADCfmean showed high AUCs for assessing p53 expression (0.694 and 0.678, respectively). CONCLUSION The FTV-derived ADC values, especially ADCfmean, can be used to assess preoperative prognostic factors in EOCs.
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Affiliation(s)
- Cheng Zhang
- The First Central Clinical School, Tianjin Medical University, Tianjin, China.
| | - Luyang Ma
- The First Central Clinical School, Tianjin Medical University, Tianjin, China.
| | - Yujiao Zhao
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
| | - Zhijing Zhang
- School of Medicine, Nankai University, Tianjin, China.
| | - Qi Zhang
- The First Central Clinical School, Tianjin Medical University, Tianjin, China.
| | - Xiaotian Li
- School of Medicine, Nankai University, Tianjin, China.
| | - Jiaming Qin
- School of Medicine, Nankai University, Tianjin, China.
| | - Yan Ren
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
| | - Zhandong Hu
- Department of Pathology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
| | - Qian Zhao
- Department of Gynecology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
| | - Wen Shen
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
| | - Yue Cheng
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
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Li X, Chai W, Sun K, Zhu H, Yan F. Whole-tumor histogram analysis of multiparametric breast magnetic resonance imaging to differentiate pure mucinous breast carcinomas from fibroadenomas with high-signal intensity on T2WI. Magn Reson Imaging 2024; 106:8-17. [PMID: 38035946 DOI: 10.1016/j.mri.2023.11.013] [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: 08/17/2023] [Revised: 11/23/2023] [Accepted: 11/24/2023] [Indexed: 12/02/2023]
Abstract
PURPOSE To investigate the utility of whole-tumor histogram analysis based on multiparametric MRI in distinguishing pure mucinous breast carcinomas (PMBCs) from fibroadenomas (FAs) with strong high-signal intensity on T2-weighted imaging (T2-SHi). MATERIAL AND METHODS The study included 20 patients (mean age, 55.80 ± 15.54 years) with single PBMCs and 29 patients (mean age, 42.31 ± 13.91 years) with single FAs exhibiting T2-SHi. A radiologist performed whole-tumor histogram analysis between PBMC and FA groups with T2-SHi using multiparametric MRI, including T2-weighted imaging (T2WI), diffusion weighted imaging (DWI) with apparent diffusion coefficient (ADC) maps, and the first (DCE_T1) and last (DCE_T4) phases of T1-weighted dynamic contrast-enhanced imaging (DCE) images, to extract 11 whole-tumor histogram parameters. Histogram parameters were compared between the two groups to identify significant variables using univariate analyses, and their diagnostic performance was assessed by receiver operating characteristic (ROC) curve analysis and logistic regression analyses. In addition, 15 breast lesions were randomly selected and histogram analysis was repeated by another radiologist to assess the intraclass correlation coefficient for each histogram feature. Pearson's correlation coefficients were used to analyze the correlations between histogram parameters and Ki-67 expression of PMBCs. RESULTS For T2WI images, mean, median, maximum, 90th percentile, variance, uniformity, and entropy significantly differed in PBMCs and FAs with T2-SHi (all P < 0.05), yielding a combined area under the curve (AUC) of 0.927. For ADC maps, entropy was significantly lower in FAs with T2-SHi than in PMBCs (P = 0.03). In both DCE_T1 and DCE_T4 sequences, FAs with T2-SHi showed significantly higher minimum values than PBMCs (P = 0.007 and 0.02, respectively). The highest AUC value of 0.956 (sensitivity, 0.862; specificity, 0.944; positive predictive value, 0.962; negative predictive value, 0.810) was obtained when all significant histogram parameters were combined. CONCLUSIONS Whole-tumor histogram analysis using multiparametric MRI is valuable for differentiating PBMCs from FAs with T2-SHi.
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Affiliation(s)
- Xue Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China; Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
| | - Weimin Chai
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China.
| | - Kun Sun
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China.
| | - Hong Zhu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China.
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China.
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Abreu-Gomez J, Lim C, Haider MA. Contemporary Approach to Prostate Imaging and Data Reporting System Score 3 Lesions. Radiol Clin North Am 2024; 62:37-51. [PMID: 37973244 DOI: 10.1016/j.rcl.2023.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
The aim of this article is to review the technical and clinical considerations encountered with PI-RADS 3 lesions, which are equivocal for clinically significant Prostate Cancer (csPCa) with detection rates ranging between 10% and 35%. The number of PI-RADS 3 lesions reported vary according to several factors including MRI quality and radiologist training/expertise among the most influential. PI-RADS v.2.1 updated definitions for scores 2 and 3 in the PZ and scores 1 and 2 in the TZ is reviewed. The role of DWI role is highlighted in the assessment of the TZ with the possibility of upgrading score 2 lesions to score 3 based on DWI score. Given the increased utilization for prostate MRI, biparametric MRI can be considered as an alternative for low-risk patients where there is a need to rule out csPCa acknowledging this technique may increase the number of indeterminate cases going for biopsies. Management of patients with equivocal lesions at mpMRI and factors influencing biopsy decision process remain as an unmet need and additional studies using molecular/imaging markers as well as artificial intelligence tools are needed to further address their role in proper patient selection for biopsy.
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Affiliation(s)
- Jorge Abreu-Gomez
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women's College Hospital, University of Toronto, 610 University Avenue, Suite 3-920, Toronto, ON M5G 2M9, Canada.
| | - Christopher Lim
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, 2075 Bayview Avenue, Room AB 279, Toronto, ON M4N 3M5, Canada
| | - Masoom A Haider
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System and the Joint Department of Medical Imaging, Sinai Health System, Princess Margaret Hospital, University of Toronto, 600 University Avenue, Toronto, ON, Canada M5G 1X5
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Fennessy FM, Maier SE. Quantitative diffusion MRI in prostate cancer: Image quality, what we can measure and how it improves clinical assessment. Eur J Radiol 2023; 167:111066. [PMID: 37651828 PMCID: PMC10623580 DOI: 10.1016/j.ejrad.2023.111066] [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: 07/05/2023] [Revised: 08/19/2023] [Accepted: 08/24/2023] [Indexed: 09/02/2023]
Abstract
Diffusion-weighted imaging is a dependable method for detection of clinically significant prostate cancer. In prostate tissue, there are several compartments that can be distinguished from each other, based on different water diffusion decay signals observed. Alterations in cell architecture, such as a relative increase in tumor infiltration and decrease in stroma, will influence the observed diffusion signal in a voxel due to impeded random motion of water molecules. The amount of restricted diffusion can be assessed quantitatively by measuring the apparent diffusion coefficient (ADC) value. This is traditionally calculated using a monoexponential decay formula represented by the slope of a line produced between the logarithm of signal intensity decay plotted against selected b-values. However, the choice and number of b-values and their distribution, has a significant effect on the measured ADC values. There have been many models that attempt to use higher-order functions to better describe the observed diffusion signal decay, requiring an increased number and range of b-values. While ADC can probe heterogeneity on a macroscopic level, there is a need to optimize advanced diffusion techniques to better interrogate prostate tissue microstructure. This could be of benefit in clinical challenges such as identifying sparse tumors in normal prostate tissue or better defining tumor margins. This paper reviews the principles of diffusion MRI and novel higher order diffusion signal analysis techniques to improve the detection of prostate cancer.
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Affiliation(s)
- Fiona M Fennessy
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
| | - Stephan E Maier
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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Zhu M, Liang Z, Feng T, Mai Z, Jin S, Wu L, Zhou H, Chen Y, Yan W. Up-to-Date Imaging and Diagnostic Techniques for Prostate Cancer: A Literature Review. Diagnostics (Basel) 2023; 13:2283. [PMID: 37443677 DOI: 10.3390/diagnostics13132283] [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: 05/18/2023] [Revised: 06/29/2023] [Accepted: 06/30/2023] [Indexed: 07/15/2023] Open
Abstract
Prostate cancer (PCa) faces great challenges in early diagnosis, which often leads not only to unnecessary, invasive procedures, but to over-diagnosis and treatment as well, thus highlighting the need for modern PCa diagnostic techniques. The review aims to provide an up-to-date summary of chronologically existing diagnostic approaches for PCa, as well as their potential to improve clinically significant PCa (csPCa) diagnosis and to reduce the proliferation and monitoring of PCa. Our review demonstrates the primary outcomes of the most significant studies and makes comparisons across the diagnostic efficacies of different PCa tests. Since prostate biopsy, the current mainstream PCa diagnosis, is an invasive procedure with a high risk of post-biopsy complications, it is vital we dig out specific, sensitive, and accurate diagnostic approaches in PCa and conduct more studies with milestone findings and comparable sample sizes to validate and corroborate the findings.
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Affiliation(s)
- Ming Zhu
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Zhen Liang
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Tianrui Feng
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Zhipeng Mai
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Shijie Jin
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Liyi Wu
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Huashan Zhou
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Yuliang Chen
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Weigang Yan
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
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He J, Che B, Li P, Li W, Huang T, Chen P, Liu M, Li G, Zhong S, Tang K. Ki67 and the apparent diffusion coefficient in postoperative prostate cancer with endocrine therapy. Front Surg 2023; 10:1140883. [PMID: 37091270 PMCID: PMC10113680 DOI: 10.3389/fsurg.2023.1140883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 03/20/2023] [Indexed: 04/08/2023] Open
Abstract
BackgroundProstate-specific antigen (PSA) is a critical part of prostate cancer (PCa) screening, diagnosis, staging, and prognosis. However, elevated PSA levels can also be caused by several external factors. To improve the specificity and sensitivity of PSA in clinical practice, we explored whether markers or parameters may be used as prognostic targets for PCa by long-term follow-up.MethodsA total of 121 PCa patients who underwent laparoscopic radical prostatectomy (LRP) were included in our study, all of whom underwent imaging and preoperative pathological diagnosis. Endocrine therapy has long been applied to treat postoperative patients. The prognosis of enrolled patients was followed, and statistics were collected. Spearman's correlation analysis was applied to examine the relationship and clinical parameters. Kaplan–Meier analysis was used to process the clinical variables of PCa patients. Cox proportional hazards regression analysis was applied to examine univariate and multivariate variables.ResultsThe Gleason score (GS), PSA, clinical stage, nerve infiltration, organ confinement, Ki67 and apparent diffusion coefficient (ADC) were significantly associated with prognosis (all P < 0.05). The GS, PSA, clinical stage, organ confined, Ki67, nerve infiltration and ADC were included in the multivariate analysis (all P < 0.05). Ultimately, Ki67 and the ADC were found to provide meaningful predictive information (both P < 0.05).ConclusionsKi67 and the ADC may be clinically and analytically valid prognostic biomarkers and imaging parameters in PCa. They may be useful for predicting the prognosis and risk of PCa patients undergoing postoperative routine endocrine therapy.
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Affiliation(s)
- Jun He
- Department of Urology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Bangwei Che
- Department of Urology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Po Li
- Department of Pathology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Wei Li
- Department of Urology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Tao Huang
- Department of Urology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Peng Chen
- Department of Urology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Miao Liu
- Department of Urology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Guangyu Li
- Department of Urology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Siwen Zhong
- Department of Urology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Kaifa Tang
- Department of Urology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
- Department of Urology and Andrology, The First Affiliated Hospital, Guizhou University of Traditional Chinese Medicine, Guiyang, China
- Correspondence: Kaifa Tang
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Prata F, Anceschi U, Cordelli E, Faiella E, Civitella A, Tuzzolo P, Iannuzzi A, Ragusa A, Esperto F, Prata SM, Sicilia R, Muto G, Grasso RF, Scarpa RM, Soda P, Simone G, Papalia R. Radiomic Machine-Learning Analysis of Multiparametric Magnetic Resonance Imaging in the Diagnosis of Clinically Significant Prostate Cancer: New Combination of Textural and Clinical Features. Curr Oncol 2023; 30:2021-2031. [PMID: 36826118 PMCID: PMC9955797 DOI: 10.3390/curroncol30020157] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/01/2023] [Accepted: 02/02/2023] [Indexed: 02/10/2023] Open
Abstract
BACKGROUND The aim of our study was to develop a radiomic tool for the prediction of clinically significant prostate cancer. METHODS From September 2020 to December 2021, 91 patients who underwent magnetic resonance imaging prostate fusion biopsy at our institution were selected. Prostate cancer aggressiveness was assessed by combining the three orthogonal planes-Llocal binary pattern the 3Dgray level co-occurrence matrix, and other first order statistical features with clinical (semantic) features. The 487 features were used to predict whether the Gleason score was clinically significant (≥7) in the final pathology. A feature selection algorithm was used to determine the most predictive features, and at the end of the process, nine features were chosen through a 10-fold cross validation. RESULTS The feature analysis revealed a detection accuracy of 83.5%, with a clinically significant precision of 84.4% and a clinically significant sensitivity of 91.5%. The resulting area under the curve was 80.4%. CONCLUSIONS Radiomic analysis allowed us to develop a tool that was able to predict a Gleason score of ≥7. This new tool may improve the detection rate of clinically significant prostate cancer and overcome the limitations of the subjective interpretation of magnetic resonance imaging, reducing the number of useless biopsies.
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Affiliation(s)
- Francesco Prata
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
- Correspondence: ; Tel.: +39-39-3437-3027; Fax: +39-062-2541-1995
| | - Umberto Anceschi
- Department of Urology, IRCCS “Regina Elena” National Cancer Institute, 00144 Rome, Italy
| | - Ermanno Cordelli
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Eliodoro Faiella
- Department of Diagnostic and Interventional Radiology, Sant’Anna Hospital, 22042 San Fermo della Battaglia, Italy
| | - Angelo Civitella
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Piergiorgio Tuzzolo
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Andrea Iannuzzi
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Alberto Ragusa
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Francesco Esperto
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Salvatore Mario Prata
- Simple Operating Unit of Lower Urinary Tract Surgery, SS. Trinità Hospital, 03039 Sora, Italy
| | - Rosa Sicilia
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Giovanni Muto
- Department of Urology, Humanitas Gradenigo University, 10153 Turin, Italy
| | - Rosario Francesco Grasso
- Department of Diagnostic and Interventional Radiology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Roberto Mario Scarpa
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Paolo Soda
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Giuseppe Simone
- Department of Urology, IRCCS “Regina Elena” National Cancer Institute, 00144 Rome, Italy
| | - Rocco Papalia
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
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Torkian S, Ebrahimi F, Shahnazi H, Rashti R, Emami M, Maracy MR. Psychometrics of the Persian version of the COVID-19-related health literacy in the Iranian population. Front Public Health 2023; 10:1085861. [PMID: 36703837 PMCID: PMC9873230 DOI: 10.3389/fpubh.2022.1085861] [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: 10/31/2022] [Accepted: 12/13/2022] [Indexed: 01/11/2023] Open
Abstract
Background Since the occurrence of the COVID-19 pandemic, information dissemination has increased rapidly. Promoting health literacy is currently crucial to prepare people to respond quickly to situations, such as the COVID-19 pandemic. Due to the importance of health literacy in this critical situation, we are looking for a questionnaire to measure COVID-19 health literacy. The COVID-19 Germany Health Literacy Questionnaire (HLS-COVID-Q22) is an excellent tool, so the study aimed to create a cultural validity of this questionnaire for the Iranian population. Methods In this validation study, 880 samples were enrolled using a convenient sampling method. The questionnaire was translated through a backward forwarding procedure. Confirmatory factor analysis (CFA) and exploratory factor analysis (EFA) were employed for Persian version validity. McDonald's omega (Ω), Cronbach's alpha, and average inter-item correlation (AIC) coefficients were assessed for reliability. Results Using EFA on the random half sample (n = 440), the EFA indicated that the scale had four factors: accessing, understanding, appraising, and applying health-related information in the COVID-19 pandemic context, which explained 59.3% of the total variance. CFA was used for the sample's second part (n = 440) to evaluate the goodness of fit of the four-factor solution. CFA showed the model fit. All indices RMSEA = 0.067, CFI = 0.934, IFI = 0.934, PCFI = 0.772, PNFI = 0.747, and CMIN/DF = 2.972 confirmed the model fit. The convergent validity of the HLS-COVID-Q22 was confirmed. McDonald's omega and Cronbach's alpha were very good (α and Ω >0.80). Conclusion The Persian version of the HLS-COVID-Q22 had acceptable psychometric properties and is applicable to measure COVID-19 health literacy.
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Affiliation(s)
- Samaneh Torkian
- Department of Epidemiology, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
| | - Fatemeh Ebrahimi
- Department of Epidemiology and Biostatistics, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hossein Shahnazi
- Department of Health Education and Promotion, School of Health Isfahan University of Medical Sciences, Isfahan, Iran
| | - Roya Rashti
- School of Public Health, Dezful University of Medical Science, Dezful, Khuzestan, Iran
| | - Mahasti Emami
- Department of Health Education and Promotion, School of Health, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Maracy
- Department of Epidemiology and Biostatistics, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
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Sun K, Zhu H, Xia B, Li X, Chai W, Fu C, Thomas B, Liu W, Grimm R, Elisabeth W, Yan F. Image quality and whole-lesion histogram and texture analysis of diffusion-weighted imaging of breast MRI based on advanced ZOOMit and simultaneous multislice readout-segmented echo-planar imaging. Front Oncol 2022; 12:913072. [PMID: 36033543 PMCID: PMC9411810 DOI: 10.3389/fonc.2022.913072] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 07/13/2022] [Indexed: 11/24/2022] Open
Abstract
Objectives To investigate the image quality and diagnostic capability a of whole-lesion histogram and texture analysis of advanced ZOOMit (A-ZOOMit) and simultaneous multislice readout-segmented echo-planar imaging (SMS-RS-EPI) to differentiate benign from malignant breast lesions. Study design From February 2020 to October 2020, diffusion-weighted imaging (DWI) using SMS-RS-EPI and A-ZOOMit were performed on 167 patients. Three breast radiologists independently ranked the image datasets. The inter-/intracorrelation coefficients (ICCs) of mean image quality scores and lesion conspicuity scores were calculated between these three readers. Histogram and texture features were extracted from the apparent diffusion coefficient (ADC) maps, respectively, based on a WL analysis. Student’s t-tests, one-way ANOVAs, Mann–Whitney U tests, and receiver operating characteristic curves were used for statistical analysis. Results The overall image quality scores and lesion conspicuity scores for A-ZOOMit and SMS-RS-EPI showed statistically significant differences (4.92 ± 0.27 vs. 3.92 ± 0.42 and 4.93 ± 0.29 vs. 3.87 ± 0.47, p < 0.0001). The ICCs for the image quality and lesion conspicuity scores had good agreements among the three readers (all ICCs >0.75). To differentiate benign and malignant breast lesions, the entropy of ADCA-Zoomit had the highest area (0.78) under the ROC curve. Conclusions A-ZOOMit achieved higher image quality and lesion conspicuity than SMS-RS-EPI. Entropy based on A-ZOOMit is recommended for differentiating benign from malignant breast lesions.
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Affiliation(s)
- Kun Sun
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- *Correspondence: Kun Sun,
| | - Hong Zhu
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Bingqing Xia
- Department of Radiology, International Peace Maternity and Child Health Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xinyue Li
- Department of Radiology, Ruijin Hospital Luwan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Weimin Chai
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Caixia Fu
- MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | - Benkert Thomas
- MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany
| | - Wei Liu
- MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | - Robert Grimm
- MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany
| | - Weiland Elisabeth
- MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
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Keenan KE, Delfino JG, Jordanova KV, Poorman ME, Chirra P, Chaudhari AS, Baessler B, Winfield J, Viswanath SE, deSouza NM. Challenges in ensuring the generalizability of image quantitation methods for MRI. Med Phys 2022; 49:2820-2835. [PMID: 34455593 PMCID: PMC8882689 DOI: 10.1002/mp.15195] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 08/17/2021] [Accepted: 08/17/2021] [Indexed: 01/31/2023] Open
Abstract
Image quantitation methods including quantitative MRI, multiparametric MRI, and radiomics offer great promise for clinical use. However, many of these methods have limited clinical adoption, in part due to issues of generalizability, that is, the ability to translate methods and models across institutions. Researchers can assess generalizability through measurement of repeatability and reproducibility, thus quantifying different aspects of measurement variance. In this article, we review the challenges to ensuring repeatability and reproducibility of image quantitation methods as well as present strategies to minimize their variance to enable wider clinical implementation. We present possible solutions for achieving clinically acceptable performance of image quantitation methods and briefly discuss the impact of minimizing variance and achieving generalizability towards clinical implementation and adoption.
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Affiliation(s)
- Kathryn E. Keenan
- Physical Measurement Laboratory, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA
| | - Jana G. Delfino
- Center for Devices and Radiological Health, US Food and Drug Administration, 10993 New Hampshire Ave, Silver Spring, MD 20993, USA
| | - Kalina V. Jordanova
- Physical Measurement Laboratory, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA
| | - Megan E. Poorman
- Physical Measurement Laboratory, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA
| | - Prathyush Chirra
- Dept of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA
| | - Akshay S. Chaudhari
- Department of Radiology, Stanford University, 450 Serra Mall, Stanford, CA 94305, USA
- Department of Biomedical Data Science, Stanford University, 450 Serra Mall, Stanford, CA 94305, USA
| | - Bettina Baessler
- University Hospital of Zurich and University of Zurich, Raemistrasse 100, 8091 Zurich, Switzerland
| | - Jessica Winfield
- Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey, SM2 5PT, UK
| | - Satish E. Viswanath
- Dept of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA
| | - Nandita M. deSouza
- Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey, SM2 5PT, UK
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11
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Peng T, Xiao J, Li L, Pu B, Niu X, Zeng X, Wang Z, Gao C, Li C, Chen L, Yang J. Can machine learning-based analysis of multiparameter MRI and clinical parameters improve the performance of clinically significant prostate cancer diagnosis? Int J Comput Assist Radiol Surg 2021; 16:2235-2249. [PMID: 34677748 PMCID: PMC8616865 DOI: 10.1007/s11548-021-02507-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 09/22/2021] [Indexed: 12/24/2022]
Abstract
Purpose To establish machine learning(ML) models for the diagnosis of clinically significant prostate cancer (csPC) using multiparameter magnetic resonance imaging (mpMRI), texture analysis (TA), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative analysis and clinical parameters and to evaluate the stability of these models in internal and temporal validation. Methods The dataset of 194 men was split into training (n = 135) and internal validation (n = 59) cohorts, and a temporal dataset (n = 58) was used for evaluation. The lesions with Gleason score ≥ 7 were defined as csPC. Logistic regression (LR), stepwise regression (SR), classical decision tree (cDT), conditional inference tree (CIT), random forest (RF) and support vector machine (SVM) models were established by combining mpMRI-TA, DCE-MRI and clinical parameters and validated by internal and temporal validation using the receiver operating characteristic (ROC) curve and Delong’s method. Results Eight variables were determined as important predictors for csPC, with the first three related to texture features derived from the apparent diffusion coefficient (ADC) mapping. RF, LR and SR models yielded larger and more stable area under the ROC curve values (AUCs) than other models. In the temporal validation, the sensitivity was lower than that of the internal validation (p < 0.05). There were no significant differences in specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV) and AUC (p > 0.05). Conclusions Each machine learning model in this study has good classification ability for csPC. Compared with internal validation, the sensitivity of each machine learning model in temporal validation was reduced, but the specificity, accuracy, PPV, NPV and AUCs remained stable at a good level. The RF, LR and SR models have better classification performance in the imaging-based diagnosis of csPC, and ADC texture-related parameters are of the highest importance. Supplementary Information The online version contains supplementary material available at 10.1007/s11548-021-02507-w.
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Affiliation(s)
- Tao Peng
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - JianMing Xiao
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - Lin Li
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - BingJie Pu
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - XiangKe Niu
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China.
| | - XiaoHui Zeng
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - ZongYong Wang
- Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - ChaoBang Gao
- College of Information Science and Technology, Chengdu University, 1 Shiling shang Street, Chengdu, 610106, Sichuan Province, China
| | - Ci Li
- Department of Pathology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - Lin Chen
- Department of Urology Surgery, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
| | - Jin Yang
- Department of Urology Surgery, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu, 610081, Sichuan Province, China
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12
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Giannini V, Mazzetti S, Defeudis A, Stranieri G, Calandri M, Bollito E, Bosco M, Porpiglia F, Manfredi M, De Pascale A, Veltri A, Russo F, Regge D. A Fully Automatic Artificial Intelligence System Able to Detect and Characterize Prostate Cancer Using Multiparametric MRI: Multicenter and Multi-Scanner Validation. Front Oncol 2021; 11:718155. [PMID: 34660282 PMCID: PMC8517452 DOI: 10.3389/fonc.2021.718155] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 09/03/2021] [Indexed: 01/06/2023] Open
Abstract
In the last years, the widespread use of the prostate-specific antigen (PSA) blood examination to triage patients who will enter the diagnostic/therapeutic path for prostate cancer (PCa) has almost halved PCa-specific mortality. As a counterpart, millions of men with clinically insignificant cancer not destined to cause death are treated, with no beneficial impact on overall survival. Therefore, there is a compelling need to develop tools that can help in stratifying patients according to their risk, to support physicians in the selection of the most appropriate treatment option for each individual patient. The aim of this study was to develop and validate on multivendor data a fully automated computer-aided diagnosis (CAD) system to detect and characterize PCas according to their aggressiveness. We propose a CAD system based on artificial intelligence algorithms that a) registers all images coming from different MRI sequences, b) provides candidates suspicious to be tumor, and c) provides an aggressiveness score of each candidate based on the results of a support vector machine classifier fed with radiomics features. The dataset was composed of 131 patients (149 tumors) from two different institutions that were divided in a training set, a narrow validation set, and an external validation set. The algorithm reached an area under the receiver operating characteristic (ROC) curve in distinguishing between low and high aggressive tumors of 0.96 and 0.81 on the training and validation sets, respectively. Moreover, when the output of the classifier was divided into three classes of risk, i.e., indolent, indeterminate, and aggressive, our method did not classify any aggressive tumor as indolent, meaning that, according to our score, all aggressive tumors would undergo treatment or further investigations. Our CAD performance is superior to that of previous studies and overcomes some of their limitations, such as the need to perform manual segmentation of the tumor or the fact that analysis is limited to single-center datasets. The results of this study are promising and could pave the way to a prediction tool for personalized decision making in patients harboring PCa.
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Affiliation(s)
- Valentina Giannini
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy.,Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Simone Mazzetti
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy.,Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Arianna Defeudis
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy.,Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Giuseppe Stranieri
- Radiology Unit, Azienda Ospedaliera Universitaria (AOU) San Luigi Gonzaga, Orbassano, Italy
| | - Marco Calandri
- Radiology Unit, Azienda Ospedaliera Universitaria (AOU) San Luigi Gonzaga, Orbassano, Italy.,Department of Oncology, University of Turin, Turin, Italy
| | - Enrico Bollito
- Department of Pathology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Italy
| | - Martino Bosco
- Department of Pathology, San Lazzaro Hospital, Alba, Italy
| | - Francesco Porpiglia
- Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Italy
| | - Matteo Manfredi
- Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Italy
| | - Agostino De Pascale
- Radiology Unit, Azienda Ospedaliera Universitaria (AOU) San Luigi Gonzaga, Orbassano, Italy
| | - Andrea Veltri
- Radiology Unit, Azienda Ospedaliera Universitaria (AOU) San Luigi Gonzaga, Orbassano, Italy.,Department of Oncology, University of Turin, Turin, Italy
| | - Filippo Russo
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - Daniele Regge
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy.,Department of Surgical Sciences, University of Turin, Turin, Italy
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13
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Pharmacokinetic modeling of dynamic contrast-enhanced (DCE)-MRI in PI-RADS category 3 peripheral zone lesions: preliminary study evaluating DCE-MRI as an imaging biomarker for detection of clinically significant prostate cancers. Abdom Radiol (NY) 2021; 46:4370-4380. [PMID: 33818626 DOI: 10.1007/s00261-021-03035-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 02/25/2021] [Accepted: 03/03/2021] [Indexed: 01/21/2023]
Abstract
PURPOSE To determine if pharmacokinetic modeling of DCE-MRI can diagnose CS-PCa in PI-RADS category 3 PZ lesions with subjective negative DCE-MRI. MATERIALS AND METHODS In the present IRB approved, bi-institutional, retrospective, case-control study, we identified 73 men with 73 PZ PI-RADS version 2.1 category 3 lesions with MRI-directed-TRUS-guided targeted biopsy yielding: 12 PZ CS-PCa (ISUP Grade Group 2; N = 9, ISUP 3; N = 3), 27 ISUP 1 PCa and 34 benign lesions. An expert blinded radiologist segmented lesions on ADC and DCE images; segmentations were overlayed onto pharmacokinetic DCE-MRI maps. Mean values were compared between groups using univariate analysis. Diagnostic accuracy was assessed by ROC. RESULTS There were no differences in age, PSA, PSAD or clinical stage between groups (p = 0.265-0.645). Mean and 10th percentile ADC did not differ comparing CS-PCa to ISUP 1 PCa and benign lesions (p = 0.376 and 0.598) but was lower comparing ISUP ≥ 1 PCa to benign lesions (p < 0.001). Mean Ktrans (p = 0.003), Ve (p = 0.003) but not Kep (p = 0.387) were higher in CS-PCa compared to ISUP 1 PCa and benign lesions. There were no differences in DCE-MRI metrics comparing ISUP ≥ 1 PCa and benign lesions (p > 0.05). AUC for diagnosis of CS-PCa using Ktrans and Ve were: 0.69 (95% CI 0.52-0.87) and 0.69 (0.49-0.88). CONCLUSION Pharmacokinetic modeling of DCE-MRI parameters in PI-RADS category 3 lesions with subjectively negative DCE-MRI show significant differences comparing CS-PCa to ISUP 1 PCa and benign lesions, in this study outperforming ADC. Studies are required to further evaluate these parameters to determine which patients should undergo targeted biopsy for PI-RADS 3 lesions.
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14
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Spohn SK, Bettermann AS, Bamberg F, Benndorf M, Mix M, Nicolay NH, Fechter T, Hölscher T, Grosu R, Chiti A, Grosu AL, Zamboglou C. Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies. Theranostics 2021; 11:8027-8042. [PMID: 34335978 PMCID: PMC8315055 DOI: 10.7150/thno.61207] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/17/2021] [Indexed: 12/14/2022] Open
Abstract
Prostate cancer (PCa) is one of the most frequently diagnosed malignancies of men in the world. Due to a variety of treatment options in different risk groups, proper diagnostic and risk stratification is pivotal in treatment of PCa. The development of precise medical imaging procedures simultaneously to improvements in big data analysis has led to the establishment of radiomics - a computer-based method of extracting and analyzing image features quantitatively. This approach bears the potential to assess and improve PCa detection, tissue characterization and clinical outcome prediction. This article gives an overview on the current aspects of methodology and systematically reviews available literature on radiomics in PCa patients, showing its potential for personalized therapy approaches. The qualitative synthesis includes all imaging modalities and focuses on validated studies, putting forward future directions.
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Affiliation(s)
- Simon K.B. Spohn
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Germany
| | - Alisa S. Bettermann
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Fabian Bamberg
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Matthias Benndorf
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Michael Mix
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Nils H. Nicolay
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
| | - Tobias Fechter
- Department of Radiation Oncology - Division of Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Tobias Hölscher
- Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Radu Grosu
- Institute of Computer Engineering, Vienne University of Technology, Vienna, Austria
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele - Milan, Italy
- IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano - Milan, Italy
| | - Anca L. Grosu
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
| | - Constantinos Zamboglou
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Germany
- German Oncology Center, European University of Cyprus, Limassol, Cyprus
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15
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Zhu G, Luo J, Ouyang Z, Cheng Z, Deng Y, Guan Y, Du G, Zhao F. The Assessment of Prostate Cancer Aggressiveness Using a Combination of Quantitative Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced Magnetic Resonance Imaging. Cancer Manag Res 2021; 13:5287-5295. [PMID: 34239327 PMCID: PMC8260110 DOI: 10.2147/cmar.s319306] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 06/18/2021] [Indexed: 12/25/2022] Open
Abstract
Objective To explore the value of combining dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative parameters with apparent diffusion coefficient (ADC) values in the diagnosis of prostate cancer. Methods The clinical data of 146 patients with prostate lesions, including 87 patients with prostate cancer (PCa) and 59 with benign prostatic hyperplasia (BPH), were collected. After DCE-MRI and diffusion-weighted imaging (DWI) prostate scans, the magnitude of the DCE-MRI transfer constant (Ktrans), rate constant (kep), the volume of the extravascular extracellular space (ve), and the ADC between the groups were compared, and the correlations between the DCE-MRI parameters and Gleason scores were analyzed. The diagnostic efficacy of these quantitative parameters was assessed by the area under the receiver operating characteristic (ROC) curve. Results The DCE-MRI parameters Ktrans and kep were significantly greater in the PCa group than in the BPH group (p < 0.05). The ROC curve showed the area under the Ktrans, kep, and ADC curves to be 0.665, 0.658, and 0.782, respectively. When all three quantitative indicators were combined, the area under the ROC curve was 0.904, with sensitivity and specificity rates of 83.6% and 93.7%, respectively. The Gleason scores were positively correlated with the Ktrans, kep, and ve (r = 0.39, 0.572, 0.30, respectively; p < 0.05) and negatively correlated with the ADC (r = –0.525; p < 0.05). Conclusion The DCE-MRI quantitative parameters Ktrans and kep, as well as the ADC value, provided effective references for the differential diagnosis of PCa and BPH, as well as more precise and reliable quantitative parameters for grading the aggressiveness of PCa.
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Affiliation(s)
- Guangbin Zhu
- Guangzhou Key Laboratory of Enhanced Recovery after Abdominal Surgery, Department of Radiology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510799, People's Republic of China
| | - Jinwen Luo
- Guangzhou Key Laboratory of Enhanced Recovery after Abdominal Surgery, Department of Radiology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510799, People's Republic of China
| | - Zhongmin Ouyang
- Guangzhou Key Laboratory of Enhanced Recovery after Abdominal Surgery, Department of Radiology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510799, People's Republic of China
| | - Zenglan Cheng
- Guangzhou Key Laboratory of Enhanced Recovery after Abdominal Surgery, Department of Radiology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510799, People's Republic of China
| | - Yi Deng
- Guangzhou Key Laboratory of Enhanced Recovery after Abdominal Surgery, Department of Radiology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510799, People's Republic of China
| | - Yubao Guan
- Guangzhou Key Laboratory of Enhanced Recovery after Abdominal Surgery, Department of Radiology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510799, People's Republic of China
| | - Guoxin Du
- Guangzhou Key Laboratory of Enhanced Recovery after Abdominal Surgery, Department of Radiology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510799, People's Republic of China
| | - Fengjin Zhao
- Department of Urology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510799, People's Republic of China
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16
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Prevalence of prostate cancer in PI-RADS version 2.1 T2-weighted transition zone 'nodule in nodule' and 'homogeneous mildly hypointense area between nodules' criteria: MRI-radical prostatectomy histopathological evaluation. Eur Radiol 2021; 31:7792-7801. [PMID: 33782767 DOI: 10.1007/s00330-021-07855-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 02/17/2021] [Accepted: 03/09/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVES To evaluate the prevalence of prostate cancer (PCa) of two PI-RADS version (v) 2.1 transition zone (TZ) features (PI-RADS 1 ['nodule in nodule'] and 2 ['homogeneous mildly hypointense area between nodules']). METHODS With an institutional review board approval, from a 5-year cohort between 2012 and 2017, we retrospectively identified 53 consecutive men with radical prostatectomy (RP) confirmed TZ tumors and MRI. Three blinded radiologists (R1/2/3) independently evaluated T2-weighted and diffusion-weighted imaging (DWI) using PI-RADS v2.1 for the presence of (1) 'nodule in nodule' (recording 'cystic change', inner nodule encapsulation, size, and DWI score) and (2) 'homogeneous mildly hypointense area between nodules' (also recording size and DWI score). MRI-RP maps established ground truth. Primary tumor was evaluated assessing PI-RADS v2.1 category, size, and presence of imaging variants. RESULTS R1/2/3 identified 26/18/22 'nodule in nodule' respectively with 7.7% (2/26; 95% confidence interval [95% CI]: 0.1-17.9%), 5.6% (1/18; 95% CI: 0.01-16.1%), and 4.5% (1/22; 95% CI: 0.01-13.3%) PCa (both Gleason score 3 + 4 = 7). Agreement was fair-to-substantial, kappa = 0.222-0.696. 'Cystic change', inner nodule absent/incomplete encapsulation and DWI score ≥ 4 for R1/R2/R2 were present in 80.8% (21/26), 46.2% (12/26), 7.7% (2/26); 94.4% (17/18), 33.3% (6/18), 5.6% (1/18); and 59.1% (13/22), 63.6% (14/22), 9.1% (2/22). Both PCa had inner nodule absent/incomplete encapsulation and DWI score ≥ 4. No other TZ tumors demonstrated 'nodule in nodule', nodule 'cystic change', or 'homogeneous mildly hypointense area between nodules'. R1/2/3 identified 5/6/13 'homogeneous mildly hypointense area between nodules' with zero PCa for any reader (upper bound 95% CI: 24.7-52.2%). Interobserver agreement was fair-to-substantial, kappa = 0.104-0.779. CONCLUSION The proportion of cancers in PI-RADS v2.1 'nodule in nodule' was low (~5-8%) with zero cancers detected in 'homogeneous mildly hypointense area between nodules'. When 'nodule in nodule' inner nodule shows absent or incomplete encapsulation with marked restricted diffusion, PCa may be considered; however, this warrants further studies. KEY POINTS • The prevalence of clinically significant prostate cancers in PI-RADS v2.1 'nodule in nodule' was low (5-8%, 95% CI: 0.1-17.9%). • Clinically significant prostate cancer was only detected in the 'nodule in nodule' variant when the inner nodule showed absent or incomplete encapsulation ('atypical nodule') with marked restricted diffusion. • 'Homogeneous mildly hypointense area between nodules' is likely benign with no cancers identified in the current study, however, with a wide 95% CI due to low prevalence.
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17
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Abouelkheir RT, Abdelhamid A, Abou El-Ghar M, El-Diasty T. Imaging of Bladder Cancer: Standard Applications and Future Trends. MEDICINA (KAUNAS, LITHUANIA) 2021; 57:medicina57030220. [PMID: 33804350 PMCID: PMC8000909 DOI: 10.3390/medicina57030220] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 02/20/2021] [Accepted: 02/23/2021] [Indexed: 12/24/2022]
Abstract
The evolution in imaging has had an increasing role in the diagnosis, staging and follow up of bladder cancer. Conventional cystoscopy is crucial in the diagnosis of bladder cancer. However, a cystoscopic procedure cannot always depict carcinoma in situ (CIS) or differentiate benign from malignant tumors prior to biopsy. This review will discuss the standard application, novel imaging modalities and their additive role in patients with bladder cancer. Staging can be performed with CT, but distinguishing between T1 and T2 BCa (bladder cancer) cannot be assessed. MRI can distinguish muscle-invasive from non-muscle-invasive tumors with accurate local staging. Vesical Imaging-Reporting and Data System (VI-RADS) score is a new diagnostic modality used for the prediction of tumor aggressiveness and therapeutic response. Bone scintigraphy is recommended in patients with muscle-invasive BCa with suspected bony metastases. CT shows low sensitivity for nodal staging; however, PET (Positron Emission Tomography)/CT is superior and highly recommended for restaging and determining therapeutic effect. PET/MRI is a new imaging technique in bladder cancer imaging and its role is promising. Texture analysis has shown significant steps in discriminating low-grade from high-grade bladder cancer. Radiomics could be a reliable method for quantitative assessment of the muscle invasion of bladder cancer.
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18
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Xie J, Li B, Min X, Zhang P, Fan C, Li Q, Wang L. Prediction of Pathological Upgrading at Radical Prostatectomy in Prostate Cancer Eligible for Active Surveillance: A Texture Features and Machine Learning-Based Analysis of Apparent Diffusion Coefficient Maps. Front Oncol 2021; 10:604266. [PMID: 33614487 PMCID: PMC7890009 DOI: 10.3389/fonc.2020.604266] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 12/18/2020] [Indexed: 12/09/2022] Open
Abstract
Objective To evaluate a combination of texture features and machine learning-based analysis of apparent diffusion coefficient (ADC) maps for the prediction of Grade Group (GG) upgrading in Gleason score (GS) ≤6 prostate cancer (PCa) (GG1) and GS 3 + 4 PCa (GG2). Materials and methods Fifty-nine patients who were biopsy-proven to have GG1 or GG2 and underwent MRI examination with the same MRI scanner prior to transrectal ultrasound (TRUS)-guided systemic biopsy were included. All these patients received radical prostatectomy to confirm the final GG. Patients were divided into training cohort and test cohort. 94 texture features were extracted from ADC maps for each patient. The independent sample t-test or Mann−Whitney U test was used to identify the texture features with statistically significant differences between GG upgrading group and GG non-upgrading group. Texture features of GG1 and GG2 were compared based on the final pathology of radical prostatectomy. We used the least absolute shrinkage and selection operator (LASSO) algorithm to filter features. Four supervised machine learning methods were employed. The prediction performance of each model was evaluated by area under the receiver operating characteristic curve (AUC). The statistical comparison between AUCs was performed. Results Six texture features were selected for the machine learning models building. These texture features were significantly different between GG upgrading group and GG non-upgrading group (P < 0.05). The six features had no significant difference between GG1 and GG2 based on the final pathology of radical prostatectomy. All machine learning methods had satisfactory predictive efficacy. The diagnostic performance of nearest neighbor algorithm (NNA) and support vector machine (SVM) was better than random forests (RF) in the training cohort. The AUC, sensitivity, and specificity of NNA were 0.872 (95% CI: 0.750−0.994), 0.967, and 0.778, respectively. The AUC, sensitivity, and specificity of SVM were 0.861 (95%CI: 0.732−0.991), 1.000, and 0.722, respectively. There had no significant difference between AUCs in the test cohort. Conclusion A combination of texture features and machine learning-based analysis of ADC maps could predict PCa GG upgrading from biopsy to radical prostatectomy non-invasively with satisfactory predictive efficacy.
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Affiliation(s)
- Jinke Xie
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Basen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiangde Min
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Peipei Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chanyuan Fan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qiubai Li
- Department of Radiology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX, United States
| | - Liang Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Gu LH, Gu GX, Wan P, Li FH, Xia Q. The utility of two-dimensional shear wave elastography and texture analysis for monitoring liver fibrosis in rat model. Hepatobiliary Pancreat Dis Int 2021; 20:46-52. [PMID: 32536521 DOI: 10.1016/j.hbpd.2020.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 05/28/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND Liver fibrosis is a common pathological change caused by a variety of etiologies. Early diagnosis and timely treatment can reverse or delay disease progression and improve the prognosis. This study aimed to assess the potential utility of two-dimensional shear wave elastography and texture analysis in dynamic monitoring of the progression of liver fibrosis in rat model. METHODS Twenty rats were divided into control group (n = 4) and experimental groups (n = 4 per group) with carbon tetrachloride administration for 2, 3, 4, and 6 weeks. The liver stiffness measurement was performed by two-dimensional shear wave elastography, while the optimal texture analysis subsets to distinguish fibrosis stage were generated by MaZda. The results of elastography and texture analysis were validated through comparing with histopathology. RESULTS Liver stiffness measurement was 6.09 ± 0.31 kPa in the control group and 7.10 ± 0.41 kPa, 7.80 ± 0.93 kPa, 8.64 ± 0.93 kPa, 9.91 ± 1.13 kPa in the carbon tetrachloride induced groups for 2, 3, 4, 6 weeks, respectively (P < 0.05). By texture analysis, histogram and co-occurrence matrix had the most frequency texture parameters in staging liver fibrosis. Receiver operating characteristic curve of liver elasticity showed that the sensitivity and specificity were 95.0% and 92.5% to discriminate liver fibrosis and non-fibrosis, respectively. In texture analysis, five optimal parameters were selected to classify liver fibrosis and non-fibrosis. CONCLUSIONS Two-dimensional shear wave elastography showed potential applications for noninvasive monitoring of the progression of hepatic fibrosis, even in mild fibrosis. Texture analysis can further extract and quantify the texture features in ultrasonic image, which was a supplementary to further visual information and acquired high diagnostic accuracy for severe fibrosis.
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Affiliation(s)
- Li-Hong Gu
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China; Department of Ultrasound, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China
| | - Guang-Xiang Gu
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China
| | - Ping Wan
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China
| | - Feng-Hua Li
- Department of Ultrasound, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China
| | - Qiang Xia
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China.
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Baek TW, Kim SH, Park SJ, Park EJ. Texture analysis on bi-parametric MRI for evaluation of aggressiveness in patients with prostate cancer. Abdom Radiol (NY) 2020; 45:4214-4222. [PMID: 32740864 DOI: 10.1007/s00261-020-02683-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 06/12/2020] [Accepted: 07/22/2020] [Indexed: 12/31/2022]
Abstract
PURPOSE To evaluate the association between texture parameters based on bi-parametric MRI and Gleason score (GS) in patients with prostate cancer (PCa) and to evaluate diagnostic performance of any significant parameter for discriminating clinically significant cancer (CSC, GS ≥ 7) from non-CSC. METHODS A total of 116 patients who had been confirmed as prostate adenocarcinoma by radical prostatectomy or biopsy were divided into a training (n = 65) and a validation dataset (n = 51). All of the patients underwent preoperative 3T-MRI. Texture analysis was performed on axial T2WI and ADC maps (generated from b values, 0 and 1000 s/mm2) using dedicated software to cover the whole tumor volume. The correlation coefficient was calculated to evaluate the association between texture parameters and GS, and subsequent multiple regression analyses were applied for the significant parameters. To extract an optimal cut-off value for prediction of CSC, ROC curve analysis was performed. RESULTS In the training dataset, gray-level co-occurrence matrix (GLCM) entropy on ADC map was the only significant indicator for GS (coefficient of determination R2, 0.4227, P = 0.0034). The AUC of GLCM entropy on ADC map was 0.825 (95% CI 0.711-0.907) with a maximum accuracy of 82%, a sensitivity of 86%, a specificity of 71%. When a cut-off value of 2.92 was applied to the validation dataset, it showed an accuracy of 92%, a sensitivity of 98%, and a specificity of 70%. CONCLUSION GLCM entropy on ADC map was associated with GS in patients with PCa and its estimated accuracy for discriminating CSC from non-CSC was 82%.
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Affiliation(s)
- Tae Wook Baek
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Korea
| | - Seung Ho Kim
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Korea.
| | - Sang Joon Park
- Department of Radiology, Seoul National University Hospital, Daehak-ro 101, Jongno-gu, Seoul, 03080, Korea
| | - Eun Joo Park
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Korea
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De Santi B, Salvi M, Giannini V, Meiburger KM, Marzola F, Russo F, Bosco M, Molinari F. Comparison of Histogram-based Textural Features between Cancerous and Normal Prostatic Tissue in Multiparametric Magnetic Resonance Images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:1671-1674. [PMID: 33018317 DOI: 10.1109/embc44109.2020.9176307] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
In the last decade, multiparametric magnetic resonance imaging (mpMRI) has been expanding its role in prostate cancer detection and characterization. In this work, 19 patients with clinically significant peripheral zone (PZ) tumours were studied. Tumour masks annotated on the whole-mount histology sections were mapped on T2-weighted (T2w) and diffusion-weighted (DW) sequences. Gray-level histograms of tumoral and normal tissue were compared using six first-order texture features. Multivariate analysis of variance (MANOVA) was used to compare group means. Mean intensity signal of ADC showed the highest showed the highest area under the receiver operator characteristics curve (AUC) equal to 0.85. MANOVA analysis revealed that ADC features allows a better separation between normal and cancerous tissue with respect to T2w features (ADC: P = 0.0003, AUC = 0.86; T2w: P = 0.03, AUC = 0.74). MANOVA proved that the combination of T2-weighted and apparent diffusion coefficient (ADC) map features increased the AUC to 0.88. Histogram-based features extracted from invivo mpMRI can help discriminating significant PZ PCa.
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Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications. Cancers (Basel) 2020; 12:cancers12061606. [PMID: 32560558 PMCID: PMC7352160 DOI: 10.3390/cancers12061606] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 06/13/2020] [Accepted: 06/14/2020] [Indexed: 11/16/2022] Open
Abstract
Significant prostate carcinoma (sPCa) classification based on MRI using radiomics or deep learning approaches has gained much interest, due to the potential application in assisting in clinical decision-making. OBJECTIVE To systematically review the literature (i) to determine which algorithms are most frequently used for sPCa classification, (ii) to investigate whether there exists a relation between the performance and the method or the MRI sequences used, (iii) to assess what study design factors affect the performance on sPCa classification, and (iv) to research whether performance had been evaluated in a clinical setting Methods: The databases Embase and Ovid MEDLINE were searched for studies describing machine learning or deep learning classification methods discriminating between significant and nonsignificant PCa on multiparametric MRI that performed a valid validation procedure. Quality was assessed by the modified radiomics quality score. We computed the median area under the receiver operating curve (AUC) from overall methods and the interquartile range. RESULTS From 2846 potentially relevant publications, 27 were included. The most frequent algorithms used in the literature for PCa classification are logistic regression (22%) and convolutional neural networks (CNNs) (22%). The median AUC was 0.79 (interquartile range: 0.77-0.87). No significant effect of number of included patients, image sequences, or reference standard on the reported performance was found. Three studies described an external validation and none of the papers described a validation in a prospective clinical trial. CONCLUSIONS To unlock the promising potential of machine and deep learning approaches, validation studies and clinical prospective studies should be performed with an established protocol to assess the added value in decision-making.
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Stanzione A, Gambardella M, Cuocolo R, Ponsiglione A, Romeo V, Imbriaco M. Prostate MRI radiomics: A systematic review and radiomic quality score assessment. Eur J Radiol 2020; 129:109095. [PMID: 32531722 DOI: 10.1016/j.ejrad.2020.109095] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/20/2020] [Accepted: 05/25/2020] [Indexed: 12/24/2022]
Abstract
BACKGROUND Radiomics have the potential to further increase the value of MRI in prostate cancer management. However, implementation in clinical practice is still far and concerns have been raised regarding the methodological quality of radiomic studies. Therefore, we aimed to systematically review the literature to assess the quality of prostate MRI radiomic studies using the radiomics quality score (RQS). METHODS Multiple medical literature archives (PubMed, Web of Science and EMBASE) were searched to retrieve original investigations focused on prostate MRI radiomic approaches up to the end of June 2019. Three researchers independently assessed each paper using the RQS. Data from the most experienced researcher were used for descriptive analysis. Inter-rater reproducibility was assessed using the intraclass correlation coefficient (ICC) on the total RQS score. RESULTS 73 studies were included in the analysis. Overall, the average RQS total score was 7.93 ± 5.13 on a maximum of 36 points, with a final average percentage of 23 ± 13%. Among the most critical items, the lack of feature robustness testing strategies and external validation datasets. The ICC resulted poor to moderate, with an average value of 0.57 and 95% Confidence Intervals between 0.44 and 0.69. CONCLUSIONS Current studies on prostate MRI radiomics still lack the quality required to allow their introduction in clinical practice.
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Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy
| | - Michele Gambardella
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy
| | - Renato Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy.
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy
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Schieda N, Lim CS, Zabihollahy F, Abreu-Gomez J, Krishna S, Woo S, Melkus G, Ukwatta E, Turkbey B. Quantitative Prostate MRI. J Magn Reson Imaging 2020; 53:1632-1645. [PMID: 32410356 DOI: 10.1002/jmri.27191] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 04/24/2020] [Accepted: 04/24/2020] [Indexed: 12/17/2022] Open
Abstract
Prostate MRI is reported in clinical practice using the Prostate Imaging and Data Reporting System (PI-RADS). PI-RADS aims to standardize, as much as possible, the acquisition, interpretation, reporting, and ultimately the performance of prostate MRI. PI-RADS relies upon mainly subjective analysis of MR imaging findings, with very few incorporated quantitative features. The shortcomings of PI-RADS are mainly: low-to-moderate interobserver agreement and modest accuracy for detection of clinically significant tumors in the transition zone. The use of a more quantitative analysis of prostate MR imaging findings is therefore of interest. Quantitative MR imaging features including: tumor size and volume, tumor length of capsular contact, tumor apparent diffusion coefficient (ADC) metrics, tumor T1 and T2 relaxation times, tumor shape, and texture analyses have all shown value for improving characterization of observations detected on prostate MRI and for differentiating between tumors by their pathological grade and stage. Quantitative analysis may therefore improve diagnostic accuracy for detection of cancer and could be a noninvasive means to predict patient prognosis and guide management. Since quantitative analysis of prostate MRI is less dependent on an individual users' assessment, it could also improve interobserver agreement. Semi- and fully automated analysis of quantitative (radiomic) MRI features using artificial neural networks represent the next step in quantitative prostate MRI and are now being actively studied. Validation, through high-quality multicenter studies assessing diagnostic accuracy for clinically significant prostate cancer detection, in the domain of quantitative prostate MRI is needed. This article reviews advances in quantitative prostate MRI, highlighting the strengths and limitations of existing and emerging techniques, as well as discussing opportunities and challenges for evaluation of prostate MRI in clinical practice when using quantitative assessment. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Nicola Schieda
- Department of Medical Imaging, The Ottawa Hospital, Ottawa, Ontario, Canada
| | - Christopher S Lim
- Department of Medical Imaging, Sunnybrook Health Sciences, Toronto, Ontario, Canada
| | | | - Jorge Abreu-Gomez
- Department of Medical Imaging, Sunnybrook Health Sciences, Toronto, Ontario, Canada
| | - Satheesh Krishna
- Joint Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada
| | - Sungmin Woo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Gerd Melkus
- Department of Medical Imaging, The Ottawa Hospital, Ottawa, Ontario, Canada
| | - Eran Ukwatta
- Faculty of Engineering, Guelph University, Guelph, Ontario, Canada
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute NIH, Bethesda, Maryland, USA
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Li M, Chen T, Zhao W, Wei C, Li X, Duan S, Ji L, Lu Z, Shen J. Radiomics prediction model for the improved diagnosis of clinically significant prostate cancer on biparametric MRI. Quant Imaging Med Surg 2020; 10:368-379. [PMID: 32190563 DOI: 10.21037/qims.2019.12.06] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Background To evaluate the potential of clinical-based model, a biparametric MRI-based radiomics model and a clinical-radiomics combined model for predicting clinically significant prostate cancer (PCa). Methods In total, 381 patients with clinically suspicious PCa were included in this retrospective study; of those, 199 patients did not have PCa upon biopsy, while 182 patients had PCa. All patients underwent 3.0-T MRI examinations with the same acquisition parameters, and clinical risk factors associated with PCa (age, prostate volume, serum PSA, etc.) were collected. We randomly stratified the training and test sets using a 6:4 ratio. The radiomic features included gradient-based histogram features, grey-level co-occurrence matrix (GLCM), run-length matrix (RLM), and grey-level size zone matrix (GLSZM). Three models were developed using multivariate logistic regression analysis to predict clinically significant PCa: a clinical model, a radiomics model and a clinical-radiomics combined model. The diagnostic performance and clinical net benefit of each model were compared via receiver operating characteristic (ROC) curve analysis and decision curves, respectively. Results Both the radiomics model (AUC: 0.98) and the clinical-radiomics combined model (AUC: 0.98) achieved greater predictive efficacy than the clinical model (AUC: 0.79). The decision curve analysis also showed that the radiomics model and combined model had higher net benefits than the clinical model. Conclusions Compared with the evaluation of clinical risk factors associated with PCa only, the radiomics-based machine learning model can improve the predictive accuracy for clinically significant PCa, in terms of both diagnostic performance and clinical net benefit.
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Affiliation(s)
- Mengjuan Li
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China
| | - Tong Chen
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China
| | - Wenlu Zhao
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China
| | - Chaogang Wei
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China
| | - Xiaobo Li
- GE Healthcare Life Science, Shanghai 200000, China
| | | | - Libiao Ji
- Department of Radiology, The Affiliated Changshu Hospital of Soochow University, Suzhou 215501, China
| | - Zhihua Lu
- Department of Radiology, The Affiliated Changshu Hospital of Soochow University, Suzhou 215501, China
| | - Junkang Shen
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China.,Institute of Radiation Oncology Therapeutics of Soochow University, Suzhou 215000, China
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Lo Gullo R, Daimiel I, Morris EA, Pinker K. Combining molecular and imaging metrics in cancer: radiogenomics. Insights Imaging 2020; 11:1. [PMID: 31901171 PMCID: PMC6942081 DOI: 10.1186/s13244-019-0795-6] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 09/25/2019] [Indexed: 02/07/2023] Open
Abstract
Background Radiogenomics is the extension of radiomics through the combination of genetic and radiomic data. Because genetic testing remains expensive, invasive, and time-consuming, and thus unavailable for all patients, radiogenomics may play an important role in providing accurate imaging surrogates which are correlated with genetic expression, thereby serving as a substitute for genetic testing. Main body In this article, we define the meaning of radiogenomics and the difference between radiomics and radiogenomics. We provide an up-to-date review of the radiomics and radiogenomics literature in oncology, focusing on breast, brain, gynecological, liver, kidney, prostate and lung malignancies. We also discuss the current challenges to radiogenomics analysis. Conclusion Radiomics and radiogenomics are promising to increase precision in diagnosis, assessment of prognosis, and prediction of treatment response, providing valuable information for patient care throughout the course of the disease, given that this information is easily obtainable with imaging. Larger prospective studies and standardization will be needed to define relevant imaging biomarkers before they can be implemented into the clinical workflow.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, New York, NY, 10065, USA.
| | - Isaac Daimiel
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, New York, NY, 10065, USA
| | - Elizabeth A Morris
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, New York, NY, 10065, USA
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, New York, NY, 10065, USA.,Department of Biomedical Imaging and Image-guided Therapy, Molecular and Gender Imaging Service, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Wien, Austria
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Lu Z, Wang L, Xia K, Jiang H, Weng X, Jiang J, Wu M. Prediction of Clinical Pathologic Prognostic Factors for Rectal Adenocarcinoma: Volumetric Texture Analysis Based on Apparent Diffusion Coefficient Maps. J Med Syst 2019; 43:331. [DOI: 10.1007/s10916-019-1464-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 09/26/2019] [Indexed: 12/14/2022]
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Liu B, Cheng J, Guo D, He X, Luo Y, Zeng Y, Li C. Prediction of prostate cancer aggressiveness with a combination of radiomics and machine learning-based analysis of dynamic contrast-enhanced MRI. Clin Radiol 2019; 74:896.e1-896.e8. [PMID: 31495546 DOI: 10.1016/j.crad.2019.07.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2019] [Accepted: 07/16/2019] [Indexed: 12/13/2022]
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Giambelluca D, Cannella R, Vernuccio F, Comelli A, Pavone A, Salvaggio L, Galia M, Midiri M, Lagalla R, Salvaggio G. PI-RADS 3 Lesions: Role of Prostate MRI Texture Analysis in the Identification of Prostate Cancer. Curr Probl Diagn Radiol 2019; 50:175-185. [PMID: 31761413 DOI: 10.1067/j.cpradiol.2019.10.009] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 10/25/2019] [Accepted: 10/28/2019] [Indexed: 12/16/2022]
Abstract
PURPOSE To determine the diagnostic performance of texture analysis of prostate MRI for the diagnosis of prostate cancer among Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions. MATERIALS AND METHODS Forty-three patients with at least 1 PI-RADS 3 lesion on prostate MRI performed between June 2016 and January 2019 were retrospectively included. Reference standard was pathological analysis of radical prostatectomy specimens or MRI-targeted biopsies. Texture analysis extraction of target lesions was performed on axial T2-weighted images and apparent diffusion coefficient (ADC) maps using a radiomic software. Lesions were categorized as prostate cancer (Gleason score [GS] ≥ 6), and no prostate cancer. Statistical analysis was performed using the generalized linear model (GLM) regression and the discriminant analysis (DA). AUROC with 95% confidence intervals were calculated to assess the diagnostic performance of standalone features and predictive models for the diagnosis of prostate cancer (GS ≥ 6) and clinically-significant prostate cancer (GS ≥ 7). RESULTS The analysis of 46 PI-RADS 3 lesions (ie, 27 [58.7%] no prostate cancers; 19 [41.3%] prostate cancers) revealed 9 and 6 independent texture parameters significantly correlated with the final histopathological results on T2-weighted and ADC maps images, respectively. The resulting GLM and DA predictive models for the diagnosis of prostate cancer yielded an AUROC of 0.775 and 0.779 on T2-weighted images or 0.815 and 0.821 on ADC maps images. For the diagnosis of clinically-significant prostate cancer, the resulting GLM and DA predictive models for the diagnosis of prostate cancer yielded an AUROC of 0.769 and 0.817 on T2-weighted images or 0.749 and 0.744 on ADC maps images. CONCLUSION Texture analysis of PI-RADS 3 lesions on T2-weighted and ADC maps images helps identifying prostate cancer. The good diagnostic performance of the combination of multiple radiomic features for the diagnosis of prostate cancer may help predicting lesions where aggressive management may be warranted.
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Affiliation(s)
- Dario Giambelluca
- Dipartimento di Biomedicina, Neuroscienze e Diagnostica avanzata (BIND), University of Palermo, Palermo, Italy
| | - Roberto Cannella
- Dipartimento di Biomedicina, Neuroscienze e Diagnostica avanzata (BIND), University of Palermo, Palermo, Italy
| | - Federica Vernuccio
- Dipartimento di Biomedicina, Neuroscienze e Diagnostica avanzata (BIND), University of Palermo, Palermo, Italy; Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (ProMISE), University of Palermo, Palermo, Italy; University Paris 7 Diderot, Sorbonne Paris Cité, Paris, France; I.R.C.C.S. Centro Neurolesi Bonino Pulejo, Messina, Italy.
| | - Albert Comelli
- Ri.MED Foundation, Palermo, Italy; Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta GA, US; Department of Industrial and Digital Innovation (DIID), University of Palermo, Italy
| | - Alice Pavone
- Dipartimento di Biomedicina, Neuroscienze e Diagnostica avanzata (BIND), University of Palermo, Palermo, Italy
| | - Leonardo Salvaggio
- Dipartimento di Biomedicina, Neuroscienze e Diagnostica avanzata (BIND), University of Palermo, Palermo, Italy
| | - Massimo Galia
- Dipartimento di Biomedicina, Neuroscienze e Diagnostica avanzata (BIND), University of Palermo, Palermo, Italy
| | - Massimo Midiri
- Dipartimento di Biomedicina, Neuroscienze e Diagnostica avanzata (BIND), University of Palermo, Palermo, Italy
| | - Roberto Lagalla
- Dipartimento di Biomedicina, Neuroscienze e Diagnostica avanzata (BIND), University of Palermo, Palermo, Italy
| | - Giuseppe Salvaggio
- Dipartimento di Biomedicina, Neuroscienze e Diagnostica avanzata (BIND), University of Palermo, Palermo, Italy
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Sun Y, Reynolds HM, Wraith D, Williams S, Finnegan ME, Mitchell C, Murphy D, Haworth A. Automatic stratification of prostate tumour aggressiveness using multiparametric MRI: a horizontal comparison of texture features. Acta Oncol 2019; 58:1118-1126. [PMID: 30994052 DOI: 10.1080/0284186x.2019.1598576] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Background: Previous studies have identified apparent diffusion coefficient (ADC) from diffusion-weighted imaging (DWI) can stratify prostate cancer into high- and low-grade disease (HG and LG, respectively). In this study, we consider the improvement of incorporating texture features (TFs) from T2-weighted (T2w) multiparametric magnetic resonance imaging (mpMRI) relative to mpMRI alone to predict HG and LG disease. Material and methods: In vivo mpMRI was acquired from 30 patients prior to radical prostatectomy. Sequences included T2w imaging, DWI and dynamic contrast enhanced (DCE) MRI. In vivo mpMRI data were co-registered with 'ground truth' histology. Tumours were delineated on the histology with Gleason scores (GSs) and classed as HG if GS ≥ 4 + 3, or LG if GS ≤ 3 + 4. Texture features based on three statistical families, namely the grey-level co-occurrence matrix (GLCM), grey-level run length matrix (GLRLM) and the grey-level size zone matrix (GLSZM), were computed from T2w images. Logistic regression models were trained using different feature subsets to classify each lesion as either HG or LG. To avoid overfitting, fivefold cross validation was applied on feature selection, model training and performance evaluation. Performance of all models generated was evaluated using the area under the curve (AUC) method. Results: Consistent with previous studies, ADC was found to discriminate between HG and LG with an AUC of 0.76. Of the three statistical TF families, GLCM (plus select mpMRI features including ADC) scored the highest AUC (0.84) with GLRLM plus mpMRI similarly performing well (AUC = 0.82). When all TFs were considered in combination, an AUC of 0.91 (95% confidence interval 0.87-0.95) was achieved. Conclusions: Incorporating T2w TFs significantly improved model performance for classifying prostate tumour aggressiveness. This result, however, requires further validation in a larger patient cohort.
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Affiliation(s)
- Yu Sun
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Australia
- School of Physics, The University of Sydney, Sydney, Australia
| | - Hayley M. Reynolds
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Australia
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Darren Wraith
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
| | - Scott Williams
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Australia
- Division of Radiation Oncology and Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Mary E. Finnegan
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
- Department of Bioengineering, Imperial College London, London, UK
| | - Catherine Mitchell
- Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Declan Murphy
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Annette Haworth
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Australia
- School of Physics, The University of Sydney, Sydney, Australia
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AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics. Eur J Nucl Med Mol Imaging 2019; 46:2673-2699. [PMID: 31292700 DOI: 10.1007/s00259-019-04414-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 06/21/2019] [Indexed: 12/13/2022]
Abstract
INTRODUCTION The quantitative imaging features (radiomics) that can be obtained from the different modalities of current-generation hybrid imaging can give complementary information with regard to the tumour environment, as they measure different morphologic and functional imaging properties. These multi-parametric image descriptors can be combined with artificial intelligence applications into predictive models. It is now the time for hybrid PET/CT and PET/MRI to take the advantage offered by radiomics to assess the added clinical benefit of using multi-parametric models for the personalized diagnosis and prognosis of different disease phenotypes. OBJECTIVE The aim of the paper is to provide an overview of current challenges and available solutions to translate radiomics into hybrid PET-CT and PET-MRI imaging for a smart and truly multi-parametric decision model.
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Toivonen J, Montoya Perez I, Movahedi P, Merisaari H, Pesola M, Taimen P, Boström PJ, Pohjankukka J, Kiviniemi A, Pahikkala T, Aronen HJ, Jambor I. Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization. PLoS One 2019; 14:e0217702. [PMID: 31283771 PMCID: PMC6613688 DOI: 10.1371/journal.pone.0217702] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 05/16/2019] [Indexed: 12/19/2022] Open
Abstract
Purpose To develop and validate a classifier system for prediction of prostate cancer (PCa) Gleason score (GS) using radiomics and texture features of T2-weighted imaging (T2w), diffusion weighted imaging (DWI) acquired using high b values, and T2-mapping (T2). Methods T2w, DWI (12 b values, 0–2000 s/mm2), and T2 data sets of 62 patients with histologically confirmed PCa were acquired at 3T using surface array coils. The DWI data sets were post-processed using monoexponential and kurtosis models, while T2w was standardized to a common scale. Local statistics and 8 different radiomics/texture descriptors were utilized at different configurations to extract a total of 7105 unique per-tumor features. Regularized logistic regression with implicit feature selection and leave pair out cross validation was used to discriminate tumors with 3+3 vs >3+3 GS. Results In total, 100 PCa lesions were analysed, of those 20 and 80 had GS of 3+3 and >3+3, respectively. The best model performance was obtained by selecting the top 1% features of T2w, ADCm and K with ROC AUC of 0.88 (95% CI of 0.82–0.95). Features from T2 mapping provided little added value. The most useful texture features were based on the gray-level co-occurrence matrix, Gabor transform, and Zernike moments. Conclusion Texture feature analysis of DWI, post-processed using monoexponential and kurtosis models, and T2w demonstrated good classification performance for GS of PCa. In multisequence setting, the optimal radiomics based texture extraction methods and parameters differed between different image types.
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Affiliation(s)
- Jussi Toivonen
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
- Dept. of Future Technologies, University of Turku, Turku, Finland
- * E-mail:
| | - Ileana Montoya Perez
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
- Dept. of Future Technologies, University of Turku, Turku, Finland
| | - Parisa Movahedi
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
- Dept. of Future Technologies, University of Turku, Turku, Finland
| | - Harri Merisaari
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
- Dept. of Future Technologies, University of Turku, Turku, Finland
- Turku PET Centre, University of Turku, Turku, Finland
| | - Marko Pesola
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
| | - Pekka Taimen
- Institute of Biomedicine, University of Turku and Dept. of Pathology, Turku University Hospital, Turku, Finland
| | | | | | - Aida Kiviniemi
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
- Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Tapio Pahikkala
- Dept. of Future Technologies, University of Turku, Turku, Finland
| | - Hannu J. Aronen
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
- Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Ivan Jambor
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
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Thomas R, Qin L, Alessandrino F, Sahu SP, Guerra PJ, Krajewski KM, Shinagare A. A review of the principles of texture analysis and its role in imaging of genitourinary neoplasms. Abdom Radiol (NY) 2019; 44:2501-2510. [PMID: 30448920 DOI: 10.1007/s00261-018-1832-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Advances in the management of genitourinary neoplasms have resulted in a trend towards providing patients with personalized care. Texture analysis of medical images, is one of the tools that is being explored to provide information such as detection and characterization of tumors, determining their aggressiveness including grade and metastatic potential and for prediction of survival rates and risk of recurrence. In this article we review the basic principles of texture analysis and then detail its current role in imaging of individual neoplasms of the genitourinary system.
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Use of Quantitative T2-Weighted and Apparent Diffusion Coefficient Texture Features of Bladder Cancer and Extravesical Fat for Local Tumor Staging After Transurethral Resection. AJR Am J Roentgenol 2019; 212:1060-1069. [PMID: 30860885 DOI: 10.2214/ajr.18.20718] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
OBJECTIVE. The purpose of this study was to determine whether quantitative T2-weighted imaging and apparent diffusion coefficient (ADC) texture features of bladder cancer and extravesical fat are predictive of muscle invasive bladder cancer (category ≥ T2) and extravesical (category ≥ T3) disease after transurethral resection of a bladder tumor (TURBT). MATERIALS AND METHODS. In this retrospective study, 36 patients (27 men, nine women; mean age, 71 years) were identified who underwent post-TURBT MRI followed by cystectomy without intervening treatment from August 2011 through October 2016. Texture features of bladder cancer and extravesical fat adjacent to the tumor on T2-weighted and ADC images were extracted and compared between category ≤ T2 versus ≥ T3 and category T1 versus ≥ T2 tumors by means of Kruskal-Wallis or Mann-Whitney U test. Multivariate logistic regression analysis was performed, and ROC curves were calculated. RESULTS. Twenty-six of the 36 (72%) tumors were ≥ T2, and 53% (19/36) were ≥ T3. In multivariate analysis, bladder cancer entropy on T2-weighted images (p = 0.006; odds ratio [OR], 4.56; 95% CI, 1.49-20.41; AUC, 0.85) and ADC maps (p = 0.019; OR, 2.24; 95% CI, 1.13-5.31; AUC, 0.80) and extravesical fat entropy on T2-weighted images (p = 0.005; OR, 17.50; 95% CI, 3.01-200.80; AUC, 0.84) and ADC maps (p = 0.002; OR, 6.54; 95% CI, 1.90-32.40; AUC, 0.82) remained greater for ≥ T3 than for ≤ T2 tumors. In multivariate analysis, bladder cancer entropy on ADC maps (p = 0.027; OR, 2.11; 95% CI, 1.08-5.03; AUC, 0.76) and extravesical fat entropy on T2-weighted images (p = 0.010; OR, 5.33; 95% CI, 1.25-3.79; AUC, 0.78) and ADC maps (p = 0.029; OR, 3.80; 95% CI, 1.25-16.97; AUC, 0.74) remained greater for category ≥ T2 compared with category T1 tumors. CONCLUSION. Greater entropy of primary bladder cancers and extravesicular fat was observed in category ≥ T3 than in category ≤ T2 and in category ≥ T2 than in category T1 tumors. MRI texture analysis can help with local bladder cancer staging in patients who have undergone TURBT and may serve as a biomarker for higher local category bladder cancers.
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Differentiating Transition Zone Cancers From Benign Prostatic Hyperplasia by Histogram Analysis of Apparent Diffusion Coefficient Maps With Standard and Ultrahigh b-value Diffusion-weighted MR Imaging. J Comput Assist Tomogr 2019; 43:235-241. [PMID: 30475249 DOI: 10.1097/rct.0000000000000829] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
OBJECTIVE To compare the diagnostic performance of standard and ultrahigh b-value Diffusion-weighted Imaging (DWI) using volumetric histogram analysis in differentiating transition zone (TZ) cancer from benign prostatic hyperplasia (BPH). METHODS 57 TZ cancer and 61 BPH patients received standard (1000 s/mm) and ultrahigh b-value (2000 s/mm) DWI. The diagnostic ability of ADC histogram parameters derived from two DWI for differentiating TZ cancer from BPH was determined by receiver operating characteristic curve. RESULTS Median, minimum, the 10th, 25th percentile ADC in both ADC1000 and ADC2000 and skewness in ADC2000 had significant differences between TZ cancer and BPH (for all, P < 0.05).The 10th percentile ADC showed highest area under the ROC curve (AUC) in both ADC1000 and ADC2000.The 10th percentile ADC of ADC2000 showed significantly higher AUC than did ADC1000 (P = 0.0385). CONCLUSIONS The 10th percentile ADC obtained from ultrahigh b-value DWI performed better for differentiating TZ cancer from BPH.
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Sun Y, Reynolds HM, Parameswaran B, Wraith D, Finnegan ME, Williams S, Haworth A. Multiparametric MRI and radiomics in prostate cancer: a review. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2019; 42:3-25. [PMID: 30762223 DOI: 10.1007/s13246-019-00730-z] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 01/22/2019] [Indexed: 12/30/2022]
Abstract
Multiparametric MRI (mpMRI) is an imaging modality that combines anatomical MR imaging with one or more functional MRI sequences. It has become a versatile tool for detecting and characterising prostate cancer (PCa). The traditional role of mpMRI was confined to PCa staging, but due to the advanced imaging techniques, its role has expanded to various stages in clinical practises including tumour detection, disease monitor during active surveillance and sequential imaging for patient follow-up. Meanwhile, with the growing speed of data generation and the increasing volume of imaging data, it is highly demanded to apply computerised methods to process mpMRI data and extract useful information. Hence quantitative analysis for imaging data using radiomics has become an emerging paradigm. The application of radiomics approaches in prostate cancer has not only enabled automatic localisation of the disease but also provided a non-invasive solution to assess tumour biology (e.g. aggressiveness and the presence of hypoxia). This article reviews mpMRI and its expanding role in PCa detection, staging and patient management. Following that, an overview of prostate radiomics will be provided, with a special focus on its current applications as well as its future directions.
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Affiliation(s)
- Yu Sun
- University of Sydney, Sydney, Australia. .,Peter MacCallum Cancer Centre, Melbourne, Australia.
| | | | | | - Darren Wraith
- Queensland University of Technology, Brisbane, Australia
| | - Mary E Finnegan
- Imperial College Healthcare NHS Trust, London, UK.,Imperial College London, London, UK
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Wu M, Krishna S, Thornhill RE, Flood TA, McInnes MD, Schieda N. Transition zone prostate cancer: Logistic regression and machine-learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis. J Magn Reson Imaging 2019; 50:940-950. [DOI: 10.1002/jmri.26674] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Revised: 01/11/2019] [Accepted: 01/11/2019] [Indexed: 12/13/2022] Open
Affiliation(s)
- Mark Wu
- Department of Medical Imaging; Ottawa Hospital, University of Ottawa; Ontario Canada
| | - Satheesh Krishna
- Joint Department of Medical Imaging; University Health Network, Mount Sinai Hospital, Women's College Hospital, University of Toronto; Ontario Canada
| | - Rebecca E. Thornhill
- Department of Medical Imaging; Ottawa Hospital, University of Ottawa; Ontario Canada
| | - Trevor A. Flood
- Department of Anatomical Pathology; Ottawa Hospital, University of Ottawa; Ontario Canada
| | - Matthew D.F. McInnes
- Department of Medical Imaging; Ottawa Hospital, University of Ottawa; Ontario Canada
| | - Nicola Schieda
- Department of Medical Imaging; Ottawa Hospital, University of Ottawa; Ontario Canada
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The value of MR textural analysis in prostate cancer. Clin Radiol 2018; 74:876-885. [PMID: 30573283 DOI: 10.1016/j.crad.2018.11.007] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 11/16/2018] [Indexed: 01/18/2023]
Abstract
Current diagnosis and treatment stratification of patients with suspected prostate cancer relies on a combination of histological and magnetic resonance imaging (MRI) findings. The aim of this article is to provide a brief overview of prostate pathological grading as well as the relevant aspects of multiparametric (MRI) mpMRI, before indicating the potential that magnetic resonance textural analysis (MRTA) offers within prostate cancer. A review of the evidence base on MRTA in prostate cancer will enable discussion of the utility of this field while also indicating recommendations to future research. Radiomic textural analysis allows the assessment of spatial inter-relationships between pixels within an image by use of mathematical methods. First-order textural analysis is better understood and may have more clinical validity than higher-order textural features. Textural features extracted from apparent diffusion coefficient maps have shown the most potential for clinical utility in MRTA of prostate cancers. Future studies should aim to integrate machine learning techniques to better represent the role of MRTA in prostate cancer clinical practice. Nomenclature should be used to reduce misidentification between first-order and second-order energy and entropy. Automated methods of segmentation should be encouraged in order to reduce problems associated with inclusion of normal tissue within regions of interest. The retrospective and small-scale nature of most published studies, make it difficult to draw meaningful conclusions. Future larger prospective studies are required to validate the textural features indicated to have potential in characterisation and/or diagnosis of prostate cancer before translation into routine clinical practice.
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Can Adrenal Adenomas Be Differentiated From Adrenal Metastases at Single-Phase Contrast-Enhanced CT? AJR Am J Roentgenol 2018; 211:1044-1050. [DOI: 10.2214/ajr.17.19276] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Mannil M, von Spiczak J, Hermanns T, Poyet C, Alkadhi H, Fankhauser CD. Three-Dimensional Texture Analysis with Machine Learning Provides Incremental Predictive Information for Successful Shock Wave Lithotripsy in Patients with Kidney Stones. J Urol 2018; 200:829-836. [DOI: 10.1016/j.juro.2018.04.059] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/08/2018] [Indexed: 10/17/2022]
Affiliation(s)
- Manoj Mannil
- Institute of Diagnostic and Interventional Radiology and Department of Urology (TH, CP, CDF), University Hospital Zurich, University of Zurich, Switzerland
| | - Jochen von Spiczak
- Institute of Diagnostic and Interventional Radiology and Department of Urology (TH, CP, CDF), University Hospital Zurich, University of Zurich, Switzerland
| | - Thomas Hermanns
- Institute of Diagnostic and Interventional Radiology and Department of Urology (TH, CP, CDF), University Hospital Zurich, University of Zurich, Switzerland
| | - Cédric Poyet
- Institute of Diagnostic and Interventional Radiology and Department of Urology (TH, CP, CDF), University Hospital Zurich, University of Zurich, Switzerland
| | - Hatem Alkadhi
- Institute of Diagnostic and Interventional Radiology and Department of Urology (TH, CP, CDF), University Hospital Zurich, University of Zurich, Switzerland
| | - Christian Daniel Fankhauser
- Institute of Diagnostic and Interventional Radiology and Department of Urology (TH, CP, CDF), University Hospital Zurich, University of Zurich, Switzerland
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Histogram analysis of apparent diffusion coefficient maps in the prognosis of patients with locally advanced head and neck squamous cell carcinoma: Comparison of different region of interest selection methods. Eur J Radiol 2018; 106:7-13. [DOI: 10.1016/j.ejrad.2018.07.004] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Revised: 05/24/2018] [Accepted: 07/03/2018] [Indexed: 11/23/2022]
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ADC Metrics From Multiparametric MRI: Histologic Downgrading of Gleason Score 9 or 10 Prostate Cancers Diagnosed at Nontargeted Transrectal Ultrasound–Guided Biopsy. AJR Am J Roentgenol 2018; 211:W158-W165. [DOI: 10.2214/ajr.17.18958] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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Krishna S, Schieda N, McInnes MDF, Flood TA, Thornhill RE. Diagnosis of transition zone prostate cancer using T2-weighted (T2W) MRI: comparison of subjective features and quantitative shape analysis. Eur Radiol 2018; 29:1133-1143. [DOI: 10.1007/s00330-018-5664-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2018] [Revised: 07/03/2018] [Accepted: 07/13/2018] [Indexed: 12/19/2022]
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John S, Cooper S, Breau RH, Flood TA, Cagiannos I, Lavallee LT, Morash C, O'sullivan J, Schieda N. Multiparametric magnetic resonance imaging - Transrectal ultrasound-guided cognitive fusion biopsy of the prostate: Clinically significant cancer detection rates stratified by the Prostate Imaging and Data Reporting System version 2 assessment category. Can Urol Assoc J 2018; 12:401-406. [PMID: 29940139 DOI: 10.5489/cuaj.5254] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
INTRODUCTION We aimed to report the clinically significant prostate cancer (PCa) detection rate in men undergoing magnetic resonance imaging-transrectal ultrasound (MRI-TRUS)-cognitive fusion (CF) targeted biopsies stratified by the Prostate Imaging and Data Reporting System (PI-RADS) version 2 (v2) scores. METHODS With a quality assurance waiver from the IRB, we identified a cohort of men who underwent MRI-TRUS-CF and synchronous template biopsy from 2015-2017. MRI (PI-RADS v2 score, lesion size, lesion location [peripheral or transition zone (PZ/TZ)]), and CF-TRUS biopsy (operator experience, TRUS visibility, and number of biopsies) features were extracted. The primary outcome was diagnosis of clinically significant (Gleason score ≥3+4=7 or International Society of Urological Pathology (ISUP) grade group ≥2) PCa. RESULTS During the study period, 131 men (with 142 PIRADS v2 score ≥3 lesions) met inclusion criteria; 98 men had previously negative template biopsy and 33 were on active surveillance for previously detected low-grade PCa. In total, 41.9% (55/131) men had clinically significant PCa - 17.6% (23/131) detected on targeted biopsy only, 8.4% (11/131) on template biopsy only, and 16.0% (21/131) on both targeted and template biopsy. Clinically significant PCa detection stratified by PI-RADS v2 scores were: 11.1% (3/27) for score 3 (indeterminate), 42.9% (24/56) for score 4 (significant cancer likely), and 35.6% (21/59) for score 5 (significant cancer very likely). Clinically significant PCa detection rates in targeted biopsies were better among PZ (41.8% [33/79]) compared to TZ (23.8% [15/63]) lesions (p=0.025) in TRUS visible lesions (p=0.033) and in the most experienced radiologists (p=0.05), with no difference by lesion size or number of additional core biopsies performed (all p>0.05). CONCLUSIONS Cognitive fusion MRI-TRUS-guided targeted biopsy yielded substantially lower rates of clinically significant cancer in PI-RADS v2 score 4 and 5 lesions when compared to published results using in-bore MR-guided or automated MRI-TRUS fusion guidance systems. Cancer detection was worst for TZ lesions.
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Affiliation(s)
- Susan John
- The University of Ottawa, The Ottawa Hospital, Ottawa, ON, Canada
| | - Steven Cooper
- The University of Ottawa, The Ottawa Hospital, Ottawa, ON, Canada
| | - Rodney H Breau
- The University of Ottawa, The Ottawa Hospital, Ottawa, ON, Canada
| | - Trevor A Flood
- The University of Ottawa, The Ottawa Hospital, Ottawa, ON, Canada
| | - Ilias Cagiannos
- The University of Ottawa, The Ottawa Hospital, Ottawa, ON, Canada
| | - Luke T Lavallee
- The University of Ottawa, The Ottawa Hospital, Ottawa, ON, Canada
| | | | | | - Nicola Schieda
- The University of Ottawa, The Ottawa Hospital, Ottawa, ON, Canada
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Prediction of successful shock wave lithotripsy with CT: a phantom study using texture analysis. Abdom Radiol (NY) 2018; 43:1432-1438. [PMID: 28840294 DOI: 10.1007/s00261-017-1309-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
OBJECTIVE To apply texture analysis (TA) in computed tomography (CT) of urinary stones and to correlate TA findings with the number of required shockwaves for successful shock wave lithotripsy (SWL). MATERIALS AND METHODS CT was performed on thirty-four urinary stones in an in vitro setting. Urinary stones underwent SWL and the number of required shockwaves for disintegration was recorded. TA was performed after post-processing for pixel spacing and image normalization. Feature selection and dimension reduction were performed according to inter- and intrareader reproducibility and by evaluating the predictive ability of the number of shock waves with the degree of redundancy between TA features. Three regression models were tested: (1) linear regression with elimination of colinear attributes (2), sequential minimal optimization regression (SMOreg) employing machine learning, and (3) simple linear regression model of a single TA feature with lowest squared error. RESULTS Highest correlations with the absolute number of required SWL shockwaves were found for the linear regression model (r = 0.55, p = 0.005) using two weighted TA features: Histogram 10th Percentile, and Gray-Level Co-Occurrence Matrix (GLCM) S(3, 3) SumAverg. Using the median number of required shockwaves (n = 72) as a threshold, receiver-operating characteristic analysis showed largest area-under-the-curve values for the SMOreg model (AUC = 0.84, r = 0.51, p < 0.001) using four weighted TA features: Histogram 10th Percentile, and GLCM S(1, 1) InvDfMom, S(3, 3) SumAverg, and S(4, -4) SumVarnc. CONCLUSION Our in vitro study illustrates the proof-of-principle of TA of urinary stone CT images for predicting the success of stone disintegration with SWL.
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Ren J, Yuan Y, Wu Y, Tao X. Differentiation of orbital lymphoma and idiopathic orbital inflammatory pseudotumor: combined diagnostic value of conventional MRI and histogram analysis of ADC maps. BMC Med Imaging 2018; 18:6. [PMID: 29716527 PMCID: PMC5930683 DOI: 10.1186/s12880-018-0246-8] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Accepted: 01/21/2018] [Indexed: 12/25/2022] Open
Abstract
Background The overlap of morphological feature and mean ADC value restricted clinical application of MRI in the differential diagnosis of orbital lymphoma and idiopathic orbital inflammatory pseudotumor (IOIP). In this paper, we aimed to retrospectively evaluate the combined diagnostic value of conventional magnetic resonance imaging (MRI) and whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps in the differentiation of the two lesions. Methods In total, 18 patients with orbital lymphoma and 22 patients with IOIP were included, who underwent both conventional MRI and diffusion weighted imaging before treatment. Conventional MRI features and histogram parameters derived from ADC maps, including mean ADC (ADCmean), median ADC (ADCmedian), skewness, kurtosis, 10th, 25th, 75th and 90th percentiles of ADC (ADC10, ADC25, ADC75, ADC90) were evaluated and compared between orbital lymphoma and IOIP. Multivariate logistic regression analysis was used to identify the most valuable variables for discriminating. Differential model was built upon the selected variables and receiver operating characteristic (ROC) analysis was also performed to determine the differential ability of the model. Results Multivariate logistic regression showed ADC10 (P = 0.023) and involvement of orbit preseptal space (P = 0.029) were the most promising indexes in the discrimination of orbital lymphoma and IOIP. The logistic model defined by ADC10 and involvement of orbit preseptal space was built, which achieved an AUC of 0.939, with sensitivity of 77.30% and specificity of 94.40%. Conclusions Conventional MRI feature of involvement of orbit preseptal space and ADC histogram parameter of ADC10 are valuable in differential diagnosis of orbital lymphoma and IOIP.
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Affiliation(s)
- Jiliang Ren
- 0000 0004 0368 8293grid.16821.3cDepartment of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Yuan
- 0000 0004 0368 8293grid.16821.3cDepartment of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingwei Wu
- 0000 0004 0368 8293grid.16821.3cDepartment of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaofeng Tao
- 0000 0004 0368 8293grid.16821.3cDepartment of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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47
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Explorative Investigation of Whole-Lesion Histogram MRI Metrics for Differentiating Uterine Leiomyomas and Leiomyosarcomas. AJR Am J Roentgenol 2018; 210:1172-1177. [DOI: 10.2214/ajr.17.18605] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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48
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Changes in quantitative CT image features of ground-glass nodules in differentiating invasive pulmonary adenocarcinoma from benign and in situ lesions: histopathological comparisons. Clin Radiol 2018; 73:504.e9-504.e16. [DOI: 10.1016/j.crad.2017.12.011] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Accepted: 12/06/2017] [Indexed: 01/15/2023]
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49
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De Paepe KN, De Keyzer F, Wolter P, Bechter O, Dierickx D, Janssens A, Verhoef G, Oyen R, Vandecaveye V. Improving lymph node characterization in staging malignant lymphoma using first-order ADC texture analysis from whole-body diffusion-weighted MRI. J Magn Reson Imaging 2018; 48:897-906. [DOI: 10.1002/jmri.26034] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Accepted: 03/17/2018] [Indexed: 12/13/2022] Open
Affiliation(s)
| | | | - Pascal Wolter
- Department of Medical Oncology; University Hospitals Leuven; Belgium
| | - Oliver Bechter
- Department of Medical Oncology; University Hospitals Leuven; Belgium
| | - Daan Dierickx
- Department of Hematology; University Hospitals Leuven; Belgium
| | - Ann Janssens
- Department of Hematology; University Hospitals Leuven; Belgium
| | - Gregor Verhoef
- Department of Hematology; University Hospitals Leuven; Belgium
| | - Raymond Oyen
- Deparment of Radiology; University Hospitals Leuven; Belgium
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50
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Conventional vs. reduced field of view diffusion weighted imaging of the prostate: Comparison of image quality, correlation with histology, and inter-reader agreement. Magn Reson Imaging 2018; 47:67-76. [DOI: 10.1016/j.mri.2017.10.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 10/10/2017] [Accepted: 10/31/2017] [Indexed: 12/31/2022]
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