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Nong HY, Cen YY, Qin M, Qin WQ, Xie YX, Li L, Liu MR, Ding K. Application of texture signatures based on multiparameter-magnetic resonance imaging for predicting microvascular invasion in hepatocellular carcinoma: Retrospective study. World J Gastrointest Oncol 2024; 16:1309-1318. [PMID: 38660663 PMCID: PMC11037072 DOI: 10.4251/wjgo.v16.i4.1309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/18/2023] [Accepted: 02/05/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Despite continuous changes in treatment methods, the survival rate for advanced hepatocellular carcinoma (HCC) patients remains low, highlighting the importance of diagnostic methods for HCC. AIM To explore the efficacy of texture analysis based on multi-parametric magnetic resonance (MR) imaging (MRI) in predicting microvascular invasion (MVI) in preoperative HCC. METHODS This study included 105 patients with pathologically confirmed HCC, categorized into MVI-positive and MVI-negative groups. We employed Original Data Analysis, Principal Component Analysis, Linear Discriminant Analysis (LDA), and Non-LDA (NDA) for texture analysis using multi-parametric MR images to predict preoperative MVI. The effectiveness of texture analysis was determined using the B11 program of the MaZda4.6 software, with results expressed as the misjudgment rate (MCR). RESULTS Texture analysis using multi-parametric MRI, particularly the MI + PA + F dimensionality reduction method combined with NDA discrimination, demonstrated the most effective prediction of MVI in HCC. Prediction accuracy in the pulse and equilibrium phases was 83.81%. MCRs for the combination of T2-weighted imaging (T2WI), arterial phase, portal venous phase, and equilibrium phase were 22.86%, 16.19%, 20.95%, and 20.95%, respectively. The area under the curve for predicting MVI positivity was 0.844, with a sensitivity of 77.19% and specificity of 91.67%. CONCLUSION Texture analysis of arterial phase images demonstrated superior predictive efficacy for MVI in HCC compared to T2WI, portal venous, and equilibrium phases. This study provides an objective, non-invasive method for preoperative prediction of MVI, offering a theoretical foundation for the selection of clinical therapy.
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Affiliation(s)
- Hai-Yang Nong
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
- Department of Radiology, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
| | - Yong-Yi Cen
- Department of Radiology, The First Affiliated Hospital of Guangxi University of Traditional Chinese Medicine, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Mi Qin
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Wen-Qi Qin
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - You-Xiang Xie
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Lin Li
- Department of Hepatobiliary Surgery, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Man-Rong Liu
- Department of Ultrasound, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Ke Ding
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
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Krauss W, Frey J, Heydorn Lagerlöf J, Lidén M, Thunberg P. Radiomics from multisite MRI and clinical data to predict clinically significant prostate cancer. Acta Radiol 2024; 65:307-317. [PMID: 38115809 DOI: 10.1177/02841851231216555] [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: 12/21/2023]
Abstract
BACKGROUND Magnetic resonance imaging (MRI) is useful in the diagnosis of clinically significant prostate cancer (csPCa). MRI-derived radiomics may support the diagnosis of csPCa. PURPOSE To investigate whether adding radiomics from biparametric MRI to predictive models based on clinical and MRI parameters improves the prediction of csPCa in a multisite-multivendor setting. MATERIAL AND METHODS Clinical information (PSA, PSA density, prostate volume, and age), MRI reviews (PI-RADS 2.1), and radiomics (histogram and texture features) were retrieved from prospectively included patients examined at different radiology departments and with different MRI systems, followed by MRI-ultrasound fusion guided biopsies of lesions PI-RADS 3-5. Predictive logistic regression models of csPCa (Gleason score ≥7) for the peripheral (PZ) and transition zone (TZ), including clinical data and PI-RADS only, and combined with radiomics, were built and compared using receiver operating characteristic (ROC) curves. RESULTS In total, 456 lesions in 350 patients were analyzed. In PZ and TZ, PI-RADS 4-5 and PSA density, and age in PZ, were independent predictors of csPCa in models without radiomics. In models including radiomics, PI-RADS 4-5, PSA density, age, and ADC energy were independent predictors in PZ, and PI-RADS 5, PSA density and ADC mean in TZ. Comparison of areas under the ROC curve (AUC) for the models without radiomics (PZ: AUC = 0.82, TZ: AUC = 0.80) versus with radiomics (PZ: AUC = 0.82, TZ: AUC = 0.82) showed no significant differences (PZ: P = 0.366; TZ: P = 0.171). CONCLUSION PSA density and PI-RADS are potent predictors of csPCa. Radiomics do not add significant information to our multisite-multivendor dataset.
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Affiliation(s)
- Wolfgang Krauss
- Department of Radiology and Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Janusz Frey
- Department of Urology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Jakob Heydorn Lagerlöf
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
- Department of Medical Physics, Karlstad Central Hospital, Sweden
| | - Mats Lidén
- Department of Radiology and Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Per Thunberg
- Department of Radiology and Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
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Li C, Deng M, Zhong X, Ren J, Chen X, Chen J, Xiao F, Xu H. Multi-view radiomics and deep learning modeling for prostate cancer detection based on multi-parametric MRI. Front Oncol 2023; 13:1198899. [PMID: 37448515 PMCID: PMC10338012 DOI: 10.3389/fonc.2023.1198899] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 06/08/2023] [Indexed: 07/15/2023] Open
Abstract
Introduction This study aims to develop an imaging model based on multi-parametric MR images for distinguishing between prostate cancer (PCa) and prostate hyperplasia. Methods A total of 236 subjects were enrolled and divided into training and test sets for model construction. Firstly, a multi-view radiomics modeling strategy was designed in which different combinations of radiomics feature categories (original, LoG, and wavelet) were compared to obtain the optimal input feature sets. Minimum-redundancy maximum-relevance (mRMR) selection and least absolute shrinkage selection operator (LASSO) were used for feature reduction, and the next logistic regression method was used for model construction. Then, a Swin Transformer architecture was designed and trained using transfer learning techniques to construct the deep learning models (DL). Finally, the constructed multi-view radiomics and DL models were combined and compared for model selection and nomogram construction. The prediction accuracy, consistency, and clinical benefit were comprehensively evaluated in the model comparison. Results The optimal input feature set was found when LoG and wavelet features were combined, while 22 and 17 radiomic features in this set were selected to construct the ADC and T2 multi-view radiomic models, respectively. ADC and T2 DL models were built by transferring learning from a large number of natural images to a relatively small sample of prostate images. All individual and combined models showed good predictive accuracy, consistency, and clinical benefit. Compared with using only an ADC-based model, adding a T2-based model to the combined model would reduce the model's predictive performance. The ADCCombinedScore model showed the best predictive performance among all and was transformed into a nomogram for better use in clinics. Discussion The constructed models in our study can be used as a predictor in differentiating PCa and BPH, thus helping clinicians make better clinical treatment decisions and reducing unnecessary prostate biopsies.
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Affiliation(s)
- Chunyu Li
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Ming Deng
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xiaoli Zhong
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jinxia Ren
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xiaohui Chen
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | | | - Feng Xiao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
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Özer H, Koplay M, Baytok A, Seher N, Demir LS, Kılınçer A, Kaynar M, Göktaş S. Texture analysis of multiparametric magnetic resonance imaging for differentiating clinically significant prostate cancer in the peripheral zone. Turk J Med Sci 2023; 53:701-711. [PMID: 37476894 PMCID: PMC10387871 DOI: 10.55730/1300-0144.5633] [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: 07/31/2022] [Accepted: 02/01/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND Texture analysis (TA) provides additional tissue heterogeneity data that may assist in differentiating peripheral zone(PZ) lesions in multiparametric magnetic resonance imaging (mpMRI). This study investigates the role of magnetic resonance imaging texture analysis (MRTA) in detecting clinically significant prostate cancer (csPCa) in the PZ. METHODS This retrospective study included 80 consecutive patients who had an mpMRI and a prostate biopsy for suspected prostate cancer. Two radiologists in consensus interpreted mpMRI and performed texture analysis based on their histopathology. The first-, second-, and higher-order texture parameters were extracted from mpMRI and were compared between groups. Univariate and multivariate logistic regression analyses were performed using the texture parameters to determine the independent predictors of csPCa. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance of the texture parameters. RESULTS : In the periferal zone, 39 men had csPCa, while 41 had benign lesions or clinically insignificant prostate cancer (cisPCa). Themajority of texture parameters showed statistically significant differences between the groups. Univariate ROC analysis showed that the ADC mean and ADC median were the best variables in differentiating csPCa (p < 0.001). The first-order logistic regression model (mean + entropy) based on the ADC maps had a higher AUC value (0.996; 95% CI: 0.989-1) than other texture-based logistic regression models (p < 0.001). DISCUSSION MRTA is useful in differentiating csPCa from other lesions in the PZ. Consequently, the first-order multivariate regressionmodel based on ADC maps had the highest diagnostic performance in differentiating csPCa.
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Affiliation(s)
- Halil Özer
- Department of Radiology, Faculty of Medicine, Selcuk University, Konya, Turkey
| | - Mustafa Koplay
- Department of Radiology, Faculty of Medicine, Selcuk University, Konya, Turkey
| | - Ahmet Baytok
- Department of Radiology, Faculty of Medicine, Selcuk University, Konya, Turkey
| | - Nusret Seher
- Department of Radiology, Faculty of Medicine, Selcuk University, Konya, Turkey
| | - Lütfi Saltuk Demir
- Department of Public Health, Faculty of Medicine, Necmettin Erbakan University, Konya, Turkey
| | - Abidin Kılınçer
- Department of Radiology, Faculty of Medicine, Selcuk University, Konya, Turkey
| | - Mehmet Kaynar
- Department of Urology, Faculty of Medicine, Selcuk University, Konya, Turkey
| | - Serdar Göktaş
- Department of Urology, Faculty of Medicine, Selcuk University, Konya, Turkey
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Costa G, Cavinato L, Fiz F, Sollini M, Chiti A, Torzilli G, Ieva F, Viganò L. Mapping Tumor Heterogeneity via Local Entropy Assessment: Making Biomarkers Visible. J Digit Imaging 2023; 36:1038-1048. [PMID: 36849835 PMCID: PMC10287605 DOI: 10.1007/s10278-023-00799-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 02/20/2023] [Accepted: 02/21/2023] [Indexed: 03/01/2023] Open
Abstract
Advanced imaging and analysis improve prediction of pathology data and outcomes in several tumors, with entropy-based measures being among the most promising biomarkers. However, entropy is often perceived as statistical data lacking clinical significance. We aimed to generate a voxel-by-voxel visual map of local tumor entropy, thus allowing to (1) make entropy explainable and accessible to clinicians; (2) disclose and quantitively characterize any intra-tumoral entropy heterogeneity; (3) evaluate associations between entropy and pathology data. We analyzed the portal phase of preoperative CT of 20 patients undergoing liver surgery for colorectal metastases. A three-dimensional core kernel (5 × 5 × 5 voxels) was created and used to compute the local entropy value for each voxel of the tumor. The map was encoded with a color palette. We performed two analyses: (a) qualitative assessment of tumors' detectability and pattern of entropy distribution; (b) quantitative analysis of the entropy values distribution. The latter data were compared with standard Hounsfield data as predictors of post-chemotherapy tumor regression grade (TRG). Entropy maps were successfully built for all tumors. Metastases were qualitatively hyper-entropic compared to surrounding parenchyma. In four cases hyper-entropic areas exceeded the tumor margin visible at CT. We identified four "entropic" patterns: homogeneous, inhomogeneous, peripheral rim, and mixed. At quantitative analysis, entropy-derived data (percentiles/mean/median/root mean square) predicted TRG (p < 0.05) better than Hounsfield-derived ones (p = n.s.). We present a standardized imaging technique to visualize tumor heterogeneity built on a voxel-by-voxel entropy assessment. The association of local entropy with pathology data supports its role as a biomarker.
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Affiliation(s)
- Guido Costa
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Lara Cavinato
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - Francesco Fiz
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Martina Sollini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Guido Torzilli
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Francesca Ieva
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy.
- CHDS - Center for Health Data Science, Human Technopole, Milan, Italy.
| | - Luca Viganò
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
- Hepatobiliary Unit, Department of Minimally Invasive General & Oncologic Surgery, Humanitas Gavazzeni University Hospital, Via M. Gavazzeni 21, 24125, Bergamo, Italy.
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Gibala S, Obuchowicz R, Lasek J, Schneider Z, Piorkowski A, Pociask E, Nurzynska K. Textural Features of MR Images Correlate with an Increased Risk of Clinically Significant Cancer in Patients with High PSA Levels. J Clin Med 2023; 12:jcm12082836. [PMID: 37109173 PMCID: PMC10146387 DOI: 10.3390/jcm12082836] [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: 03/17/2023] [Revised: 04/06/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND Prostate cancer, which is associated with gland biology and also with environmental risks, is a serious clinical problem in the male population worldwide. Important progress has been made in the diagnostic and clinical setups designed for the detection of prostate cancer, with a multiparametric magnetic resonance diagnostic process based on the PIRADS protocol playing a key role. This method relies on image evaluation by an imaging specialist. The medical community has expressed its desire for image analysis techniques that can detect important image features that may indicate cancer risk. METHODS Anonymized scans of 41 patients with laboratory diagnosed PSA levels who were routinely scanned for prostate cancer were used. The peripheral and central zones of the prostate were depicted manually with demarcation of suspected tumor foci under medical supervision. More than 7000 textural features in the marked regions were calculated using MaZda software. Then, these 7000 features were used to perform region parameterization. Statistical analyses were performed to find correlations with PSA-level-based diagnosis that might be used to distinguish suspected (different) lesions. Further multiparametrical analysis using MIL-SVM machine learning was used to obtain greater accuracy. RESULTS Multiparametric classification using MIL-SVM allowed us to reach 92% accuracy. CONCLUSIONS There is an important correlation between the textural parameters of MRI prostate images made using the PIRADS MR protocol with PSA levels > 4 mg/mL. The correlations found express dependence between image features with high cancer markers and hence the cancer risk.
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Affiliation(s)
- Sebastian Gibala
- Urology Department, Ultragen Medical Center, 31-572 Krakow, Poland
| | - Rafal Obuchowicz
- Department of Diagnostic Imaging, Jagiellonian University Medical College, 31-501 Krakow, Poland
| | - Julia Lasek
- Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Zofia Schneider
- Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Adam Piorkowski
- Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Elżbieta Pociask
- Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Karolina Nurzynska
- Department of Algorithmics and Software, Silesian University of Technology, 44-100 Gliwice, Poland
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He K, Zhang Y, Li S, Yuan G, Liang P, Zhang Q, Xie Q, Xiao P, Li H, Meng X, Li Z. Incremental prognostic value of ADC histogram analysis in patients with high-risk prostate cancer receiving adjuvant hormonal therapy after radical prostatectomy. Front Oncol 2023; 13:1076400. [PMID: 36761966 PMCID: PMC9907778 DOI: 10.3389/fonc.2023.1076400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/05/2023] [Indexed: 01/26/2023] Open
Abstract
Purpose To investigate the incremental prognostic value of preoperative apparent diffusion coefficient (ADC) histogram analysis in patients with high-risk prostate cancer (PCa) who received adjuvant hormonal therapy (AHT) after radical prostatectomy (RP). Methods Sixty-two PCa patients in line with the criteria were enrolled in this study. The 10th, 50th, and 90th percentiles of ADC (ADC10, ADC50, ADC90), the mean value of ADC (ADCmean), kurtosis, and skewness were obtained from the whole-lesion ADC histogram. The Kaplan-Meier method and Cox regression analysis were used to analyze the relationship between biochemical recurrence-free survival (BCR-fs) and ADC parameters and other clinicopathological factors. Prognostic models were constructed with and without ADC parameters. Results The median follow-up time was 53.4 months (range, 41.1-79.3 months). BCR was found in 19 (30.6%) patients. Kaplan-Meier curves showed that lower ADCmean, ADC10, ADC50, and ADC90 and higher kurtosis could predict poorer BCR-fs (all p<0.05). After adjusting for clinical parameters, ADC50 and kurtosis remained independent prognostic factors for BCR-fs (HR: 0.172, 95% CI: 0.055-0.541, p=0.003; HR: 7.058, 95% CI: 2.288-21.773, p=0.001, respectively). By adding ADC parameters to the clinical model, the C index and diagnostic accuracy for the 24- and 36-month BCR-fs were improved. Conclusion ADC histogram analysis has incremental prognostic value in patients with high-risk PCa who received AHT after RP. Combining ADC50, kurtosis and clinical parameters can improve the accuracy of BCR-fs prediction.
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Affiliation(s)
- Kangwen He
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yucong Zhang
- Department of Geriatrics, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shichao Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Guanjie Yuan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ping Liang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qingpeng Zhang
- School of Data Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Qingguo Xie
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Peng Xiao
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Heng Li
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China,*Correspondence: Heng Li, ; Xiaoyan Meng,
| | - Xiaoyan Meng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China,*Correspondence: Heng Li, ; Xiaoyan Meng,
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Shiraishi M, Igarashi T, Hiroaki F, Oe R, Ohki K, Ojiri H. Radiomics based on diffusion-weighted imaging for differentiation between focal-type autoimmune pancreatitis and pancreatic carcinoma. Br J Radiol 2022; 95:20210456. [PMID: 35946923 PMCID: PMC9733621 DOI: 10.1259/bjr.20210456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 07/31/2022] [Accepted: 08/04/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To evaluate the parameters of support vector machine (SVM) using imaging data generated from the apparent diffusion coefficient (ADC) to differentiate between focal-type autoimmune pancreatitis (f-AIP) and pancreatic ductal adenocarcinoma (PDAC) when using SVM based on diffusion-weighted imaging. METHODS The 2D-ADCmean and texture parameters (16 texture features × [non-filter+17 filters]) were retrospectively segmented by 2 readers in 28 patients with f-AIP and 77 patients with pathologically proven PDAC. The diagnostic accuracy of the SVM model was evaluated by receiver operating characteristic curve analysis and calculation of the area under the curve (AUC). Interreader reliability was assessed by intraclass correlation coefficient (ICC). RESULTS The 2D-ADCmean and 3D-ADCmean were significantly lower in cases of f-AIP (1.10-1.15 × 10-3 mm2/s and 1.21-1.23× 10-3 mm2/s, respectively) vs PDAC (1.29-1.33 × 10-3 mm2/s and 1.41-1.43 × 10-3 mm2/s, respectively), with excellent and good interreader reliability, respectively (ICC = 0.909 and 0.891, respectively). Among the texture parameters, energy with exponential filtering yielded the highest AUC (Reader 1: 74.7%, Reader 2: 81.5%), with fair interreader reliability (ICC = 0.707). The non-linear SVM, a combination of 2D-ADCmean, object volume and exponential-energy showed an AUC value of 96.2% in the testing cohorts. CONCLUSION Our results suggest that non-linear SVM using a combination of 2D-ADCmean, object volume, and exponential-energy may assist in differentiating f-AIP from PDAC. ADVANCES IN KNOWLEDGE The radiomics based on an apparent diffusion coefficient value may assist in differentiating f-AIP from PDAC.
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Affiliation(s)
- Megumi Shiraishi
- Department of Radiology, The Jikei University School of Medicine, Tokyo, Japan
| | - Takao Igarashi
- Department of Radiology, The Jikei University School of Medicine, Tokyo, Japan
| | - Fujioka Hiroaki
- Department of Radiology, The Jikei University School of Medicine, Tokyo, Japan
| | - Rika Oe
- Department of Radiology, The Jikei University School of Medicine, Tokyo, Japan
| | - Kazuyoshi Ohki
- Department of Radiology, The Jikei University School of Medicine, Tokyo, Japan
| | - Hiroya Ojiri
- Department of Radiology, The Jikei University School of Medicine, Tokyo, Japan
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Zhu JJ, Shen J, Zhang W, Wang F, Yuan M, Xu H, Yu TF. Quantitative texture analysis based on dynamic contrast enhanced MRI for differential diagnosis between primary thymic lymphoma from thymic carcinoma. Sci Rep 2022; 12:12629. [PMID: 35871647 PMCID: PMC9309158 DOI: 10.1038/s41598-022-16393-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 07/08/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractTo evaluate the value of texture analysis based on dynamic contrast enhanced MRI (DCE-MRI) in the differential diagnosis of thymic carcinoma and thymic lymphoma. Sixty-nine patients with pathologically confirmed (thymic carcinoma, n = 32; thymic lymphoma, n = 37) were enrolled in this retrospective study. Ktrans, Kep and Ve maps were automatically generated, and texture features were extracted, including mean, median, 5th/95th percentile, skewness, kurtosis, diff-variance, diff-entropy, contrast and entropy. The differences in parameters between the two groups were compared and the diagnostic efficacy was calculated. The Ktrans-related significant features yielded an area under the curve (AUC) of 0.769 (sensitivity 90.6%, specificity 51.4%) for the differentiation between thymic carcinoma and thymic lymphoma. The Kep-related significant features yielded an AUC of 0.780 (sensitivity 87.5%, specificity 62.2%). The Ve-related significant features yielded an AUC of 0.807 (sensitivity 75.0%, specificity 78.4%). The combination of DCE-MRI textural features yielded an AUC of 0.962 (sensitivity 93.8%, specificity 89.2%). Five parameters were screened out, including age, Ktrans-entropy, Kep-entropy, Ve-entropy, and Ve-P95. The combination of these five parameters yielded the best discrimination efficiency (AUC of 0.943, 93.7% sensitivity, 81.1% specificity). Texture analysis of DCE-MRI may be helpful to distinguish thymic carcinoma from thymic lymphoma.
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Fiz F, Jayakody Arachchige VS, Gionso M, Pecorella I, Selvam A, Wheeler DR, Sollini M, Viganò L. Radiomics of Biliary Tumors: A Systematic Review of Current Evidence. Diagnostics (Basel) 2022; 12:diagnostics12040826. [PMID: 35453878 PMCID: PMC9024804 DOI: 10.3390/diagnostics12040826] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 03/19/2022] [Accepted: 03/25/2022] [Indexed: 02/06/2023] Open
Abstract
Biliary tumors are rare diseases with major clinical unmet needs. Standard imaging modalities provide neither a conclusive diagnosis nor robust biomarkers to drive treatment planning. In several neoplasms, texture analyses non-invasively unveiled tumor characteristics and aggressiveness. The present manuscript aims to summarize the available evidence about the role of radiomics in the management of biliary tumors. A systematic review was carried out through the most relevant databases. Original, English-language articles published before May 2021 were considered. Three main outcome measures were evaluated: prediction of pathology data; prediction of survival; and differential diagnosis. Twenty-seven studies, including a total of 3605 subjects, were identified. Mass-forming intrahepatic cholangiocarcinoma (ICC) was the subject of most studies (n = 21). Radiomics reliably predicted lymph node metastases (range, AUC = 0.729−0.900, accuracy = 0.69−0.83), tumor grading (AUC = 0.680−0.890, accuracy = 0.70−0.82), and survival (C-index = 0.673−0.889). Textural features allowed for the accurate differentiation of ICC from HCC, mixed HCC-ICC, and inflammatory masses (AUC > 0.800). For all endpoints (pathology/survival/diagnosis), the predictive/prognostic models combining radiomic and clinical data outperformed the standard clinical models. Some limitations must be acknowledged: all studies are retrospective; the analyzed imaging modalities and phases are heterogeneous; the adoption of signatures/scores limits the interpretability and applicability of results. In conclusion, radiomics may play a relevant role in the management of biliary tumors, from diagnosis to treatment planning. It provides new non-invasive biomarkers, which are complementary to the standard clinical biomarkers; however, further studies are needed for their implementation in clinical practice.
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Affiliation(s)
- Francesco Fiz
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy; (F.F.); (M.S.)
| | - Visala S Jayakody Arachchige
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (V.S.J.A.); (M.G.); (I.P.); (A.S.); (D.R.W.)
| | - Matteo Gionso
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (V.S.J.A.); (M.G.); (I.P.); (A.S.); (D.R.W.)
| | - Ilaria Pecorella
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (V.S.J.A.); (M.G.); (I.P.); (A.S.); (D.R.W.)
| | - Apoorva Selvam
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (V.S.J.A.); (M.G.); (I.P.); (A.S.); (D.R.W.)
| | - Dakota Russell Wheeler
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (V.S.J.A.); (M.G.); (I.P.); (A.S.); (D.R.W.)
| | - Martina Sollini
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy; (F.F.); (M.S.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (V.S.J.A.); (M.G.); (I.P.); (A.S.); (D.R.W.)
| | - Luca Viganò
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (V.S.J.A.); (M.G.); (I.P.); (A.S.); (D.R.W.)
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy
- Correspondence: ; Tel.: +39-02-8224-7361
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11
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Peng F, Zheng T, Tang X, Liu Q, Sun Z, Feng Z, Zhao H, Gong L. Magnetic Resonance Texture Analysis in Myocardial Infarction. Front Cardiovasc Med 2021; 8:724271. [PMID: 34778395 PMCID: PMC8581163 DOI: 10.3389/fcvm.2021.724271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 09/27/2021] [Indexed: 11/13/2022] Open
Abstract
Texture analysis (TA) is a newly arisen field that can detect the invisible MRI signal changes among image pixels. Myocardial infarction (MI) is cardiomyocyte necrosis caused by myocardial ischemia and hypoxia, becoming the primary cause of death and disability worldwide. In recent years, various TA studies have been performed in patients with MI and show a good clinical application prospect. This review briefly presents the main pathogenesis and pathophysiology of MI, introduces the overview and workflow of TA, and summarizes multiple magnetic resonance TA (MRTA) clinical applications in MI. We also discuss the facing challenges currently for clinical utilization and propose the prospect.
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Affiliation(s)
- Fei Peng
- Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Tian Zheng
- Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xiaoping Tang
- Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Qiao Liu
- Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zijing Sun
- Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhaofeng Feng
- Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Heng Zhao
- Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Lianggeng Gong
- Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, 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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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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14
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Hectors SJ, Chen C, Chen J, Wang J, Gordon S, Yu M, Al Hussein Al Awamlh B, Sabuncu MR, Margolis DJA, Hu JC. Magnetic Resonance Imaging Radiomics-Based Machine Learning Prediction of Clinically Significant Prostate Cancer in Equivocal PI-RADS 3 Lesions. J Magn Reson Imaging 2021; 54:1466-1473. [PMID: 33970516 DOI: 10.1002/jmri.27692] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 04/25/2021] [Accepted: 04/27/2021] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND While Prostate Imaging Reporting and Data System (PI-RADS) 4 and 5 lesions typically warrant prostate biopsy and PI-RADS 1 and 2 lesions may be safely observed, PI-RADS 3 lesions are equivocal. PURPOSE To construct and cross-validate a machine learning model based on radiomics features from T2 -weighted imaging (T2 WI) of PI-RADS 3 lesions to identify clinically significant prostate cancer (csPCa), that is, pathological Grade Group ≥ 2. STUDY TYPE Single-center retrospective study. POPULATION A total of 240 patients were included (training cohort, n = 188, age range 43-82 years; test cohort, n = 52, age range 41-79 years). Eligibility criteria were 1) magnetic resonance imaging (MRI)-targeted biopsy between 2015 and 2020; 2) PI-RADS 3 index lesion identified on multiparametric MRI; (3) biopsy performed within 1 year of MRI. The percentages of csPCa lesions were 10.6% and 15.4% in the training and test cohorts, respectively. FIELD STRENGTH/SEQUENCE A 3 T; T2 WI turbo-spin echo, diffusion-weighted spin-echo echo planar imaging, dynamic contrast-enhanced MRI with time-resolved T1-weighted imaging. ASSESSMENT Multislice volumes-of-interest (VOIs) were drawn in the PI-RADS 3 index lesions on T2 WI. A total of 107 radiomics features (first-order histogram and second-order texture) were extracted from the segmented lesions. STATISTICAL TESTS A random forest classifier using the radiomics features as input was trained and validated for prediction of csPCa. The performance of the machine learning classifier, prostate specific antigen (PSA) density, and prostate volume for csPCa prediction was evaluated using receiver operating characteristic (ROC) analysis. RESULTS The trained random forest classifier constructed from the T2 WI radiomics features good and statistically significant area-under-the-curves (AUCs) of 0.76 (P = 0.022) for prediction of csPCa in the test set. Prostate volume and PSA density showed moderate and nonsignificant performance (AUC 0.62, P = 0.275 and 0.61, P = 0.348, respectively) for csPCa prediction in the test set. CONCLUSION The machine learning classifier based on T2 WI radiomic features demonstrated good performance for prediction of csPCa in PI-RADS 3 lesions. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: 2.
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Affiliation(s)
- Stefanie J Hectors
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Christine Chen
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Johnson Chen
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Jade Wang
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Sharon Gordon
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Miko Yu
- Department of Urology, Weill Cornell Medicine, New York, New York, USA
| | | | - Mert R Sabuncu
- School of Electrical and Computer Engineering, Cornell University, New York, USA
| | | | - Jim C Hu
- Department of Urology, Weill Cornell Medicine, New York, New York, USA
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15
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Björeland U, Nyholm T, Jonsson J, Skorpil M, Blomqvist L, Strandberg S, Riklund K, Beckman L, Thellenberg-Karlsson C. Impact of neoadjuvant androgen deprivation therapy on magnetic resonance imaging features in prostate cancer before radiotherapy. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2021; 17:117-123. [PMID: 33898790 PMCID: PMC8058024 DOI: 10.1016/j.phro.2021.01.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 01/19/2021] [Accepted: 01/19/2021] [Indexed: 01/01/2023]
Abstract
Background and purpose In locally advanced prostate cancer (PC), androgen deprivation therapy (ADT) in combination with whole prostate radiotherapy (RT) is the standard treatment. ADT affects the prostate as well as the tumour on multiparametric magnetic resonance imaging (MRI) with decreased PC conspicuity and impaired localisation of the prostate lesion. Image texture analysis has been suggested to be of aid in separating tumour from normal tissue. The aim of the study was to investigate the impact of ADT on baseline defined MRI features in prostate cancer with the goal to investigate if it might be of use in radiotherapy planning. Materials and methods Fifty PC patients were included. Multiparametric MRI was performed before, and three months after ADT. At baseline, a tumour volume was delineated on apparent diffusion coefficient (ADC) maps with suspected tumour content and a reference volume in normal prostatic tissue. These volumes were transferred to MRIs after ADT and were analysed with first-order -and invariant Haralick -features. Results At baseline, the median value and several of the invariant Haralick features of ADC, showed a significant difference between tumour and reference volumes. After ADT, only ADC median value could significantly differentiate the two volumes. Conclusions Invariant Haralick -features could not distinguish between baseline MRI defined PC and normal tissue after ADT. First-order median value remained significantly different in tumour and reference volumes after ADT, but the difference was less pronounced than before ADT.
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Affiliation(s)
- Ulrika Björeland
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
- Corresponding author at: Department of Medical Physics, Sundsvall Hospital, 85186 Sundsvall, Sweden.
| | - Tufve Nyholm
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Joakim Jonsson
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Mikael Skorpil
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Lennart Blomqvist
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Sara Strandberg
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Katrine Riklund
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Lars Beckman
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
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16
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Li M, Yang L, Yue Y, Xu J, Huang C, Song B. Use of Radiomics to Improve Diagnostic Performance of PI-RADS v2.1 in Prostate Cancer. Front Oncol 2021; 10:631831. [PMID: 33680954 PMCID: PMC7925826 DOI: 10.3389/fonc.2020.631831] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Accepted: 12/30/2020] [Indexed: 02/05/2023] Open
Abstract
Objective To investigate whether a radiomics model can help to improve the performance of PI-RADS v2.1 in prostate cancer (PCa). Methods This was a retrospective analysis of 203 patients with pathologically confirmed PCa or non-PCa between March 2015 and December 2016. Patients were divided into a training set (n = 141) and a validation set (n = 62). The radiomics model (Rad-score) was developed based on multi-parametric MRI including T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), apparent diffusion coefficient (ADC) imaging, and dynamic contrast enhanced (DCE) imaging. The combined model involving Rad-score and PI-RADS was compared with PI-RADS for the diagnosis of PCa by using the receiver operating characteristic curve (ROC) analysis. Results A total of 112 (55.2%) patients had PCa, and 91 (44.8%) patients had benign lesions. For PCa versus non-PCa, the Rad-score had a significantly higher area under the ROC curve (AUC) [0.979 (95% CI, 0.940–0.996)] than PI-RADS [0.905 (0.844–0.948), P = 0.002] in the training set. However, the AUC between them was insignificant in the validation set [0.861 (0.749–0.936) vs. 0.845 (0.731–0.924), P = 0.825]. When Rad-score was added to PI-RADS, the performance of the PI-RADS was significantly improved for the PCa diagnosis (AUC = 0.989, P < 0.001 for the training set and AUC = 0.931, P = 0.038 for the validation set). Conclusions The radiomics based on multi-parametric MRI can help to improve the diagnostic performance of PI-RADS v2.1 in PCa.
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Affiliation(s)
- Mou Li
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Ling Yang
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Yufeng Yue
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Jingxu Xu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
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17
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Chianca V, Cuocolo R, Gitto S, Albano D, Merli I, Badalyan J, Cortese MC, Messina C, Luzzati A, Parafioriti A, Galbusera F, Brunetti A, Sconfienza LM. Radiomic Machine Learning Classifiers in Spine Bone Tumors: A Multi-Software, Multi-Scanner Study. Eur J Radiol 2021; 137:109586. [PMID: 33610852 DOI: 10.1016/j.ejrad.2021.109586] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 11/22/2020] [Accepted: 02/04/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE Spinal lesion differential diagnosis remains challenging even in MRI. Radiomics and machine learning (ML) have proven useful even in absence of a standardized data mining pipeline. We aimed to assess ML diagnostic performance in spinal lesion differential diagnosis, employing radiomic data extracted by different software. METHODS Patients undergoing MRI for a vertebral lesion were retrospectively analyzed (n = 146, 67 males, 79 females; mean age 63 ± 16 years, range 8-89 years) and constituted the train (n = 100) and internal test cohorts (n = 46). Part of the latter had additional prior exams which constituted a multi-scanner, external test cohort (n = 35). Lesions were labeled as benign or malignant (2-label classification), and benign, primary malignant or metastases (3-label classification) for classification analyses. Features extracted via 3D Slicer heterogeneityCAD module (hCAD) and PyRadiomics were independently used to compare different combinations of feature selection methods and ML classifiers (n = 19). RESULTS In total, 90 and 1548 features were extracted by hCAD and PyRadiomics, respectively. The best feature selection method-ML algorithm combination was selected by 10 iterations of 10-fold cross-validation in the training data. For the 2-label classification ML obtained 94% accuracy in the internal test cohort, using hCAD data, and 86% in the external one. For the 3-label classification, PyRadiomics data allowed for 80% and 69% accuracy in the internal and external test sets, respectively. CONCLUSIONS MRI radiomics combined with ML may be useful in spinal lesion assessment. More robust pre-processing led to better consistency despite scanner and protocol heterogeneity.
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Affiliation(s)
- Vito Chianca
- Clinica di Radiologia EOC, Istituto di Imaging della Svizzera Italiana (IIMSI), Lugano, Switzerland; Ospedale Evangelico Betania, Napoli, Italy
| | - Renato Cuocolo
- Dipartimento di Scienze Biomediche Avanzate, Università degli Studi di Napoli (")Federico II", Napoli, Italy; Laboratory of Augmented Reality for Health Monitoring (ARHeMLab), Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Salvatore Gitto
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milano, Italy.
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milano, Italy; Sezione di Scienze Radiologiche, Dipartimento di Biomedicina, Neuroscienze e Diagnostica Avanzata, Università degli Studi di Palermo, Italy
| | - Ilaria Merli
- UOC Radiodiagnostica, Presidio San Carlo Borromeo, ASST Santi Paolo e Carlo, Milano, Italy
| | - Julietta Badalyan
- International Medical School, University of Milan and Russian National Research Medical University, Milano, Italy
| | - Maria Cristina Cortese
- Istituto di Radiologia, Fondazione Policlinico A. Gemelli IRCCS - Università Cattolica Sacro Cuore, Roma, Italy
| | - Carmelo Messina
- IRCCS Istituto Ortopedico Galeazzi, Milano, Italy; Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milano, Italy
| | | | | | | | - Arturo Brunetti
- Dipartimento di Scienze Biomediche Avanzate, Università degli Studi di Napoli (")Federico II", Napoli, Italy
| | - Luca Maria Sconfienza
- IRCCS Istituto Ortopedico Galeazzi, Milano, Italy; Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milano, Italy
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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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Kang Z, Xu A, Wang L. Predictive role of T2WI and ADC-derived texture parameters in differentiating Gleason score 3 + 4 and 4 + 3 prostate cancer. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2021; 29:307-315. [PMID: 33522997 DOI: 10.3233/xst-200785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
BACKGROUND Since Gleason score (GS) 4 + 3 prostate cancer (PCa) has a worse prognosis than GS 3 + 4 PCa, differentiating these two types of PCa is of clinical significance. OBJECTIVE To assess the predictive roles of using T2WI and ADC-derived image texture parameters in differentiating GS 3 + 4 from GS 4 + 3 PCa. METHODS Forty-eight PCa patients of GS 3 + 4 and 37 patients of GS 4 + 3 are retrieved and randomly divided into training (60%) and testing (40%) sets. Axial image showing the maximum tumor size is selected in the T2WI and ADC maps for further image texture feature analysis. Three hundred texture features are computed from each region of interest (ROI) using MaZda software. Feature reduction is implemented to obtain 30 optimal features, which are then used to generate the most discriminative features (MDF). Receiver operating characteristic (ROC) curve analysis is performed on MDF values in the training sets to achieve cutoff values for determining the correct rates of discrimination between two Gleason patterns in the testing sets. RESULTS ROC analysis on T2WI and ADC-derived MDF values in the training set (n = 51) results in a mean area under the curve (AUC) of 0.953±0.025 (with sensitivity 0.9274±0.0615 and specificity 0.897±0.069), and 0.985±0.013 (with sensitivity 0.9636±0.0446 and specificity 0.9726±0.0258), respectively. Using the corresponding MDF cutoffs, 95.3% (ranges from 76.5% to 100%) and 94.1% (ranged from 76.5% to 100%) of test cases (n = 34) are correctly discriminated using T2WI and ADC-derived MDF values, respectively. CONCLUSIONS The study demonstrates that using T2WI and ADC-derived image texture parameters has a potential predictive role in differentiating GS 3 + 4 and GS 4 + 3 PCa.
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Affiliation(s)
- Zhen Kang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Technology and Science, Jiefang Avenue, Wuhan, China
| | - Anhui Xu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Technology and Science, Jiefang Avenue, Wuhan, China
| | - Liang Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Technology and Science, Jiefang Avenue, Wuhan, China
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Cai JH, He Y, Zhong XL, Lei H, Wang F, Luo GH, Zhao H, Liu JC. Magnetic Resonance Texture Analysis in Alzheimer's disease. Acad Radiol 2020; 27:1774-1783. [PMID: 32057617 DOI: 10.1016/j.acra.2020.01.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 01/05/2020] [Accepted: 01/06/2020] [Indexed: 12/11/2022]
Abstract
Texture analysis is an emerging field that allows mathematical detection of changes in MRI signals that are not visible among image pixels. Alzheimer's disease, a progressive neurodegenerative disease, is the most common cause of dementia. Recently, multiple texture analysis studies in patients with Alzheimer's disease have been performed. This review summarizes the main contributors to Alzheimer's disease-associated cognitive decline, presents a brief overview of texture analysis, followed by review of various MR imaging texture analysis applications in Alzheimer's disease. We also discuss the current challenges for widespread clinical utilization. MR texture analysis could potentially be applied to develop neuroimaging biomarkers for use in Alzheimer's disease clinical trials and diagnosis.
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Affiliation(s)
- Jia-Hui Cai
- Department of Radiology, The First Affiliated Hospital of University of South China, Chuanshan Road No. 69, Hengyang 421000, Hunan, China
| | - Yuan He
- Department of Radiology, The First Affiliated Hospital of University of South China, Chuanshan Road No. 69, Hengyang 421000, Hunan, China
| | - Xiao-Lin Zhong
- Institute of Clinical Medicine, The First Affiliated Hospital of University of South China, Hengyang, Hunan, China
| | - Hao Lei
- Department of Radiology, The First Affiliated Hospital of University of South China, Chuanshan Road No. 69, Hengyang 421000, Hunan, China
| | - Fang Wang
- Department of Radiology, The First Affiliated Hospital of University of South China, Chuanshan Road No. 69, Hengyang 421000, Hunan, China
| | - Guang-Hua Luo
- Department of Radiology, The First Affiliated Hospital of University of South China, Chuanshan Road No. 69, Hengyang 421000, Hunan, China
| | - Heng Zhao
- Department of Radiology, The First Affiliated Hospital of University of South China, Chuanshan Road No. 69, Hengyang 421000, Hunan, China; Department of Radiology, Shengjing Hospital of China Medical University, Sanhao Street No. 36, Heping District, Shenyang 110004, China.
| | - Jin-Cai Liu
- Department of Radiology, The First Affiliated Hospital of University of South China, Chuanshan Road No. 69, Hengyang 421000, Hunan, China
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Zhang Y, Li X, Lv Y, Gu X. Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma. Tomography 2020; 6:325-332. [PMID: 33364422 PMCID: PMC7744193 DOI: 10.18383/j.tom.2020.00039] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The diagnosis of patients with suspected angiomyolipoma relies on the detection of abundant macroscopic intralesional fat, which is always of no use to differentiate fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma and diagnosis of fp-AML excessively depends on individual experience. Texture analysis was proven to be a potentially useful biomarker for distinguishing between benign and malignant tumors because of its capability of providing objective and quantitative assessment of lesions by analyzing features that are not visible to the human eye. This review aimed to summarize the literature on the use of texture analysis to diagnose patients with fat-poor angiomyolipoma vs those with renal cell carcinoma and to evaluate its current application, limitations, and future challenges in order to avoid unnecessary surgical resection.
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Affiliation(s)
- Yuhan Zhang
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, China; and
| | - Xu Li
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, China; and
| | - Yang Lv
- Department of Anesthesia, The Second Hospital of Jilin University, Changchun, China
| | - Xinquan Gu
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, China; and
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22
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Impact of radiomics on prostate cancer detection: a systematic review of clinical applications. Curr Opin Urol 2020; 30:754-781. [DOI: 10.1097/mou.0000000000000822] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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23
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Nelson CR, Ekberg J, Fridell K. Prostate Cancer Detection in Screening Using Magnetic Resonance Imaging and Artificial Intelligence. ACTA ACUST UNITED AC 2020. [DOI: 10.2174/1874061802006010001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Prostate cancer is a leading cause of death among men who do not participate in a screening programme. MRI forms a possible alternative for prostate analysis of a higher level of sensitivity than the PSA test or biopsy. Magnetic resonance is a non-invasive method and magnetic resonance tomography produces a large amount of data. If a screening programme were implemented, a dramatic increase in radiologist workload and patient waiting time will follow. Computer Aided-Diagnose (CAD) could assist radiologists to decrease reading times and cost, and increase diagnostic effectiveness. CAD mimics radiologist and imaging guidelines to detect prostate cancer.
Aim:
The purpose of this study was to analyse and describe current research in MRI prostate examination with the aid of CAD. The aim was to determine if CAD systems form a reliable method for use in prostate screening.
Methods:
This study was conducted as a systematic literature review of current scientific articles. Selection of articles was carried out using the “Preferred Reporting Items for Systematic Reviews and for Meta-Analysis” (PRISMA). Summaries were created from reviewed articles and were then categorised into relevant data for results.
Results:
CAD has shown that its capability concerning sensitivity or specificity is higher than a radiologist. A CAD system can reach a peak sensitivity of 100% and two CAD systems showed a specificity of 100%. CAD systems are highly specialised and chiefly focus on the peripheral zone, which could mean missing cancer in the transition zone. CAD systems can segment the prostate with the same effectiveness as a radiologist.
Conclusion:
When CAD analysed clinically-significant tumours with a Gleason score greater than 6, CAD outperformed radiologists. However, their focus on the peripheral zone would require the use of more than one CAD system to analyse the entire prostate.
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Hamm CA, Beetz NL, Savic LJ, Penzkofer T. [Artificial intelligence and radiomics in MRI-based prostate diagnostics]. Radiologe 2020; 60:48-55. [PMID: 31802148 DOI: 10.1007/s00117-019-00613-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
CLINICAL/METHODICAL ISSUE In view of the diagnostic complexity and the large number of examinations, modern radiology is challenged to identify clinically significant prostate cancer (PCa) with high sensitivity and specificity. Meanwhile overdiagnosis and overtreatment of clinically nonsignificant carcinomas need to be avoided. STANDARD RADIOLOGICAL METHODS Increasingly, international guidelines recommend multiparametric magnetic resonance imaging (mpMRI) as first-line investigation in patients with suspected PCa. METHODICAL INNOVATIONS Image interpretation according to the PI-RADS criteria is limited by interobserver variability. Thus, rapid developments in the field of automated image analysis tools, including radiomics and artificial intelligence (AI; machine learning, deep learning), give hope for further improvement in patient care. PERFORMANCE AI focuses on the automated detection and classification of PCa, but it also attempts to stratify tumor aggressiveness according to the Gleason score. Recent studies present good to very good results in radiomics or AI-supported mpMRI diagnosis. Nevertheless, these systems are not widely used in clinical practice. ACHIEVEMENTS AND PRACTICAL RECOMMENDATIONS In order to apply these innovative technologies, a growing awareness for the need of structured data acquisition, development of robust systems and an increased acceptance of AI as diagnostic support are needed. If AI overcomes these obstacles, it may play a key role in the quantitative and reproducible image-based diagnosis of ever-increasing prostate MRI examination volumes.
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Affiliation(s)
- Charlie Alexander Hamm
- Institute of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Deutschland
| | - Nick Lasse Beetz
- Institute of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Deutschland
| | - Lynn Jeanette Savic
- Institute of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Deutschland
| | - Tobias Penzkofer
- Institute of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Deutschland. .,Berlin Institute of Health, 10178, Berlin, Deutschland.
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25
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Barrett T, Rajesh A. Special issue on prostate imaging. Clin Radiol 2019; 74:821-822. [PMID: 31345556 DOI: 10.1016/j.crad.2019.06.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 06/27/2019] [Indexed: 12/13/2022]
Affiliation(s)
- T Barrett
- Department of Radiology, Addenbrooke's Hospital and the University of Cambridge, Cambridge, CB2 0QQ, UK.
| | - A Rajesh
- Department of Radiology, University Hospitals of Leicester NHS Trust, Gwendolen Road, Leicester, LE5 4PW, UK
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