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Gao J, Zhou J, Liu C, Pan Y, Lin X, Zhang Y. Outcome prediction of SSTR-RADS-3A and SSTR-RADS-3B lesions in patients with neuroendocrine tumors based on 68Ga-DOTATATE PET/MR. J Cancer Res Clin Oncol 2024; 150:272. [PMID: 38795250 PMCID: PMC11127844 DOI: 10.1007/s00432-024-05776-5] [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: 03/04/2024] [Accepted: 05/03/2024] [Indexed: 05/27/2024]
Abstract
PURPOSE Somatostatin receptor (SSTR)-targeted PET imaging has emerged as a common approach to evaluating those patients with well-differentiated neuroendocrine tumors (NETs). The SSTR reporting and data system (SSTR-RADS) version 1.0 provides a means of categorizing lesions from 1 to 5 according to the likelihood of NET involvement, with SSTR-RADS-3A (soft-tissue) and SSTR-RADS-3B (bone) lesions being those suggestive of but without definitive NET involvement. The goal of the present study was to assess the ability of 68Ga-DOTATATE PET/MR imaging data to predict outcomes for indeterminate SSTR-RADS-3A and 3B lesions. METHODS NET patients with indeterminate SSTR-RADS-3A or SSTR-RADS-3B lesions who underwent 68Ga-DOTATATE PET/MR imaging from April 2020 through August 2023 were retrospectively evaluated. All patients underwent follow-up through December 2023 (median, 17 months; (3-31 months)), with imaging follow-up or biopsy findings ultimately being used to classify lesions as malignant or benign. Lesion maximum standardized uptake value (SUVmax) along with minimum and mean apparent diffusion coefficient (ADCmin and ADCmean) values were measured and assessed for correlations with outcomes on follow-up. RESULTS In total, 33 indeterminate SSTR-RADS-3 lesions from 22 patients (19 SSTR-RADS-3A and 14 SSTR-RADS-3B) were identified based upon baseline 68Ga-DOTATATE PET/MR findings. Over the course of follow-up, 16 of these lesions (48.5%) were found to exhibit true NET positivity, including 9 SSTR-RADS-3A and 7 SSTR-RADS-3B lesions. For SSTR-RADS-3A lymph nodes, a diameter larger than 0.7 cm and an ADCmin of 779 × 10-6mm2/s or lower were identified as being more likely to be associated with metastatic lesions. Significant differences in ADCmin and ADCmean were identified when comparing metastatic and non-metastatic SSTR-RADS-3B bone lesions (P < 0.05), with these parameters offering a high predictive ability (AUC = 0.94, AUC = 0.86). CONCLUSION Both diameter and ADCmin can aid in the accurate identification of the nature of lesions associated with SSTR-RADS-3A lymph nodes, whereas ADCmin and ADCmean values can inform the accurate interpretation of SSTR-RADS-3B bone lesions.
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Affiliation(s)
- Jing Gao
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Road, Shanghai, 200025, China
| | - Jinxin Zhou
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Road, Shanghai, 200025, China
| | - Chang Liu
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Road, Shanghai, 200025, China
| | - Yu Pan
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Road, Shanghai, 200025, China
| | - Xiaozhu Lin
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Road, Shanghai, 200025, China.
| | - Yifan Zhang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Road, Shanghai, 200025, China.
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Xinyang S, Shuang Z, Tianci S, Xiangyu H, Yangyang W, Mengying D, Jingran Z, Feng Y. A machine learning radiomics model based on bpMRI to predict bone metastasis in newly diagnosed prostate cancer patients. Magn Reson Imaging 2024; 107:15-23. [PMID: 38181835 DOI: 10.1016/j.mri.2023.12.009] [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: 01/31/2023] [Revised: 09/07/2023] [Accepted: 12/28/2023] [Indexed: 01/07/2024]
Abstract
OBJECTIVES To develop and evaluate a machine learning radiomics model based on biparametric magnetic resonance imaging MRI (bpMRI) to predict bone metastasis (BM) status in newly diagnosed prostate cancer (PCa) patients. METHODS We retrospectively analyzed bpMRI scans of PCa patients from multiple centers between January 2016 and October 2021. 348 PCa patients were recruited from two institutions for this study. The first institution contributed 284 patients, stratified and randomly divided into training and internal validation cohorts at a 7:3 ratio. The remaining 64 patients were sourced from the second institution and comprised the external validation cohort. Radiomics features were extracted from axial T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) tumor regions. We developed the radiomics prediction model for BM in the training cohort and validated it in the internal and external validation cohorts. As a benchmark, we trained the logistic regression model with lasso feature reduction (LFR-LRM) in the training cohort and further compared it with Naive Bayes, eXtreme Gradient Boosting (XGboost), Random Forest (RF), GBDT, SVM, Adaboost, and KNN algorithms and validated in both the internal and external cohorts. The performance of several predictive models was assessed by receiver operating characteristic (ROC). RESULTS The LFR-LRM model achieved an area under the receiver operating characteristic curve (AUC) of 0.89 (95% CI: 0.822-0.974) and an accuracy of 0.828 (95% CI: 0.713-0.911). The AUC and accuracy in external validation were 0.866 (95% CI: 0.784-0.948) and 0.769 (95% CI: 0.648-0.864), respectively. The RF and XGBoost models outperformed the LFR-LRM, with AUCs of 0.907 (95% CI: 0.863-0.949) and 0.928 (95% CI: 0.882-0.974) and accuracies of 0.831 (95% CI: 0.727-0.907) and 0.884 (95% CI: 0.792-0.946). External validation for these models yielded AUCs and accuracies of 0.911 (95% CI: 0.861-0.966), 0.921 (95% CI: 0.889-0.953), and 0.846 (95% CI: 0.735-0.923) and 0.876 (95% CI: 0.771-0.945), respectively. CONCLUSIONS The XGboost machine learning model is more accurate than LFR-LRM for predicting BM in patients with newly confirmed PCa.
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Affiliation(s)
- Song Xinyang
- Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China
| | - Zhang Shuang
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441000, China
| | - Shen Tianci
- Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China
| | - Hu Xiangyu
- Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China
| | - Wang Yangyang
- Department of Orthopedics, Xiangyang No. 1 People's Hospital, Jinzhou Medical University Union Training Base, Xiangyang 441000, China
| | - Du Mengying
- Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China
| | - Zhou Jingran
- Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China.
| | - Yang Feng
- Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China.
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Gautam SK, Dalal V, Sajja BR, Gupta S, Gulati M, Dwivedi NV, Aithal A, Cox JL, Rachagani S, Liu Y, Chung V, Salgia R, Batra SK, Jain M. Endothelin-axis antagonism enhances tumor perfusion in pancreatic cancer. Cancer Lett 2022; 544:215801. [PMID: 35732216 PMCID: PMC10198578 DOI: 10.1016/j.canlet.2022.215801] [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: 03/14/2022] [Revised: 06/02/2022] [Accepted: 06/16/2022] [Indexed: 11/20/2022]
Abstract
Delivery of therapeutic agents in pancreatic cancer (PC) is impaired due to its hypovascular and desmoplastic tumor microenvironment. The Endothelin (ET)-axis is the major regulator of vasomotor tone under physiological conditions and is highly upregulated in multiple cancers. We investigated the effect of dual endothelin receptor antagonist bosentan on perfusion and macromolecular transport in a PC cell-fibroblast co-implantation tumor model using Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI). Following bosentan treatment, the contrast enhancement ratio and wash-in rates in tumors were two- and nine times higher, respectively, compared to the controls, whereas the time to peak was significantly shorter (7.29 ± 1.29 min v/s 22.08 ± 5.88 min; p = 0.04). Importantly, these effects were tumor selective as the magnitudes of change for these parameters were much lower in muscles. Bosentan treatment also reduced desmoplasia and improved intratumoral distribution of high molecular weight FITC-dextran. Overall, these findings support that targeting the ET-axis can serve as a potential strategy to selectively enhance tumor perfusion and improve the delivery of therapeutic agents in pancreatic tumors.
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Affiliation(s)
- Shailendra K Gautam
- Department of Biochemistry and Molecular Biology, College of Medicine, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Vipin Dalal
- Department of Biochemistry and Molecular Biology, College of Medicine, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Balasrinivasa R Sajja
- Department of Radiology, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Suprit Gupta
- Department of Biochemistry and Molecular Biology, College of Medicine, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Mansi Gulati
- Department of Biochemistry and Molecular Biology, College of Medicine, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Nidhi V Dwivedi
- Department of Biochemistry and Molecular Biology, College of Medicine, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Abhijit Aithal
- Department of Biochemistry and Molecular Biology, College of Medicine, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Jesse L Cox
- Department of Pathology and Microbiology, College of Medicine, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Satyanarayana Rachagani
- Department of Biochemistry and Molecular Biology, College of Medicine, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Yutong Liu
- Department of Radiology, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Vincent Chung
- Department of Medical Oncology and Experimental Therapeutics, City of Hope Comprehensive Cancer Center, Duarte, CA, 91010, USA
| | - Ravi Salgia
- Department of Medical Oncology and Experimental Therapeutics, City of Hope Comprehensive Cancer Center, Duarte, CA, 91010, USA
| | - Surinder K Batra
- Department of Biochemistry and Molecular Biology, College of Medicine, University of Nebraska Medical Center, Omaha, NE, 68198, USA; Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Maneesh Jain
- Department of Biochemistry and Molecular Biology, College of Medicine, University of Nebraska Medical Center, Omaha, NE, 68198, USA; Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
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Wang J, Huang J, Guo Y, Fu Y, Cao Y, Zhou K, Ma J, Lv B, Huang W. Identification and functional analysis of LncRNA-XIST ceRNA network in prostate cancer. BMC Cancer 2022; 22:935. [PMID: 36038831 PMCID: PMC9426231 DOI: 10.1186/s12885-022-10007-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 08/16/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Long non-coding RNAs (lncRNAs) play a functional role in the progression of prostate cancer (PCa). However, the molecular mechanism, expression, or function of the lncRNA XIST in PCa is not well understood. Therefore, the major goal of this study was to investigate the involvement of XIST in PCa. METHODS We used the The Cancer Genome Atlas (TCGA) database to conduct a pan-cancer bioinformatics analysis of XIST and identified that it may play an important role in prostate cancer. This finding was verified using clinical samples and in vitro assays. Finally, we constructed an XIST ceRNA network for prostate cancer. RESULTS Our in vitro and in vivo results showed that the XIST gene expression level was higher in PCa derived cells and tissues compared to that in normal cells and tissues. XIST gene expression level was positively correlated with the invasion and proliferation of tumour cells. Furthermore, the downregulation of XIST inhibited the growth of subcutaneous 22Rv1 xenografts in nude mice. In addition, we constructed a XIST ceRNA network. Consistent with previous studies, we found that the role of XIST is mediated through via sponges, such as miRNA -96-5p, miRNA -153-3p, and miRNA-182-5p. CONCLUSION High expression level of XIST can lead to enhanced carcinogenicity in PCa. Therefore, XIST has the potential to be used as a prognostic marker and may become a new research focus for the treatment of PCa.
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Affiliation(s)
- Jie Wang
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Jie Huang
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yingxue Guo
- College of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yuli Fu
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yifang Cao
- Urology Department, Jiaxing First Hospital, Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang, China
| | - Kang Zhou
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Jianxiong Ma
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Bodong Lv
- Department of Urology, School of Medicine, The Second Affiliated Hospital, Zhejia-Ng University, Hangzhou, China.
| | - Wenjie Huang
- Department of Urology, School of Medicine, The Second Affiliated Hospital, Zhejia-Ng University, Hangzhou, China.
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Synthetic correlated diffusion imaging hyperintensity delineates clinically significant prostate cancer. Sci Rep 2022; 12:3376. [PMID: 35232991 PMCID: PMC8888633 DOI: 10.1038/s41598-022-06872-7] [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: 09/05/2021] [Accepted: 02/08/2022] [Indexed: 11/08/2022] Open
Abstract
Prostate cancer (PCa) is the second most common cancer in men worldwide and the most frequently diagnosed cancer among men in more developed countries. The prognosis of PCa is excellent if detected at an early stage, making early screening crucial for detection and treatment. In recent years, a new form of diffusion magnetic resonance imaging called correlated diffusion imaging (CDI) was introduced, and preliminary results show promise as a screening tool for PCa. In the largest study of its kind, we investigate the relationship between PCa presence and a new variant of CDI we term synthetic correlated diffusion imaging (CDI\documentclass[12pt]{minimal}
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\begin{document}$$^s$$\end{document}s), as well as its performance for PCa delineation compared to current standard MRI techniques [T2-weighted (T2w) imaging, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging] across a cohort of 200 patient cases. Statistical analyses reveal that hyperintensity in CDI\documentclass[12pt]{minimal}
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\begin{document}$$^s$$\end{document}s is a strong indicator of PCa presence and achieves strong delineation of clinically significant cancerous tissue compared to T2w, DWI, and DCE. These results suggest that CDI\documentclass[12pt]{minimal}
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\begin{document}$$^s$$\end{document}s hyperintensity may be a powerful biomarker for the presence of PCa, and may have a clinical impact as a diagnostic aid for improving PCa screening.
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Siddiqui K, Magsi K, Iqbal J, Ahmed A, Fazal A, Siddiqui I, Khan S. To Determine the Diagnostic Accuracy of Diffusion-Weighted Imaging in the Diagnosis of Prostate Carcinoma Taking Histopathology As the Gold Standard. Cureus 2021; 13:e19508. [PMID: 34912645 PMCID: PMC8666136 DOI: 10.7759/cureus.19508] [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] [Accepted: 11/12/2021] [Indexed: 11/05/2022] Open
Abstract
Introduction Carcinoma of the prostate is the most common malignancy among males. Serum prostate-specific antigen (PSA) levels and digital rectal examination (DRE) are the initial investigations for the detection of prostate cancer. In recent years, some investigators have used color Doppler ultrasound and diffusion-weighted imaging (DWI) for the diagnosis of prostate cancer and avoided invasive and painful investigation, i.e., biopsy. The purpose of the study is to determine the validity of DWI in detecting prostatic cancer taking histopathology as the gold standard. Material and methods This cross-sectional study was conducted prospectively in the radiology department of a tertiary care hospital from January 1, 2019, to December 31, 2020. This study was approved by the Departmental Research Committee. A total of 272 male patients were included in our study who have elevated PSA levels (>4.0 ng/ml) with symptoms of hematuria and urinary retention. All included subjects were sent to the radiology department for DWI imaging. The DWI imaging was analyzed for prostate cancer and the results were correlated with histopathological diagnosis. Results The average age of patients was 50.28±9.93 years. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of DWI in the diagnoses of prostate cancer were 86.7%, 87.8%, 75.8%, 93.8%, and 87.5%, respectively. Conclusion DWI is an informative and non-invasive imaging modality with high diagnostic accuracy for the diagnosis of prostate carcinoma.
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Affiliation(s)
| | - Kausar Magsi
- Radiology, Sindh Institute of Urology and Transplantation, Karachi, PAK
| | - Junaid Iqbal
- Radiology, Aga Khan University Hospital, Karachi, PAK
| | - Anwar Ahmed
- Radiology, The Aga Khan University, Karachi, PAK
| | - Adnan Fazal
- Cardiology, National Institute of Cardiovascular Diseases, Karachi, PAK
| | | | - Shahmeer Khan
- Radiology, Aga Khan University Hospital, Karachi, PAK
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Imaging of Neuroendocrine Prostatic Carcinoma. Cancers (Basel) 2021; 13:cancers13225765. [PMID: 34830919 PMCID: PMC8616225 DOI: 10.3390/cancers13225765] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/07/2021] [Accepted: 11/10/2021] [Indexed: 12/27/2022] Open
Abstract
Neuroendocrine prostate cancer (NEPC) is an aggressive subtype of prostate cancer that typically has a high metastatic potential and poor prognosis in comparison to the adenocarcinoma subtype. Although it can arise de novo, NEPC much more commonly occurs as a mechanism of treatment resistance during therapy for conventional prostatic adenocarcinoma, the latter is also termed as castration-resistant prostate cancer (CRPC). The incidence of NEPC increases after hormonal therapy and they represent a challenge, both in the radiological and pathological diagnosis, as well as in the clinical management. This article provides a comprehensive imaging review of prostatic neuroendocrine tumors.
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Fatty Acid Metabolism Reprogramming in Advanced Prostate Cancer. Metabolites 2021; 11:metabo11110765. [PMID: 34822423 PMCID: PMC8618281 DOI: 10.3390/metabo11110765] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 11/01/2021] [Accepted: 11/03/2021] [Indexed: 12/23/2022] Open
Abstract
Prostate cancer (PCa) is a carcinoma in which fatty acids are abundant. Fatty acid metabolism is rewired during PCa development. Although PCa can be treated with hormone therapy, after prolonged treatment, castration-resistant prostate cancer can develop and can lead to increased mortality. Changes to fatty acid metabolism occur systemically and locally in prostate cancer patients, and understanding these changes may lead to individualized treatments, especially in advanced, castration-resistant prostate cancers. The fatty acid metabolic changes are not merely reflective of oncogenic activity, but in many cases, these represent a critical factor in cancer initiation and development. In this review, we analyzed the literature regarding systemic changes to fatty acid metabolism in PCa patients and how these changes relate to obesity, diet, circulating metabolites, and peri-prostatic adipose tissue. We also analyzed cellular fatty acid metabolism in prostate cancer, including fatty acid uptake, de novo lipogenesis, fatty acid elongation, and oxidation. This review broadens our view of fatty acid switches in PCa and presents potential candidates for PCa treatment and diagnosis.
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Precise Identification of Prostate Cancer from DWI Using Transfer Learning. SENSORS 2021; 21:s21113664. [PMID: 34070290 PMCID: PMC8197382 DOI: 10.3390/s21113664] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 05/17/2021] [Accepted: 05/18/2021] [Indexed: 12/23/2022]
Abstract
Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions and reduce the dependence on invasive techniques. In this study, a CAD system that detects and identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. Methods: The proposed system first uses non-negative matrix factorization (NMF) to integrate three different types of features for the accurate segmentation of prostate regions. Then, discriminatory features in the form of apparent diffusion coefficient (ADC) volumes are estimated from the segmented regions. The ADC maps that constitute these volumes are labeled by a radiologist to identify the ADC maps with malignant or benign tumors. Finally, transfer learning is used to fine-tune two different previously-trained convolutional neural network (CNN) models (AlexNet and VGGNet) for detecting and identifying prostate cancer. Results: Multiple experiments were conducted to evaluate the accuracy of different CNN models using DWI datasets acquired at nine distinct b-values that included both high and low b-values. The average accuracy of AlexNet at the nine b-values was 89.2±1.5% with average sensitivity and specificity of 87.5±2.3% and 90.9±1.9%. These results improved with the use of the deeper CNN model (VGGNet). The average accuracy of VGGNet was 91.2±1.3% with sensitivity and specificity of 91.7±1.7% and 90.1±2.8%. Conclusions: The results of the conducted experiments emphasize the feasibility and accuracy of the developed system and the improvement of this accuracy using the deeper CNN.
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Krishnan MA, Chelvam V. Developing μSpherePlatform Using a Commercial Hairbrush: An Agarose 3D Culture Platform for Deep-Tissue Imaging of Prostate Cancer. ACS APPLIED BIO MATERIALS 2021; 4:4254-4270. [DOI: 10.1021/acsabm.1c00086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Mena A. Krishnan
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore 453 552, India
| | - Venkatesh Chelvam
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore 453 552, India
- Department of Chemistry, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore 453 552, India
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Ragheb SR, Bassiouny RH. Can mean ADC value and ADC ratio of benign prostate tissue to prostate cancer assist in the prediction of clinically significant prostate cancer within the PI-RADSv2 scoring system? THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2020. [DOI: 10.1186/s43055-020-00347-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
The aim of this study is to investigate whether quantitative DW metrics can provide additive value to the reliable categorization of lesions within existing PI-RADSv2 guidelines. Fifty-eight patients with clinically suspicious prostate cancer who underwent PR examination, PSA serum levels, sextant TRUS-guided biopsies, and bi-parametric MR imaging were included in the study.
Results
Sixty-six lesions were detected by histopathological analysis of surgical specimens. The mean ADC values were significantly lower in tumor than non-tumor tissue. The mean ADC value inversely correlated with Gleason score of tumors with a significant p value < 0.001.Conversely, a positive relationship was found between the ADC ratio (ADC of benign prostatic tissue to prostate cancer) and the pathologic Gleason score with a significant elevation of the ADC ratio along with an increase of the pathologic Gleason score (p < 0.001). ROC curves constructed for the tumor ADC and ADC ratio helped to distinguish pathologically aggressive (Gleason score ≥ 7) from non-aggressive (Gleason score ≤ 6) tumors and to correlate it with PIRADSv2 scoring to predict the presence of clinically significant PCA (PIRADSv2 DW ≥ 4). The ability of the tumor ADC and ADC ratio to predict highly aggressive tumors (GS> 7) was high (AUC for ADC and ADC ratio, 0.946 and 0.897; p = 0.014 and 0.039, respectively). The ADC cut-off value for GS ≥ 7 was < 0.7725 and for GS ≤ 6 was > 0.8620 with sensitivity and specificity 97 and 94%. The cutoff ADC ratio for predicting (GS > 7) was 1.42 and for GS ≤ 6 was > 1.320 with sensitivity and specificity 97 and 92%. By applying this ADC ratio cut-off value the sensitivity and specificity of reader 1 for correct categorization of PIRADSv2 DW > 4 increased from 90 and 68% to 95 and 90% and that of reader 2 increased from 94 and 88% to 97 and 92%, respectively.
Conclusion
Estimation of DW metrics (ADC and ADC ratio between benign prostatic tissue and prostate cancer) allow the non-invasive assessment of biological aggressiveness of prostate cancer and allow reliable application of the PIRADSv2 scoring to determine clinically significant cancer (DW score > 4) which may contribute in planning initial treatment strategies.
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Muglia VF, Vargas HA. Doctor, a patient is on the phone asking about the endorectal coil! Abdom Radiol (NY) 2020; 45:4003-4011. [PMID: 32300836 DOI: 10.1007/s00261-020-02528-0] [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] [Indexed: 01/01/2023]
Abstract
The question referred to in the title of this article is a relatively common situation when performing prostate MRI in some healthcare settings. Moreover, the answer is not always straightforward. The decisions on type of receiver coil for prostate MRI and whether or not an endorectal coil (ERC) should be used is based on several factors. These relate to the patient (e.g., body habitus, presence of metallic devices in the pelvis), the focus of the exam (diagnosis, staging, recurrence), and characteristics of the MRI system (e.g., magnetic field strength and hardware components including coil design and number of elements/channels available in the surface coil). Historically, the combined use of an ERC and a surface coil was the optimal combination for maximizing the signal-to-noise ratio (SNR), particularly for low-strength magnetic fields (1.5T). However, there are several disadvantages associated with the use of an ERC, and several studies have advocated equivalent clinical performance of modern MRI systems for diagnosis and staging of prostate cancer (PCa), either with ERC or surface alone. Accordingly, there is a wide variation in the precise imaging technique across institutions. This article focuses on the most relevant aspects of the decision of whether to use an ERC for PCa MR imaging.
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Affiliation(s)
- Valdair Francisco Muglia
- Department of Medical Images, Radiation Therapy and Oncohematology, Ribeirao Preto Medical School, Hospital Clinicas, University of São Paulo, Av. Bandeirantes 3900, Campus Monte Alegre, Ribeirão Prêto, 14049-900, Brazil.
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A Hybrid End-to-End Approach Integrating Conditional Random Fields into CNNs for Prostate Cancer Detection on MRI. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10010338] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Prostate Cancer (PCa) is the most common oncological disease in Western men. Even though a growing effort has been carried out by the scientific community in recent years, accurate and reliable automated PCa detection methods on multiparametric Magnetic Resonance Imaging (mpMRI) are still a compelling issue. In this work, a Deep Neural Network architecture is developed for the task of classifying clinically significant PCa on non-contrast-enhanced MR images. In particular, we propose the use of Conditional Random Fields as a Recurrent Neural Network (CRF-RNN) to enhance the classification performance of XmasNet, a Convolutional Neural Network (CNN) architecture specifically tailored to the PROSTATEx17 Challenge. The devised approach builds a hybrid end-to-end trainable network, CRF-XmasNet, composed of an initial CNN component performing feature extraction and a CRF-based probabilistic graphical model component for structured prediction, without the need for two separate training procedures. Experimental results show the suitability of this method in terms of classification accuracy and training time, even though the high-variability of the observed results must be reduced before transferring the resulting architecture to a clinical environment. Interestingly, the use of CRFs as a separate postprocessing method achieves significantly lower performance with respect to the proposed hybrid end-to-end approach. The proposed hybrid end-to-end CRF-RNN approach yields excellent peak performance for all the CNN architectures taken into account, but it shows a high-variability, thus requiring future investigation on the integration of CRFs into a CNN.
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CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study. NEURAL APPROACHES TO DYNAMICS OF SIGNAL EXCHANGES 2020. [DOI: 10.1007/978-981-13-8950-4_25] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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15
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USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.07.006] [Citation(s) in RCA: 123] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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16
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Afifi AHAA, Etaby AN, Ahmad MAY, Farghaly YT. Value of diffusion weighted magnetic resonance imaging in the prediction of cancer prostate. ALEXANDRIA JOURNAL OF MEDICINE 2019. [DOI: 10.1016/j.ajme.2012.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/27/2022] Open
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17
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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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18
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Lee H, Hwang SI, Lee HJ, Byun SS, Lee SE, Hong SK. Diagnostic performance of diffusion-weighted imaging for prostate cancer: Peripheral zone versus transition zone. PLoS One 2018; 13:e0199636. [PMID: 29933396 PMCID: PMC6014656 DOI: 10.1371/journal.pone.0199636] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 06/11/2018] [Indexed: 01/22/2023] Open
Abstract
OBJECTIVES Diffusion-weighted imaging (DWI) has been shown to be an important component of multiparametric magnetic resonance imaging (mpMRI). We compared performance of DWI for detection of prostate cancer (PCa) in peripheral zone (PZ) and transition zone (TZ) of prostate. MATERIALS AND METHODS We reviewed data of 460 subjects who underwent preoperative 3.0-Tesla mpMRI and subsequently radical prostatectomy. Level of suspicion for PCa was graded using 5-grade Likert-scale from DWI. Topographic analyses were performed for location of tumor foci at each surgical specimen. Among those with DWI grade ≥ III, we analyzed concordance rate on the location of radiologic and pathologic index lesions between DWI and surgical specimens. RESULTS Among 460 patients, 351 (76.3%) patients showed suspicious DWI lesions (57.5% in PZ, 42.5% in TZ). Multivariates regression analyses revealed significant associations between high DWI grade and adverse pathologic outcomes including pathologic stage, Gleason score, tumor volume and extracapsular extension (all p < 0.05). Overall concordance rates between DWI and surgical specimen were 75.8%, significantly higher in PZ than TZ (82.2% vs. 67.1% p = 0.002). Such concordance rate showed a positive linear association with increase in DWI grading (p < 0.001). Among 109 patients with DWI grade I-II, 28 (25.7%) harbored high grade disease (pathologic Gleason score ≥ 4 + 3). CONCLUSIONS DWI detects tumors in PZ of prostate more accurately than those in TZ. Such accuracy of DWI was shown to be more evident with higher DWI grade. Meanwhile, a negative DWI did not guarantee absence of high grade PCa.
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Affiliation(s)
- Hakmin Lee
- Department of Urology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Sung Il Hwang
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Hak Jong Lee
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Seok-Soo Byun
- Department of Urology, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Urology, Seoul National University College of Medicine, Seoul, Korea
| | - Sang Eun Lee
- Department of Urology, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Urology, Seoul National University College of Medicine, Seoul, Korea
| | - Sung Kyu Hong
- Department of Urology, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Urology, Seoul National University College of Medicine, Seoul, Korea
- * E-mail:
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19
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Dou S, Bai Y, Shandil A, Ding D, Shi D, Haacke EM, Wang M. Detecting prostate cancer and prostatic calcifications using advanced magnetic resonance imaging. Asian J Androl 2018; 19:439-443. [PMID: 27004542 PMCID: PMC5507089 DOI: 10.4103/1008-682x.177840] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Prostate cancer and prostatic calcifications have a high incidence in elderly men. We aimed to investigate the diagnostic capabilities of susceptibility-weighted imaging in detecting prostate cancer and prostatic calcifications. A total number of 156 men, including 34 with prostate cancer and 122 with benign prostate were enrolled in this study. Computed tomography, conventional magnetic resonance imaging, diffusion-weighted imaging, and susceptibility-weighted imaging were performed on all the patients. One hundred and twelve prostatic calcifications were detected in 87 patients. The sensitivities and specificities of the conventional magnetic resonance imaging, apparent diffusion coefficient, and susceptibility-filtered phase images in detecting prostate cancer and prostatic calcifications were calculated. McNemar's Chi-square test was used to compare the differences in sensitivities and specificities between the techniques. The results showed that the sensitivity and specificity of susceptibility-filtered phase images in detecting prostatic cancer were greater than that of conventional magnetic resonance imaging and apparent diffusion coefficient (P < 0.05). In addition, the sensitivity and specificity of susceptibility-filtered phase images in detecting prostatic calcifications were comparable to that of computed tomography and greater than that of conventional magnetic resonance imaging and apparent diffusion coefficient (P < 0.05). Given the high incidence of susceptibility-weighted imaging (SWI) abnormality in prostate cancer, we conclude that susceptibility-weighted imaging is more sensitive and specific than conventional magnetic resonance imaging, diffusion-weighted imaging, and computed tomography in detecting prostate cancer. Furthermore, susceptibility-weighted imaging can identify prostatic calcifications similar to computed tomography, and it is much better than conventional magnetic resonance imaging and diffusion-weighted imaging.
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Affiliation(s)
- Shewei Dou
- Department of Radiology, Henan Provincial People's Hospital and The People's Hospital of Zhengzhou University, Zhengzhou 450003, Henan, China
| | - Yan Bai
- Department of Radiology, Henan Provincial People's Hospital and The People's Hospital of Zhengzhou University, Zhengzhou 450003, Henan, China
| | - Ankit Shandil
- Department of Postgraduate Education, School of International Education, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Degang Ding
- Department of Urology, Henan Provincial People's Hospital and The People's Hospital of Zhengzhou University, Zhengzhou 450003, Henan, China
| | - Dapeng Shi
- Department of Radiology, Henan Provincial People's Hospital and The People's Hospital of Zhengzhou University, Zhengzhou 450003, Henan, China
| | - E Mark Haacke
- Department of Radiology, Wayne State University, Detroit 48202, MI, USA.,Department of Administration, Magnetic Resonance Innovations Inc., Detroit 48202, USA
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People's Hospital and The People's Hospital of Zhengzhou University, Zhengzhou 450003, Henan, China
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20
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Taha Ali TF, ElHariri MA, Riad MM. Diffusion-weighted MRI in prostatic lesions: Diagnostic performance of normalized ADC using normal peripheral prostatic zone as a reference. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2018. [DOI: 10.1016/j.ejrnm.2017.09.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
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21
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Giganti F, Gambarota G, Moore CM, Robertson NL, McCartan N, Jameson C, Bott SRJ, Winkler M, Whitcher B, Castro-Santamaria R, Emberton M, Allen C, Kirkham A. Prostate cancer detection using quantitative T 2 and T 2 -weighted imaging: The effects of 5-alpha-reductase inhibitors in men on active surveillance. J Magn Reson Imaging 2017; 47:1646-1653. [PMID: 29135073 DOI: 10.1002/jmri.25891] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 10/25/2017] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND T2 -weighted imaging (T2 -WI) information has been used in a qualitative manner in the assessment of prostate cancer. Quantitative derivatives (T2 relaxation time) can be generated from T2 -WI. These outputs may be useful in helping to discriminate clinically significant prostate cancer from background signal. PURPOSE/HYPOTHESIS To investigate changes in quantitative T2 parameters in lesions and noncancerous tissue of men on active surveillance for prostate cancer taking dutasteride 0.5 mg or placebo daily for 6 months. STUDY TYPE Retrospective. POPULATION/SUBJECTS Forty men randomized to 6 months of daily dutasteride (n = 20) or placebo (n = 20). FIELD STRENGTH/SEQUENCE Multiparametric 3T MRI at baseline and 6 months. This included a multiecho MR sequence for quantification of the T2 relaxation times, in three regions of interest (index lesion, noncancerous peripheral [PZ] and transitional [TZ] zones). A synthetic signal contrast (T2 Q contrast) between lesion and noncancerous tissue was assessed using quantitative T2 values. Signal contrast was calculated using the T2 -weighted sequence (T2 W contrast). ASSESSMENT Two radiologists reviewed the scans in consensus according to Prostate Imaging Reporting and Data System (PI-RADS v. 2) guidelines. STATISTICAL TESTS Wilcoxon and Mann-Whitney U-tests, Spearman's correlation. RESULTS When compared to noncancerous tissue, shorter T2 values were observed within lesions at baseline (83.5 and 80.5 msec) and 6 months (81.5 and 81.9 msec) in the placebo and dutasteride arm, respectively. No significant differences for T2 W contrast at baseline and after 6 months were observed, both in the placebo (0.40 [0.29-0.49] vs. 0.43 [0.25-0.49]; P = 0.881) and dutasteride arm (0.35 [0.24-0.47] vs. 0.37 [0.22-0.44]; P = 0.668). There was a significant, positive correlation between the T2 Q contrast and the T2 W contrast values (r = 0.786; P < 0.001). DATA CONCLUSION The exposure to antiandrogen therapy did not significantly influence the T2 contrast or the T2 relaxation values in men on active surveillance for prostate cancer. LEVEL OF EVIDENCE 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1646-1653.
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Affiliation(s)
- Francesco Giganti
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK.,Division of Surgery & Interventional Science, University College London, London, UK
| | - Giulio Gambarota
- INSERM, U1099, Rennes, France.,Université de Rennes 1, LTSI, Rennes, France
| | - Caroline M Moore
- Division of Surgery & Interventional Science, University College London, London, UK.,Department of Urology, University College London Hospital NHS Foundation Trust, London, UK
| | - Nicola L Robertson
- Division of Surgery & Interventional Science, University College London, London, UK.,Department of Urology, University College London Hospital NHS Foundation Trust, London, UK
| | - Neil McCartan
- Division of Surgery & Interventional Science, University College London, London, UK
| | - Charles Jameson
- Department of Pathology, University College London Hospital NHS Foundation Trust, London, UK
| | - Simon R J Bott
- Department of Urology, Frimley Park Hospital, Surrey, UK
| | - Mathias Winkler
- Department of Urology, Charing Cross Hospital, Imperial College NHS Trust, London, UK
| | - Brandon Whitcher
- Klarismo, London, UK.,Department of Mathematics, Imperial College London, UK
| | | | - Mark Emberton
- Division of Surgery & Interventional Science, University College London, London, UK.,Department of Urology, University College London Hospital NHS Foundation Trust, London, UK
| | - Clare Allen
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK
| | - Alex Kirkham
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK
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22
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Shin T, Kaji Y, Shukuya T, Nozaki M, Soh S, Okada H. Significant changes of T2 value in the peripheral zone and seminal vesicles after ejaculation. Eur Radiol 2017; 28:1009-1015. [DOI: 10.1007/s00330-017-5077-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Revised: 08/25/2017] [Accepted: 09/12/2017] [Indexed: 11/28/2022]
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23
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Automated Prostate Gland Segmentation Based on an Unsupervised Fuzzy C-Means Clustering Technique Using Multispectral T1w and T2w MR Imaging. INFORMATION 2017. [DOI: 10.3390/info8020049] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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24
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Furuya K, Kawahara T, Narahara M, Tokita T, Fukui S, Imano M, Mitome T, Ito Y, Izumi K, Osaka K, Yokomizo Y, Hayashi N, Hasumi H, Nawata S, Kawano T, Yao M, Uemura H. Measurement of serum isoform [-2]proPSA derivatives shows superior accuracy to magnetic resonance imaging in the diagnosis of prostate cancer in patients with a total prostate-specific antigen level of 2-10 ng/ml. Scand J Urol 2017; 51:251-257. [PMID: 28351193 DOI: 10.1080/21681805.2017.1298155] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
OBJECTIVE More accurate diagnostic procedures for prostate cancer are needed to avoid unnecessary biopsy due to the low specificity of prostate-specific antigen (PSA). Recent studies showed that the percentage of serum isoform [-2]proPSA (p2PSA) to free PSA (%p2PSA), the Prostate Health Index (PHI) and magnetic resonance imaging (MRI) were more accurate than PSA. The aim of this study was to test the accuracy of %p2PSA, PHI and MRI in discriminating patients with and without prostate cancer. MATERIALS AND METHODS The subjects were 50 consecutive men with a PSA level of 2.0-10.0 ng/ml, who underwent prostate biopsy from October 2012 to July 2014. These patients underwent multiparametric MRI before biopsy, and their serum samples were measured for PSA, free PSA and p2PSA. The sensitivity, specificity and accuracy of PHI, %p2PSA and MRI were compared with PSA in the diagnosis of biopsy-confirmed prostate cancer. RESULTS In a univariate analysis, %p2PSA [area under the curve (AUC): 0.811] and PHI (AUC 0.795) were more accurate than MRI (AUC: 0.583) and PSA (AUC: 0.554) for prostate cancer detection. At 60% sensitivity, the specificity of PHI (76.5%) was higher than that of MRI (52.9%). For significant cancer detection, %p2PSA (AUC: 0.745), PHI (AUC: 0.791) and MRI (AUC: 0.739) were marginally more accurate than PSA (AUC: 0.696). At 85% sensitivity, the specificity of MRI (62.1%) was higher than that of PHI (34.5%). CONCLUSION PHI and %p2PSA can be used for screening the general population and MRI can be used for detection of significant cancer in patients suspected, from screening tests, of having prostate cancer.
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Affiliation(s)
- Kazuhiro Furuya
- a Department of Urology , Graduate School of Medicine, Yokohama City University , Yokohama , Japan
| | - Takashi Kawahara
- b Department of Urology , Yokohama City University Medical Center , Yokohama , Japan
| | - Masaki Narahara
- b Department of Urology , Yokohama City University Medical Center , Yokohama , Japan
| | - Takashi Tokita
- a Department of Urology , Graduate School of Medicine, Yokohama City University , Yokohama , Japan
| | - Sachi Fukui
- a Department of Urology , Graduate School of Medicine, Yokohama City University , Yokohama , Japan
| | - Masashi Imano
- a Department of Urology , Graduate School of Medicine, Yokohama City University , Yokohama , Japan
| | - Taku Mitome
- a Department of Urology , Graduate School of Medicine, Yokohama City University , Yokohama , Japan
| | - Yusuke Ito
- a Department of Urology , Graduate School of Medicine, Yokohama City University , Yokohama , Japan
| | - Koji Izumi
- a Department of Urology , Graduate School of Medicine, Yokohama City University , Yokohama , Japan
| | - Kimito Osaka
- a Department of Urology , Graduate School of Medicine, Yokohama City University , Yokohama , Japan
| | - Yumiko Yokomizo
- a Department of Urology , Graduate School of Medicine, Yokohama City University , Yokohama , Japan
| | - Narihiko Hayashi
- a Department of Urology , Graduate School of Medicine, Yokohama City University , Yokohama , Japan
| | - Hisashi Hasumi
- a Department of Urology , Graduate School of Medicine, Yokohama City University , Yokohama , Japan
| | - Shintaro Nawata
- c Department of Radiology , Graduate School of Medicine, Yokohama City University , Yokohama , Japan
| | - Tsuyoshi Kawano
- c Department of Radiology , Graduate School of Medicine, Yokohama City University , Yokohama , Japan
| | - Masahiro Yao
- a Department of Urology , Graduate School of Medicine, Yokohama City University , Yokohama , Japan
| | - Hiroji Uemura
- b Department of Urology , Yokohama City University Medical Center , Yokohama , Japan
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25
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Goffin KE, Joniau S, Tenke P, Slawin K, Klein EA, Stambler N, Strack T, Babich J, Armor T, Wong V. Phase 2 Study of 99mTc-Trofolastat SPECT/CT to Identify and Localize Prostate Cancer in Intermediate- and High-Risk Patients Undergoing Radical Prostatectomy and Extended Pelvic LN Dissection. J Nucl Med 2017; 58:1408-1413. [DOI: 10.2967/jnumed.116.187807] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Accepted: 03/01/2017] [Indexed: 12/23/2022] Open
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26
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Sun Y, Reynolds H, Wraith D, Williams S, Finnegan ME, Mitchell C, Murphy D, Ebert MA, Haworth A. Predicting prostate tumour location from multiparametric MRI using Gaussian kernel support vector machines: a preliminary study. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2017; 40:39-49. [PMID: 28120144 DOI: 10.1007/s13246-016-0515-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Accepted: 12/12/2016] [Indexed: 01/08/2023]
Abstract
The performance of a support vector machine (SVM) algorithm was investigated to predict prostate tumour location using multi-parametric MRI (mpMRI) data. The purpose was to obtain information of prostate tumour location for the implementation of bio-focused radiotherapy. In vivo mpMRI data were collected from 16 patients prior to radical prostatectomy. Sequences included T2-weighted imaging, diffusion-weighted imaging and dynamic contrast enhanced imaging. In vivo mpMRI was registered with 'ground truth' histology, using ex vivo MRI as an intermediate registration step to improve accuracy. Prostate contours were delineated by a radiation oncologist and tumours were annotated on histology by a pathologist. Five patients with minimal imaging artefacts were selected for this study. A Gaussian kernel SVM was trained and tested on different patient data subsets. Parameters were optimised using leave-oneout cross validation. Signal intensities of mpMRI were used as features and histology annotations as true labels. Prediction accuracy, as well as area under the curve (AUC) of the receiver operating characteristics (ROC) curve, were used to assess performance. Results demonstrated the prediction accuracy ranged from 70.4 to 87.1% and AUC of ROC ranged from 0.81 to 0.94. Additional investigations showed the apparent diffusion coefficient map from diffusion weighted imaging was the most important imaging modality for predicting tumour location. Future work will incorporate additional patient data into the framework to increase the sensitivity and specificity of the model, and will be extended to incorporate predictions of biological characteristics of the tumour which will be used in bio-focused radiotherapy optimisation.
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Affiliation(s)
- Yu Sun
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia. .,Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
| | - Hayley Reynolds
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia.,Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Darren Wraith
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| | - Scott Williams
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia.,Division of Radiation Oncology and Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Mary E Finnegan
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK.,Department of Bioengineering, Imperial College London, London, UK
| | - Catherine Mitchell
- Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Declan Murphy
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Martin A Ebert
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, WA, Australia.,School of Physics, University of Western Australia, Perth, WA, Australia
| | - Annette Haworth
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia.,Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
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27
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Trigui R, Mitéran J, Walker P, Sellami L, Ben Hamida A. Automatic classification and localization of prostate cancer using multi-parametric MRI/MRS. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.07.015] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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28
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Abstract
Magnetic resonance spectroscopy (MRS) is a noninvasive functional technique to evaluate the biochemical behavior of human tissues. This property has been widely used in assessment and therapy monitoring of brain tumors. MRS studies can be implemented outside the brain, with successful and promising results in the evaluation of prostate and breast cancer, although still with limited reproducibility. As a result of technical improvements, malignancies of the musculoskeletal system and abdominopelvic organs can benefit from the molecular information that MRS provides. The technical challenges and main applications in oncology of (1)H MRS in a clinical setting are the focus of this review.
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Rampun A, Zheng L, Malcolm P, Tiddeman B, Zwiggelaar R. Computer-aided detection of prostate cancer in T2-weighted MRI within the peripheral zone. Phys Med Biol 2016; 61:4796-825. [DOI: 10.1088/0031-9155/61/13/4796] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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30
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Role of multiparametric MRI in the diagnosis of prostate cancer: update. Urologia 2016; 83:61-7. [PMID: 26350047 DOI: 10.5301/uro.5000138] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/10/2015] [Indexed: 01/04/2023]
Abstract
Prostate cancer is the most common malignancy of the male gender. The role of magnetic resonance imaging has evolved very rapidly over the years to be currently recognized as a fundamental tool in the diagnosis, treatment and follow-up of prostate cancer.
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31
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Abstract
This article discusses MR imaging of the normal prostate and of disease conditions of the prostate including prostatitis, cystic lesions, amyloidosis, calculi, hematospermia, benign prostatic hyperplasia, and malignancy.
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Affiliation(s)
- Ekta Gupta
- Department of Radiology, Hospital of the University of Pennsylvania, University of Pennsylvania School of Medicine, 3400 Spruce Street, Philadelphia, PA 19104, USA
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32
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Multiparametric MRI of the anterior prostate gland: clinical–radiological–histopathological correlation. Clin Radiol 2016; 71:405-17. [DOI: 10.1016/j.crad.2016.01.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2015] [Revised: 08/19/2015] [Accepted: 01/04/2016] [Indexed: 11/19/2022]
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33
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Rampun A, Chen Z, Malcolm P, Tiddeman B, Zwiggelaar R. Computer-aided diagnosis: detection and localization of prostate cancer within the peripheral zone. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2016; 32:e02745. [PMID: 26313267 DOI: 10.1002/cnm.2745] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2014] [Revised: 05/21/2015] [Accepted: 08/24/2015] [Indexed: 06/04/2023]
Abstract
We propose a methodology for prostate cancer detection and localization within the peripheral zone based on combining multiple segmentation techniques. We extract four image features using Gaussian and median filters. Subsequently, we use each image feature separately to generate binary segmentations. Finally, we take the intersection of all four binary segmentations, incorporating a model of the peripheral zone, and perform erosion to remove small false-positive regions. The initial evaluation of this method is based on 275 MRI images from 37 patients, and 86% of the slices were classified correctly with 87% and 86% sensitivity and specificity achieved, respectively. This paper makes two contributions: firstly, a novel computer-aided diagnosis approach, which is based on combining multiple segmentation techniques using only a small number of simple image features, and secondly, the development of the proposed method and its application in prostate cancer detection and localization using a single MRI modality with the results comparable with the state-of-the-art multimodality and advanced computer vision methods in the literature. Copyright © 2015 John Wiley & Sons, Ltd.
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Affiliation(s)
- Andrik Rampun
- Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UK
| | - Zhili Chen
- Information and Control Engineering Faculty, Shenyang Jianzhu University, Liaoning, 110168, China
| | - Paul Malcolm
- Department of Radiology, Norfolk Norwich University Hospital, Norwich, NR4 7UY, UK
| | - Bernie Tiddeman
- Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UK
| | - Reyer Zwiggelaar
- Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UK
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Fusco R, Sansone M, Petrillo M, Setola SV, Granata V, Botti G, Perdonà S, Borzillo V, Muto P, Petrillo A. Multiparametric MRI for prostate cancer detection: Preliminary results on quantitative analysis of dynamic contrast enhanced imaging, diffusion-weighted imaging and spectroscopy imaging. Magn Reson Imaging 2016; 34:839-45. [PMID: 27071309 DOI: 10.1016/j.mri.2016.04.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2016] [Accepted: 04/03/2016] [Indexed: 10/22/2022]
Abstract
INTRODUCTION Early promising data suggest that combined use of both morphological and functional MRI (multi-parametric MR, mpMRI) including MRSI, DWI and DCE may be of additional value for prostate cancer localization and its local staging. The objective of this paper is to evaluate the diagnostic performance of mpMRI in the detection of prostate cancer. METHODS Thirty-one consecutive male patients were screened to be enrolled in a single center prospective observational study. All eligible patients underwent multi-parametric MRI and TRUS (Trans Rectal Ultra Sound) guided prostate biopsies. A register, approved by the Institutional Ethics Committee, included patients enrolled in this study. All patients who decided to undergo the MRI examination signed an explicit informed consensus. MRI data were aligned on a common spatial grid and several functional parameters (perfusion, diffusion and metabolic parameters) were computed. Statistical analysis was conducted in order to compare mpMRI with biopsy-based analysis. RESULTS Statistically significant differences between median values in high Gleason score (≥5) and low Gleason score (<5) to Wilcox on rank sum test were obtained for MRSI parameters and for plasma fraction (Tofts model) of DCE-MRI. The area under curve obtained with ROC analysis showed that the best-performing single-parameter was vp (plasma fraction of Tofts model), while the best parameters combination to discriminate the area with high Gleason score were (Cho+Cr)/Cit and Cho+Cr. Linear Discrimination Analysis showed that the best results were obtained considering the linear combination of all MRSI parameters and the linear combination of all features (perfusion, diffusion and metabolic parameters). CONCLUSIONS In conclusion, our findings showed that by combining morphological MRI, DWI, DCE-MRI and MRSI, an increase in sensitivity and specificity correlated to biopsy Gleason grade could be obtained. Furthermore, morphological and functional MRI could have a diagnostic role in patients with prostate cancer, identifying those patients who will have a negative work-up and those patients at high risk for a high Gleason score cancer of the prostate.
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Affiliation(s)
- Roberta Fusco
- Department of Diagnostic Imaging, Radiant and Metabolic Therapy, "Istituto Nazionale Tumori Fondazione Giovanni Pascale - IRCCS", Via Mariano Semmola, 80131, Naples, Italy.
| | - Mario Sansone
- Department of Electrical Engineering and Information Technologies, University "Federico II" of Naples, Via Claudio 21, 80125, Naples, Italy
| | - Mario Petrillo
- Department of Diagnostic Imaging, Radiant and Metabolic Therapy, "Istituto Nazionale Tumori Fondazione Giovanni Pascale - IRCCS", Via Mariano Semmola, 80131, Naples, Italy
| | - Sergio Venanzio Setola
- Department of Diagnostic Imaging, Radiant and Metabolic Therapy, "Istituto Nazionale Tumori Fondazione Giovanni Pascale - IRCCS", Via Mariano Semmola, 80131, Naples, Italy
| | - Vincenza Granata
- Department of Diagnostic Imaging, Radiant and Metabolic Therapy, "Istituto Nazionale Tumori Fondazione Giovanni Pascale - IRCCS", Via Mariano Semmola, 80131, Naples, Italy
| | - Gerardo Botti
- Department of Pathology, "Istituto Nazionale Tumori Fondazione Giovanni Pascale - IRCCS", Via Mariano Semmola, 80131, Naples, Italy
| | - Sisto Perdonà
- Department of Urology, "Istituto Nazionale Tumori Fondazione Giovanni Pascale - IRCCS", Via Mariano Semmola, 80131, Naples, Italy
| | - Valentina Borzillo
- Department of Diagnostic Imaging, Radiant and Metabolic Therapy, "Istituto Nazionale Tumori Fondazione Giovanni Pascale - IRCCS", Via Mariano Semmola, 80131, Naples, Italy
| | - Paolo Muto
- Department of Diagnostic Imaging, Radiant and Metabolic Therapy, "Istituto Nazionale Tumori Fondazione Giovanni Pascale - IRCCS", Via Mariano Semmola, 80131, Naples, Italy
| | - Antonella Petrillo
- Department of Diagnostic Imaging, Radiant and Metabolic Therapy, "Istituto Nazionale Tumori Fondazione Giovanni Pascale - IRCCS", Via Mariano Semmola, 80131, Naples, Italy
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Lopes Dias J, Lucas R, Magalhães Pina J, João R, Costa NV, Leal C, Bilhim T, Campos Pinheiro L, Mateus Marques R. Post-treated prostate cancer: normal findings and signs of local relapse on multiparametric magnetic resonance imaging. ACTA ACUST UNITED AC 2016; 40:2814-38. [PMID: 26105522 DOI: 10.1007/s00261-015-0473-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The use of multiparametric magnetic resonance imaging (mp-MRI) for prostate cancer has increased over recent years, mainly for detection, staging, and active surveillance. However, suspicion of recurrence in the set of biochemical failure is becoming a significant reason for clinicians to request mp-MRI. Radiologists should be able to recognize the normal post-treatment MRI findings. Fibrosis and atrophic remnant seminal vesicles after prostatectomy are often found and must be differentiated from local relapse. Moreover, brachytherapy, external beam radiotherapy, cryosurgery, and hormonal therapy tend to diffusely decrease the signal intensity of the peripheral zone on T2-weighted images (T2WI) due to the loss of water content, consequently mimicking tumor and hemorrhage. The combination of T2WI and functional studies like diffusion-weighted imaging and dynamic contrast-enhanced improves the identification of local relapse. Tumor recurrence tends to restrict on diffusion images and avidly enhances after contrast administration either within or outside the gland. The authors provide a pictorial review of the normal findings and the signs of local tumor relapse after radical prostatectomy, external beam radiotherapy, brachytherapy, cryosurgery, and hormonal therapy.
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Affiliation(s)
- João Lopes Dias
- Department of Radiology, Hospital de S. José, Rua José António Serrano, 1150-199, Lisbon, Portugal. .,, Rua Herculano Rodrigues, n15, 2esq, 2695-044, Lisbon, Portugal. .,Faculdade de Ciências Médicas, Nova Medica School, Lisbon, Portugal.
| | - Rita Lucas
- Department of Radiology, Hospital de Santo António dos Capuchos, Alameda de Santo António dos Capuchos, 1169-050, Lisbon, Portugal
| | - João Magalhães Pina
- Department of Urology, Hospital de S. José, Rua José António Serrano, 1150-199, Lisbon, Portugal.,Faculdade de Ciências Médicas, Nova Medica School, Lisbon, Portugal
| | - Raquel João
- Department of Urology, Hospital de S. José, Rua José António Serrano, 1150-199, Lisbon, Portugal.,Faculdade de Ciências Médicas, Nova Medica School, Lisbon, Portugal
| | - Nuno Vasco Costa
- Department of Radiology, Hospital de S. José, Rua José António Serrano, 1150-199, Lisbon, Portugal.,Faculdade de Ciências Médicas, Nova Medica School, Lisbon, Portugal
| | - Cecília Leal
- Department of Radiology, Hospital de Santa Marta, Rua de Santa Marta, 1169-1024, Lisbon, Portugal.,Faculdade de Ciências Médicas, Nova Medica School, Lisbon, Portugal
| | - Tiago Bilhim
- Department of Radiology, Hospital de S. José, Rua José António Serrano, 1150-199, Lisbon, Portugal.,Faculdade de Ciências Médicas, Nova Medica School, Lisbon, Portugal
| | - Luís Campos Pinheiro
- Department of Radiology, Hospital de S. José, Rua José António Serrano, 1150-199, Lisbon, Portugal.,Faculdade de Ciências Médicas, Nova Medica School, Lisbon, Portugal
| | - Rui Mateus Marques
- Department of Radiology, Hospital de S. José, Rua José António Serrano, 1150-199, Lisbon, Portugal.,Faculdade de Ciências Médicas, Nova Medica School, Lisbon, Portugal
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Sahibzada I, Batura D, Hellawell G. Validating multiparametric MRI for diagnosis and monitoring of prostate cancer in patients for active surveillance. Int Urol Nephrol 2016; 48:529-33. [PMID: 26759330 DOI: 10.1007/s11255-016-1212-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Accepted: 01/04/2016] [Indexed: 12/12/2022]
Abstract
PURPOSE To compare multiparametric magnetic resonance imaging (mpMRI) with transrectal ultrasound-guided prostate biopsy (TRUS-bx) for the diagnosis and monitoring of small-volume prostate cancer (PCa) in patients on active surveillance (AS). METHODS In a retrospective cross-sectional validation study, 100 patients on AS for PCa underwent a systematic 12-core TRUS-bx (the gold standard) as well as mpMRI, on either a 1.5 or 3 Tesla scanner (32 and 68 patients, respectively). Three pathologists reported biopsy histology separately. A single, experienced radiologist scored mpMRI scans using the PI-RADS system. We compared left- and right-sided PI-RADS scores of the peripheral zone with TRUS-bx results of the relevant prostate lobe. We then estimated the specificity and sensitivity of mpMRI in diagnosing low-grade low-risk PCa in our AS cohort. RESULTS The sensitivity of mpMRI was 37% (95% CI 28-47%) and specificity was 85% (CI 76-92%) for cancer. The negative predictive value was 51% (CI 44-60%), and the positive predictive value was 76% (CI 62-87%). The positive and negative likelihood ratios were 2.5 and 0.7, respectively. CONCLUSION Because of its low specificity and low negative predictive value, mpMRI is not suitable for diagnosing low-grade small-volume PCa. However, because of a specificity of 85% and a negative likelihood ratio of 0.7, mpMRI may be useful for follow-up of previously TRUS-bx diagnosed patients who are on AS.
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Affiliation(s)
- Iqbal Sahibzada
- Department of Urology, London North West Healthcare NHS Trust, Harrow, London, Middlesex, HA1 3UJ, UK
| | - Deepak Batura
- Department of Urology, London North West Healthcare NHS Trust, Harrow, London, Middlesex, HA1 3UJ, UK.
| | - Giles Hellawell
- Department of Urology, London North West Healthcare NHS Trust, Harrow, London, Middlesex, HA1 3UJ, UK
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Zidan S, Tantawy HI. Prostate carcinoma: Accuracy of diagnosis and differentiation with Dynamic Contrast-Enhanced MRI and Diffusion Weighted Imaging. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2015. [DOI: 10.1016/j.ejrnm.2015.06.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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Value of functional MRI in evaluation of patients with suspected prostate cancer. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2015. [DOI: 10.1016/j.ejrnm.2015.07.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
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Dikaios N, Alkalbani J, Abd-Alazeez M, Sidhu HS, Kirkham A, Ahmed HU, Emberton M, Freeman A, Halligan S, Taylor S, Atkinson D, Punwani S. Zone-specific logistic regression models improve classification of prostate cancer on multi-parametric MRI. Eur Radiol 2015; 25:2727-37. [PMID: 25680730 DOI: 10.1007/s00330-015-3636-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 12/18/2014] [Accepted: 01/21/2015] [Indexed: 11/26/2022]
Abstract
OBJECTIVES To assess the interchangeability of zone-specific (peripheral-zone (PZ) and transition-zone (TZ)) multiparametric-MRI (mp-MRI) logistic-regression (LR) models for classification of prostate cancer. METHODS Two hundred and thirty-one patients (70 TZ training-cohort; 76 PZ training-cohort; 85 TZ temporal validation-cohort) underwent mp-MRI and transperineal-template-prostate-mapping biopsy. PZ and TZ uni/multi-variate mp-MRI LR-models for classification of significant cancer (any cancer-core-length (CCL) with Gleason > 3 + 3 or any grade with CCL ≥ 4 mm) were derived from the respective cohorts and validated within the same zone by leave-one-out analysis. Inter-zonal performance was tested by applying TZ models to the PZ training-cohort and vice-versa. Classification performance of TZ models for TZ cancer was further assessed in the TZ validation-cohort. ROC area-under-curve (ROC-AUC) analysis was used to compare models. RESULTS The univariate parameters with the best classification performance were the normalised T2 signal (T2nSI) within the TZ (ROC-AUC = 0.77) and normalized early contrast-enhanced T1 signal (DCE-nSI) within the PZ (ROC-AUC = 0.79). Performance was not significantly improved by bi-variate/tri-variate modelling. PZ models that contained DCE-nSI performed poorly in classification of TZ cancer. The TZ model based solely on maximum-enhancement poorly classified PZ cancer. CONCLUSION LR-models dependent on DCE-MRI parameters alone are not interchangable between prostatic zones; however, models based exclusively on T2 and/or ADC are more robust for inter-zonal application. KEY POINTS • The ADC and T2-nSI of benign/cancer PZ are higher than benign/cancer TZ. • DCE parameters are significantly different between benign PZ and TZ, but not between cancerous PZ and TZ. • Diagnostic models containing contrast enhancement parameters have reduced performance when applied across zones.
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Affiliation(s)
- Nikolaos Dikaios
- Centre for Medical Imaging, University College London, Level 3 East, 250 Euston Road, London, NW1 2PG, UK
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40
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Abstract
Multiparametric-magnetic resonance imaging (mp-MRI) has shown promising results in diagnosis, localization, risk stratification and staging of clinically significant prostate cancer. It has also opened up opportunities for focal treatment of prostate cancer. Combinations of T2-weighted imaging, diffusion imaging, perfusion (dynamic contrast-enhanced imaging) and spectroscopic imaging have been used in mp-MRI assessment of prostate cancer, but T2 morphologic assessment and functional assessment by diffusion imaging remains the mainstay for prostate cancer diagnosis on mp-MRI. Because assessment on mp-MRI can be subjective, use of the newly developed standardized reporting Prostate Imaging and Reporting Archiving Data System scoring system and education of specialist radiologists are essential for accurate interpretation. This review focuses on the present status of mp-MRI in prostate cancer and its evolving role in the management of prostate cancer.
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Affiliation(s)
- Sangeet Ghai
- Joint Department of Medical Imaging, University Health Network, University of Toronto, Ontario, Canada
| | - Masoom A Haider
- Department of Medical Imaging, Sunnybrook Health Sciences Center, University of Toronto, Ontario, Canada
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41
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Hedgire SS, Eberhardt SC, Borczuk R, McDermott S, Harisinghani MG. Interpretation and reporting multiparametric prostate MRI: a primer for residents and novices. ACTA ACUST UNITED AC 2015; 39:1036-51. [PMID: 24566965 DOI: 10.1007/s00261-014-0097-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Multiparametric MRI has developed as a tool for prostate cancer lesion detection, characterization, staging, surveillance, and imaging of local recurrence. Given the disease frequency and the growing importance of imaging, as reliance on PSA declines, radiologists involved in prostate MRI imaging must become proficient with the fundamentals of multiparametric prostate MRI (T2WI, DWI, DCE-MRI, and MR spectroscopy). Interpretation and reporting must yield accuracy, consistency, and add value to clinical care. This review provides a primer to novices and trainees learning about multiparametric prostate MRI. MRI technique is presented along with the use of particular MRI sequences. Relevant prostate anatomy is outlined and imaging features of prostate cancer with staging are discussed. Finally structured reporting is introduced, and some limitations of prostate MRI are discussed.
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Affiliation(s)
- Sandeep S Hedgire
- Department of Abdominal Imaging and Intervention, Massachusetts General Hospital, 55 Fruit St, Boston, MA, 02114, USA,
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Dynamic contrast-enhanced MRI for the detection of prostate cancer: meta-analysis. AJR Am J Roentgenol 2015; 204:W439-48. [PMID: 25794093 DOI: 10.2214/ajr.14.13373] [Citation(s) in RCA: 77] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
OBJECTIVE The purpose of this study was to systematically review and meta-analyze dynamic contrast-enhanced MRI (DCE-MRI) for the detection of prostate cancer in comparison with standard evaluation with T2-weighted imaging. MATERIALS AND METHODS A PubMed electronic database search for the terms "dynamic contrast-enhanced," "prostate," and "MRI" was completed for articles up to September 17, 2013. All included studies had histopathologic correlation. Two by two contingency data were constructed for each study. A binormal bayesian ROC model was used to estimate and compare sensitivity, specificity, and AUC among eligible modalities. RESULTS Both DCE-MRI (0.82-0.86) and diffusion-weighted MRI (DWI) (0.84-0.88) yielded significantly better AUC than T2-weighted imaging (0.68-0.77). Moreover, partial AUC for the combination of DCE-MRI, DWI, and T2-weighted imaging was improved significantly (0.111; 0.103-0.119) when compared with DCE-MRI alone (0.079; 0.072-0.085) and T2-weighted imaging alone (0.079; 0.074-0.084) but not DWI alone (0.099; 0.091-0.108). Sensitivity and specificity were similar among the four modalities. CONCLUSION DCE-MRI improves AUC of tumor detection overall compared with T2-weighted imaging alone. Methods for DCE-MRI analysis require standardization, but visual analysis performs similar to semiquantitative methods. A two-parameter approach using DCE-MRI and T2-weighted imaging or DWI and T2-weighted imaging may be sufficient, and the latter may be more favorable for most routine prostate cancer imaging.
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Yang L, Xie Y, Li B, Xie M, Wang X, Zhang J. Symmetry based prostate cancer detection. Br J Radiol 2015; 88:20150132. [PMID: 25899893 DOI: 10.1259/bjr.20150132] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To retrospectively assess the value of left and right half symmetry analysis in prostate T2 weighted images (T2WI) for improving prostate cancer (PCa) screening. METHODS T2WI and other data of a total of 66 males were collected; the control group and cancer group had 33 patients each. Thresholding geometric active contours algorithm was used for prostate region segmentation, and the measure of local reflectional symmetry algorithm was applied to extract the longitudinal symmetry axes. After that, cross-correlation coefficients (CCs) of the left and right halves of each prostate were obtained. RESULTS Data analysis showed that the mean and variance of the value of the left and right half CCs of prostate T2WI in the cancer group and control group were 0.73 ± 0.05 and 0.82 ± 0.06, respectively. The area under the receiver operating characteristic curve was 0.87, and the specificity and the sensitivity were 91% and 70%, respectively. The p < 0.001 indicated that the value of CCs of the prostates between the two groups was significantly different. CONCLUSION The symmetry in T2WI is a potential useful index for PCa screening and has a potential value for PCa detection and localizations of tumours for biopsy. ADVANCES IN KNOWLEDGE Texture bilateral symmetry of prostate T2WI is employed to screen the suspected prostate tumour.
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Affiliation(s)
- L Yang
- 1 College of Electronic Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China
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Lemaître G, Martí R, Freixenet J, Vilanova JC, Walker PM, Meriaudeau F. Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review. Comput Biol Med 2015; 60:8-31. [PMID: 25747341 DOI: 10.1016/j.compbiomed.2015.02.009] [Citation(s) in RCA: 121] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 02/11/2015] [Accepted: 02/12/2015] [Indexed: 12/30/2022]
Abstract
Prostate cancer is the second most diagnosed cancer of men all over the world. In the last few decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed to improve diagnosis. In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systems have been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field of research for the last 10 years. This survey aims to provide a comprehensive review of the state-of-the-art in this lapse of time, focusing on the different stages composing the work-flow of a computer-aided system. We also provide a comparison between studies and a discussion about the potential avenues for future research. In addition, this paper presents a new public online dataset which is made available to the research community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey.
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Affiliation(s)
- Guillaume Lemaître
- LE2I-UMR CNRS 6306, Université de Bourgogne, 12 rue de la Fonderie, 71200 Le Creusot, France; ViCOROB, Universitat de Girona, Campus Montilivi, Edifici P4, 17071 Girona, Spain.
| | - Robert Martí
- ViCOROB, Universitat de Girona, Campus Montilivi, Edifici P4, 17071 Girona, Spain.
| | - Jordi Freixenet
- ViCOROB, Universitat de Girona, Campus Montilivi, Edifici P4, 17071 Girona, Spain.
| | - Joan C Vilanova
- Department of Magnetic Resonance, Clínica Girona, Lorenzana 36, 17002 Girona, Spain
| | - Paul M Walker
- LE2I-UMR CNRS 6306, Université de Bourgogne, Avenue Alain Savary, 21000 Dijon, France.
| | - Fabrice Meriaudeau
- LE2I-UMR CNRS 6306, Université de Bourgogne, 12 rue de la Fonderie, 71200 Le Creusot, France.
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Dikaios N, Alkalbani J, Sidhu HS, Fujiwara T, Abd-Alazeez M, Kirkham A, Allen C, Ahmed H, Emberton M, Freeman A, Halligan S, Taylor S, Atkinson D, Punwani S. Logistic regression model for diagnosis of transition zone prostate cancer on multi-parametric MRI. Eur Radiol 2015; 25:523-32. [PMID: 25226842 PMCID: PMC4291517 DOI: 10.1007/s00330-014-3386-4] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2014] [Accepted: 08/05/2014] [Indexed: 12/29/2022]
Abstract
OBJECTIVES We aimed to develop logistic regression (LR) models for classifying prostate cancer within the transition zone on multi-parametric magnetic resonance imaging (mp-MRI). METHODS One hundred and fifty-five patients (training cohort, 70 patients; temporal validation cohort, 85 patients) underwent mp-MRI and transperineal-template-prostate-mapping (TPM) biopsy. Positive cores were classified by cancer definitions: (1) any-cancer; (2) definition-1 [≥Gleason 4 + 3 or ≥ 6 mm cancer core length (CCL)] [high risk significant]; and (3) definition-2 (≥Gleason 3 + 4 or ≥ 4 mm CCL) cancer [intermediate-high risk significant]. For each, logistic-regression mp-MRI models were derived from the training cohort and validated internally and with the temporal cohort. Sensitivity/specificity and the area under the receiver operating characteristic (ROC-AUC) curve were calculated. LR model performance was compared to radiologists' performance. RESULTS Twenty-eight of 70 patients from the training cohort, and 25/85 patients from the temporal validation cohort had significant cancer on TPM. The ROC-AUC of the LR model for classification of cancer was 0.73/0.67 at internal/temporal validation. The radiologist A/B ROC-AUC was 0.65/0.74 (temporal cohort). For patients scored by radiologists as Prostate Imaging Reporting and Data System (Pi-RADS) score 3, sensitivity/specificity of radiologist A 'best guess' and LR model was 0.14/0.54 and 0.71/0.61, respectively; and radiologist B 'best guess' and LR model was 0.40/0.34 and 0.50/0.76, respectively. CONCLUSIONS LR models can improve classification of Pi-RADS score 3 lesions similar to experienced radiologists. KEY POINTS • MRI helps find prostate cancer in the anterior of the gland • Logistic regression models based on mp-MRI can classify prostate cancer • Computers can help confirm cancer in areas doctors are uncertain about.
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Affiliation(s)
- Nikolaos Dikaios
- Centre for Medical Imaging, University College London, 3rd Floor East Wing, 250 Euston Road, London, UK NW1 2PG
- Departments of Radiology, University College London Hospital, 235 Euston Road, London, UK NW1 2BU
| | - Jokha Alkalbani
- Centre for Medical Imaging, University College London, 3rd Floor East Wing, 250 Euston Road, London, UK NW1 2PG
| | - Harbir Singh Sidhu
- Centre for Medical Imaging, University College London, 3rd Floor East Wing, 250 Euston Road, London, UK NW1 2PG
| | - Taiki Fujiwara
- Centre for Medical Imaging, University College London, 3rd Floor East Wing, 250 Euston Road, London, UK NW1 2PG
| | - Mohamed Abd-Alazeez
- Research Department of Urology, University College London, London, UK NW1 2PG
| | - Alex Kirkham
- Departments of Radiology, University College London Hospital, 235 Euston Road, London, UK NW1 2BU
| | - Clare Allen
- Departments of Radiology, University College London Hospital, 235 Euston Road, London, UK NW1 2BU
| | - Hashim Ahmed
- Research Department of Urology, University College London, London, UK NW1 2PG
| | - Mark Emberton
- Research Department of Urology, University College London, London, UK NW1 2PG
| | - Alex Freeman
- Department of Histopathology, University College London Hospital, London, UK NW1 2PG
| | - Steve Halligan
- Centre for Medical Imaging, University College London, 3rd Floor East Wing, 250 Euston Road, London, UK NW1 2PG
- Departments of Radiology, University College London Hospital, 235 Euston Road, London, UK NW1 2BU
| | - Stuart Taylor
- Centre for Medical Imaging, University College London, 3rd Floor East Wing, 250 Euston Road, London, UK NW1 2PG
- Departments of Radiology, University College London Hospital, 235 Euston Road, London, UK NW1 2BU
| | - David Atkinson
- Centre for Medical Imaging, University College London, 3rd Floor East Wing, 250 Euston Road, London, UK NW1 2PG
- Departments of Radiology, University College London Hospital, 235 Euston Road, London, UK NW1 2BU
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, 3rd Floor East Wing, 250 Euston Road, London, UK NW1 2PG
- Departments of Radiology, University College London Hospital, 235 Euston Road, London, UK NW1 2BU
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Abstract
OBJECTIVE The purpose of this study was to evaluate the effect of increasing the spatial resolution of the prostate DWI protocol on image quality and lesion conspicuity. SUBJECTS AND METHODS Twenty-nine patients with biopsy-proven prostate cancer undergoing MRI examinations were imaged with two diffusion-weighted imaging (DWI) protocols: current standard clinical protocol (6.7 mm(3) voxels) and a new high-resolution protocol (3.1 mm(3) voxels). Diffusion-weighted images were independently and subjectively scored on lesion conspicuity, internal architecture definition, and overall image quality by two radiologists. Average apparent diffusion coefficient (ADC) values were measured in normal tissue and cancerous lesions on both sequences. Reader scores and ADC and contrast values were compared between the two protocols. Cancer ADC values were correlated with Gleason scores. RESULTS The signal-to-noise ratio of the new high-resolution DWI protocol was 40% lower than that of the standard protocol. The reader scores were higher by 0.73 (range, 0.29-1.16) grades, or 19% (range, 7-32%), on average, for the new protocol, indicating better image quality. The average ADC values were 8% higher with the new protocol, with ADC contrast values between cancer and normal prostate unchanged. There was marginally significant correlation of cancer ADC values with Gleason scores (p = 0.05, r ≈ -0.36). CONCLUSION We showed that for DWI of the prostate at 3-7 mm(3) voxel sizes the benefits of higher spatial resolution outweigh the effects of reduced signal-to-noise and contrast-to-noise ratios, potentially improving the sensitivity to small or sparse prostate cancers. Radiologists can consider using higher-spatial-resolution DWI sequences in their practices.
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Sharma S. Imaging and intervention in prostate cancer: Current perspectives and future trends. Indian J Radiol Imaging 2014; 24:139-48. [PMID: 25024523 PMCID: PMC4094966 DOI: 10.4103/0971-3026.134399] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Prostate cancer is the commonest malignancy in men that causes significant morbidity and mortality worldwide. Screening by digital rectal examination (DRE) and serum prostate-specific antigen (PSA) is used despite its limitations. Gray-scale transrectal ultrasound (TRUS), used to guide multiple random prostatic biopsies, misses up to 20% cancers and frequently underestimates the grade of malignancy. Increasing the number of biopsy cores marginally increases the yield. Evolving techniques of real-time ultrasound elastography (RTE) and contrast-enhanced ultrasound (CEUS) are being investigated to better detect and improve the yield by allowing “targeted” biopsies. Last decade has witnessed rapid developments in magnetic resonance imaging (MRI) for improved management of prostate cancer. In addition to the anatomical information, it is capable of providing functional information through diffusion-weighted imaging (DWI), magnetic resonance spectroscopy (MRS), and dynamic contrast-enhanced (DCE) MRI. Multi-parametric MRI has the potential to exclude a significant cancer in majority of cases. Inclusion of MRI before prostatic biopsy can reduce the invasiveness of the procedure by limiting the number of cores needed to make a diagnosis and support watchful waiting in others. It is made possible by targeted biopsies as opposed to random. With the availability of minimally invasive therapeutic modalities like high-intensity focused ultrasound (HIFU) and interstitial laser therapy, detecting early cancer is even more relevant today. [18F]--fluorodeoxyglucose positron emission tomography/computed tomography (18FDG PET/CT) has no role in the initial evaluation of prostate cancer. Choline PET has been recently found to be more useful. Fluoride-PET has a higher sensitivity and resolution than a conventional radionuclide bone scan in detecting skeletal metastases.
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Affiliation(s)
- Sanjay Sharma
- Department of Radiodiagnosis, All Institute of Medical Sciences, New Delhi, India
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Prostate cancer: role of pretreatment multiparametric 3-T MRI in predicting biochemical recurrence after radical prostatectomy. AJR Am J Roentgenol 2014; 202:W459-65. [PMID: 24758681 DOI: 10.2214/ajr.13.11381] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
OBJECTIVE The purpose of this study is to retrospectively investigate whether pretreatment multiparametric MRI findings can predict biochemical recurrence in patients who underwent radical prostatectomy (RP) for localized prostate cancer. MATERIALS AND METHODS In this study, 282 patients with biopsy-proven prostate cancer who received RP underwent pretreatment MRI using a phased-array coil at 3 T, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE-MRI). MRI variables included apparent tumor presence on combined imaging sequences, extracapsular extension, and tumor size on DWI or DCE-MRI. Clinical variables included baseline prostate-specific antigen (PSA) level, clinical stage, and Gleason score at biopsy. The relationship between clinical and imaging variables and biochemical recurrence was evaluated using Cox regression analysis. RESULTS After a median follow-up of 26 months, biochemical recurrence developed in 61 patients (22%). Univariate analysis revealed that all the imaging and clinical variables were significantly associated with biochemical recurrence (p < 0.01). On multivariate analysis, however, baseline PSA level (p = 0.002), Gleason score at biopsy (p = 0.024), and apparent tumor presence on combined T2WI, DWI, and DCE-MRI (p = 0.047) were the only significant independent predictors of biochemical recurrence. Of the independent predictors, apparent tumor presence on combined T2WI, DWI, and DCE-MRI showed the highest hazard ratio (2.38) compared with baseline PSA level (hazard ratio, 1.05) and Gleason score at biopsy (hazard ratio, 1.34). CONCLUSION The apparent tumor presence on combined T2WI, DWI, and DCE-MRI of pretreatment MRI is an independent predictor of biochemical recurrence after RP. This finding may be used to construct a predictive model for biochemical recurrence after surgery.
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García Molina JF, Zheng L, Sertdemir M, Dinter DJ, Schönberg S, Rädle M. Incremental learning with SVM for multimodal classification of prostatic adenocarcinoma. PLoS One 2014; 9:e93600. [PMID: 24699716 PMCID: PMC3974761 DOI: 10.1371/journal.pone.0093600] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2013] [Accepted: 03/06/2014] [Indexed: 11/18/2022] Open
Abstract
Robust detection of prostatic cancer is a challenge due to the multitude of variants and their representation in MR images. We propose a pattern recognition system with an incremental learning ensemble algorithm using support vector machines (SVM) tackling this problem employing multimodal MR images and a texture-based information strategy. The proposed system integrates anatomic, texture, and functional features. The data set was preprocessed using B-Spline interpolation, bias field correction and intensity standardization. First- and second-order angular independent statistical approaches and rotation invariant local phase quantization (RI-LPQ) were utilized to quantify texture information. An incremental learning ensemble SVM was implemented to suit working conditions in medical applications and to improve effectiveness and robustness of the system. The probability estimation of cancer structures was calculated using SVM and the corresponding optimization was carried out with a heuristic method together with a 3-fold cross-validation methodology. We achieved an average sensitivity of 0.844 ± 0.068 and a specificity of 0.780 ± 0.038, which yielded superior or similar performance to current state of the art using a total database of only 41 slices from twelve patients with histological confirmed information, including cancerous, unhealthy non-cancerous and healthy prostate tissue. Our results show the feasibility of an ensemble SVM being able to learn additional information from new data while preserving previously acquired knowledge and preventing unlearning. The use of texture descriptors provides more salient discriminative patterns than the functional information used. Furthermore, the system improves selection of information, efficiency and robustness of the classification. The generated probability map enables radiologists to have a lower variability in diagnosis, decrease false negative rates and reduce the time to recognize and delineate structures in the prostate.
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Affiliation(s)
- José Fernando García Molina
- Institute of Experimental Radiation Oncology, Department of Radiation Oncology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany
| | - Lei Zheng
- Institute of Experimental Radiation Oncology, Department of Radiation Oncology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany
| | - Metin Sertdemir
- Institute for Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany
| | - Dietmar J. Dinter
- Institute for Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany
| | - Stefan Schönberg
- Institute for Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany
| | - Matthias Rädle
- Institute of Process Control and Innovative Energy Conversion (PI), Hochschule Mannheim, University of Applied Sciences, Mannheim, Germany
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AbdelMaboud NM, Elsaid HH, Aboubeih EA. The role of diffusion – Weighted MRI in evaluation of prostate cancer. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2014. [DOI: 10.1016/j.ejrnm.2013.11.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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