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Wang LJ, Jinzaki M, Tan CH, Oh YT, Shinmoto H, Lee CH, Patel NU, Chang SD, Westphalen AC, Kim CK. Use of Imaging and Biopsy in Prostate Cancer Diagnosis: A Survey From the Asian Prostate Imaging Working Group. Korean J Radiol 2023; 24:1102-1113. [PMID: 37899520 PMCID: PMC10613851 DOI: 10.3348/kjr.2023.0644] [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: 07/10/2023] [Revised: 08/14/2023] [Accepted: 08/25/2023] [Indexed: 10/31/2023] Open
Abstract
OBJECTIVE To elucidate the use of radiological studies, including nuclear medicine, and biopsy for the diagnosis and staging of prostate cancer (PCA) in clinical practice and understand the current status of PCA in Asian countries via an international survey. MATERIALS AND METHODS The Asian Prostate Imaging Working Group designed a survey questionnaire with four domains focused on prostate magnetic resonance imaging (MRI), other prostate imaging, prostate biopsy, and PCA backgrounds. The questionnaire was sent to 111 members of professional affiliations in Korea, Japan, Singapore, and Taiwan who were representatives of their working hospitals, and their responses were analyzed. RESULTS This survey had a response rate of 97.3% (108/111). The rates of using 3T scanners, antispasmodic agents, laxative drugs, and prostate imaging-reporting and data system reporting for prostate MRI were 21.6%-78.9%, 22.2%-84.2%, 2.3%-26.3%, and 59.5%-100%, respectively. Respondents reported using the highest b-values of 800-2000 sec/mm² and fields of view of 9-30 cm. The prostate MRI examinations per month ranged from 1 to 600, and they were most commonly indicated for biopsy-naïve patients suspected of PCA in Japan and Singapore and staging of proven PCA in Korea and Taiwan. The most commonly used radiotracers for prostate positron emission tomography are prostate-specific membrane antigen in Singapore and fluorodeoxyglucose in three other countries. The most common timing for prostate MRI was before biopsy (29.9%). Prostate-targeted biopsies were performed in 63.8% of hospitals, usually by MRI-ultrasound fusion approach. The most common presentation was localized PCA in all four countries, and it was usually treated with radical prostatectomy. CONCLUSION This survey showed the diverse technical details and the availability of imaging and biopsy in the evaluation of PCA. This suggests the need for an educational program for Asian radiologists to promote standardized evidence-based imaging approaches for the diagnosis and staging of PCA.
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Affiliation(s)
- Li-Jen Wang
- Department of Medical Imaging and Intervention, New Taipei Municipal Tucheng Hospital, Chang Gung Medical Foundation, New Taipei, Taiwan
- Department of Medical Imaging and Intervention, Linkou Chang Gung Medical Hospital, Taoyuan, Taiwan
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Masahiro Jinzaki
- Department of Radiology, Keio University, School of Medicine, Tokyo, Japan
| | - Cher Heng Tan
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, National Health Care Group, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Young Taik Oh
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hiroshi Shinmoto
- Department of Radiology, National Defense Medical College, Saitama, Japan
| | - Chau Hung Lee
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, National Health Care Group, Singapore
| | - Nayana U Patel
- Department of Radiology, UNM Health Sciences Center, University of New Mexico, Albuquerque, NM, USA
| | - Silvia D Chang
- Department of Radiology, Vancouver General Hospital, University of British Columbia, Vancouver, BC, Canada
| | | | - Chan Kyo Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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Shin HJ, Son NH, Hwang SH, Song K. Reliability of synthetic diffusion-weighted imaging with a high b-value for paediatric abdominal MRI. Clin Radiol 2023; 78:616-621. [PMID: 37149417 DOI: 10.1016/j.crad.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/01/2023] [Accepted: 04/10/2023] [Indexed: 05/08/2023]
Abstract
AIM To evaluate the reliability of synthetic diffusion-weighted imaging (DWI) using a high b-value in comparison to conventional DWI for paediatric abdominal MRI. MATERIALS AND METHODS Paediatric patients (<19 years old) who underwent liver or pancreatobiliary MRI with DWI using 10 b-values (b = 0, 25, 50, 75, 100, 200, 400, 600, 800, 1,500 s/mm2) from March to October 2021 were included in this retrospective study. Using the software, synthetic DWI using b = 1,500 s/mm2 was generated automatically by selecting the b-value required as output. Conventional and synthetic DWI values for b = 1,500 s/mm2 were measured at the liver, spleen, paraspinal muscle, and mass lesions, if present, and apparent diffusion coefficient (ADC) values were calculated using the mono-exponential model. Intraclass correlation coefficients (ICCs) were calculated to assess the reliability of conventional and synthetic DWI and ADC values with b = 1,500 s/mm2. RESULTS Thirty paediatric patients (M:F = 22:8, mean 10.8 ± 3.1 years old) were included and four had tumours on abdominal MRI. ICC values were 0.906-0.995 between conventional and synthetic DWI and ADC with b = 1,500 s/mm2 in the liver, spleen and muscle. For mass lesions, ICC values were 0.997-0.999 for both synthetic DWI and ADC images. CONCLUSIONS Synthetic DWI and ADC values obtained using a high b-value showed excellent agreement with conventional DWI for the liver, spleen, muscle, and mass in paediatric MRI.
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Affiliation(s)
- H J Shin
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 16995, Republic of Korea.
| | - N-H Son
- Department of Statistics, Keimyung University, 1095 Dalgubeol-daero, Dalseo-gu, Daegu, 42601, Republic of Korea
| | - S H Hwang
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 16995, Republic of Korea
| | - K Song
- Department of Paediatrics, Division of Paediatric Endocrinology, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 16995, Republic of Korea
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Hong S, Kim SH, Yoo B, Kim JY. Deep Learning Algorithm for Tumor Segmentation and Discrimination of Clinically Significant Cancer in Patients with Prostate Cancer. Curr Oncol 2023; 30:7275-7285. [PMID: 37623009 PMCID: PMC10453750 DOI: 10.3390/curroncol30080528] [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: 06/11/2023] [Revised: 07/05/2023] [Accepted: 07/25/2023] [Indexed: 08/26/2023] Open
Abstract
BACKGROUND We investigated the feasibility of a deep learning algorithm (DLA) based on apparent diffusion coefficient (ADC) maps for the segmentation and discrimination of clinically significant cancer (CSC, Gleason score ≥ 7) from non-CSC in patients with prostate cancer (PCa). METHODS Data from a total of 149 consecutive patients who had undergone 3T-MRI and been pathologically diagnosed with PCa were initially collected. The labelled data (148 images for GS6, 580 images for GS7) were applied for tumor segmentation using a convolutional neural network (CNN). For classification, 93 images for GS6 and 372 images for GS7 were used. For external validation, 22 consecutive patients from five different institutions (25 images for GS6, 70 images for GS7) representing different MR machines were recruited. RESULTS Regarding segmentation and classification, U-Net and DenseNet were used, respectively. The tumor Dice scores for internal and external validation were 0.822 and 0.7776, respectively. As for classification, the accuracies of internal and external validation were 73 and 75%, respectively. For external validation, diagnostic predictive values for CSC (sensitivity, specificity, positive predictive value and negative predictive value) were 84, 48, 82 and 52%, respectively. CONCLUSIONS Tumor segmentation and discrimination of CSC from non-CSC is feasible using a DLA developed based on ADC maps (b2000) alone.
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Affiliation(s)
- Sujin Hong
- Department of Radiology, Inje University, College of Medicine, Haeundae Paik Hospital, Busan 48108, Republic of Korea
| | - Seung Ho Kim
- Department of Radiology, Inje University, College of Medicine, Haeundae Paik Hospital, Busan 48108, Republic of Korea
| | | | - Joo Yeon Kim
- Department of Pathology, Inje University, College of Medicine, Haeundae Paik Hospital, Busan 48108, Republic of Korea
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Zhang G, Xu Z, Zheng J, Wang M, Ren J, Wei X, Huan Y, Zhang J. Ultra-high b-Value DWI in predicting progression risk of locally advanced rectal cancer: a comparative study with routine DWI. Cancer Imaging 2023; 23:59. [PMID: 37308941 DOI: 10.1186/s40644-023-00582-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 06/02/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND The prognosis prediction of locally advanced rectal cancer (LARC) was important to individualized treatment, we aimed to investigate the performance of ultra-high b-value DWI (UHBV-DWI) in progression risk prediction of LARC and compare with routine DWI. METHODS This retrospective study collected patients with rectal cancer from 2016 to 2019. Routine DWI (b = 0, 1000 s/mm2) and UHBV-DWI (b = 0, 1700 ~ 3500 s/mm2) were processed with mono-exponential model to generate ADC and ADCuh, respectively. The performance of the ADCuh was compared with ADC in 3-year progression free survival (PFS) assessment using time-dependent ROC and Kaplan-Meier curve. Prognosis model was constructed with ADCuh, ADC and clinicopathologic factors using multivariate COX proportional hazard regression analysis. The prognosis model was assessed with time-dependent ROC, decision curve analysis (DCA) and calibration curve. RESULTS A total of 112 patients with LARC (TNM-stage II-III) were evaluated. ADCuh performed better than ADC for 3-year PFS assessment (AUC = 0.754 and 0.586, respectively). Multivariate COX analysis showed that ADCuh and ADC were independent factors for 3-year PFS (P < 0.05). Prognostic model 3 (TNM-stage + extramural venous invasion (EMVI) + ADCuh) was superior than model 2 (TNM-stage + EMVI + ADC) and model 1 (TNM-stage + EMVI) for 3-year PFS prediction (AUC = 0.805, 0.719 and 0.688, respectively). DCA showed that model 3 had higher net benefit than model 2 and model 1. Calibration curve demonstrated better agreement of model 1 than model 2 and model 1. CONCLUSIONS ADCuh from UHBV-DWI performed better than ADC from routine DWI in predicting prognosis of LARC. The model based on combination of ADCuh, TNM-stage and EMVI could help to indicate progression risk before treatment.
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Affiliation(s)
- Guangwen Zhang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, No.127, Chang Le West Road, Xi'an, Shaanxi, 710032, China
| | - Ziliang Xu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, No.127, Chang Le West Road, Xi'an, Shaanxi, 710032, China
| | - Jianyong Zheng
- Department of Gastrointestinal Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China
| | - Mian Wang
- Department of Gastrointestinal Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China
| | - Jialiang Ren
- Department of Pharmaceuticals Diagnostics, GE Healthcare China, Beijing, 100176, China
| | - Xiaocheng Wei
- Department of MR Research, GE Healthcare China, Beijing, 100176, China
| | - Yi Huan
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, No.127, Chang Le West Road, Xi'an, Shaanxi, 710032, China
| | - Jinsong Zhang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, No.127, Chang Le West Road, Xi'an, Shaanxi, 710032, China.
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Bashkanov O, Rak M, Meyer A, Engelage L, Lumiani A, Muschter R, Hansen C. Automatic detection of prostate cancer grades and chronic prostatitis in biparametric MRI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 239:107624. [PMID: 37271051 DOI: 10.1016/j.cmpb.2023.107624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 05/13/2023] [Accepted: 05/25/2023] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE With emerging evidence to improve prostate cancer (PCa) screening, multiparametric magnetic prostate imaging is becoming an essential noninvasive component of the diagnostic routine. Computer-aided diagnostic (CAD) tools powered by deep learning can help radiologists interpret multiple volumetric images. In this work, our objective was to examine promising methods recently proposed in the multigrade prostate cancer detection task and to suggest practical considerations regarding model training in this context. METHODS We collected 1647 fine-grained biopsy-confirmed findings, including Gleason scores and prostatitis, to form a training dataset. In our experimental framework for lesion detection, all models utilized 3D nnU-Net architecture that accounts for anisotropy in the MRI data. First, we explore an optimal range of b-values for diffusion-weighted imaging (DWI) modality and its effect on the detection of clinically significant prostate cancer (csPCa) and prostatitis using deep learning, as the optimal range is not yet clearly defined in this domain. Next, we propose a simulated multimodal shift as a data augmentation technique to compensate for the multimodal shift present in the data. Third, we study the effect of incorporating the prostatitis class alongside cancer-related findings at three different granularities of the prostate cancer class (coarse, medium, and fine) and its impact on the detection rate of the target csPCa. Furthermore, ordinal and one-hot encoded (OHE) output formulations were tested. RESULTS An optimal model configuration with fine class granularity (prostatitis included) and OHE has scored the lesion-wise partial Free-Response Receiver Operating Characteristic (FROC) area under the curve (AUC) of 1.94 (CI 95%: 1.76-2.11) and patient-wise ROC AUC of 0.874 (CI 95%: 0.793-0.938) in the detection of csPCa. Inclusion of the auxiliary prostatitis class has demonstrated a stable relative improvement in specificity at a false positive rate (FPR) of 1.0 per patient, with an increase of 3%, 7%, and 4% for coarse, medium, and fine class granularities. CONCLUSIONS This paper examines several configurations for model training in the biparametric MRI setup and proposes optimal value ranges. It also shows that the fine-grained class configuration, including prostatitis, is beneficial for detecting csPCa. The ability to detect prostatitis in all low-risk cancer lesions suggests the potential to improve the quality of the early diagnosis of prostate diseases. It also implies an improved interpretability of the results by the radiologist.
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Affiliation(s)
- Oleksii Bashkanov
- Faculty of Computer Science and Research Campus STIMULATE, University of Magdeburg, Universitätsplatz 2, Magdeburg 39106, Germany.
| | - Marko Rak
- Faculty of Computer Science and Research Campus STIMULATE, University of Magdeburg, Universitätsplatz 2, Magdeburg 39106, Germany
| | - Anneke Meyer
- Faculty of Computer Science and Research Campus STIMULATE, University of Magdeburg, Universitätsplatz 2, Magdeburg 39106, Germany
| | | | | | | | - Christian Hansen
- Faculty of Computer Science and Research Campus STIMULATE, University of Magdeburg, Universitätsplatz 2, Magdeburg 39106, Germany
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Kim YJ, Kim SH, Baek TW, Park H, Lim YJ, Jung HK, Kim JY. Comparison of Computed Diffusion-Weighted Imaging b2000 and Acquired Diffusion-Weighted Imaging b2000 for Detection of Prostate Cancer. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2022; 83:1059-1070. [PMID: 36276208 PMCID: PMC9574295 DOI: 10.3348/jksr.2022.0004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 03/05/2022] [Accepted: 03/21/2022] [Indexed: 11/15/2022]
Abstract
Purpose To compare the sensitivity of tumor detection and inter-observer agreement between acquired diffusion-weighted imaging (aDWI) b2000 and computed DWI (cDWI) b2000 in patients with prostate cancer (PCa). Materials and Methods Eighty-eight patients diagnosed with PCa by radical prostatectomy and having undergone pre-operative 3 Tesla-MRI, including DWI (b, 0, 100, 1000, 2000 s/mm2), were included in the study. cDWI b2000 was obtained from aDWI b0, b100, and b1000. Two independent reviewers performed a review of the aDWI b2000 and cDWI b2000 images in random order at 4-week intervals. A region of interest was drawn for the largest tumor on each dataset, and a Prostate Imaging-Reporting and Data System (PI-RADS) score based on PI-RADS v2.1 was recorded. Histologic topographic maps served as the reference standard. Results The study population's Gleason scores were 6 (n = 16), 7 (n = 53), 8 (n = 9), and 9 (n = 10). According to the reviewers, the sensitivities of cDWI b2000 and aDWI b2000 showed no significant differences (for reviewer 1, both 94% [83/88]; for reviewer 2, both 90% [79/88]; p = 1.000, respectively). The kappa values of cDWI b2000 and aDWI b2000 for the PI-RADS score were 0.422 (95% confidence interval [CI], 0.240-0.603) and 0.495 (95% CI, 0.308-0.683), respectively. Conclusion cDWI b2000 showed comparable sensitivity with aDWI b2000, in addition to sustained moderate inter-observer agreement, in the detection of PCa.
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Hernando D, Zhang Y, Pirasteh A. Quantitative diffusion MRI of the abdomen and pelvis. Med Phys 2021; 49:2774-2793. [PMID: 34554579 DOI: 10.1002/mp.15246] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 08/05/2021] [Accepted: 09/15/2021] [Indexed: 12/14/2022] Open
Abstract
Diffusion MRI has enormous potential and utility in the evaluation of various abdominal and pelvic disease processes including cancer and noncancer imaging of the liver, prostate, and other organs. Quantitative diffusion MRI is based on acquisitions with multiple diffusion encodings followed by quantitative mapping of diffusion parameters that are sensitive to tissue microstructure. Compared to qualitative diffusion-weighted MRI, quantitative diffusion MRI can improve standardization of tissue characterization as needed for disease detection, staging, and treatment monitoring. However, similar to many other quantitative MRI methods, diffusion MRI faces multiple challenges including acquisition artifacts, signal modeling limitations, and biological variability. In abdominal and pelvic diffusion MRI, technical acquisition challenges include physiologic motion (respiratory, peristaltic, and pulsatile), image distortions, and low signal-to-noise ratio. If unaddressed, these challenges lead to poor technical performance (bias and precision) and clinical outcomes of quantitative diffusion MRI. Emerging and novel technical developments seek to address these challenges and may enable reliable quantitative diffusion MRI of the abdomen and pelvis. Through systematic validation in phantoms, volunteers, and patients, including multicenter studies to assess reproducibility, these emerging techniques may finally demonstrate the potential of quantitative diffusion MRI for abdominal and pelvic imaging applications.
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Affiliation(s)
- Diego Hernando
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Yuxin Zhang
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Ali Pirasteh
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
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