|
1
|
He K, Meng X, Wang Y, Feng C, Liu Z, Li Z, Niu Y. Progress of Multiparameter Magnetic Resonance Imaging in Bladder Cancer: A Comprehensive Literature Review. Diagnostics (Basel) 2024; 14:442. [PMID: 38396481 PMCID: PMC10888296 DOI: 10.3390/diagnostics14040442] [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: 12/21/2023] [Revised: 01/25/2024] [Accepted: 02/13/2024] [Indexed: 02/25/2024] Open
Abstract
Magnetic resonance imaging (MRI) has been proven to be an indispensable imaging method in bladder cancer, and it can accurately identify muscular invasion of bladder cancer. Multiparameter MRI is a promising tool widely used for preoperative staging evaluation of bladder cancer. Vesical Imaging-Reporting and Data System (VI-RADS) scoring has proven to be a reliable tool for local staging of bladder cancer with high accuracy in preoperative staging, but VI-RADS still faces challenges and needs further improvement. Artificial intelligence (AI) holds great promise in improving the accuracy of diagnosis and predicting the prognosis of bladder cancer. Automated machine learning techniques based on radiomics features derived from MRI have been utilized in bladder cancer diagnosis and have demonstrated promising potential for practical implementation. Future work should focus on conducting more prospective, multicenter studies to validate the additional value of quantitative studies and optimize prediction models by combining other biomarkers, such as urine and serum biomarkers. This review assesses the value of multiparameter MRI in the accurate evaluation of muscular invasion of bladder cancer, as well as the current status and progress of its application in the evaluation of efficacy and prognosis.
Collapse
Affiliation(s)
- Kangwen He
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China (X.M.); (Z.L.)
| | - Xiaoyan Meng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China (X.M.); (Z.L.)
| | - Yanchun Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China (X.M.); (Z.L.)
| | - Cui Feng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China (X.M.); (Z.L.)
| | - Zheng Liu
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China (X.M.); (Z.L.)
| | - Yonghua Niu
- Department of Pediatric Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| |
Collapse
|
|
2
|
Xu Y, Wan Q, Ren X, Jiang Y, Wang F, Yao J, Wu P, Shen A, Wang P. Amide proton transfer-weighted MRI for renal tumors: Comparison with diffusion-weighted imaging. Magn Reson Imaging 2024; 106:104-109. [PMID: 38135260 DOI: 10.1016/j.mri.2023.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 12/07/2023] [Accepted: 12/10/2023] [Indexed: 12/24/2023]
Abstract
OBJECTIVE To investigate the potential of amide proton transfer-weighted (APTw) MRI in identifying benign and malignant renal tumors and to evaluate whether APTw MRI can add diagnostic value to diffusion-weighted imaging (DWI). MATERIALS AND METHODS Participants with renal tumor underwent preoperative multiparametric MRI, including APTw MRI and DWI. The APTw and apparent diffusion coefficient (ADC) of malignant tumors and benign tumors were calculated independently by two radiologists and compared. The value of the mean APTw and the mean ADC for differentiating malignant and benign tumors was evaluated by receiver operating characteristic analysis. RESULTS In total, 65 participants (mean age, 59 years ±14; 41 men) were evaluated: 54 with malignant and 11 with benign renal tumors. Malignant renal tumors showed higher mean APTw values [2.03% (1.63) vs 1.00% (1.60); P < 0.01] and lower mean ADC values (1.22 × 10-3 mm2/s ± 0.37 vs 1.51 × 10-3 mm2/s ± 0.37; P < 0.05) than benign renal tumors. The area under the receiver operating characteristic curve (AUC) of APTw, ADC and the combination of them for the identification of benign and malignant renal tumors was 0.78(95% CI: 0.66, 0.87; P < 0.001),0.70(95% CI: 0.54, 0.86; P < 0.05) and 0.79 (95% CI: 0.67, 0.88; P < 0.001). The optimal cutoff value for mean APTw was 2.14% (sensitivity, 74%; specificity, 73%). There was no difference between these three parameters for differentiating malignant from benign renal tumors (P > 0.05). CONCLUSION The APTw MRI has the potential use as an imaging biomarker for renal malignant and benign tumors.
Collapse
Affiliation(s)
- Yun Xu
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Qingxuan Wan
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Xihui Ren
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Yutao Jiang
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Fang Wang
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Jing Yao
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China
| | - Peng Wu
- Philips Healthcare, Shanghai 200072, China
| | - Aijun Shen
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China.
| | - Peijun Wang
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200065, China.
| |
Collapse
|