1
|
Wang F, Sun YN, Zhang BT, Yang Q, He AD, Xu WY, Liu J, Liu MX, Li XH, Yu YQ, Zhu J. Value of fractional-order calculus (FROC) model diffusion-weighted imaging combined with simultaneous multi-slice (SMS) acceleration technology for evaluating benign and malignant breast lesions. BMC Med Imaging 2024; 24:190. [PMID: 39075336 PMCID: PMC11285176 DOI: 10.1186/s12880-024-01368-4] [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: 03/19/2024] [Accepted: 07/16/2024] [Indexed: 07/31/2024] Open
Abstract
BACKGROUND This study explores the diagnostic value of combining fractional-order calculus (FROC) diffusion-weighted model with simultaneous multi-slice (SMS) acceleration technology in distinguishing benign and malignant breast lesions. METHODS 178 lesions (73 benign, 105 malignant) underwent magnetic resonance imaging with diffusion-weighted imaging using multiple b-values (14 b-values, highest 3000 s/mm2). Independent samples t-test or Mann-Whitney U test compared image quality scores, FROC model parameters (D,, ), and ADC values between two groups. Multivariate logistic regression analysis identified independent variables and constructed nomograms. Model discrimination ability was assessed with receiver operating characteristic (ROC) curve and calibration chart. Spearman correlation analysis and Bland-Altman plot evaluated parameter correlation and consistency. RESULTS Malignant lesions exhibited lower D, and ADC values than benign lesions (P < 0.05), with higher values (P < 0.05). In SSEPI-DWI and SMS-SSEPI-DWI sequences, the AUC and diagnostic accuracy of D value are maximal, with D value demonstrating the highest diagnostic sensitivity, while value exhibits the highest specificity. The D and combined model had the highest AUC and accuracy. D and ADC values showed high correlation between sequences, and moderate. Bland-Altman plot demonstrated unbiased parameter values. CONCLUSION SMS-SSEPI-DWI FROC model provides good image quality and lesion characteristic values within an acceptable time. It shows consistent diagnostic performance compared to SSEPI-DWI, particularly in D and values, and significantly reduces scanning time.
Collapse
Affiliation(s)
- Fei Wang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No.218, Jixi Road, Hefei, 230032, China
- Department of Radiology, Anqing Municipal Hospital, No.352, Renmin Road, Anqing, 246003, China
| | - Yi-Nan Sun
- Department of Radiology, Anqing Municipal Hospital, No.352, Renmin Road, Anqing, 246003, China
| | - Bao-Ti Zhang
- Department of Radiology, Anqing Municipal Hospital, No.352, Renmin Road, Anqing, 246003, China
| | - Qing Yang
- Department of Radiology, Anqing Municipal Hospital, No.352, Renmin Road, Anqing, 246003, China
| | - An-Dong He
- Department of Radiology, Anqing Municipal Hospital, No.352, Renmin Road, Anqing, 246003, China
| | - Wang-Yan Xu
- Department of Radiology, Anqing Municipal Hospital, No.352, Renmin Road, Anqing, 246003, China
| | - Jun Liu
- Department of Radiology, Anqing Municipal Hospital, No.352, Renmin Road, Anqing, 246003, China
| | - Meng-Xiao Liu
- MR Research & Marketing Department, Siemens Healthineers Co., Ltd, No.278, Zhouzugong Road, Shanghai, 201318, China
| | - Xiao-Hu Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No.218, Jixi Road, Hefei, 230032, China
| | - Yong-Qiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No.218, Jixi Road, Hefei, 230032, China.
| | - Juan Zhu
- Department of Radiology, Anqing Municipal Hospital, No.352, Renmin Road, Anqing, 246003, China.
| |
Collapse
|
2
|
Fan Z, Guo J, Zhang X, Chen Z, Wang B, Jiang Y, Li Y, Wang Y, Yang G, Wang X. Non-Gaussian diffusion metrics with whole-tumor histogram analysis for bladder cancer diagnosis: muscle invasion and histological grade. Insights Imaging 2024; 15:138. [PMID: 38853200 PMCID: PMC11162990 DOI: 10.1186/s13244-024-01701-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 04/13/2024] [Indexed: 06/11/2024] Open
Abstract
PURPOSE To investigate the performance of histogram features of non-Gaussian diffusion metrics for diagnosing muscle invasion and histological grade in bladder cancer (BCa). METHODS Patients were prospectively allocated to MR scanner1 (training cohort) or MR2 (testing cohort) for conventional diffusion-weighted imaging (DWIconv) and multi-b-value DWI. Metrics of continuous time random walk (CTRW), diffusion kurtosis imaging (DKI), fractional-order calculus (FROC), intravoxel incoherent motion (IVIM), and stretched exponential model (SEM) were simultaneously calculated using multi-b-value DWI. Whole-tumor histogram features were extracted from DWIconv and non-Gaussian diffusion metrics for logistic regression analysis to develop diffusion models diagnosing muscle invasion and histological grade. The models' performances were quantified by area under the receiver operating characteristic curve (AUC). RESULTS MR1 included 267 pathologically-confirmed BCa patients (median age, 67 years [IQR, 46-82], 222 men) and MR2 included 83 (median age, 65 years [IQR, 31-82], 73 men). For discriminating muscle invasion, CTRW achieved the highest testing AUC of 0.915, higher than DWIconv's 0.805 (p = 0.014), and similar to the combined diffusion model's AUC of 0.885 (p = 0.076). For differentiating histological grade of non-muscle-invasion bladder cancer, IVIM outperformed a testing AUC of 0.897, higher than DWIconv's 0.694 (p = 0.020), and similar to the combined diffusion model's AUC of 0.917 (p = 0.650). In both tasks, DKI, FROC, and SEM failed to show diagnostic superiority over DWIconv (p > 0.05). CONCLUSION CTRW and IVIM are two potential non-Gaussian diffusion models to improve the MRI application in assessing muscle invasion and histological grade of BCa, respectively. CRITICAL RELEVANCE STATEMENT Our study validates non-Gaussian diffusion imaging as a reliable, non-invasive technique for early assessment of muscle invasion and histological grade in BCa, enhancing accuracy in diagnosis and improving MRI application in BCa diagnostic procedures. KEY POINTS Muscular invasion largely determines bladder salvageability in bladder cancer patients. Evaluated non-Gaussian diffusion metrics surpassed DWIconv in BCa muscle invasion and histological grade diagnosis. Non-Gaussian diffusion imaging improved MRI application in preoperative diagnosis of BCa.
Collapse
Affiliation(s)
- Zhichang Fan
- Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Junting Guo
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Xiaoyue Zhang
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Zeke Chen
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Bin Wang
- Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Yueluan Jiang
- Department of MR Research Collaboration, Siemens Healthineers, Beijing, China
| | - Yan Li
- Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Yongfang Wang
- Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Guoqiang Yang
- Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Xiaochun Wang
- Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
| |
Collapse
|
3
|
Zhou M, Bao D, Huang H, Chen M, Jiang W. Utilization of diffusion-weighted derived mathematical models to predict prognostic factors of resectable rectal cancer. Abdom Radiol (NY) 2024:10.1007/s00261-024-04239-2. [PMID: 38744701 DOI: 10.1007/s00261-024-04239-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/05/2024] [Accepted: 02/05/2024] [Indexed: 05/16/2024]
Abstract
PURPOSE This study explored models of monoexponential diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), stretched exponential (SEM), fractional-order calculus (FROC), and continuous-time random-walk (CTRW) as diagnostic tools for assessing pathological prognostic factors in patients with resectable rectal cancer (RRC). METHODS RRC patients who underwent radical surgery were included. The apparent diffusion coefficient (ADC), the mean kurtosis (MK) and mean diffusion (MD) from the DKI model, the distributed diffusion coefficient (DDC) and α from the SEM model, D, β and u from the FROC model, and D, α and β from the CTRW model were assessed. RESULTS There were a total of 181 patients. The area under the receiver operating characteristic (ROC) curve (AUC) of CTRW-α for predicting histology type was significantly higher than that of FROC-u (0.780 vs. 0.671, p = 0.043). The AUC of CTRW-α for predicting pT stage was significantly higher than that of FROC-u and ADC (0.786 vs.0.683, p = 0.043; 0.786 vs. 0.682, p = 0.030), the difference in predictive efficacy of FROC-u between ADC and MK was not statistically significant [0.683 vs. 0.682, p = 0.981; 0.683 vs. 0.703, p = 0.720]; the difference between the predictive efficacy of MK and ADC was not statistically significant (p = 0.696). The AUC of CTRW (α + β) (0.781) was significantly higher than that of FROC-u (0.781 vs. 0.625, p = 0.003) in predicting pN stage but not significantly different from that of MK (p = 0.108). CONCLUSION The CTRW and DKI models may serve as imaging biomarkers to predict pathological prognostic factors in RRC patients before surgery.
Collapse
Affiliation(s)
- Mi Zhou
- Department of Radiology, Sichuan Provincial Orthopedic Hospital, Chengdu, China.
| | - Deying Bao
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Hongyun Huang
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Meining Chen
- Department of MR Scientific Marketing, Siemens Healthineers, Shanghai, 200135, China
| | - Wenli Jiang
- Department of Radiology, Second Affiliated Hospital of Chongqing University of Medical Sciences, Chongqing, 400010, China
| |
Collapse
|
4
|
Guo R, Lu F, Lin J, Fu C, Liu M, Yang S. Multi-b-value DWI to evaluate the synergistic antiproliferation and anti-heterogeneity effects of bufalin plus sorafenib in an orthotopic HCC model. Eur Radiol Exp 2024; 8:43. [PMID: 38467904 PMCID: PMC10928042 DOI: 10.1186/s41747-024-00448-y] [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: 09/22/2023] [Accepted: 02/06/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Multi-b-value diffusion-weighted imaging (DWI) with different postprocessing models allows for evaluating hepatocellular carcinoma (HCC) proliferation, spatial heterogeneity, and feasibility of treatment strategies. We assessed synergistic effects of bufalin+sorafenib in orthotopic HCC-LM3 xenograft nude mice by using intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), a stretched exponential model (SEM), and a fractional-order calculus (FROC) model. METHODS Twenty-four orthotopic HCC-LM3 xenograft mice were divided into bufalin+sorafenib, bufalin, sorafenib treatment groups, and a control group. Multi-b-value DWI was performed using a 3-T scanner after 3 weeks' treatment to obtain true diffusion coefficient Dt, pseudo-diffusion coefficient Dp, perfusion fraction f, mean diffusivity (MD), mean kurtosis (MK), distributed diffusion coefficient (DDC), heterogeneity index α, diffusion coefficient D, fractional order parameter β, and microstructural quantity μ. Necrotic fraction (NF), standard deviation (SD) of hematoxylin-eosin staining, and microvessel density (MVD) of anti-CD31 staining were evaluated. Correlations of DWI parameters with histopathological results were analyzed, and measurements were compared among four groups. RESULTS In the final 22 mice, f positively correlated with MVD (r = 0.679, p = 0.001). Significantly good correlations of MK (r = 0.677), α (r = -0.696), and β (r= -0.639) with SD were observed (all p < 0.010). f, MK, MVD, and SD were much lower, while MD, α, β, and NF were higher in bufalin plus sorafenib group than control group (all p < 0.050). CONCLUSION Evaluated by IVIM, DKI, SEM, and FROC, bufalin+sorafenib was found to inhibit tumor proliferation and angiogenesis and reduce spatial heterogeneity in HCC-LM3 models. RELEVANCE STATEMENT Multi-b-value DWI provides potential metrics for evaluating the efficacy of treatment in HCC. KEY POINTS • Bufalin plus sorafenib combination may increase the effectiveness of HCC therapy. • Multi-b-value DWI depicted HCC proliferation, angiogenesis, and spatial heterogeneity. • Multi-b-value DWI may be a noninvasive method to assess HCC therapeutic efficacy.
Collapse
Affiliation(s)
- Ran Guo
- Department of Radiology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 274 Middle Zhi-jiang Road, Shanghai, 200071, People's Republic of China
| | - Fang Lu
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, People's Republic of China
| | - Jiang Lin
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, People's Republic of China
| | - Caixia Fu
- MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, 518057, People's Republic of China
| | - Mengxiao Liu
- MR scientific Marketing, Diagnostic Imaging, Siemens Healthineers Ltd, Shanghai, 201318, People's Republic of China
| | - Shuohui Yang
- Department of Radiology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 274 Middle Zhi-jiang Road, Shanghai, 200071, People's Republic of China.
| |
Collapse
|
5
|
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
|
6
|
Kong L, Wen Z, Cai Q, Lin Y, Chen Y, Cao W, Li M, Qian L, Chen J, Guo Y, Wang H. Amide Proton Transfer-Weighted MRI and Diffusion-Weighted Imaging in Bladder Cancer: A Complementary Tool to the VI-RADS. Acad Radiol 2024; 31:564-571. [PMID: 37821347 DOI: 10.1016/j.acra.2023.09.005] [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: 08/08/2023] [Revised: 08/27/2023] [Accepted: 09/03/2023] [Indexed: 10/13/2023]
Abstract
RATIONALE AND OBJECTIVES To investigate the feasibility of amide proton transfer-weighted (APTw) and diffusion-weighted Magnetic Resonance Imaging (MRI) as a means by which to add value to the Vesical Imaging Reporting and Data System (VI-RADS) for discriminating muscle invasive bladder cancer (MIBC) from nonmuscle invasive bladder cancer (NMIBC). MATERIALS AND METHODS This prospective study enrolled participants with pathologically confirmed bladder cancer (BCa) who underwent preoperative multiparametric MRI, including APTw and diffusion-weighted MRI, from July 2020 to January 2023. The exclusion criteria were lesions smaller than 10 mm, missing smooth muscle layer in the operation specimen, neoadjuvant therapy before MRI, inadequate image quality, and malignancy other than urothelial neoplasm. Two radiologists independently assigned the VI-RADS score for each participant. Quantitative parameters derived from APTw and diffusion-weighted MRI were obtained by another two radiologists. Receiver operating characteristic (ROC) curve analysis with the area under the ROC curve (AUC) was performed to evaluate the diagnostic performances of quantitative parameters for discriminating BCa detrusor muscle invasion status. RESULTS A total of 106 participants were enrolled (mean age, 64 ± 12 years [SD]; 90 men): 32 with MIBC and 74 with NMIBC. Lower apparent diffusion coefficient (ADC) values (0.88 × 10-3 mm2/s ± 0.12 vs. 1.08 × 10-3 mm2/s ± 0.25; P < 0.001) and higher APTw values (6.89% [interquartile range {IQR}, 5.05%-12.17%] vs. 3.61% [IQR, 2.23%-6.83%]; P < 0.001) were observed in the MIBC group. Compared to VI-RADS alone, both APTw (P = 0.003) and ADC (P = 0.020) values could improve the diagnostic performance of VI-RADS in differentiating MIBC from NMIBC. The combination of the three yielded the highest diagnostic performance (AUC, 0.93; 95% CI:0.87,0.97) for evaluating muscle invasion status. The addition of the APTw values to the combination of VI-RADS and ADC values notably improved the diagnostic performance for differentiating NMIBC from MIBC (VI-RADS+ADC vs. VI-RADS+APTw+ADC, P = 0.046). CONCLUSION MRI parameters derived from APTw and diffusion-weighted MRI can be used to accurately assess muscle invasion status in BCa and provide additional value to VI-RADS.
Collapse
Affiliation(s)
- Lingmin Kong
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, PR China (L.K., Z.W., Q.C., Y.L., Y.C., W.C., M.L., Y.G., H.W.)
| | - Zhihua Wen
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, PR China (L.K., Z.W., Q.C., Y.L., Y.C., W.C., M.L., Y.G., H.W.)
| | - Qian Cai
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, PR China (L.K., Z.W., Q.C., Y.L., Y.C., W.C., M.L., Y.G., H.W.)
| | - Yingyu Lin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, PR China (L.K., Z.W., Q.C., Y.L., Y.C., W.C., M.L., Y.G., H.W.)
| | - Yanling Chen
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, PR China (L.K., Z.W., Q.C., Y.L., Y.C., W.C., M.L., Y.G., H.W.)
| | - Wenxin Cao
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, PR China (L.K., Z.W., Q.C., Y.L., Y.C., W.C., M.L., Y.G., H.W.)
| | - Meiqin Li
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, PR China (L.K., Z.W., Q.C., Y.L., Y.C., W.C., M.L., Y.G., H.W.)
| | - Long Qian
- MR Research, GE Healthcare, Beijing, China (L.Q.)
| | - Junxing Chen
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, PR China (J.C.)
| | - Yan Guo
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, PR China (L.K., Z.W., Q.C., Y.L., Y.C., W.C., M.L., Y.G., H.W.)
| | - Huanjun Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, PR China (L.K., Z.W., Q.C., Y.L., Y.C., W.C., M.L., Y.G., H.W.).
| |
Collapse
|
7
|
Zhong Z, Ryu K, Mao J, Sun K, Dan G, Vasanawala SS, Zhou XJ. Accelerating High b-Value Diffusion-Weighted MRI Using a Convolutional Recurrent Neural Network (CRNN-DWI). Bioengineering (Basel) 2023; 10:864. [PMID: 37508891 PMCID: PMC10376839 DOI: 10.3390/bioengineering10070864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/05/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023] Open
Abstract
PURPOSE To develop a novel convolutional recurrent neural network (CRNN-DWI) and apply it to reconstruct a highly undersampled (up to six-fold) multi-b-value, multi-direction diffusion-weighted imaging (DWI) dataset. METHODS A deep neural network that combines a convolutional neural network (CNN) and recurrent neural network (RNN) was first developed by using a set of diffusion images as input. The network was then used to reconstruct a DWI dataset consisting of 14 b-values, each with three diffusion directions. For comparison, the dataset was also reconstructed with zero-padding and 3D-CNN. The experiments were performed with undersampling rates (R) of 4 and 6. Standard image quality metrics (SSIM and PSNR) were employed to provide quantitative assessments of the reconstructed image quality. Additionally, an advanced non-Gaussian diffusion model was employed to fit the reconstructed images from the different approaches, thereby generating a set of diffusion parameter maps. These diffusion parameter maps from the different approaches were then compared using SSIM as a metric. RESULTS Both the reconstructed diffusion images and diffusion parameter maps from CRNN-DWI were better than those from zero-padding or 3D-CNN. Specifically, the average SSIM and PSNR of CRNN-DWI were 0.750 ± 0.016 and 28.32 ± 0.69 (R = 4), and 0.675 ± 0.023 and 24.16 ± 0.77 (R = 6), respectively, both of which were substantially higher than those of zero-padding or 3D-CNN reconstructions. The diffusion parameter maps from CRNN-DWI also yielded higher SSIM values for R = 4 (>0.8) and for R = 6 (>0.7) than the other two approaches (for R = 4, <0.7, and for R = 6, <0.65). CONCLUSIONS CRNN-DWI is a viable approach for reconstructing highly undersampled DWI data, providing opportunities to reduce the data acquisition burden.
Collapse
Affiliation(s)
- Zheng Zhong
- Departments of Radiology, Stanford University, Stanford, CA 94305, USA
- Center for Magnetic Resonance Research, Chicago, IL 60612, USA
| | - Kanghyun Ryu
- Departments of Radiology, Stanford University, Stanford, CA 94305, USA
| | - Jonathan Mao
- Henry M. Gunn High School, Palo Alto, CA 94306, USA
| | - Kaibao Sun
- Center for Magnetic Resonance Research, Chicago, IL 60612, USA
| | - Guangyu Dan
- Center for Magnetic Resonance Research, Chicago, IL 60612, USA
| | | | - Xiaohong Joe Zhou
- Center for Magnetic Resonance Research, Chicago, IL 60612, USA
- Department of Radiology, Neurosurgery and Biomedical Engineering, University of Illinois Chicago, Chicago, IL 60607, USA
| |
Collapse
|
8
|
Meng X, Li S, He K, Hu H, Feng C, Li Z, Wang Y. Evaluation of Whole-Tumor Texture Analysis Based on MRI Diffusion Kurtosis and Biparametric VI-RADS Model for Staging and Grading Bladder Cancer. Bioengineering (Basel) 2023; 10:745. [PMID: 37508772 PMCID: PMC10376391 DOI: 10.3390/bioengineering10070745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/16/2023] [Accepted: 06/18/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND to evaluate the feasibility of texture analysis (TA) based on diffusion kurtosis imaging (DKI) in staging and grading bladder cancer (BC) and to compare it with apparent diffusion coefficient (ADC) and biparametric vesical imaging reporting and data system (VI-RADS). MATERIALS AND METHODS In this retrospective study, 101 patients with pathologically confirmed BC underwent MRI with multiple-b values ranging from 0 to 2000 s/mm2. ADC- and DKI-derived parameters, including mean kurtosis (MK) and mean diffusivity (MD), were obtained. First-order texture histogram parameters of MK and MD, including the mean; 5th, 25th, 50th, 75th, and 90th percentiles; inhomogeneity; skewness: kurtosis; and entropy; were extracted. The VI-RADS score was evaluated based on the T2WI and DWI. The Mann-Whitney U-test was used to compare the texture parameters and ADC values between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC), as well as between low and high grades. Receiver operating characteristic analysis was used to evaluate the diagnostic performance of each significant parameter and their combinations. RESULTS The NMIBC and low-grade group had higher MDmean, MD5th, MD25th, MD50th, MD75th, MD90th, and ADC values than those of the MIBC and the high-grade group. The NMIBC and low-grade group yielded lower MKmean, MK25th, MK50th, MK75th, and MK90th than the MIBC and high-grade group. Among all histogram parameters, MD75th and MD90th yielded the highest AUC in differentiating MIBC from NMIBC (both AUCs were 0.87), while the AUC for ADC was 0.86. The MK75th and MK90th had the highest AUC (both 0.79) in differentiating low- from high-grade BC, while ADC had an AUC of 0.68. The AUC (0.92) of the combination of DKI histogram parameters (MD75th, MD90th, and MK90th) with biparametric VI-RADS in staging BC was higher than that of the biparametric VI-RADS (0.89). CONCLUSIONS Texture-analysis-derived DKI is useful in evaluating both the staging and grading of bladder cancer; in addition, the histogram parameters of the DKI (MD75th, MD90th, and MK90th) can provide additional value to VI-RADS.
Collapse
Affiliation(s)
- Xiaoyan Meng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Shichao Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Kangwen He
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Henglong Hu
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Cui Feng
- Department of Radiology, 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
| | - Yanchun Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| |
Collapse
|
9
|
Mehta R, Bu Y, Zhong Z, Dan G, Zhong PS, Zhou C, Hu W, Zhou XJ, Xu M, Wang S, Karaman MM. Characterization of breast lesions using multi-parametric diffusion MRI and machine learning. Phys Med Biol 2023; 68:085006. [PMID: 36808921 DOI: 10.1088/1361-6560/acbde0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 02/21/2023] [Indexed: 02/23/2023]
Abstract
Objective. To investigate quantitative imaging markers based on parameters from two diffusion-weighted imaging (DWI) models, continuous-time random-walk (CTRW) and intravoxel incoherent motion (IVIM) models, for characterizing malignant and benign breast lesions by using a machine learning algorithm.Approach. With IRB approval, 40 women with histologically confirmed breast lesions (16 benign, 24 malignant) underwent DWI with 11b-values (50 to 3000 s/mm2) at 3T. Three CTRW parameters,Dm,α, andβand three IVIM parametersDdiff,Dperf, andfwere estimated from the lesions. A histogram was generated and histogram features of skewness, variance, mean, median, interquartile range; and the value of the 10%, 25% and 75% quantiles were extracted for each parameter from the regions-of-interest. Iterative feature selection was performed using the Boruta algorithm that uses the Benjamin Hochberg False Discover Rate to first determine significant features and then to apply the Bonferroni correction to further control for false positives across multiple comparisons during the iterative procedure. Predictive performance of the significant features was evaluated using Support Vector Machine, Random Forest, Naïve Bayes, Gradient Boosted Classifier (GB), Decision Trees, AdaBoost and Gaussian Process machine learning classifiers.Main Results. The 75% quantile, and median ofDm; 75% quantile off;mean, median, and skewness ofβ;kurtosis ofDperf; and 75% quantile ofDdiffwere the most significant features. The GB differentiated malignant and benign lesions with an accuracy of 0.833, an area-under-the-curve of 0.942, and an F1 score of 0.87 providing the best statistical performance (p-value < 0.05) compared to the other classifiers.Significance. Our study has demonstrated that GB with a set of histogram features from the CTRW and IVIM model parameters can effectively differentiate malignant and benign breast lesions.
Collapse
Affiliation(s)
- Rahul Mehta
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Yangyang Bu
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
| | - Zheng Zhong
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Guangyu Dan
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Ping-Shou Zhong
- Department of Mathematics, Statistics, and Computer Science, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Changyu Zhou
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
| | - Weihong Hu
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
| | - Xiaohong Joe Zhou
- Departments of Radiology and Neurosurgery, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Maosheng Xu
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
| | - Shiwei Wang
- The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China
| | - M Muge Karaman
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States of America
| |
Collapse
|
10
|
Li J, Cao K, Lin H, Deng L, Yang S, Gao Y, Liang M, Lin C, Zhang W, Xie C, Zhang K, Luo J, Pan Z, Yue P, Zou Y, Huang B. Predicting muscle invasion in bladder cancer by deep learning analysis of MRI: comparison with vesical imaging-reporting and data system. Eur Radiol 2023; 33:2699-2709. [PMID: 36434397 DOI: 10.1007/s00330-022-09272-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 09/18/2022] [Accepted: 10/24/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVES To compare the diagnostic performance of a novel deep learning (DL) method based on T2-weighted imaging with the vesical imaging-reporting and data system (VI-RADS) in predicting muscle invasion in bladder cancer (MIBC). METHODS A total of 215 tumours (129 for training and 31 for internal validation, centre 1; 55 for external validation, centre 2) were included. MIBC was confirmed by pathological examination. VI-RADS scores were provided by two groups of radiologists (readers 1 and readers 2) independently. A deep convolutional neural network was constructed in the training set, and validation was conducted on the internal and external validation sets. ROC analysis was performed to evaluate the performance for MIBC diagnosis. RESULTS The AUCs of the DL model, readers 1, and readers 2 were as follows: in the internal validation set, 0.963, 0.843, and 0.852, respectively; in the external validation set, 0.861, 0.808, and 0.876, respectively. The accuracy of the DL model in the tumours scored VI-RADS 2 or 3 was higher than that of radiologists in the external validation set: for readers 1, 0.886 vs. 0.600, p = 0.006; for readers 2, 0.879 vs. 0.636, p = 0.021. The average processing time (38 s and 43 s in two validation sets) of the DL method was much shorter than the readers, with a reduction of over 100 s in both validation sets. CONCLUSIONS Compared to radiologists using VI-RADS, the DL method had a better diagnostic performance, shorter processing time, and robust generalisability, indicating good potential for diagnosing MIBC. KEY POINTS • The DL model shows robust performance for MIBC diagnosis in both internal and external validation. • The diagnostic performance of the DL model in the tumours scored VI-RADS 2 or 3 is better than that obtained by radiologists using VI-RADS. • The DL method shows potential in the preoperative assessment of MIBC.
Collapse
Affiliation(s)
- Jianpeng Li
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, Guangdong, China
| | - Kangyang Cao
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Hongxin Lin
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Lei Deng
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, Guangdong, China
| | - Shuiqing Yang
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, Guangdong, China
| | - Yun Gao
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, Guangdong, China
| | - Manqiu Liang
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, Guangdong, China
| | - Chuxuan Lin
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Weijing Zhang
- Imaging Department, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Chuanmiao Xie
- Imaging Department, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Kunlin Zhang
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, Guangdong, China
| | - Jiexin Luo
- Department of Urology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, Guangdong, China
| | - Zhaohong Pan
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Peiyan Yue
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Yujian Zou
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, Guangdong, China.
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China.
| |
Collapse
|
11
|
Wang C, Wang G, Zhang Y, Dai Y, Yang D, Wang C, Li J. Differentiation of benign and malignant breast lesions using diffusion-weighted imaging with a fractional-order calculus model. Eur J Radiol 2023; 159:110646. [PMID: 36577184 DOI: 10.1016/j.ejrad.2022.110646] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 11/25/2022] [Accepted: 12/07/2022] [Indexed: 12/23/2022]
Abstract
PURPOSE To assess the feasibility of using three diffusion parameters (D, β, and μ) derived from fractional-order calculus (FROC) diffusion model for improving the differentiation between benign and malignant breast lesions. METHOD In this prospective study, 103 patients with breast lesions were enrolled. All subjects underwent diffusion-weighted imaging (DWI) with 12b values. Inter-observer agreement with respect to quantification of parameters by two radiologists was assessed using intraclass coefficient. Conventional apparent diffusion coefficient (ADC) and three FROC model parameters D, β, and μ were compared between the benign lesion and malignant lesion groups using the Mann-Whitney U test. Then, a comprehensive prediction model was created by using binary logistic regression. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the parameters using histopathological diagnosis as the reference standard. RESULTS The FROC parameters and ADC all exhibited significant differences between benign lesions and malignant lesions (P<0.001). Among the individual parameters, the sensitivity of μ was higher than ADC (95.92% for μ vs 91.84% for ADC), and the specificity of β was higher than ADC (72.22% for β vs 70.37% for ADC). The combination of ADC and FROC parameters (D and β) generated the largest area under the ROC curve (0.841) when compared with individual parameters, indicating an improved performance for differentiating benign lesions from malignant lesions. CONCLUSIONS This study demonstrated the feasibility of using the FROC diffusion model to improve the accuracy of identifying malignant breast lesions.
Collapse
Affiliation(s)
- Chunhong Wang
- Department of Radiology, Xinyang Central Hospital, No. 01 Xinyang Siyi Road, Xinyang 464000, Henan, China
| | - Guanying Wang
- Department of Radiology, Xinyang Central Hospital, No. 01 Xinyang Siyi Road, Xinyang 464000, Henan, China
| | - Yunfei Zhang
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Yongming Dai
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Dan Yang
- Department of Radiology, Xinyang Central Hospital, No. 01 Xinyang Siyi Road, Xinyang 464000, Henan, China
| | - Changfu Wang
- Imaging department, Huaihe Hospital, Henan University, Kaifeng, 475000, Henan, China
| | - Jianhong Li
- Department of Radiology, Xinyang Central Hospital, No. 01 Xinyang Siyi Road, Xinyang 464000, Henan, China.
| |
Collapse
|
12
|
Radiomics Nomogram Based on High-b-Value Diffusion-Weighted Imaging for Distinguishing the Grade of Bladder Cancer. Life (Basel) 2022; 12:life12101510. [DOI: 10.3390/life12101510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/03/2022] [Accepted: 09/23/2022] [Indexed: 12/24/2022] Open
Abstract
Background: The aim was to evaluate the feasibility of radiomics features based on diffusion-weighted imaging (DWI) at high b-values for grading bladder cancer and to compare the possible advantages of high-b-value DWI over the standard b-value DWI. Methods: Seventy-four participants with bladder cancer were included in this study. DWI sequences using a 3 T MRI with b-values of 1000, 1700, and 3000 s/mm2 were acquired, and the corresponding ADC maps were generated, followed with feature extraction. Patients were randomly divided into training and testing cohorts with a ratio of 8:2. The radiomics features acquired from the ADC1000, ADC1700, and ADC3000 maps were compared between low- and high-grade bladder cancers by using the Wilcox analysis, and only the radiomics features with significant differences were selected. The least absolute shrinkage and selection operator method and a logistic regression were performed for the feature selection and establishing the radiomics model. A receiver operating characteristic (ROC) analysis was conducted to assess the diagnostic performance of the radiomics models. Results: In the training cohorts, the AUCs of the ADC1000, ADC1700, and ADC3000 model for discriminating between low- from high-grade bladder cancer were 0.901, 0.920, and 0.901, respectively. In the testing cohorts, the AUCs of ADC1000, ADC1700, and ADC3000 were 0.582, 0.745, and 0.745, respectively. Conclusions: The radiomics features extracted from the ADC1700 maps could improve the diagnostic accuracy over those extracted from the conventional ADC1000 maps.
Collapse
|
13
|
Hu X, Li G, Wu S. Advances in Diagnosis and Therapy for Bladder Cancer. Cancers (Basel) 2022; 14:cancers14133181. [PMID: 35804953 PMCID: PMC9265007 DOI: 10.3390/cancers14133181] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/19/2022] [Accepted: 06/24/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary The clinical management of bladder cancer has been developing in the past decade, including diagnostic tools and treatment options. Both monotherapy and combination therapy have been undoubtedly upgraded. Multiple diagnostic techniques and therapeutic strategies have been developed to meet the urgent clinical needs, resulting in the emergence of various explorations for cancer diagnosis and therapy. In this review, we mainly focus on the advances in the diagnosis and treatment of bladder cancer. Abstract Bladder cancer (BCa) is one of the most common and expensive urinary system malignancies for its high recurrence and progression rate. In recent years, immense amounts of studies have been carried out to bring a more comprehensive cognition and numerous promising clinic approaches for BCa therapy. The development of innovative enhanced cystoscopy techniques (optical techniques, imaging systems) and tumor biomarkers-based non-invasive urine screening (DNA methylation-based urine test) would dramatically improve the accuracy of tumor detection, reducing the risk of recurrence and progression of BCa. Moreover, intravesical instillation and systemic therapeutic strategies (cocktail therapy, immunotherapy, vaccine therapy, targeted therapy) also provide plentiful measures to break the predicament of BCa. Several exploratory clinical studies, including novel surgical approaches, pharmaceutical compositions, and bladder preservation techniques, emerged continually, which are supposed to be promising candidates for BCa clinical treatment. Here, recent advances and prospects of diagnosis, intravesical or systemic treatment, and novel drug delivery systems for BCa therapy are reviewed in this paper.
Collapse
Affiliation(s)
- Xinzi Hu
- Institute of Urology, The Affiliated Luohu Hospital of Shenzhen University, Shenzhen University, Shenzhen 518000, China; (X.H.); (G.L.)
- Department of Urology, South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Guangzhi Li
- Institute of Urology, The Affiliated Luohu Hospital of Shenzhen University, Shenzhen University, Shenzhen 518000, China; (X.H.); (G.L.)
- Department of Urology, South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Song Wu
- Institute of Urology, The Affiliated Luohu Hospital of Shenzhen University, Shenzhen University, Shenzhen 518000, China; (X.H.); (G.L.)
- Department of Urology, South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
- Correspondence:
| |
Collapse
|
14
|
Shao X, An L, Liu H, Feng H, Zheng L, Dai Y, Yu B, Zhang J. Cervical Carcinoma: Evaluation Using Diffusion MRI With a Fractional Order Calculus Model and its Correlation With Histopathologic Findings. Front Oncol 2022; 12:851677. [PMID: 35480091 PMCID: PMC9036957 DOI: 10.3389/fonc.2022.851677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/03/2022] [Indexed: 11/13/2022] Open
Abstract
Objective The objective of the study is to investigate the feasibility of using the fractional order calculus (FROC) model to reflect tumor subtypes and histological grades of cervical carcinoma. Methods Sixty patients with untreated cervical carcinoma underwent multi-b-value diffusion-weighted imaging (DWI) at 3.0T magnetic resonance imaging (MRI). The mono-exponential and the FROC models were fitted. The differences in the histological subtypes and grades were evaluated by the Mann–Whitney U test. Receiver operating characteristic (ROC) analyses were performed to assess the diagnostic performance and to determine the best predictor for both univariate analysis and multivariate analysis. Differences between ROC curves were tested using the Hanley and McNeil test, while the sensitivity, specificity, and accuracy were compared using the McNemar test. P-value <0.05 was considered as significant difference. The Bonferroni corrections were applied to reduce problems associated with multiple comparisons. Results Only the parameter β, derived from the FROC model could differentiate cervical carcinoma subtypes (P = 0.03) and the squamous cell carcinoma (SCC) lesions exhibited significantly lower β than that in the adenocarcinoma (ACA) lesions. All the individual parameters, namely, ADC, β, D, and μ derived from the FROC model, could differentiate low-grade cervical carcinomas from high-grade ones (P = 0.022, 0.009, 0.004, and 0.015, respectively). The combination of all the FROC parameters showed the best overall performance, providing the highest sensitivity (81.2%) and AUC (0.829). Conclusion The parameters derived from the FROC model were able to differentiate the subtypes and grades of cervical carcinoma.
Collapse
Affiliation(s)
- Xian Shao
- Department of Anesthesiology, The Fourth Hospital of Shijiazhuang, Shijiazhuang, China
| | - Li An
- Department of Anesthesiology, The Fourth Hospital of Shijiazhuang, Shijiazhuang, China
| | - Hui Liu
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Hui Feng
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Liyun Zheng
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Yongming Dai
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Bin Yu
- Department of Emergency, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jin Zhang
- Department of Anesthesiology, The Fourth Hospital of Shijiazhuang, Shijiazhuang, China
| |
Collapse
|
15
|
Proposal for a new Vesical Imaging-Reporting and Data System (VI-RADS)-based algorithm for the management of bladder cancer: A paradigm shift from the current transurethral resection of bladder tumor (TURBT)-dependent practice. Clin Genitourin Cancer 2022; 20:e291-e295. [DOI: 10.1016/j.clgc.2022.03.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 02/27/2022] [Accepted: 03/01/2022] [Indexed: 11/19/2022]
|
16
|
Apparent Diffusion Coefficient Value as a Biomarker for Detecting Muscle-Invasive and High-Grade Bladder Cancer: A Systematic Review. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031278] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background: Several studies have investigated the potential role of the apparent diffusion coefficient (ADC) value of diffusion-weighted magnetic resonance imaging as a biomarker of high-grade and invasive bladder cancer. Methods: PubMed and the Cochrane Library were systematically searched in September 2021 to extract studies that evaluated the associations between ADC values, pathological T stage, and histological grade bladder cancers. The diagnostic performance of ADC values in detecting muscle-invasive bladder cancer (MIBC) and high-grade disease was systematically reviewed. Results: Six studies were included in this systematic review. MIBC showed significantly lower ADC values than non-muscle-invasive bladder cancer (NMIBC) in all six studies. The median (range) sensitivity, specificity, and area under the curve (AUC) of ADC values to detect MIBC among the four eligible studies were 73.5% (68.8–90.0%), 79.9% (66.7–84.4%), and 0.762 (0.730–0.884), respectively. Similarly, high-grade disease showed significantly lower ADC values than did low-grade disease in all four eligible studies. The median (range) sensitivity, specificity, and AUC of ADC values for detecting high-grade disease among the three eligible studies were 75.0% (73.0–76.5%), 95.8% (76.2–100%), and 0.902 (0.804–0.906), respectively. Conclusions: The ADC value is a non-invasive diagnostic biomarker for discriminating muscle-invasive and high-grade bladder cancer.
Collapse
|
17
|
Predicting the aggressiveness of peripheral zone prostate cancer using a fractional order calculus diffusion model. Eur J Radiol 2021; 143:109913. [PMID: 34464907 DOI: 10.1016/j.ejrad.2021.109913] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 08/01/2021] [Accepted: 08/12/2021] [Indexed: 11/21/2022]
Abstract
PURPOSE To evaluate the performance of parameters D, β, μ from the Fractional Order Calculus (FROC) model at differentiating peripheral zone (PZ) prostate cancer (PCa) MATERIAL AND METHODS: 75 patients who underwent targeted MRI-guided TRUS prostate biopsy within 6 months of MRI were reviewed retrospectively. Regions of interest (ROI) were placed on suspicious lesions on MRI scans. ROIs were then correlated to pathological results based on core biopsy location. The final tumor count is a total: 23 of GS 6 (3 + 3), 36 of GS 7 (3 + 4), 18 of GS 7 (4 + 3), and 19 of GS ≥ 8. Diffusion-weighted imaging (DWI) scans were fitted into the FROC and monoexponential model to calculate ADC and FROC parameters: anomalous diffusion coefficient D, intravoxel diffusion heterogeneity β, and spatial parameter μ. The performance of FROC parameters and ADC at differentiating PCa grade was evaluated with receiver operating characteristic (ROC) analysis. RESULTS In differentiating low (GS 6) vs. intermediate (GS 7) risk PZ PCa, combination of (D, β) provides the best performance with AUC of 0.829 with significance of p = 0.018 when compared to ADC (AUC of 0.655). In differentiating clinically significant (GS 6) vs. clinically significant (GS ≥ 7) PCa, combination of (D, β, μ) provides highest AUC of 0.802 when compared to ADC (AUC of 0.671) with significance of p = 0.038. Stratification of intermediate (GS 7) and high (GS ≥ 8) risk PCa with FROC did not reach a significant difference when compared to ADC. CONCLUSION Combination of FROC parameters shows greater performance than ADC at differentiating low vs. intermediate risk and clinically insignificant vs. significant prostate cancers in peripheral zone lesions. The FROC diffusion model holds promise as a quantitative imaging technique for non-invasive evaluation of PZ PCa.
Collapse
|