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Donners R, Candito A, Rata M, Sharp A, Messiou C, Koh DM, Tunariu N, Blackledge MD. Inter- and Intra-Patient Repeatability of Radiomic Features from Multiparametric Whole-Body MRI in Patients with Metastatic Prostate Cancer. Cancers (Basel) 2024; 16:1647. [PMID: 38730599 PMCID: PMC11083580 DOI: 10.3390/cancers16091647] [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: 02/12/2024] [Revised: 04/13/2024] [Accepted: 04/22/2024] [Indexed: 05/13/2024] Open
Abstract
(1) Background: We assessed the test-re-test repeatability of radiomics in metastatic castration-resistant prostate cancer (mCPRC) bone disease on whole-body diffusion-weighted (DWI) and T1-weighted Dixon MRI. (2) Methods: In 10 mCRPC patients, 1.5 T MRI, including DWI and T1-weighted gradient-echo Dixon sequences, was performed twice on the same day. Apparent diffusion coefficient (ADC) and relative fat-fraction-percentage (rFF%) maps were calculated. Per study, up to 10 target bone metastases were manually delineated on DWI and Dixon images. All 106 radiomic features included in the Pyradiomics toolbox were derived for each target volume from the ADC and rFF% maps. To account for inter- and intra-patient measurement repeatability, the log-transformed individual target measurements were fitted to a hierarchical model, represented as a Bayesian network. Repeatability measurements, including the intraclass correlation coefficient (ICC), were derived. Feature ICCs were compared with mean ADC and rFF ICCs. (3) Results: A total of 65 DWI and 47 rFF% targets were analysed. There was no significant bias for any features. Pairwise correlation revealed fifteen ADC and fourteen rFF% feature sub-groups, without specific patterns between feature classes. The median intra-patient ICC was generally higher than the inter-patient ICC. Features that describe extremes in voxel values (minimum, maximum, range, skewness, and kurtosis) showed generally lower ICCs. Several mostly shape-based texture features were identified, which showed high inter- and intra-patient ICCs when compared with the mean ADC or mean rFF%, respectively. (4) Conclusions: Pyradiomics texture features of mCRPC bone metastases varied greatly in inter- and intra-patient repeatability. Several features demonstrated good repeatability, allowing for further exploration as diagnostic parameters in mCRPC bone disease.
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Affiliation(s)
- Ricardo Donners
- University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland
| | - Antonio Candito
- The Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, UK; (A.C.); (M.R.); (A.S.); (C.M.); (D.-M.K.); (N.T.)
| | - Mihaela Rata
- The Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, UK; (A.C.); (M.R.); (A.S.); (C.M.); (D.-M.K.); (N.T.)
| | - Adam Sharp
- The Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, UK; (A.C.); (M.R.); (A.S.); (C.M.); (D.-M.K.); (N.T.)
| | - Christina Messiou
- The Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, UK; (A.C.); (M.R.); (A.S.); (C.M.); (D.-M.K.); (N.T.)
| | - Dow-Mu Koh
- The Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, UK; (A.C.); (M.R.); (A.S.); (C.M.); (D.-M.K.); (N.T.)
| | - Nina Tunariu
- The Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, UK; (A.C.); (M.R.); (A.S.); (C.M.); (D.-M.K.); (N.T.)
| | - Matthew D. Blackledge
- The Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, UK; (A.C.); (M.R.); (A.S.); (C.M.); (D.-M.K.); (N.T.)
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Dai Y, Hu W, Wu G, Wu D, Zhu M, Luo Y, Wang J, Zhou Y, Hu P. Grading Clear Cell Renal Cell Carcinoma Grade Using Diffusion Relaxation Correlated MR Spectroscopic Imaging. J Magn Reson Imaging 2024; 59:699-710. [PMID: 37209407 DOI: 10.1002/jmri.28777] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 05/01/2023] [Accepted: 05/02/2023] [Indexed: 05/22/2023] Open
Abstract
BACKGROUND Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC, and accurate grading is crucial for prognosis and treatment selection. Biopsy is the reference standard for grading, but MRI methods can improve and complement the grading procedure. PURPOSE Assess the performance of diffusion relaxation correlation spectroscopic imaging (DR-CSI) in grading ccRCC. STUDY TYPE Prospective. SUBJECTS 79 patients (age: 58.1 +/- 11.5 years; 55 male) with ccRCC confirmed by histopathology (grade 1, 7; grade 2, 45; grade 3, 18; grade 4, 9) following surgery. FIELD STRENGTH/SEQUENCE 3.0 T MRI scanner. DR-CSI with a diffusion-weighted echo-planar imaging sequence and T2-mapping with a multi-echo spin echo sequence. ASSESSMENT DR-CSI results were analyzed for the solid tumor regions of interest using spectrum segmentation with five sub-region volume fraction metrics (VA , VB , VC , VD , and VE ). The regulations for spectrum segmentation were determined based on the D-T2 spectra of distinct macro-components. Tumor size, voxel-wise T2, and apparent diffusion coefficient (ADC) values were obtained. Histopathology assessed tumor grade (G1-G4) for each case. STATISTICAL TESTS One-way ANOVA or Kruskal-Wallis test, Spearman's correlation (coefficient, rho), multivariable logistic regression analysis, receiver operating characteristic curve analysis, and DeLong's test. Significance criteria: P < 0.05. RESULTS Significant differences were found in ADC, T2, DR-CSI VB , and VD among the ccRCC grades. Correlations were found for ccRCC grade to tumor size (rho = 0.419), age (rho = 0.253), VB (rho = 0.553) and VD (rho = -0.378). AUC of VB was slightly larger than ADC in distinguishing low-grade (G1-G2) from high-grade (G3-G4) ccRCC (0.801 vs. 0.762, P = 0.406) and G1 from G2 to G4 (0.796 vs. 0.647, P = 0.175), although not significant. Combining VB , VD , and VE had better diagnostic performance than combining ADC and T2 for differentiating G1 from G2-G4 (AUC: 0.814 vs 0.643). DATA CONCLUSION DR-CSI parameters are correlated with ccRCC grades, and may help to differentiate ccRCC grades. EVIDENCE LEVEL 2 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Yongming Dai
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Wentao Hu
- Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Guangyu Wu
- Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Dongmei Wu
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Mengying Zhu
- Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yuansheng Luo
- Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jieying Wang
- Clinical Research Center, Renji hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yan Zhou
- Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Peng Hu
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
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Zhu HX, Zheng WC, Chen H, Chen JY, Lin F, Chen SH, Xue XY, Zheng QS, Liang M, Xu N, Chen DN, Sun XL. Exploring Novel Genome Instability-associated lncRNAs and their Potential Function in Pan-Renal Cell Carcinoma. Comb Chem High Throughput Screen 2024; 27:1788-1807. [PMID: 37957851 DOI: 10.2174/0113862073258779231020052115] [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: 06/01/2023] [Revised: 09/14/2023] [Accepted: 09/21/2023] [Indexed: 11/15/2023]
Abstract
OBJECTIVE Genomic instability can drive clonal evolution, continuous modification of tumor genomes, and tumor genomic heterogeneity. The molecular mechanism of genomic instability still needs further investigation. This study aims to identify novel genome instabilityassociated lncRNAs (GI-lncRNAs) and investigate the role of genome instability in pan-Renal cell carcinoma (RCC). MATERIALS AND METHODS A mutator hypothesis was employed, combining the TCGA database of somatic mutation (SM) information, to identify GI-lncRNAs. Subsequently, a training cohort (n = 442) and a testing cohort (n = 439) were formed by randomly dividing all RCC patients. Based on the training cohort dataset, a multivariate Cox regression analysis lncRNAs risk model was created. Further validations were performed in the testing cohort, TCGA cohort, and different RCC subtypes. To confirm the relative expression levels of lncRNAs in HK-2, 786-O, and 769-P cells, qPCR was carried out. Functional pathway enrichment analyses were performed for further investigation. RESULTS A total of 170 novel GI-lncRNAs were identified. The lncRNA prognostic risk model was constructed based on LINC00460, AC073218.1, AC010789.1, and COLCA1. This risk model successfully differentiated patients into distinct risk groups with significantly different clinical outcomes. The model was further validated in multiple independent patient cohorts. Additionally, functional and pathway enrichment analyses revealed that GI-lncRNAs play a crucial role in GI. Furthermore, the assessments of immune response, drug sensitivity, and cancer stemness revealed a significant relationship between GI-lncRNAs and tumor microenvironment infiltration, mutational burden, microsatellite instability, and drug resistance. CONCLUSIONS In this study, we discovered four novel GI-lncRNAs and developed a novel signature that effectively predicted clinical outcomes in pan-RCC. The findings provide valuable insights for pan-RCC immunotherapy and shed light on potential underlying mechanisms.
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Affiliation(s)
- Hui-Xin Zhu
- Department of Surgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
| | - Wen-Cai Zheng
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Hang Chen
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Jia-Yin Chen
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Fei Lin
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Shao-Hao Chen
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China
| | - Xue-Yi Xue
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
- Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
| | - Qing-Shui Zheng
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Min Liang
- Department of Anesthesiology, Anesthesiology Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, China
| | - Ning Xu
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
- Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
| | - Dong-Ning Chen
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Xiong-Lin Sun
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
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Woo S, Becker AS, Das JP, Ghafoor S, Arita Y, Benfante N, Gangai N, Teo MY, Goh AC, Vargas HA. Evaluating residual tumor after neoadjuvant chemotherapy for muscle-invasive urothelial bladder cancer: diagnostic performance and outcomes using biparametric vs. multiparametric MRI. Cancer Imaging 2023; 23:110. [PMID: 37964386 PMCID: PMC10644594 DOI: 10.1186/s40644-023-00632-0] [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: 06/02/2023] [Accepted: 11/03/2023] [Indexed: 11/16/2023] Open
Abstract
BACKGROUND Neoadjuvant chemotherapy (NAC) before radical cystectomy is standard of care in patients with muscle-invasive bladder cancer (MIBC). Response assessment after NAC is important but suboptimal using CT. We assessed MRI without vs. with intravenous contrast (biparametric [BP] vs. multiparametric [MP]) for identifying residual disease on cystectomy and explored its prognostic role. METHODS Consecutive MIBC patients that underwent NAC, MRI, and cystectomy between January 2000-November 2022 were identified. Two radiologists reviewed BP-MRI (T2 + DWI) and MP-MRI (T2 + DWI + DCE) for residual tumor. Diagnostic performances were compared using receiver operating characteristic curve analysis. Kaplan-Meier curves and Cox proportional-hazards models were used to evaluate association with disease-free survival (DFS). RESULTS 61 patients (36 men and 25 women; median age 65 years, interquartile range 59-72) were included. After NAC, no residual disease was detected on pathology in 19 (31.1%) patients. BP-MRI was more accurate than MP-MRI for detecting residual disease after NAC: area under the curve = 0.75 (95% confidence interval (CI), 0.62-0.85) vs. 0.58 (95% CI, 0.45-0.70; p = 0.043). Sensitivity were identical (65.1%; 95% CI, 49.1-79.0) but specificity was higher in BP-MRI compared with MP-MRI for determining residual disease: 77.8% (95% CI, 52.4-93.6) vs. 38.9% (95% CI, 17.3-64.3), respectively. Positive BP-MRI and residual disease on pathology were both associated with worse DFS: hazard ratio (HR) = 4.01 (95% CI, 1.70-9.46; p = 0.002) and HR = 5.13 (95% CI, 2.66-17.13; p = 0.008), respectively. Concordance between MRI and pathology results was significantly associated with DFS. Concordant positive (MRI+/pathology+) patients showed worse DFS than concordant negative (MRI-/pathology-) patients (HR = 8.75, 95% CI, 2.02-37.82; p = 0.004) and compared to the discordant group (MRI+/pathology- or MRI-/pathology+) with HR = 3.48 (95% CI, 1.39-8.71; p = 0.014). CONCLUSION BP-MRI was more accurate than MP-MRI for identifying residual disease after NAC. A negative BP-MRI was associated with better outcomes, providing complementary information to pathological assessment of cystectomy specimens.
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Affiliation(s)
- Sungmin Woo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA.
- Department of Radiology, NYU Langone Health, 660 1st Avenue, New York, NY, 10016, USA.
| | - Anton S Becker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
- Department of Radiology, NYU Langone Health, 660 1st Avenue, New York, NY, 10016, USA
| | - Jeeban P Das
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Soleen Ghafoor
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Rämistrasse 100, Zürich, CH-8091, Switzerland
| | - Yuki Arita
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Nicole Benfante
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Natalie Gangai
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Min Yuen Teo
- Genitourinary Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Alvin C Goh
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Hebert A Vargas
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
- Department of Radiology, NYU Langone Health, 660 1st Avenue, New York, NY, 10016, USA
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Donners R, Candito A, Blackledge M, Rata M, Messiou C, Koh DM, Tunariu N. Repeatability of quantitative individual lesion and total disease multiparametric whole-body MRI measurements in prostate cancer bone metastases. Br J Radiol 2023; 96:20230378. [PMID: 37660399 PMCID: PMC10607420 DOI: 10.1259/bjr.20230378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/07/2023] [Accepted: 07/18/2023] [Indexed: 09/05/2023] Open
Abstract
OBJECTIVES To assess the repeatability of quantitative multiparametric whole-body MRI (mpWB-MRI) parameters in advanced prostate cancer (APC) bone metastases. METHODS 1.5T MRI was performed twice on the same day in 10 APC patients. MpWB-MRI-included diffusion weighted imaging (DWI) and T1-weighted gradient-echo 2-point Dixon sequences. ADC and relative fat-fraction percentage (rFF%) maps were calculated, respectively. A radiologist delineated up to 10 target bone metastases per study. Means of ADC, b900 signal intensity(SI), normalised b900 SI, rFF% and maximum diameter (MD) for each target lesion and overall parameter averages across all targets per patient were recorded. The total disease volume (tDV in ml) was manually delineated on b900 images and mean global (g)ADC was derived. Bland-Altman analyses were performed with calculation of 95% repeatability coefficients (RC). RESULTS Seventy-three individual targets (median MD 26 mm) were included. Lesion mean ADC RC was 12.5%, mean b900 SI RC 137%, normalised mean b900 SI RC 110%, rFF% RC 3.2 and target MD RC 5.5 mm (16.3%). Patient target lesion average mean ADC RC was 6.4%, b900 SI RC 104% and normalised mean b900 SI RC 39.6%. Target average rFF% RC was 1.8, average MD RC 1.3 mm (4.8%). tDV segmentation RC was 6.4% and mean gADC RC 5.3%. CONCLUSIONS APC bone metastases' ADC, rFF% and maximum diameter, tDV and gADC show good repeatability. ADVANCES IN KNOWLEDGE APC bone metastases' mean ADC and rFF% measurements of single lesions and global disease volumes are repeatable, supporting their potential role as quantitative biomarkers in metastatic bone disease.
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Affiliation(s)
| | - Antonio Candito
- Cancer Research UK Cancer Imaging Centre, The Institute of Cancer Research, Sutton, United Kingdom
| | - Matthew Blackledge
- Cancer Research UK Cancer Imaging Centre, The Institute of Cancer Research, Sutton, United Kingdom
| | - Mihaela Rata
- Cancer Research UK Cancer Imaging Centre, The Institute of Cancer Research, Sutton, United Kingdom
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Zou J, Ye J, Zhu W, Wu J, Chen W, Chen R, Zhu Q. Diffusion-weighted and diffusion kurtosis imaging analysis of microstructural differences in clear cell renal cell carcinoma: a comparative study. Br J Radiol 2023; 96:20230146. [PMID: 37393526 PMCID: PMC10546464 DOI: 10.1259/bjr.20230146] [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: 02/12/2023] [Revised: 06/12/2023] [Accepted: 06/13/2023] [Indexed: 07/03/2023] Open
Abstract
OBJECTIVE To quantitatively compare the diagnostic values of conventional diffusion-weighted imaging (DWI) and diffusion kurtosis imaging (DKI) analysis of microstructural differences for clear cell renal cell carcinoma (CRCC). METHODS A total of 108 patients with pathologically confirmed CRCC, including 38 Grade I, 37 Grade II, 18 Grade III and 15 Grade IV, were enrolled and divided into groups according to tumor grade [low grade (Ⅰ+Ⅱ, n = 75) and high grade (Ⅲ+Ⅳ, n = 33)]. Apparent diffusion coefficient (ADC), mean diffusivity (MD), mean kurtosis (MK), kurtosis anisotropy (KA) and radial kurtosis (RK) were performed. RESULTS Both the ADC (r = -0.803) and MD (-0.867) values showed a negative correlation with tumor grading (p < 0.05) and MK (r = 0.812), KA (0.816) and RK (0.853) values a positive correlation with tumor grading (p < 0.05). Mean FA values showed no significant differences among CRCC grades (p > 0.05). ROC curve analyses showed that MD values had the highest diagnostic efficacy in differentiating low/high and Ⅱ/Ⅲ tumor grading. MD values gave AUC: 0.937 (0.896); sensitivity: 92.0% (86.5%); specificity: 78.8% (77.8%) and accuracy: 90.7% (87.3%). ADC performed worse than MD, MK, KA or RK (all p < 0.05) during pair-wise comparisons of ROC curves to show diagnostic efficacy. CONCLUSION DKI analysis performs better than ADC in differentiating CRCC grading. ADVANCES IN KNOWLEDGE Both the ADC and MD values correlated negatively with CRCC grading.The MK, KA and RK values correlated positively with CRCC grading.MD values had the highest diagnostic efficacy in differentiating low/high and Ⅱ/Ⅲ CRCC grading.
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Affiliation(s)
- Jinzhao Zou
- Department of Medical Imaging, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Jing Ye
- Department of Medical Imaging, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Wenrong Zhu
- Department of Medical Imaging, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Jingtao Wu
- Department of Medical Imaging, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Wenxin Chen
- Department of Medical Imaging, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Rui Chen
- Department of Kidney internal medicine, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Qingqiang Zhu
- Department of Medical Imaging, Clinical Medical College, Yangzhou University, Yangzhou, China
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Stabinska J, Zöllner HJ, Thiel TA, Wittsack HJ, Ljimani A. Image downsampling expedited adaptive least-squares (IDEAL) fitting improves intravoxel incoherent motion (IVIM) analysis in the human kidney. Magn Reson Med 2023; 89:1055-1067. [PMID: 36416075 DOI: 10.1002/mrm.29517] [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: 06/09/2022] [Revised: 10/13/2022] [Accepted: 10/14/2022] [Indexed: 11/24/2022]
Abstract
PURPOSE To improve the reliability of intravoxel incoherent motion (IVIM) model parameter estimation for the DWI in the kidney using a novel image downsampling expedited adaptive least-squares (IDEAL) approach. METHODS The robustness of IDEAL was investigated using simulated DW-MRI data corrupted with different levels of Rician noise. Subsequently, the performance of the proposed method was tested by fitting bi- and triexponential IVIM model to in vivo renal DWI data acquired on a clinical 3 Tesla MRI scanner and compared to conventional approaches (fixed D* and segmented fitting). RESULTS The numerical simulations demonstrated that the IDEAL algorithm provides robust estimates of the IVIM parameters in the presence of noise (SNR of 20) as indicated by relatively low absolute percentage bias (maximal sMdPB <20%) and normalized RMSE (maximal RMSE <28%). The analysis of the in vivo data showed that the IDEAL-based IVIM parameter maps were less noisy and more visually appealing than those obtained using the fixed D* and segmented methods. Further, coefficients of variation for nearly all IVIM parameters were significantly reduced in cortex and medulla for IDEAL-based biexponential (coefficients of variation: 4%-50%) and triexponential (coefficients of variation: 7.5%-75%) IVIM modelling compared to the segmented (coefficients of variation: 4%-120%) and fixed D* (coefficients of variation: 17%-174%) methods, reflecting greater accuracy of this method. CONCLUSION The proposed fitting algorithm yields more robust IVIM parameter estimates and is less susceptible to poor SNR than the conventional fitting approaches. Thus, the IDEAL approach has the potential to improve the reliability of renal DW-MRI analysis for clinical applications.
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Affiliation(s)
- Julia Stabinska
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, 21205, USA
- Division of MR Research, The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine University Dusseldorf, Düsseldorf, Germany
| | - Helge J Zöllner
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, 21205, USA
- Division of MR Research, The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Thomas A Thiel
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine University Dusseldorf, Düsseldorf, Germany
| | - Hans-Jörg Wittsack
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine University Dusseldorf, Düsseldorf, Germany
| | - Alexandra Ljimani
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine University Dusseldorf, Düsseldorf, Germany
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Posada Calderon L, Eismann L, Reese SW, Reznik E, Hakimi AA. Advances in Imaging-Based Biomarkers in Renal Cell Carcinoma: A Critical Analysis of the Current Literature. Cancers (Basel) 2023; 15:cancers15020354. [PMID: 36672304 PMCID: PMC9856305 DOI: 10.3390/cancers15020354] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/31/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
Cross-sectional imaging is the standard diagnostic tool to determine underlying biology in renal masses, which is crucial for subsequent treatment. Currently, standard CT imaging is limited in its ability to differentiate benign from malignant disease. Therefore, various modalities have been investigated to identify imaging-based parameters to improve the noninvasive diagnosis of renal masses and renal cell carcinoma (RCC) subtypes. MRI was reported to predict grading of RCC and to identify RCC subtypes, and has been shown in a small cohort to predict the response to targeted therapy. Dynamic imaging is promising for the staging and diagnosis of RCC. PET/CT radiotracers, such as 18F-fluorodeoxyglucose (FDG), 124I-cG250, radiolabeled prostate-specific membrane antigen (PSMA), and 11C-acetate, have been reported to improve the identification of histology, grading, detection of metastasis, and assessment of response to systemic therapy, and to predict oncological outcomes. Moreover, 99Tc-sestamibi and SPECT scans have shown promising results in distinguishing low-grade RCC from benign lesions. Radiomics has been used to further characterize renal masses based on semantic and textural analyses. In preliminary studies, integrated machine learning algorithms using radiomics proved to be more accurate in distinguishing benign from malignant renal masses compared to radiologists' interpretations. Radiomics and radiogenomics are used to complement risk classification models to predict oncological outcomes. Imaging-based biomarkers hold strong potential in RCC, but require standardization and external validation before integration into clinical routines.
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Affiliation(s)
- Lina Posada Calderon
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Lennert Eismann
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Stephen W. Reese
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ed Reznik
- Computational Oncology, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Abraham Ari Hakimi
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Correspondence:
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9
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Li S, He K, Yuan G, Yong X, Meng X, Feng C, Zhang Y, Kamel IR, Li Z. WHO/ISUP grade and pathological T stage of clear cell renal cell carcinoma: value of ZOOMit diffusion kurtosis imaging and chemical exchange saturation transfer imaging. Eur Radiol 2022; 33:4429-4439. [PMID: 36472697 DOI: 10.1007/s00330-022-09312-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/07/2022] [Accepted: 11/21/2022] [Indexed: 12/12/2022]
Abstract
OBJECTIVES To evaluate the value of ZOOMit diffusion kurtosis imaging (DKI) and chemical exchange saturation transfer (CEST) imaging in predicting WHO/ISUP grade and pathological T stage in clear cell renal cell carcinoma (ccRCC). METHODS Forty-six patients with ccRCC were included in this retrospective study. All participants underwent MRI including ZOOMit DKI and CEST. The non-Gaussian mean kurtosis (MK), mean diffusivity (MD), magnetization transfer ratio asymmetry (MTRasym (3.5 ppm)), and Ssat (3.5 ppm)/S0 were analyzed based on different WHO/ISUP grades and pT stages. Binary logistic regression was used to identify the best combination of the parameters. Pearson's correlation coefficients were calculated between CEST and diffusion-related parameters. RESULTS The ADC, MD, and Ssat (3.5 ppm)/S0 values were significantly lower for higher WHO/ISUP grade tumors, whereas the MK and MTRasym (3.5 ppm) were higher in higher WHO/ISUP grade and higher pT stage tumors. MTRasym (3.5 ppm) combined with MD (AUC, 0.930; 95% CI, 0.858-1.000) showed the best diagnostic efficacy in evaluating the WHO/ISUP grade. MTRasym (3.5 ppm) and MK were mildly positively correlated (r = 0.324, p = 0.028). Ssat (3.5 ppm)/S0 was moderately positively correlated with ADC (r = 0.580, p < 0.001), mildly positively correlated with MD (r = 0.412, p = 0.005), and moderately negatively correlated with MK (r = -0.575, p < .001). CONCLUSION The microstructural and biochemical assessment of ZOOMit DKI and CEST allowed for the characterization of different WHO/ISUP grades and pT stages in ccRCC. MTRasym (3.5 ppm) combined with MD showed the best diagnostic performance for WHO/ISUP grading. KEY POINTS • Both diffusion kurtosis imaging (DKI) and chemical exchange saturation transfer (CEST) can be used to predict the WHO/ISUP grade and pathological T stage. • MTRasym (3.5 ppm) combined with MD showed the highest AUC (0.930; 95% CI, 0.858-1.000) in WHO/ISUP grading. • MTRasym at 3.5 ppm showed a positive correlation with mean kurtosis.
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Affiliation(s)
- Shichao Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Kangwen He
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Guanjie Yuan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xingwang Yong
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaoyan Meng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Cui Feng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Ihab R Kamel
- Russell H. Morgan Department of Radiology and Radiological Science, the Johns Hopkins Medical Institutions, Baltimore, Maryland, USA
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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10
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Shi B, Xue K, Yin Y, Xu Q, Shi B, Wu D, Ye J. Grading of clear cell renal cell carcinoma using diffusion MRI with a fractional order calculus model. Acta Radiol 2022; 64:421-430. [PMID: 35040361 DOI: 10.1177/02841851211072482] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
BACKGROUND The fractional order calculus (FROC) model has been developed to describe restrained motion of water molecules as well as microstructural heterogeneity, providing a novel tool for non-invasive tumor grading. PURPOSE To evaluate the role of the FROC model in characterizing clear cell renal cell carcinoma (ccRCC) grades. MATERIAL AND METHODS A total of 59 patients diagnosed with ccRCC were included in this prospective study. The diffusion metrics derived from the mono-exponential model (apparent diffusion coefficient [ADC]), intra-voxel incoherent motion [IVIM] model [D, D*, f], and FROC model [Dfroc, β, μ]) were calculated and compared between low- and high-grade ccRCCs. Binary logistic regression analysis was performed to establish the diagnostic models. Receiver operating characteristic (ROC) analysis and DeLong test were performed to evaluate and compare the diagnostic performance of metrics in grading ccRCC. RESULTS All the metrics except D* and f exhibited statistical differences between low- and high-grade ccRCCs. ROC analysis showed individual FROC parameters, μ, Dfroc, and β, outperformed ADC and IVIM parameters in grading ccRCC. For single parameter, μ demonstrated the highest AUC value, sensitivity, and diagnostic accuracy in discriminating the two ccRCC groups while β exhibited the optimal specificity. Importantly, the combination of Dfroc, μ, and β could further improve the diagnostic performance. CONCLUSION The FROC parameters were superior to ADC and IVIM parameters in grading ccRCC, indicating the great potential of the FROC model in distinguishing low- and high-grade ccRCCs.
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Affiliation(s)
- Bowen Shi
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, PR China
| | - Ke Xue
- Central Research Institute, United Imaging Healthcare, Shanghai, PR China
| | - Yili Yin
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, PR China
| | - Qing Xu
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, PR China
| | - Binbin Shi
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, PR China
| | - Dongmei Wu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronics Science, East China Normal University, Shanghai, PR China
| | - Jing Ye
- Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, PR China
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11
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Grajo JR, Batra NV, Bozorgmehri S, Magnelli LL, O'Malley P, Terry R, Su LM, Crispen PL. Association between nuclear grade of renal cell carcinoma and the aorta-lesion-attenuation-difference. Abdom Radiol (NY) 2021; 46:5629-5638. [PMID: 34463815 DOI: 10.1007/s00261-021-03260-z] [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: 06/19/2021] [Revised: 08/19/2021] [Accepted: 08/20/2021] [Indexed: 11/27/2022]
Abstract
INTRODUCTION AND BACKGROUND Several features noted on renal mass biopsy (RMB) can influence treatment selection including tumor histology and nuclear grade. However, there is poor concordance between renal cell carcinoma (RCC) nuclear grade on RMB compared to nephrectomy specimens. Here, we evaluate the association of nuclear grade with aorta-lesion-attenuation-difference (ALAD) values determined on preoperative CT scan. METHODS AND MATERIALS A retrospective review of preoperative CT scans and surgical pathology was performed on patients undergoing nephrectomy for solid renal masses. ALAD was calculated by measuring the difference in Hounsfield units (HU) between the aorta and the lesion of interest on the same image slice on preoperative CT scan. The discriminative ability of ALAD to differentiate low-grade (nuclear grade 1 and 2) and high-grade (nuclear grade 3 and 4) tumors was evaluated by sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under curve (AUC) using ROC analysis. Sub-group analysis by histologic sub-type was also performed. RESULTS A total of 368 preoperative CT scans in patients with RCC on nephrectomy specimen were reviewed. Median patient age was 61 years (IQR 52-68). The majority of patients were male, 66% (243/368). Tumor histology was chromophobe RCC in 7.6%, papillary RCC in 15.5%, and clear cell RCC in 76.9%. The majority, 69.3% (253/365) of tumors, were stage T1a. Nuclear grade was grade 1 in 5.46% (19/348), grade 2 in 64.7% (225/348), grade 3 in 26.2% (91/348), and grade 4 in 3.2% (11/348). Nephrographic ALAD values for grade 1, 2, 3, and 4 were 73.7, 46.5, 36.4, and 43.1, respectively (p = 0.0043). Nephrographic ALAD was able to differentiate low-grade from high-grade RCC with a sensitivity of 32%, specificity of 89%, PPV of 86%, and NPV of 36%. ROC analysis demonstrated the predictive utility of nephrographic ALAD to predict high- versus low-grade RCC with an AUC of 0.60 (95% CI 0.51-0.69). CONCLUSION ALAD was significantly associated with nuclear grade in our nephrectomy series. Strong specificity and PPV for the nephrographic phrase demonstrate a potential role for ALAD in the pre-operative setting that may augment RMB findings in assessing nuclear grade of RCC. Although this association was statistically significant, the clinical utility is limited at this time given the results of the statistical analysis (relatively poor ROC analysis). Sub-group analysis by histologic subtype yielded very similar diagnostic performance and limitations of ALAD. Further studies are necessary to evaluate this relationship further.
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Affiliation(s)
- Joseph R Grajo
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, 32610, USA.
| | - Nikhil V Batra
- Department of Urology, University of Florida College of Medicine, Gainesville, FL, 32610, USA
| | - Shahab Bozorgmehri
- Department of Epidemiology, University of Florida College of Medicine, Gainesville, FL, 32610, USA
| | - Laura L Magnelli
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, 32610, USA
| | - Padraic O'Malley
- Department of Urology, University of Florida College of Medicine, Gainesville, FL, 32610, USA
| | - Russell Terry
- Department of Urology, University of Florida College of Medicine, Gainesville, FL, 32610, USA
| | - Li-Ming Su
- Department of Urology, University of Florida College of Medicine, Gainesville, FL, 32610, USA
| | - Paul L Crispen
- Department of Urology, University of Florida College of Medicine, Gainesville, FL, 32610, USA
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12
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Xu F, Liang Y, Guo W, Liang Z, Li L, Xiong Y, Ye G, Zeng X. Diagnostic Performance of Diffusion-Weighted Imaging for Differentiating Malignant From Benign Intraductal Papillary Mucinous Neoplasms of the Pancreas: A Systematic Review and Meta-Analysis. Front Oncol 2021; 11:637681. [PMID: 34290974 PMCID: PMC8287206 DOI: 10.3389/fonc.2021.637681] [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: 12/04/2020] [Accepted: 06/16/2021] [Indexed: 11/21/2022] Open
Abstract
Objectives To assess the diagnostic accuracy of diffusion-weighted imaging (DWI) in predicting the malignant potential in patients with intraductal papillary mucinous neoplasms (IPMNs) of the pancreas. Methods A systematic search of articles investigating the diagnostic performance of DWI for prediction of malignant potential in IPMNs was conducted from PubMed, Embase, and Web of Science from January 1997 to 10 February 2020. QUADAS-2 tool was used to evaluate the study quality. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), positive likelihood ratios (PLR), negative likelihood ratios (NLR), and their 95% confidence intervals (CIs) were calculated. The summary receiver operating characteristic (SROC) curve was then plotted, and meta-regression was also performed to explore the heterogeneity. Results Five articles with 307 patients were included. The pooled sensitivity and specificity of DWI were 0.74 (95% CI: 0.65, 0.82) and 0.94 (95% CI: 0.78, 0.99), in evaluating the malignant potential of IPMNs. The PLR was 13.5 (95% CI: 3.1, 58.7), the NLR was 0.27 (95% CI: 0.20, 0.37), and DOR was 50.0 (95% CI: 11.0, 224.0). The area under the curve (AUC) of SROC curve was 0.84 (95% CI: 0.80, 0.87). The meta-regression showed that the slice thickness of DWI (p = 0.02) and DWI parameter (p= 0.01) were significant factors affecting the heterogeneity. Conclusions DWI is an effective modality for the differential diagnosis between benign and malignant IPMNs. The slice thickness of DWI and DWI parameter were the main factors influencing diagnostic specificity.
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Affiliation(s)
- Fan Xu
- Department of Radiology, Guangzhou Red Cross Hospital, Medical College, Jinan University, Guangzhou, China
| | - Yingying Liang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Wei Guo
- Department of Radiology, Wuhan Third Hospital (Tongren Hospital of WuHan University), Wuhan, China
| | - Zhiping Liang
- Department of Radiology, Guangzhou Red Cross Hospital, Medical College, Jinan University, Guangzhou, China
| | - Liqi Li
- Department of Radiology, Guangzhou Red Cross Hospital, Medical College, Jinan University, Guangzhou, China
| | - Yuchao Xiong
- Department of Radiology, Guangzhou Red Cross Hospital, Medical College, Jinan University, Guangzhou, China
| | - Guoxi Ye
- Department of Radiology, Guangzhou Red Cross Hospital, Medical College, Jinan University, Guangzhou, China
| | - Xuwen Zeng
- Department of Radiology, Guangzhou Red Cross Hospital, Medical College, Jinan University, Guangzhou, China
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Wang Y, Yan K, Wang L, Bi J. Genome instability-related long non-coding RNA in clear renal cell carcinoma determined using computational biology. BMC Cancer 2021; 21:727. [PMID: 34167490 PMCID: PMC8229419 DOI: 10.1186/s12885-021-08356-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 04/29/2021] [Indexed: 12/04/2022] Open
Abstract
Background There is evidence that long non-coding RNA (lncRNA) is related to genetic stability. However, the complex biological functions of these lncRNAs are unclear. Method TCGA - KIRC lncRNAs expression matrix and somatic mutation information data were obtained from TCGA database. “GSVA” package was applied to evaluate the genomic related pathway in each samples. GO and KEGG analysis were performed to show the biological function of lncRNAs-mRNAs. “Survival” package was applied to determine the prognostic significance of lncRNAs. Multivariate Cox proportional hazard regression analysis was applied to conduct lncRNA prognosis model. Results In the present study, we applied computational biology to identify genome-related long noncoding RNA and identified 26 novel genomic instability-associated lncRNAs in clear cell renal cell carcinoma. We identified a genome instability-derived six lncRNA-based gene signature that significantly divided clear renal cell samples into high- and low-risk groups. We validated it in test cohorts. To further elucidate the role of the six lncRNAs in the model’s genome stability, we performed a gene set variation analysis (GSVA) on the matrix. We performed Pearson correlation analysis between the GSVA scores of genomic stability-related pathways and lncRNA. It was determined that LINC00460 and LINC01234 could be used as critical factors in this study. They may influence the genome stability of clear cell carcinoma by participating in mediating critical targets in the base excision repair pathway, the DNA replication pathway, homologous recombination, mismatch repair pathway, and the P53 signaling pathway. Conclusion subsections These data suggest that LINC00460 and LINC01234 are crucial for the stability of the clear cell renal cell carcinoma genome. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-021-08356-9.
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Affiliation(s)
- Yutao Wang
- Department of Urology, China Medical University, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Kexin Yan
- Department of Dermatology, China Medical University, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Linhui Wang
- Department of Urology, China Medical University, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Jianbin Bi
- Department of Urology, China Medical University, The First Hospital of China Medical University, Shenyang, Liaoning, China.
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Zhu J, Luo X, Gao J, Li S, Li C, Chen M. Application of diffusion kurtosis tensor MR imaging in characterization of renal cell carcinomas with different pathological types and grades. Cancer Imaging 2021; 21:30. [PMID: 33726862 PMCID: PMC7962255 DOI: 10.1186/s40644-021-00394-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 02/19/2021] [Indexed: 12/13/2022] Open
Abstract
Background To probe the feasibility and reproducibility of diffusion kurtosis tensor imaging (DKTI) in renal cell carcinoma (RCC) and to apply DKTI in distinguishing the subtypes of RCC and the grades of clear cell RCC (CCRCC). Methods Thirty-eight patients with pathologically confirmed RCCs [CCRCC for 30 tumors, papillary RCC (PRCC) for 5 tumors and chromophobic RCC (CRCC) for 3 tumors] were involved in the study. Diffusion kurtosis tensor MR imaging were performed with 3 b-values (0, 500, 1000s/mm2) and 30 diffusion directions. The mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr) values and mean diffusity (MD) for RCC and contralateral normal parenchyma were acquired. The inter-observer agreements of all DKTI metrics of contralateral renal cortex and medulla were evaluated using Bland-Altman plots. Statistical comparisons with DKTI metrics of 3 RCC subtypes and between low-grade (Furman grade I ~ II, 22 cases) and high-grade (Furman grade III ~ IV, 8 cases) CCRCC were performed with ANOVA test and Student t test separately. Receiver operating characteristic (ROC) curve analyses were used to compare the diagnostic efficacy of DKTI metrics for predicting nuclear grades of CCRCC. Correlations between DKTI metrics and nuclear grades were also evaluated with Spearman correlation analysis. Results Inter-observer measurements for each metric showed great reproducibility with excellent ICCs ranging from 0.81 to 0.87. There were significant differences between the DKTI metrics of RCCs and contralateral renal parenchyma, also among the subtypes of RCC. MK and Ka values of CRCC were significantly higher than those of CCRCC and PRCC. Statistical difference of the MK, Ka, Kr and MD values were also obtained between CCRCC with high- and low-grades. MK values were more effective for distinguishing between low- and high- grade CCRCC (area under the ROC curve: 0.949). A threshold value of 0.851 permitted distinction with high sensitivity (90.9%) and specificity (87.5%). Conclusion Our preliminary results suggest a possible role of DKTI in differentiating CRCC from CCRCC and PRCC. MK, the principle DKTI metric might be a surrogate biomarker to predict nuclear grades of CCRCC. Trial registration ChiCTC, ChiCTR-DOD-17010833, Registered 10 March, 2017, retrospectively registered, http://www.chictr.org.cn/showproj.aspx?proj=17559.
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Affiliation(s)
- Jie Zhu
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, P. R. China
| | - Xiaojie Luo
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, P. R. China
| | - Jiayin Gao
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, P. R. China
| | - Saying Li
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, P. R. China
| | - Chunmei Li
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, P. R. China
| | - Min Chen
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, P. R. China.
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Lai S, Sun L, Wu J, Wei R, Luo S, Ding W, Liu X, Yang R, Zhen X. Multiphase Contrast-Enhanced CT-Based Machine Learning Models to Predict the Fuhrman Nuclear Grade of Clear Cell Renal Cell Carcinoma. Cancer Manag Res 2021; 13:999-1008. [PMID: 33568946 PMCID: PMC7869703 DOI: 10.2147/cmar.s290327] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Accepted: 01/08/2021] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE To investigate the predictive performance of different machine learning models for the discrimination of low and high nuclear grade clear cell renal cell carcinoma (ccRCC) by using multiphase computed tomography (CT)-based radiomic features. MATERIALS AND METHODS A total of 137 consecutive patients with pathologically proven ccRCC (including 96 low-grade [grade 1 or 2] and 41 high-grade [grade 3 or 4] ccRCC) from January 2011 to January 2019 were enrolled in this retrospective study. Target region of interest (ROI) delineation followed by texture extraction was performed on a representative slice with the largest section of the tumor on the four-phase (unenhanced phase [UP], corticomedullary phase [CMP], nephrographic phase [NP] and excretory phase [EP]) CT images. Fifteen concatenations of the four-phase features were fed into 176 classification models (built with 8 classifiers and 22 feature selection methods), the classification performances of the 2640 resultant discriminative models were compared, and the top-ranked features were analyzed. RESULTS Image features extracted from the unenhanced phase (UP) CT images demonstrated a dominant classification performance over features from the other three phases. The discriminative model "Bagging + CMIM" achieved the highest classification AUC of 0.75. The top-ranked features from the UP included one shape-based feature and five first-order statistical features. CONCLUSION Image features extracted from the UP are more effective than other CT phases in differentiating low and high nuclear grade ccRCC based on machine learning-based classification modeling.
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Affiliation(s)
- Shengsheng Lai
- School of Medical Equipment, Guangdong Food and Drug Vocational College, Guangzhou, Guangdong, 510520, People’s Republic of China
| | - Lei Sun
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China
| | - Jialiang Wu
- Department of Radiology, The University of Hong Kong Shenzhen Hospital, Shenzhen, Guangdong, 518000, People’s Republic of China
| | - Ruili Wei
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People’s Republic of China
| | - Shiwei Luo
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People’s Republic of China
| | - Wenshuang Ding
- Department of Pathology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People’s Republic of China
| | - Xilong Liu
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China
| | - Ruimeng Yang
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People’s Republic of China
| | - Xin Zhen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China
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Stabinska J, Ljimani A, Zöllner HJ, Wilken E, Benkert T, Limberg J, Esposito I, Antoch G, Wittsack HJ. Spectral diffusion analysis of kidney intravoxel incoherent motion MRI in healthy volunteers and patients with renal pathologies. Magn Reson Med 2021; 85:3085-3095. [PMID: 33462838 DOI: 10.1002/mrm.28631] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 10/22/2020] [Accepted: 11/12/2020] [Indexed: 12/12/2022]
Abstract
PURPOSE To assess the feasibility of measuring tubular and vascular signal fractions in the human kidney using nonnegative least-square (NNLS) analysis of intravoxel incoherent motion data collected in healthy volunteers and patients with renal pathologies. METHODS MR imaging was performed at 3 Tesla in 12 healthy subjects and 3 patients with various kidney pathologies (fibrotic kidney disease, failed renal graft, and renal masses). Relative signal fractions f and mean diffusivities of the diffusion components in the cortex, medulla, and renal lesions were obtained using the regularized NNLS fitting of the intravoxel incoherent motion data. Test-retest repeatability of the NNLS approach was tested in 5 volunteers scanned twice. RESULTS In the healthy kidneys, the NNLS method yielded diffusion spectra with 3 distinguishable components that may be linked to the slow tissue water diffusion, intermediate tubular and vascular flow, and fast blood flow in larger vessels with the relative signal fractions, fslow , finterm and ffast , respectively. In the pathological kidneys, the diffusion spectra varied substantially from those acquired in the healthy kidneys. Overall, the renal cyst showed substantially higher finterm and lower fslow , whereas the fibrotic kidney, failed renal graft, and renal cell carcinoma demonstrated the opposite trend. CONCLUSION NNLS-based intravoxel incoherent motion could potentially become a valuable tool in assessing changes in tubular and vascular volume fractions under pathophysiological conditions.
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Affiliation(s)
- Julia Stabinska
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich Heine University Dusseldorf, Düsseldorf, Germany
| | - Alexandra Ljimani
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich Heine University Dusseldorf, Düsseldorf, Germany
| | - Helge Jörn Zöllner
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich Heine University Dusseldorf, Düsseldorf, Germany.,Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.,F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Enrica Wilken
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich Heine University Dusseldorf, Düsseldorf, Germany
| | - Thomas Benkert
- MR Application Development, Siemens Healthcare GmbH, Erlangen, Germany
| | - Juliane Limberg
- Institute of Pathology, Medical Faculty, Heinrich Heine University Dusseldorf, Düsseldorf, Germany
| | - Irene Esposito
- Institute of Pathology, Medical Faculty, Heinrich Heine University Dusseldorf, Düsseldorf, Germany
| | - Gerald Antoch
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich Heine University Dusseldorf, Düsseldorf, Germany
| | - Hans-Jörg Wittsack
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich Heine University Dusseldorf, Düsseldorf, Germany
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17
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Cao J, Luo X, Zhou Z, Duan Y, Xiao L, Sun X, Shang Q, Gong X, Hou Z, Kong D, He B. Comparison of diffusion-weighted imaging mono-exponential mode with diffusion kurtosis imaging for predicting pathological grades of clear cell renal cell carcinoma. Eur J Radiol 2020; 130:109195. [PMID: 32763475 DOI: 10.1016/j.ejrad.2020.109195] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 07/01/2020] [Accepted: 07/20/2020] [Indexed: 12/24/2022]
Abstract
PURPOSE To evaluate the role of diffusion kurtosis imaging (DKI1) in the characterization of clear cell renal cell carcinoma (ccRCC2) compared with standard diffusion-weighted imaging (DWI3). METHODS 89 patients with histologically proven ccRCC were evaluated by DKI and DWI on a 3-T scanner. All ccRCCs were classified as grade 1-4 according to the Fuhrman classification system. The apparent diffusion coefficient (ADC4), fractional anisotropy (FA5), mean diffusivity (MD6), mean kurtosis (MK7), axial kurtosis (Ka8) and radial kurtosis (Kr9) values were recorded. The differences in DWI and DKI parameters were evaluated by independent-sample t test and a receiver operating characteristic (ROC10) analysis was performed. The DeLong test was performed to compare the ROCs. RESULTS Compared to normal renal parenchyma, ADC and MD values of ccRCC decreased and MK, Ka, and Kr values increased (p < 0.05). ADC and MD values of ccRCC decreased with the increase in pathological grade, while MK, Ka, and Kr values were increased (p < 0.05). ADC could discriminate G1 vs G3, G1 vs G4, G2 vs G3, G2 vs G4, and G3 vs G4 (p < 0.05) except for G1 vs G2 (p > 0.05). Ka and Kr could discriminate G1 vs G2, G1 vs G3, G1 vs G4, G2 vs G4, and G3 vs G4 (p < 0.05) except for G2 vs G3 (p > 0.05). MD and MK could discriminate G1 vs G2, G1 vs G3, G1 vs G4, G2 vs G3, G2 vs G4, and G3 vs G4 (p < 0.05). The AUC of MK was the highest. The DeLong test showed that there were significant differences regarding ROCs between ADC/MK, ADC/Ka, ADC/Kr in grading G1/G2, and ADC/MK, MK/Ka in grading G3/G4 (p < 0.05). CONCLUSION DKI was superior compared to the mono-exponential mode of DWI in grading ccRCC.
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Affiliation(s)
- Jinfeng Cao
- Department of Radiology, Zibo Central Hospital, Zibo, Shandong, China; Zibo Key Laboratory of Precision Neuroimaging, China
| | - Xin Luo
- Department of Radiology, Zibo Central Hospital, Zibo, Shandong, China; Zibo Key Laboratory of Precision Neuroimaging, China
| | - Zhongmin Zhou
- Department of Nephrology, Zibo Central Hospital, Shandong, China
| | - Yanhua Duan
- Department of Radiology, Shandong Medical Imaging Research Institute, Shandong University, Jinan, Shandong, China
| | - Lianxiang Xiao
- Department of Radiology, Shandong Medical Imaging Research Institute, Shandong University, Jinan, Shandong, China
| | - Xinru Sun
- Department of Radiology, Zibo Central Hospital, Zibo, Shandong, China; Zibo Key Laboratory of Precision Neuroimaging, China
| | - Qun Shang
- Department of Radiology, Zibo Central Hospital, Zibo, Shandong, China; Zibo Key Laboratory of Precision Neuroimaging, China
| | - Xiao Gong
- Department of Radiology, Zibo Central Hospital, Zibo, Shandong, China; Zibo Key Laboratory of Precision Neuroimaging, China
| | - Zhenbo Hou
- Department of Pathology, Zibo Central Hospital, Zibo, Shandong, China
| | - Demin Kong
- Department of Radiology, Zibo Central Hospital, Zibo, Shandong, China; Zibo Key Laboratory of Precision Neuroimaging, China
| | - Bing He
- Department of Radiology, Zibo Central Hospital, Zibo, Shandong, China; Zibo Key Laboratory of Precision Neuroimaging, China.
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Diffusion-Weighted Imaging in Oncology: An Update. Cancers (Basel) 2020; 12:cancers12061493. [PMID: 32521645 PMCID: PMC7352852 DOI: 10.3390/cancers12061493] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 05/28/2020] [Accepted: 06/01/2020] [Indexed: 02/06/2023] Open
Abstract
To date, diffusion weighted imaging (DWI) is included in routine magnetic resonance imaging (MRI) protocols for several cancers. The real additive role of DWI lies in the "functional" information obtained by probing the free diffusivity of water molecules into intra and inter-cellular spaces that in tumors mainly depend on cellularity. Although DWI has not gained much space in some oncologic scenarios, this non-invasive tool is routinely used in clinical practice and still remains a hot research topic: it has been tested in almost all cancers to differentiate malignant from benign lesions, to distinguish different malignant histotypes or tumor grades, to predict and/or assess treatment responses, and to identify residual or recurrent tumors in follow-up examinations. In this review, we provide an up-to-date overview on the application of DWI in oncology.
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Diagnostic test accuracy of ADC values for identification of clear cell renal cell carcinoma: systematic review and meta-analysis. Eur Radiol 2020; 30:4023-4038. [PMID: 32144458 DOI: 10.1007/s00330-020-06740-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 01/14/2020] [Accepted: 02/11/2020] [Indexed: 12/11/2022]
Abstract
OBJECTIVES To perform a systematic review on apparent diffusion coefficient (ADC) values of renal tumor subtypes and meta-analysis on the diagnostic performance of ADC for differentiation of localized clear cell renal cell carcinoma (ccRCC) from other renal tumor types. METHODS Medline, Embase, and the Cochrane Library databases were searched for studies published until May 1, 2019, that reported ADC values of renal tumors. Methodological quality was evaluated. For the meta-analysis on diagnostic test accuracy of ADC for differentiation of ccRCC from other renal lesions, we applied a bivariate random-effects model and compared two subgroups of ADC measurement with vs. without cystic and necrotic areas. RESULTS We included 48 studies (2588 lesions) in the systematic review and 13 studies (1126 lesions) in the meta-analysis. There was no significant difference in ADC of renal parenchyma using b values of 0-800 vs. 0-1000 (p = 0.08). ADC measured on selected portions (sADC) excluding cystic and necrotic areas differed significantly from whole-lesion ADC (wADC) (p = 0.002). Compared to ccRCC, minimal-fat angiomyolipoma, papillary RCC, and chromophobe RCC showed significantly lower sADC while oncocytoma exhibited higher sADC. Summary estimates of sensitivity and specificity to differentiate ccRCC from other tumors were 80% (95% CI, 0.76-0.88) and 78% (95% CI, 0.64-0.89), respectively, for sADC and 77% (95% CI, 0.59-0.90) and 77% (95% CI, 0.69-0.86) for wADC. sADC offered a higher area under the receiver operating characteristic curve than wADC (0.852 vs. 0.785, p = 0.02). CONCLUSIONS ADC values of kidney tumors that exclude cystic or necrotic areas more accurately differentiate ccRCC from other renal tumor types than whole-lesion ADC values. KEY POINTS • Selective ADC of renal tumors, excluding cystic and necrotic areas, provides better discriminatory ability than whole-lesion ADC to differentiate clear cell RCC from other renal lesions, with area under the receiver operating characteristic curve (AUC) of 0.852 vs. 0.785, respectively (p = 0.02). • Selective ADC of renal masses provides moderate sensitivity and specificity of 80% and 78%, respectively, for differentiation of clear cell renal cell carcinoma (RCC) from papillary RCC, chromophobe RCC, oncocytoma, and minimal-fat angiomyolipoma. • Selective ADC excluding cystic and necrotic areas are preferable to whole-lesion ADC as an additional tool to multiphasic MRI to differentiate clear cell RCC from other renal lesions whether the highest b value is 800 or 1000.
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Rouvière O, Cornelis F, Brunelle S, Roy C, André M, Bellin MF, Boulay I, Eiss D, Girouin N, Grenier N, Hélénon O, Lapray JF, Lefèvre A, Matillon X, Ménager JM, Millet I, Ronze S, Sanzalone T, Tourniaire J, Rocher L, Renard-Penna R. Imaging protocols for renal multiparametric MRI and MR urography: results of a consensus conference from the French Society of Genitourinary Imaging. Eur Radiol 2020; 30:2103-2114. [PMID: 31900706 DOI: 10.1007/s00330-019-06530-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 09/19/2019] [Accepted: 10/18/2019] [Indexed: 12/23/2022]
Abstract
OBJECTIVES To develop technical guidelines for magnetic resonance imaging aimed at characterising renal masses (multiparametric magnetic resonance imaging, mpMRI) and at imaging the bladder and upper urinary tract (magnetic resonance urography, MRU). METHODS The French Society of Genitourinary Imaging organised a Delphi consensus conference with a two-round Delphi survey followed by a face-to-face meeting. Two separate questionnaires were issued for renal mpMRI and for MRU. Consensus was strictly defined using a priori criteria. RESULTS Forty-two expert uroradiologists completed both survey rounds with no attrition between the rounds. Fifty-six of 84 (67%) statements of the mpMRI questionnaire and 44/71 (62%) statements of the MRU questionnaire reached final consensus. For mpMRI, there was consensus that no injection of furosemide was needed and that the imaging protocol should include T2-weighted imaging, dual chemical shift imaging, diffusion-weighted imaging (use of multiple b-values; maximal b-value, 1000 s/mm2) and fat-saturated single-bolus multiphase (unenhanced, corticomedullary, nephrographic) contrast-enhanced imaging; late imaging (more than 10 min after injection) was judged optional. For MRU, the patients should void their bladder before the examination. The protocol must include T2-weighted imaging, anatomical fast T1/T2-weighted imaging, diffusion-weighted imaging (use of multiple b-values; maximal b-value, 1000 s/mm2) and fat-saturated single-bolus multiphase (unenhanced, corticomedullary, nephrographic, excretory) contrast-enhanced imaging. An intravenous injection of furosemide is mandatory before the injection of contrast medium. Heavily T2-weighted cholangiopancreatography-like imaging was judged optional. CONCLUSION This expert-based consensus conference provides recommendations to standardise magnetic resonance imaging of kidneys, ureter and bladder. KEY POINTS • Multiparametric magnetic resonance imaging (mpMRI) aims at characterising renal masses; magnetic resonance urography (MRU) aims at imaging the urinary bladder and the collecting systems. • For mpMRI, no injection of furosemide is needed. • For MRU, an intravenous injection of furosemide is mandatory before the injection of contrast medium; heavily T2-weighted cholangiopancreatography-like imaging is optional.
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Affiliation(s)
- Olivier Rouvière
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, 5, place d'Arsonval, 69347, Lyon, France.
- Faculté de médecine Lyon Est, Université de Lyon, Université Lyon 1, Lyon, France.
| | - François Cornelis
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Serge Brunelle
- Department of Radiology, Institut Paoli-Calmettes, Marseille, France
| | - Catherine Roy
- Department of Radiology B, CHU de Strasbourg, Nouvel Hôpital Civil, Strasbourg, France
| | - Marc André
- Department of Radiology, Hôpital Européen, Marseille, France
| | - Marie-France Bellin
- Department of Diagnostic and Interventional Radiology, Groupe Hospitalier Paris Sud, Assistance Publique-Hôpitaux de Paris, Le Kremlin Bicêtre, France
- Université Paris Sud, Le Kremlin Bicêtre, France
- IR4M, UMR 8081, Service hospitalier Joliot Curie, Orsay, France
| | - Isabelle Boulay
- Department of Radiology, Fondation Hôpital Saint Joseph, Paris, France
| | - David Eiss
- Department of Adult Radiology, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
- Paris Descartes University, Sorbonne Paris Cité, Paris, France
| | | | - Nicolas Grenier
- Department of Diagnostic and Interventional Adult Imaging, CHU de Bordeaux, Bordeaux, France
- Université de Bordeaux, Bordeaux, France
| | - Olivier Hélénon
- Department of Adult Radiology, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
- Paris Descartes University, Sorbonne Paris Cité, Paris, France
| | | | - Arnaud Lefèvre
- Centre d'Imagerie Médicale Tourville, Paris, France
- Department of Radiology, American Hospital of Paris, Neuilly, France
| | - Xavier Matillon
- Faculté de médecine Lyon Est, Université de Lyon, Université Lyon 1, Lyon, France
- Department of Urology and Transplantation, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
- CarMeN Laboratory, INSERM U1060, Lyon, France
| | | | - Ingrid Millet
- Department of Radiology, Hôpital Lapeyronie, Montpellier, France
- Université de Montpellier, Montpellier, France
| | - Sébastien Ronze
- Imagerie médicale Val d'Ouest Charcot (IMVOC), Ecully, France
| | - Thomas Sanzalone
- Department of Radiology, Centre Hospitalier de Valence, Valence, France
| | - Jean Tourniaire
- Department of Radiology, Clinique Rhône Durance, Avignon, France
| | - Laurence Rocher
- Department of Diagnostic and Interventional Radiology, Groupe Hospitalier Paris Sud, Assistance Publique-Hôpitaux de Paris, Le Kremlin Bicêtre, France
- Université Paris Sud, Le Kremlin Bicêtre, France
- IR4M, UMR 8081, Service hospitalier Joliot Curie, Orsay, France
| | - Raphaële Renard-Penna
- Academic Department of Radiology, Hôpital Pitié-Salpêtrière and Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, Paris, France
- Sorbonne Universités, GRC no 5, ONCOTYPE-URO, Paris, France
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Diagnostic Imaging in Renal Tumors. KIDNEY CANCER 2020. [DOI: 10.1007/978-3-030-28333-9_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Role of MR texture analysis in histological subtyping and grading of renal cell carcinoma: a preliminary study. Abdom Radiol (NY) 2019; 44:3336-3349. [PMID: 31300850 DOI: 10.1007/s00261-019-02122-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
PURPOSE The study evaluated the usefulness of magnetic resonance imaging (MRI) texture parameters in differentiating clear cell renal carcinoma (CC-RCC) from non-clear cell carcinoma (NC-RCC) and in the histological grading of CC-RCC. MATERIALS AND METHODS After institutional ethical approval, this retrospective study analyzed 33 patients with 34 RCC masses (29 CC-RCC and five NC-RCC; 19 low-grade and 10 high-grade CC-RCC), who underwent MRI between January 2011 and December 2012 on a 1.5-T scanner (Avanto, Siemens, Erlangen, Germany). The MRI protocol included T2-weighted imaging (T2WI), diffusion-weighted imaging [DWI; at b 0, 500 and 1000 s/mm2 with apparent diffusion coefficient (ADC) maps] and T1-weighted pre and postcontrast [corticomedullary (CM) and nephrographic (NG) phase] acquisition. MR texture analysis (MRTA) was performed using the TexRAD research software (Feedback Medical Ltd., Cambridge, UK) by a single reader who placed free-hand polygonal region of interest (ROI) on the slice showing the maximum viable tumor. Filtration histogram-based texture analysis was used to generate six first-order statistical parameters [mean intensity, standard deviation (SD), mean of positive pixels (MPP), entropy, skewness and kurtosis] at five spatial scaling factors (SSF) as well as on the unfiltered image. Mann-Whitney test was used to compare the texture parameters of CC-RCC versus NC-RCC, and high-grade versus low-grade CC-RCC. P value < 0.05 was considered significant. A 3-step feature selection was used to obtain the best texture metrics for each MRI sequence and included the receiver-operating characteristic (ROC) curve analysis and Pearson's correlation test. RESULTS The best performing texture parameters in differentiating CC-RCC from NC-RCC for each sequence included (area under the curve in parentheses): entropy at SSF 4 (0.807) on T2WI, SD at SSF 4 (0.814) on DWI b500, SD at SSF 6 (0.879) on DWI b1000, mean at SSF 0 (0.848) on ADC, skewness at SSF 2 (0.854) on T1WI and skewness at SSF 3 (0.908) on CM phase. In differentiating high from low-grade CC-RCC, the best parameters were: entropy at SSF 6 (0.823) on DWI b1000, mean at SSF 3 (0.889) on CM phase and MPP at SSF 5 (0.870) on NG phase. CONCLUSION Several MR texture parameters showed excellent diagnostic performance (AUC > 0.8) in differentiating CC-RCC from NC-RCC, and high-grade from low-grade CC-RCC. MRTA could serve as a useful non-invasive tool for this purpose.
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Can MRI be used to diagnose histologic grade in T1a (< 4 cm) clear cell renal cell carcinomas? Abdom Radiol (NY) 2019; 44:2841-2851. [PMID: 31041495 DOI: 10.1007/s00261-019-02018-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
OBJECTIVE To assess whether MRI can differentiate low-grade from high-grade T1a cc-RCC. MATERIALS AND METHODS With IRB approval, 49 consecutive solid < 4 cm cc-RCC (low grade [Grade 1 or 2] N = 38, high grade [Grade 3] N = 11) with pre-operative MRI before nephrectomy were identified between 2013 and 2018. Tumor size, apparent diffusion coefficient (ADC) histogram analysis, enhancement wash-in and wash-out rates, and chemical shift signal intensity index (SI index) were assessed by a blinded radiologist. Subjectively, two blinded Radiologists also assessed for (1) microscopic fat, (2) homogeneity (5-point Likert scale), and (3) ADC signal (relative to renal cortex); discrepancies were resolved by consensus. Outcomes were studied using Chi square, multivariate analysis, logistic regression modeling, and ROC. Inter-observer agreement was assessed using Cohen's kappa. RESULTS Tumor size was 24 ± 7 (13-39) mm with no association to grade (p = 0.45). Among quantitative features studied, corticomedullary phase wash-in index (p = 0.015), SI index (p = 0.137), and tenth-centile ADC (p = 0.049) were higher in low-grade tumors. 36.8% (14/38) low-grade tumors versus zero high-grade tumors demonstrated microscopic fat (p = 0.015; Kappa = 0.67). Microscopic fat was specific for low-grade disease (100.0% [71.5-100.0]) with low sensitivity (36.8% [21.8-54.6]). Other subjective features did not differ between groups (p > 0.05). A logistic regression model combining microscopic fat + wash-in index + tenth-centile-ADC yielded area under ROC curve 0.98 (Confidence Intervals 0.94-1.0) with sensitivity/specificity 87.5%/100%. CONCLUSION The combination of microscopic fat, higher corticomedullary phase wash-in and higher tenth-centile ADC is highly accurate for diagnosis of low-grade disease among T1a clear cell RCC.
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Hallscheidt P. Tumors of the Urinary Tract. CURRENT RADIOLOGY REPORTS 2019. [DOI: 10.1007/s40134-019-0334-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Unenhanced CT Texture Analysis of Clear Cell Renal Cell Carcinomas: A Machine Learning-Based Study for Predicting Histopathologic Nuclear Grade. AJR Am J Roentgenol 2019; 212:W132-W139. [PMID: 30973779 DOI: 10.2214/ajr.18.20742] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
OBJECTIVE. The purpose of this study is to investigate the predictive performance of machine learning (ML)-based unenhanced CT texture analysis in distinguishing low (grades I and II) and high (grades III and IV) nuclear grade clear cell renal cell carcinomas (RCCs). MATERIALS AND METHODS. For this retrospective study, 81 patients with clear cell RCC (56 high and 25 low nuclear grade) were included from a public database. Using 2D manual segmentation, 744 texture features were extracted from unenhanced CT images. Dimension reduction was done in three consecutive steps: reproducibility analysis by two radiologists, collinearity analysis, and feature selection. Models were created using artificial neural network (ANN) and binary logistic regression, with and without synthetic minority oversampling technique (SMOTE), and were validated using 10-fold cross-validation. The reference standard was histopathologic nuclear grade (low vs high). RESULTS. Dimension reduction steps yielded five texture features for the ANN and six for the logistic regression algorithm. None of clinical variables was selected. ANN alone and ANN with SMOTE correctly classified 81.5% and 70.5%, respectively, of clear cell RCCs, with AUC values of 0.714 and 0.702, respectively. The logistic regression algorithm alone and with SMOTE correctly classified 75.3% and 62.5%, respectively, of the tumors, with AUC values of 0.656 and 0.666, respectively. The ANN performed better than the logistic regression (p < 0.05). No statistically significant difference was present between the model performances created with and without SMOTE (p > 0.05). CONCLUSION. ML-based unenhanced CT texture analysis using ANN can be a promising noninvasive method in predicting the nuclear grade of clear cell RCCs.
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Wang K, Cheng J, Wang Y, Wu G. Renal cell carcinoma: preoperative evaluate the grade of histological malignancy using volumetric histogram analysis derived from magnetic resonance diffusion kurtosis imaging. Quant Imaging Med Surg 2019; 9:671-680. [PMID: 31143658 DOI: 10.21037/qims.2019.04.14] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Background To investigate the value of histogram analysis of magnetic resonance (MR) diffusion kurtosis imaging (DKI) in the assessment of renal cell carcinoma (RCC) grading before surgery. Methods A total of 73 RCC patients who had undergone preoperative MR imaging and DKI were classified into either a low- grade group or a high-grade group. Parametric DKI maps of each tumor were obtained using in-house software, and histogram metrics between the two groups were analyzed. Receiver operating characteristic (ROC) curve analysis was used for obtaining the optimum diagnostic thresholds, the area under the ROC curve (AUC), sensitivity, specificity and accuracy of the parameters. Results Significant differences were observed in 3 metrics of ADC histogram parameters and 8 metrics of DKI histogram parameters (P<0.05). ROC curve analyses showed that Kapp mean had the highest diagnostic efficacy in differentiating RCC grades. The AUC, sensitivity, and specificity of the Kapp mean were 0.889, 87.9% and 80%, respectively. Conclusions DKI histogram parameters can effectively distinguish high- and low- grade RCC. Kapp mean is the best parameter to distinguish RCC grades.
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Affiliation(s)
- Ke Wang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan 437100, China
| | - Jingyun Cheng
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan 437100, China
| | - Yan Wang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan 437100, China
| | - Guangyao Wu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan 437100, China.,Radiology Department, Shenzhen University General Hospital and Shenzhen University Clinical Medical Academy, Shenzhen 518000, China
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Assessment of the extracellular volume fraction for the grading of clear cell renal cell carcinoma: first results and histopathological findings. Eur Radiol 2019; 29:5832-5843. [PMID: 30887194 DOI: 10.1007/s00330-019-06087-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 01/23/2019] [Accepted: 02/08/2019] [Indexed: 12/28/2022]
Abstract
OBJECTIVES To assess the potential of T1 mapping-based extracellular volume fraction (ECV) for the identification of higher grade clear cell renal cell carcinoma (cRCC), based on histopathology as the reference standard. METHODS For this single-center, institutional review board-approved prospective study, 27 patients (17 men, median age 62 ± 12.4 years) with pathologic diagnosis of cRCC (nucleolar International Society of Urological Pathology (ISUP) grading) received abdominal MRI scans at 1.5 T using a modified Look-Locker inversion recovery (MOLLI) sequence between January 2017 and June 2018. Quantitative T1 values were measured at different time points (pre- and postcontrast agent administration) and quantification of the ECV was performed on MRI and histological sections (H&E staining). RESULTS Reduction in T1 value after contrast agent administration and MR-derived ECV were reliable predictors for differentiating higher from lower grade cRCC. Postcontrast T1diff values (T1diff = T1 difference between the native and nephrogenic phase) and MR-derived ECV were significantly higher for higher grade cRCC (ISUP grades 3-4) compared with lower grade cRCC (ISUP grades 1-2) (p < 0.001). A cutoff value of 700 ms could distinguish higher grade from lower grade tumors with 100% (95% CI 0.69-1.00) sensitivity and 82% (95% CI 0.57-0.96) specificity. There was a positive and strong correlation between MR-derived ECV and histological ECV (p < 0.01, r = 0.88). Interobserver agreement for quantitative longitudinal relaxation times in the T1 maps was excellent. CONCLUSIONS T1 mapping with ECV measurement could represent a novel in vivo biomarker for the classification of cRCC regarding their nucleolar grade, providing incremental diagnostic value as a quantitative MR marker. KEY POINTS • Reduction in MRI T1 relaxation times after contrast agent administration and MR-derived extracellular volume fraction are useful parameters for grading of clear cell renal cell carcinoma (cRCC). • T1 differences between the native and the nephrogenic phase are higher for higher grade cRCC compared with lower grade cRCC and MRI-derived extracellular volume fraction (ECV) and histological ECV show a strong correlation. • T1 mapping with ECV measurement may be helpful for the noninvasive assessment of cRCC pathology, being a safe and feasible method, and it has potential to optimize individualized treatment options, e.g., in the decision of active surveillance.
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Diagnostic Accuracy of MRI for Detecting Inferior Vena Cava Wall Invasion in Renal Cell Carcinoma Tumor Thrombus Using Quantitative and Subjective Analysis. AJR Am J Roentgenol 2019; 212:562-569. [DOI: 10.2214/ajr.18.20209] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Native T1 Mapping as an In Vivo Biomarker for the Identification of Higher-Grade Renal Cell Carcinoma. Invest Radiol 2019; 54:118-128. [DOI: 10.1097/rli.0000000000000515] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Guan HX, Pan YY, Wang YJ, Tang DZ, Zhou SC, Xia LM. Comparison of Various Parameters of DWI in Distinguishing Solitary Pulmonary Nodules. Curr Med Sci 2018; 38:920-924. [PMID: 30341530 DOI: 10.1007/s11596-018-1963-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Revised: 09/12/2018] [Indexed: 12/19/2022]
Abstract
In order to prospectively assess various parameters of diffusion weighted imaging (DWI) in differential diagnosis of benign and malignant solitary pulmonary nodules (SPNs), 58 patients (40 men and 18 women, and mean age of 48.1±10.4 years old) with SPNs undergoing conventional MR, DWI using b=500 s/mm2 on a 1.5T MR scanner, were studied. Various DWI parameters [apparent diffusion coefficient (ADC), lesion-tospinal cord signal intensity ratio (LSR), signal intensity (SI) score] were calculated and compared between malignant and benign SPNs groups. A receiver operating characteristic (ROC) curve analysis was employed to compare the diagnostic capabilities of all the parameters for discrimination between benign and malignant SPNs. The results showed that there were 42 malignant and 16 benign SPNs. The ADC was significantly lower in malignant SPNs (1.40±0.44)×10-3 mm2/s than in benign SPNs (1.81±0.58)×10-3 mm2/s. The LSR and SI scores were significantly increased in malignant SPNs (0.90±0.37 and 2.8±1.2) as compared with those in benign SPNs (0.68±0.39 and 2.2±1.2). The area under the ROC curves (AUC) of all parameters was not significantly different between malignant SPNs and benign SPNs. It was suggested that as three reported parameters for DWI, ADC, LSR and SI scores are all feasible for discrimination of malignant and benign SPNs. The three parameters have equal diagnostic performance.
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Affiliation(s)
- Han-Xiong Guan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Yue-Ying Pan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Yu-Jin Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Da-Zong Tang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Shu-Chang Zhou
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Li-Ming Xia
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
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Bektas CT, Kocak B, Yardimci AH, Turkcanoglu MH, Yucetas U, Koca SB, Erdim C, Kilickesmez O. Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade. Eur Radiol 2018; 29:1153-1163. [PMID: 30167812 DOI: 10.1007/s00330-018-5698-2] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 07/19/2018] [Accepted: 07/31/2018] [Indexed: 12/16/2022]
Abstract
OBJECTIVE To evaluate the performance of quantitative computed tomography (CT) texture analysis using different machine learning (ML) classifiers for discriminating low and high nuclear grade clear cell renal cell carcinomas (cc-RCCs). MATERIALS AND METHODS This retrospective study included 53 patients with pathologically proven 54 cc-RCCs (31 low-grade [grade 1 or 2]; 23 high-grade [grade 3 or 4]). In one patient, two synchronous cc-RCCs were included in the analysis. Mean age was 57.5 years. Thirty-four (64.1%) patients were male and 19 were female (35.9%). Mean tumour size based on the maximum diameter was 57.4 mm (range, 16-145 mm). Forty patients underwent radical nephrectomy and 13 underwent partial nephrectomy. Following pre-processing steps, two-dimensional CT texture features were extracted using portal-phase contrast-enhanced CT. Reproducibility of texture features was assessed with the intra-class correlation coefficient (ICC). Nested cross-validation with a wrapper-based algorithm was used in feature selection and model optimisation. The ML classifiers were support vector machine (SVM), multilayer perceptron (MLP, a sort of neural network), naïve Bayes, k-nearest neighbours, and random forest. The performance of the classifiers was compared by certain metrics. RESULTS Among 279 texture features, 241 features with an ICC equal to or higher than 0.80 (excellent reproducibility) were included in the further feature selection process. The best model was created using SVM. The selected subset of features for SVM included five co-occurrence matrix (ICC range, 0.885-0.998), three run-length matrix (ICC range, 0.889-0.992), one gradient (ICC = 0.998), and four Haar wavelet features (ICC range, 0.941-0.997). The overall accuracy, sensitivity (for detecting high-grade cc-RCCs), specificity (for detecting high-grade cc-RCCs), and overall area under the curve of the best model were 85.1%, 91.3%, 80.6%, and 0.860, respectively. CONCLUSIONS The ML-based CT texture analysis can be a useful and promising non-invasive method for prediction of low and high Fuhrman nuclear grade cc-RCCs. KEY POINTS • Based on the percutaneous biopsy literature, ML-based CT texture analysis has a comparable predictive performance with percutaneous biopsy. • Highest predictive performance was obtained with use of the SVM. • SVM correctly classified 85.1% of cc-RCCs in terms of nuclear grade, with an AUC of 0.860.
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Affiliation(s)
- Ceyda Turan Bektas
- Department of Radiology, Istanbul Training and Research Hospital, Istanbul, Turkey
| | - Burak Kocak
- Department of Radiology, Istanbul Training and Research Hospital, Istanbul, Turkey.
| | - Aytul Hande Yardimci
- Department of Radiology, Istanbul Training and Research Hospital, Istanbul, Turkey
| | - Mehmet Hamza Turkcanoglu
- Department of Radiology, Batman Women and Children's Health Training and Research Hospital, Batman, Turkey
| | - Ugur Yucetas
- Department of Urology, Istanbul Training and Research Hospital, Istanbul, Turkey
| | - Sevim Baykal Koca
- Department of Pathology, Istanbul Training and Research Hospital, Istanbul, Turkey
| | - Cagri Erdim
- Department of Radiology, Istanbul Training and Research Hospital, Istanbul, Turkey
| | - Ozgur Kilickesmez
- Department of Radiology, Istanbul Training and Research Hospital, Istanbul, Turkey
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Diagnostic Accuracy of Unenhanced CT Analysis to Differentiate Low-Grade From High-Grade Chromophobe Renal Cell Carcinoma. AJR Am J Roentgenol 2018; 210:1079-1087. [PMID: 29547054 DOI: 10.2214/ajr.17.18874] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
OBJECTIVE The objective of our study was to evaluate tumor attenuation and texture on unenhanced CT for potential differentiation of low-grade from high-grade chromophobe renal cell carcinoma (RCC). MATERIALS AND METHODS A retrospective study of 37 consecutive patients with chromophobe RCC (high-grade, n = 13; low-grade, n = 24) who underwent preoperative unenhanced CT between 2011 and 2016 was performed. Two radiologists (readers 1 and 2) blinded to the histologic grade of the tumor and outcome of the patients subjectively evaluated tumor homogeneity (3-point scale: completely homogeneous, mildly heterogeneous, or mostly heterogeneous). A third radiologist, also blinded to tumor grade and patient outcome, measured attenuation and contoured tumors for quantitative texture analysis. Comparisons were performed between high-grade and low-grade tumors using the chi-square test for subjective variables and sex, independent t tests for patient age and tumor attenuation, and Mann-Whitney U tests for texture analysis. Logistic regression models and ROC curves were computed. RESULTS There were no differences in age or sex between the groups (p = 0.652 and 0.076). High-grade tumors were larger (mean ± SD, 62.6 ± 34.9 mm [range, 17.0-141.0 mm] vs 39.0 ± 17.9 mm [16.0-72.3 mm]; p = 0.009) and had higher attenuation (mean ± SD, 45.5 ± 8.2 HU [range, 29.0-55.0 HU] vs 35.3 ± 8.5 HU [14.0-51.0 HU]; p = 0.001) than low-grade tumors. CT size and attenuation achieved good accuracy to diagnose high-grade chromophobe RCC: The AUC ± standard error was 0.85 ± 0.08 (p < 0.0001) with a sensitivity of 69.0% and a specificity of 100%. Subjectively, high-grade tumors were more heterogeneous (mildly or markedly heterogeneous: 69.2% [9/13] for reader 1 and 76.9% [10/13] for reader 2; reader 1, p = 0.024; reader 2, p = 0.001) with moderate agreement (κ = 0.57). Combined texture features diagnosed high-grade tumors with a maximal AUC of 0.84 ± 0.06 (p < 0.0001). CONCLUSION Tumor attenuation and heterogeneity assessed on unenhanced CT are associated with high-grade chromophobe RCC and correlate well with the histopathologic chromophobe tumor grading system.
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