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Dai C, Xiong Y, Zhu P, Yao L, Lin J, Yao J, Zhang X, Huang R, Wang R, Hou J, Wang K, Shi Z, Chen F, Guo J, Zeng M, Zhou J, Wang S. Deep Learning Assessment of Small Renal Masses at Contrast-enhanced Multiphase CT. Radiology 2024; 311:e232178. [PMID: 38742970 DOI: 10.1148/radiol.232178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Background Accurate characterization of suspicious small renal masses is crucial for optimized management. Deep learning (DL) algorithms may assist with this effort. Purpose To develop and validate a DL algorithm for identifying benign small renal masses at contrast-enhanced multiphase CT. Materials and Methods Surgically resected renal masses measuring 3 cm or less in diameter at contrast-enhanced CT were included. The DL algorithm was developed by using retrospective data from one hospital between 2009 and 2021, with patients randomly allocated in a training and internal test set ratio of 8:2. Between 2013 and 2021, external testing was performed on data from five independent hospitals. A prospective test set was obtained between 2021 and 2022 from one hospital. Algorithm performance was evaluated by using the area under the receiver operating characteristic curve (AUC) and compared with the results of seven clinicians using the DeLong test. Results A total of 1703 patients (mean age, 56 years ± 12 [SD]; 619 female) with a single renal mass per patient were evaluated. The retrospective data set included 1063 lesions (874 in training set, 189 internal test set); the multicenter external test set included 537 lesions (12.3%, 66 benign) with 89 subcentimeter (≤1 cm) lesions (16.6%); and the prospective test set included 103 lesions (13.6%, 14 benign) with 20 (19.4%) subcentimeter lesions. The DL algorithm performance was comparable with that of urological radiologists: for the external test set, AUC was 0.80 (95% CI: 0.75, 0.85) versus 0.84 (95% CI: 0.78, 0.88) (P = .61); for the prospective test set, AUC was 0.87 (95% CI: 0.79, 0.93) versus 0.92 (95% CI: 0.86, 0.96) (P = .70). For subcentimeter lesions in the external test set, the algorithm and urological radiologists had similar AUC of 0.74 (95% CI: 0.63, 0.83) and 0.81 (95% CI: 0.68, 0.92) (P = .78), respectively. Conclusion The multiphase CT-based DL algorithm showed comparable performance with that of radiologists for identifying benign small renal masses, including lesions of 1 cm or less. Published under a CC BY 4.0 license. Supplemental material is available for this article.
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Affiliation(s)
- Chenchen Dai
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Ying Xiong
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Pingyi Zhu
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Linpeng Yao
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Jinglai Lin
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Jiaxi Yao
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Xue Zhang
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Risheng Huang
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Run Wang
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Jun Hou
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Kang Wang
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Zhang Shi
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Feng Chen
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Jianming Guo
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Mengsu Zeng
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Jianjun Zhou
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Shuo Wang
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
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Vazquez LC, Xi Y, Rasmussen RG, Venzor JER, Kapur P, Zhong H, Dai JC, Morgan TN, Cadeddu JA, Pedrosa I. Characterization of Demographical Histologic Diversity in Small Renal Masses With the Clear Cell Likelihood Score. J Comput Assist Tomogr 2024; 48:370-377. [PMID: 38213063 DOI: 10.1097/rct.0000000000001567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
OBJECTIVE This study aimed to develop a diagnostic model to estimate the distribution of small renal mass (SRM; ≤4 cm) histologic subtypes for patients with different demographic backgrounds and clear cell likelihood score (ccLS) designations. MATERIALS AND METHODS A bi-institution retrospective cohort study was conducted where 347 patients (366 SRMs) underwent magnetic resonance imaging and received a ccLS before pathologic confirmation between June 2016 and November 2021. Age, sex, race, ethnicity, socioeconomic status, body mass index (BMI), and the ccLS were tabulated. The socioeconomic status for each patient was determined using the Area Deprivation Index associated with their residential address. The magnetic resonance imaging-derived ccLS assists in the characterization of SRMs by providing a likelihood of clear cell renal cell carcinoma (ccRCC). Pathological subtypes were grouped into four categories (ccRCC, papillary renal cell carcinoma, other renal cell carcinomas, or benign). Generalized estimating equations were used to estimate probabilities of the pathological subtypes across different patient subgroups. RESULTS Race and ethnicity, BMI, and ccLS were significant predictors of histology (all P < 0.001). Obese (BMI, ≥30 kg/m 2 ) Hispanic patients with ccLS of ≥4 had the highest estimated rate of ccRCC (97.1%), and normal-weight (BMI, <25 kg/m 2 ) non-Hispanic Black patients with ccLS ≤2 had the lowest (0.2%). The highest estimated rates of papillary renal cell carcinoma were found in overweight (BMI, 25-30 kg/m 2 ) non-Hispanic Black patients with ccLS ≤2 (92.3%), and the lowest, in obese Hispanic patients with ccLS ≥4 (<0.1%). CONCLUSIONS Patient race, ethnicity, BMI, and ccLS offer synergistic information to estimate the probabilities of SRM histologic subtypes.
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Affiliation(s)
| | - Yin Xi
- From the Department of Radiology, University of Texas Southwestern School of Medicine
| | - Robert G Rasmussen
- From the Department of Radiology, University of Texas Southwestern School of Medicine
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Gao Y, Wang X, Zhao X, Zhu C, Li C, Li J, Wu X. Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (< 4 cm) clear cell renal cell carcinoma. BMC Cancer 2023; 23:953. [PMID: 37814228 PMCID: PMC10561466 DOI: 10.1186/s12885-023-11454-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 09/27/2023] [Indexed: 10/11/2023] Open
Abstract
BACKGROUND Small (< 4 cm) clear cell renal cell carcinoma (ccRCC) is the most common type of small renal cancer and its prognosis is poor. However, conventional radiological characteristics obtained by computed tomography (CT) are not sufficient to predict the nuclear grade of small ccRCC before surgery. METHODS A total of 113 patients with histologically confirmed ccRCC were randomly assigned to the training set (n = 67) and the testing set (n = 46). The baseline and CT imaging data of the patients were evaluated statistically to develop a clinical model. A radiomics model was created, and the radiomics score (Rad-score) was calculated by extracting radiomics features from the CT images. Then, a clinical radiomics nomogram was developed using multivariate logistic regression analysis by combining the Rad-score and critical clinical characteristics. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination of small ccRCC in both the training and testing sets. RESULTS The radiomics model was constructed using six features obtained from the CT images. The shape and relative enhancement value of the nephrographic phase (REV of the NP) were found to be independent risk factors in the clinical model. The area under the curve (AUC) values for the training and testing sets for the clinical radiomics nomogram were 0.940 and 0.902, respectively. Decision curve analysis (DCA) revealed that the radiomics nomogram model was a better predictor, with the highest degree of coincidence. CONCLUSION The CT-based radiomics nomogram has the potential to be a noninvasive and preoperative method for predicting the WHO/ISUP grade of small ccRCC.
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Affiliation(s)
- Yankun Gao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Xia Wang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Xiaoying Zhao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Chao Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Cuiping Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China
| | - Jianying Li
- CT Research Center, GE Healthcare China, Shanghai, 210000, China
| | - Xingwang Wu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China.
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4
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Shang W, Hong G, Li W. MRI for the detection of small malignant renal masses: a systematic review and meta-analysis. Front Oncol 2023; 13:1194128. [PMID: 37876965 PMCID: PMC10591109 DOI: 10.3389/fonc.2023.1194128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 09/12/2023] [Indexed: 10/26/2023] Open
Abstract
Objective We aimed to review the available evidence on the diagnostic performance of magnetic resonance imaging in differentiating malignant from benign small renal masses. Methods An electronic literature search of Web of Science, MEDLINE (Ovid and PubMed), Cochrane Library, EMBASE, and Google Scholar was performed to identify relevant articles up to 31 January 2023. We included studies that reported the diagnostic accuracy of using magnetic resonance imaging to differentiate small (≤4 cm) malignant from benign renal masses. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated using the bivariate model and the hierarchical summary receiver operating characteristic model. The study quality evaluation was performed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Results A total of 10 studies with 860 small renal masses (815 patients) were included in the current meta-analysis. The pooled sensitivity and specificity of the studies for the detection of malignant masses were 0.85 (95% CI 0.79-0.90) and 0.83 (95% CI 0.67-0.92), respectively. Conclusions MRI had a moderate diagnostic performance in differentiating small malignant renal masses from benign ones. Substantial heterogeneity was observed between studies for both sensitivity and specificity.
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Affiliation(s)
| | | | - Wei Li
- Department of Medical Imaging, Jiangsu Vocational College of Medicine, Yancheng, China
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5
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Pickovsky JS, Alo Nasiyabi K, Eldihimi F, Schieda N. Utility of multiparametric renal CT for differentiation of low-grade from high-grade cT1a clear cell renal cell carcinoma. Br J Radiol 2023; 96:20221087. [PMID: 37428147 PMCID: PMC10546453 DOI: 10.1259/bjr.20221087] [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: 11/21/2022] [Revised: 03/23/2023] [Accepted: 03/28/2023] [Indexed: 07/11/2023] Open
Abstract
OBJECTIVE To determine if CT can differentiate low-grade from high-grade clear cell renal cell carcinoma (ccRCC) in cT1a solid ccRCC. METHODS AND MATERIALS This retrospective cross-sectional study evaluated 78 < 4 cm solid (>25% enhancing) ccRCC in 78 patients with renal CT within 12 months of surgery between January 2016 and December 2019. Two radiologists (R1/R2), blinded to pathology, independently recorded mass:size, calcification, attenuation and heterogeneity (5-point Likert scale) and recorded a 5-point ccRCC CT Score. Multivariate logistic regression (LR) was performed. RESULTS There were 64.1% (50/78) low-grade (5/50 Grade 1 and 45/50 Grade 2) and 35.9% (28/78) high-grade (27/28 Grade 3 and 1/28 Grade 4) tumors.Unenhanced CT attenuation was higher (35.9±10.3 R1 and 34.9±10.7 R2 high-grade vs 29.7±10.2 R1 and 29.5±9.8 R2 low-grade, p=0.01-0.02), absolute corticomedullary phase attenuation ratio (CMphase-ratio; 0.67±0.16 R1 and 0.66±0.16 R2 vs 0.93±0.83 R1 and 0.80±0.33 R2, p=0.04-0.05) and 3-tiered stratification of CMphase-ratio (p=0.02) lower in high-grade tumors.A two-variable LR-model including unenhanced CT attenuation and CM.phase-ratio achieved area under the receiver operator characteristic curve of: 73% (95% confidence intervals 59-86%) and 72% (59-84%) for R1 and R2.ccRCC CT score differed by ccRCC grade (p<0.01 R1, R2) with high-grade tumors occurring most commonly in moderately enhancing ccRCC score 4 (46.4% [13/28] R1 and 54% [15/28]). CONCLUSION Among cT1a ccRCC, high-grade tumors have higher unenhanced CT attenuation and are less avidly enhancing. ADVANCES IN KNOWLEDGE High-grade ccRCC have higher attenuation (possibly due to less microscopic fat) and lower corticomedullary phase enhancement compared to low-grade tumors. This may result in categorization of high-grade tumors in lower ccRCC diagnostic algorithm categories.
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Affiliation(s)
- Jana S Pickovsky
- Department of Medical Imaging, The Ottawa Hospital, Ottawa, Canada
| | | | | | - Nicola Schieda
- Department of Medical Imaging, The Ottawa Hospital, Ottawa, Canada
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6
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Lemieux S, Shen L, Liang T, Lo E, Chu Y, Kamaya A, Tse JR. External Validation of a Five-Tiered CT Algorithm for the Diagnosis of Clear Cell Renal Cell Carcinoma: A Retrospective Five-Reader Study. AJR Am J Roentgenol 2023; 221:334-343. [PMID: 37162037 DOI: 10.2214/ajr.23.29151] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
BACKGROUND. In 2022, a five-tiered CT algorithm was proposed for predicting whether a small (cT1a) solid renal mass represents clear cell renal cell carcinoma (ccRCC). OBJECTIVE. The purpose of this external validation study was to evaluate the proposed CT algorithm for diagnosis of ccRCC among small solid renal masses. METHODS. This retrospective study included 93 patients (median age, 62 years; 42 women, 51 men) with 97 small solid renal masses that were seen on corticomedullary phase contrast-enhanced CT performed between January 2012 and July 2022 and subsequently underwent surgical resection. Five readers (three attending radiologists, two clinical fellows) independently evaluated masses for the mass-to-cortex corticomedullary attenuation ratio and heterogeneity score; these scores were used to derive the CT score by use of the previously proposed CT algorithm. The CT score's sensitivity, specificity, and PPV for ccRCC were calculated at threshold of 4 or greater, and the NPV for ccRCC was calculated at a threshold of 3 or greater (consistent with thresholds in studies of the MRI-based clear cell likelihood score and the CT algorithm's initial study). The CT score's sensitivity and specificity for papillary RCC were calculated at a threshold of 2 or less. Interreader agreement was assessed using the Gwet agreement coefficient (AC1). RESULTS. Overall, 61 of 97 masses (63%) were malignant and 43 of 97 (44%) were ccRCC. Across readers, CT score had sensitivity ranging from 47% to 95% (pooled sensitivity, 74% [95% CI, 68-80%]), specificity ranging from 19% to 83% (pooled specificity, 59% [95% CI, 52-67%]), PPV ranging from 48% to 76% (pooled PPV, 59% [95% CI, 49-71%]), and NPV ranging from 83% to 100% (pooled NPV, 90% [95% CI, 84-95%]), for ccRCC. A CT score of 2 or less had sensitivity ranging from 44% to 100% and specificity ranging from 77% to 98% for papillary RCC (representing nine of 97 masses). Interreader agreement was substantial for attenuation score (AC1 = 0.70), poor for heterogeneity score (AC1 = 0.17), fair for five-tiered CT score (AC1 = 0.32), and fair for dichotomous CT score at a threshold of 4 or greater (AC1 = 0.24 [95% CI, 0.14-0.33]). CONCLUSION. The five-tiered CT algorithm for evaluation of small solid renal masses was tested in an external sample and showed high NPV for ccRCC. CLINICAL IMPACT. The CT algorithm may be used for risk stratification and patient selection for active surveillance by identifying patients unlikely to have ccRCC.
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Affiliation(s)
- Simon Lemieux
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, Rm H-1307, Stanford, CA 94305
| | - Luyao Shen
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, Rm H-1307, Stanford, CA 94305
| | - Tie Liang
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, Rm H-1307, Stanford, CA 94305
| | - Edward Lo
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, Rm H-1307, Stanford, CA 94305
| | - Youngmin Chu
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, Rm H-1307, Stanford, CA 94305
| | - Aya Kamaya
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, Rm H-1307, Stanford, CA 94305
| | - Justin R Tse
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, Rm H-1307, Stanford, CA 94305
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7
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Qarni B, McGrath T, Aldhufian M, Schieda N. Prevalence of malignant or possibly malignant renal masses among homogeneous low-attenuation masses that are too small to characterize at computed tomography. Abdom Radiol (NY) 2023; 48:2628-2635. [PMID: 37166461 DOI: 10.1007/s00261-023-03946-6] [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: 02/18/2023] [Revised: 04/28/2023] [Accepted: 05/01/2023] [Indexed: 05/12/2023]
Abstract
BACKGROUND Homogeneous low-attenuation renal masses that are too small to characterize (tstc) are considered clinically insignificant; however, based primarily on expert opinion. OBJECTIVE To determine the prevalence of malignant or possibly malignant masses among homogeneous low-attenuation renal masses that are tstc. MATERIALS AND METHODS This retrospective cross-sectional study evaluated 75 patients with 104 tstc who underwent renal CT and MRI between Jan 2016 and Jul 2022. Low-attenuation renal masses measuring < 1 cm in size were identified and, independently evaluated by two blinded radiologists measuring attenuation (Hounsfield Units, HU) at non-contrast enhanced CT (NECT) and nephrographic phase contrast-enhanced (CE)-CT when possible. Reference standard for benign cyst was MRI and for other renal masses was pathology or MRI showing enhancement. RESULTS Average tstc size was 6 ± 2 (range 2-10) mm. Considering only incidental tstc (CT performed for another reason), 100% (98/98, 95%CI 96-100%) tstc were benign. Overall, considering both incidental and tstc referred for further characterization, there were 94% (98/104; 95% Confidence Intervals [CIs] 88-98%) benign cysts and 6% (6/104; 95%CI 2-12%) other masses (1 Bosniak 2F cystic mass, 2 probable renal cell carcinoma (RCC), three metastases). Pseudoenhancement, attenuation change > 10 HU or > 20 HU, was present in 29% (15/59) and 12% (7/59) benign cysts. All six other masses enhanced by > 20 HU. CECT threshold of ≤ 30 HU correctly classified 62% of benign cysts (61/98). All six other masses measured > 30 HU at CECT. CONCLUSION The prevalence of malignant or possibly malignant renal masses among homogeneous low-attenuation too small to characterize masses among incidental tstc masses is near zero. Attenuation measurements misclassify a substantial proportion of these cysts, likely due to their small size.
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Affiliation(s)
- Bilal Qarni
- Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Room C159, Ottawa, ON, K1Y 4E9, Canada
| | - Trevor McGrath
- Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Room C159, Ottawa, ON, K1Y 4E9, Canada
| | - Meshary Aldhufian
- Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Room C159, Ottawa, ON, K1Y 4E9, Canada
| | - Nicola Schieda
- Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Avenue, Room C159, Ottawa, ON, K1Y 4E9, Canada.
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8
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Shen L, Tse JR, Lemieux S, Yoon L, Mullane PC, Liang T, Davenport MS, Pedrosa I, Silverman SG. Risk of malignancy in T1-hyperintense Bosniak version 2019 class II and IIF cystic renal masses. Abdom Radiol (NY) 2023; 48:2636-2648. [PMID: 37202641 DOI: 10.1007/s00261-023-03955-5] [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: 03/01/2023] [Revised: 05/04/2023] [Accepted: 05/04/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUND Bosniak classification version 2019 includes cystic masses in class II and IIF based partly on their hyperintense appearance at T1-weighted MRI. The prevalence of malignancy in non-enhancing heterogeneously T1-hyperintense masses is unknown, nor whether the pattern of T1 hyperintensity affects malignancy likelihood. PURPOSE To determine the malignancy proportion among six patterns of T1 hyperintensity within non-enhancing cystic renal masses. METHODS This retrospective, single-institution study included 72 Bosniak class II and IIF, non-enhancing, T1-hyperintense cystic renal masses. Diagnosis was confirmed by histopathology or by follow-up imaging demonstrating 5-year size and morphologic stability, decreased in size by ≥ 30%, resolution, or Bosniak down-classification. Six patterns of T1 hyperintensity were pre-defined: homogeneous (pattern A), fluid-fluid level (pattern B), peripherally markedly T1-hyperintense (pattern C), containing a T1-hyperintense non-enhancing nodule (pattern D), peripherally T1-hypointense (pattern E), and heterogeneously T1-hyperintense without a distinct pattern (pattern F). Three readers independently assigned each mass to a pattern. Individual and mean malignancy proportion were determined. Mann-Whitney test and Fischer's exact test compared the likelihood of malignancy between patterns. Inter-reader agreement was analyzed with Gwet's agreement coefficient (AC). RESULTS Among 72 masses, the mean number of masses assigned was 11 (15%) to pattern A, 21 (29%) to pattern B, 6 (8%) to pattern C, 7 (10%) to pattern D, 5 (7%) to pattern E, and 22 (31%) to pattern F. Five of 72 masses (7%) were malignant; none was assigned pattern A, B, or D. Mean malignancy proportion was 5% (0/9, 1/6, and 0/4) for pattern C, 13% (0/4, 1/3, and 1/7) for pattern E, and 18% (5/20, 3/21, and 4/25) for pattern F. Malignant masses were more likely assigned to pattern E or F (p = 0.003-0.039). Inter-reader agreement was substantial (Gwet's AC: 0.68). CONCLUSION Bosniak version 2019 class IIF masses that are non-enhancing and heterogeneously T1-hyperintense with a fluid-fluid level are likely benign. Those that are non-enhancing and heterogeneously T1-hyperintense without a distinct pattern have a malignancy proportion up to 25% (5/20).
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Affiliation(s)
- Luyao Shen
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA.
| | - Justin R Tse
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - Simon Lemieux
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - Luke Yoon
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - Patrick C Mullane
- Department of Pathology, Stanford University School of Medicine, Lane Building, L235, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - Tie Liang
- Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - Matthew S Davenport
- Department of Radiology and Urology, Michigan Medicine, University of Michigan, 1500 E Medical Center Dr, Ann Arbor, MI, B2-A209A48109, USA
| | - Ivan Pedrosa
- Department of Radiology, University of Texas Southwestern Medical Center, 2201 Inwood Rd. 2nd Floor, Suite 202, Dallas, TX, 75390, USA
| | - Stuart G Silverman
- Department of Radiology, Brigham and Women's Hospital, Harvard University, 75 Francis St., Boston, MA, 02115, USA
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9
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Shetty AS, Fraum TJ, Ballard DH, Hoegger MJ, Itani M, Rajput MZ, Lanier MH, Cusworth BM, Mehrsheikh AL, Cabrera-Lebron JA, Chu J, Cunningham CR, Hirschi RS, Mokkarala M, Unteriner JG, Kim EH, Siegel CL, Ludwig DR. Renal Mass Imaging with MRI Clear Cell Likelihood Score: A User's Guide. Radiographics 2023; 43:e220209. [PMID: 37319026 DOI: 10.1148/rg.220209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Small solid renal masses (SRMs) are frequently detected at imaging. Nearly 20% are benign, making careful evaluation with MRI an important consideration before deciding on management. Clear cell renal cell carcinoma (ccRCC) is the most common renal cell carcinoma subtype with potentially aggressive behavior. Thus, confident identification of ccRCC imaging features is a critical task for the radiologist. Imaging features distinguishing ccRCC from other benign and malignant renal masses are based on major features (T2 signal intensity, corticomedullary phase enhancement, and the presence of microscopic fat) and ancillary features (segmental enhancement inversion, arterial-to-delayed enhancement ratio, and diffusion restriction). The clear cell likelihood score (ccLS) system was recently devised to provide a standardized framework for categorizing SRMs, offering a Likert score of the likelihood of ccRCC ranging from 1 (very unlikely) to 5 (very likely). Alternative diagnoses based on imaging appearance are also suggested by the algorithm. Furthermore, the ccLS system aims to stratify which patients may or may not benefit from biopsy. The authors use case examples to guide the reader through the evaluation of major and ancillary MRI features of the ccLS algorithm for assigning a likelihood score to an SRM. The authors also discuss patient selection, imaging parameters, pitfalls, and areas for future development. The goal is for radiologists to be better equipped to guide management and improve shared decision making between the patient and treating physician. © RSNA, 2023 Quiz questions for this article are available in the supplemental material. See the invited commentary by Pedrosa in this issue.
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Affiliation(s)
- Anup S Shetty
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Tyler J Fraum
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - David H Ballard
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Mark J Hoegger
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Malak Itani
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Mohamed Z Rajput
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Michael H Lanier
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Brian M Cusworth
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Amanda L Mehrsheikh
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Jorge A Cabrera-Lebron
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Jia Chu
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Christopher R Cunningham
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Ryan S Hirschi
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Mahati Mokkarala
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Jackson G Unteriner
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Eric H Kim
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Cary L Siegel
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Daniel R Ludwig
- From the Mallinckrodt Institute of Radiology (A.S.S., T.J.F., D.H.B., M.J.H., M.I., M.Z.R., M.H.L., B.M.C., A.L.M., J.A.C.L., J.C., C.R.C., R.S.H., M.M., J.G.U., C.L.S., D.R.L.) and Division of Urologic Surgery (E.H.K.), Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
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10
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Magnetta MJ, Schieda N, Murphy P, Miller FH. Accumulation of iodine or other similar K-edge equivalent element within renal cysts mimics enhancing masses at single-phase dual-energy CT. Br J Radiol 2023; 96:20221079. [PMID: 36802978 PMCID: PMC10078865 DOI: 10.1259/bjr.20221079] [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: 11/18/2022] [Revised: 01/31/2023] [Accepted: 02/06/2023] [Indexed: 02/22/2023] Open
Abstract
OBJECTIVE To describe instances of iodine, or other element with similar K-edge to iodine, accumulating in benign renal cysts and simulating solid renal masses (SRM) at single-phase contrast-enhanced (CE) dual-energy CT (DECT). METHODS During the course of routine clinical practice, instances of benign renal cysts (reference standard true non-contrast enhanced CT [NCCT] homogeneous attenuation <10 HU and not enhancing, or MRI) simulating SRM at follow-up single-phase CE-DECT due to iodine (or other element) accumulation were documented in two institutions over a 3-month observation period in 2021. RESULTS Five Bosniak one renal cysts (12 ± 7 mm) in five patients changed nature on follow-up imaging simulating SRM at CE-DECT. At time of DECT, cyst attenuation on true NCCT (mean 91 ± 25 HU [Range 56-120]) was significantly higher compared to virtual NCCT (mean 11 ± 22 HU [-23-30], p = 0.003) and all five cysts showed internal iodine content on DECT iodine maps with concentration >1.9 mg ml-1 (mean 8.2 ± 7.6 mg ml-1 [2.8-20.9]). CONCLUSION The accumulation of iodine, or other element with similar K-edge to iodine, in benign renal cysts could simulate enhancing renal masses at single-phase contrast-enhanced DECT.
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Affiliation(s)
- Michael J Magnetta
- Department of Radiology, Northwestern Memorial Hospital, Northwestern University, Chicago, IL, USA
| | - Nicola Schieda
- Department of Medical Imaging, The Ottawa Hospital, The University of Ottawa, 1053 Carling Ave, Ottawa, Canada
| | - Patrick Murphy
- Department of Radiology, Northwestern Memorial Hospital, Northwestern University, Chicago, IL, USA
| | - Frank H Miller
- Department of Radiology, Northwestern Memorial Hospital, Northwestern University, Chicago, IL, USA
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11
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Duus LA, Junker T, Rasmussen BS, Bojsen JA, Pedersen AL, Anthonsen A, Lund L, Pedersen M, Graumann O. Safety, efficacy, and mid-term oncological outcomes of computed tomography-guided cryoablation of T1 renal cancer. Acta Radiol 2023; 64:814-820. [PMID: 35297745 DOI: 10.1177/02841851221081825] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
BACKGROUND Cryoablation is a promising minimally invasive, nephron-sparing treatment of small renal carcinoma (RCC) in co-morbid patients. PURPOSE To assess the safety, efficacy, and cancer-specific outcomes of computed tomography (CT)-guided cryoablation of stage T1 (RCC). MATERIAL AND METHODS A retrospective evaluation of 122 consecutive patients with 128 tumors treated with cryoablation during 2016-2017. All patients had biopsy-verified T1 RCC. RESULTS Median age was 69 years (IQR=59-76); 69% were male. Median tumor size was 26 mm (± 20-33); 9% were stage T1b. Mean follow-up time was 36.3±12.0 months. In total, 14 (11%) procedures led to complications, of which 4 (3%) were intraoperative, 5 (4%) appeared ≤30 days and 5 (4%) >30 days after treatment. Major complications arose after 4 (3%) procedures. Statistically significant associations were found between major complications and stage T1b (P = 0.039), RENAL score (P = 0.010), and number of needles used in cryoablation (P = 0.004). Residual tumor was detected after 4 (3%) procedures and 5 (4%) tumors had local tumor progression. Of 122 patients, 3 (2%) advanced to metastatic disease. Significant statistical associations were found between local tumor progression and T1b stage tumors and number of needles used in cryoablation (P = 0.05 and P = 0.004, respectively). For patients with T1a tumors, the one- and three-year disease-free survival was 98% and 95%, respectively, and for T1b 100% after one year and 75% after three years. CONCLUSIONS This study showed that cryoablation is a safe and effective treatment of stage T1 RCC and suggests that in selecting candidates for cryoablation of RCC, the tumor characteristics are more critical than patients' baseline health status.
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Affiliation(s)
- Louise A Duus
- Department of Radiology, 11286Odense University Hospital (OUH), Odense C, Denmark.,Research and Innovation Unit of Radiology, 6174University of Southern Denmark (SDU), Odense C, Denmark.,OPEN, Odense Patient data Explorative Network, SDU, Odense C, Denmark
| | - Theresa Junker
- Department of Radiology, 11286Odense University Hospital (OUH), Odense C, Denmark.,Research and Innovation Unit of Radiology, 6174University of Southern Denmark (SDU), Odense C, Denmark.,OPEN, Odense Patient data Explorative Network, SDU, Odense C, Denmark
| | - Benjamin S Rasmussen
- Department of Radiology, 11286Odense University Hospital (OUH), Odense C, Denmark.,Research and Innovation Unit of Radiology, 6174University of Southern Denmark (SDU), Odense C, Denmark
| | - Jonas A Bojsen
- Department of Radiology, 11286Odense University Hospital (OUH), Odense C, Denmark.,Research and Innovation Unit of Radiology, 6174University of Southern Denmark (SDU), Odense C, Denmark
| | - Allan L Pedersen
- Research and Innovation Unit of Radiology, 6174University of Southern Denmark (SDU), Odense C, Denmark
| | - Andrea Anthonsen
- Research and Innovation Unit of Radiology, 6174University of Southern Denmark (SDU), Odense C, Denmark
| | - Lars Lund
- Department of Urology, OUH, Odense C, Denmark.,Institute of Clinical Research, SDU, Odense C, Denmark
| | - Michael Pedersen
- Research and Innovation Unit of Radiology, 6174University of Southern Denmark (SDU), Odense C, Denmark.,Comparative Medicine Lab, Department of Clinical Medicine, Aarhus University, Aarhus N, Denmark
| | - Ole Graumann
- Department of Radiology, 11286Odense University Hospital (OUH), Odense C, Denmark.,Research and Innovation Unit of Radiology, 6174University of Southern Denmark (SDU), Odense C, Denmark.,OPEN, Odense Patient data Explorative Network, SDU, Odense C, Denmark
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12
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Ferro M, Crocetto F, Barone B, del Giudice F, Maggi M, Lucarelli G, Busetto GM, Autorino R, Marchioni M, Cantiello F, Crocerossa F, Luzzago S, Piccinelli M, Mistretta FA, Tozzi M, Schips L, Falagario UG, Veccia A, Vartolomei MD, Musi G, de Cobelli O, Montanari E, Tătaru OS. Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review. Ther Adv Urol 2023; 15:17562872231164803. [PMID: 37113657 PMCID: PMC10126666 DOI: 10.1177/17562872231164803] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 03/04/2023] [Indexed: 04/29/2023] Open
Abstract
Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.
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Affiliation(s)
| | - Felice Crocetto
- Department of Neurosciences and Reproductive
Sciences and Odontostomatology, University of Naples Federico II, Naples,
Italy
| | - Biagio Barone
- Department of Neurosciences and Reproductive
Sciences and Odontostomatology, University of Naples Federico II, Naples,
Italy
| | - Francesco del Giudice
- Department of Maternal Infant and Urologic
Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, Rome,
Italy
| | - Martina Maggi
- Department of Maternal Infant and Urologic
Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, Rome,
Italy
| | - Giuseppe Lucarelli
- Urology, Andrology and Kidney Transplantation
Unit, Department of Emergency and Organ Transplantation, University of Bari,
Bari, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ
Transplantation, University of Foggia, Foggia, Italy
| | | | - Michele Marchioni
- Department of Medical, Oral and
Biotechnological Sciences, Urology Unit, SS Annunziata Hospital, G.
d’Annunzio University of Chieti, Chieti, Italy
- Department of Urology, ASL Abruzzo 2, Chieti,
Italy
| | - Francesco Cantiello
- Department of Urology, Magna Graecia
University of Catanzaro, Catanzaro, Italy
| | - Fabio Crocerossa
- Department of Urology, Magna Graecia
University of Catanzaro, Catanzaro, Italy
| | - Stefano Luzzago
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Università degli Studi di Milano, Milan,
Italy
| | - Mattia Piccinelli
- Cancer Prognostics and Health Outcomes Unit,
Division of Urology, University of Montréal Health Center, Montréal, QC,
Canada
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Marco Tozzi
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Università degli Studi di Milano, Milan,
Italy
| | - Luigi Schips
- Department of Medical, Oral and
Biotechnological Sciences, Urology Unit, SS Annunziata Hospital, G.
d’Annunzio University of Chieti, Chieti, Italy
| | | | - Alessandro Veccia
- Urology Unit, Azienda Ospedaliera
Universitaria Integrata Verona, University of Verona, Verona, Italy
| | - Mihai Dorin Vartolomei
- Department of Cell and Molecular Biology,
George Emil Palade University of Medicine, Pharmacy, Science and Technology
of Târgu Mures, Târgu Mures, Romania
- Department of Urology, Medical University of
Vienna, Vienna, Austria
| | - Gennaro Musi
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Ottavio de Cobelli
- Department of Urology, IEO – European
Institute of Oncology, IRCCS – Istituto di Ricovero e Cura a Carattere
Scientifico, Milan, Italy
- Department of Oncology and
Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Emanuele Montanari
- Department of Urology, Foundation IRCCS Ca’
Granda – Ospedale Maggiore Policlinico, Department of Clinical Sciences and
Community Health, University of Milan, Milan, Italy
| | - Octavian Sabin Tătaru
- Institution Organizing University Doctoral
Studies (IOSUD), George Emil Palade University of Medicine, Pharmacy,
Science and Technology of Târgu Mures, Târgu Mures, Romania
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13
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Development of a Multiparametric Renal CT Algorithm for Diagnosis of Clear Cell Renal Cell Carcinoma Among Small (≤ 4 cm) Solid Renal Masses. AJR Am J Roentgenol 2022; 219:814-823. [PMID: 35766532 DOI: 10.2214/ajr.22.27971] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND. The MRI clear cell likelihood score predicts the likelihood that a renal mass is clear cell renal cell carcinoma (ccRCC). A CT-based algorithm has not yet been established. OBJECTIVE. The purpose of our study was to develop and evaluate a CT-based algorithm for diagnosing ccRCC among small (≤ 4 cm) solid renal masses. METHODS. This retrospective study included 148 patients (73 men, 75 women; mean age, 58 ± 12 [SD] years) with 148 small (≤ 4 cm) solid (> 25% enhancing tissue) renal masses that underwent renal mass CT (unenhanced, corticomedullary, and nephrographic phases) before resection between January 2016 and December 2019. Two radiologists independently evaluated CT examinations and recorded calcification, mass attenuation in all phases, mass-to-cortex corticomedullary attenuation ratio, and heterogeneity score (score on a 5-point Likert scale, assessed in corticomedullary phase). Features associated with ccRCC were identified by multivariable logistic regression analysis and then used to create a five-tiered CT score for diagnosing ccRCC. RESULTS. The masses comprised 53% (78/148) ccRCC and 47% (70/148) other histologic diagnoses. The mass-to-cortex corticomedullary attenuation ratio was higher for ccRCC than for other diagnoses (reader 1: 0.84 ± 0.68 vs 0.68 ± 0.65, p = .02; reader 2: 0.75 ± 0.29 vs 0.59 ± 0.25, p = .02). The heterogeneity score was higher for ccRCC than other diagnoses (reader 1: 4.0 ± 1.1 vs 1.5 ± 1.6, p < .001; reader 2: 4.4 ± 0.9 vs 3.3 ± 1.5, p < .001). Other features showed no difference. A five-tiered diagnostic algorithm including the mass-to-cortex corticomedullary attenuation ratio and heterogeneity score had interobserver agreement of 0.71 (weighted κ) and achieved an AUC for diagnosing ccRCC of 0.75 (95% CI, 0.68-0.82) for reader 1 and 0.72 (95% CI, 0.66-0.82) for reader 2. A CT score of 4 or greater achieved sensitivity, specificity, and PPV of 71% (95% CI, 59-80%), 79% (95% CI, 67-87%), and 79% (95% CI, 67-87%) for reader 1 and 42% (95% CI, 31-54%), 81% (95% CI, 70-90%), and 72% (95% CI, 56-84%) for reader 2. A CT score of 2 or less had NPV of 85% (95% CI, 69-95%) for reader 1 and 88% (95% CI, 69-97%) for reader 2. CONCLUSION. A five-tiered renal CT algorithm, including the mass-to-cortex corticomedullary attenuation ratio and heterogeneity score, had substantial interobserver agreement, moderate AUC and PPV, and high NPV for diagnosing ccRCC. CLINICAL IMPACT. The CT algorithm, if validated, may represent a useful clinical tool for diagnosing ccRCC.
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14
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Tian J, Teng F, Xu H, Zhang D, Chi Y, Zhang H. Systematic review and meta-analysis of multiparametric MRI clear cell likelihood scores for classification of small renal masses. Front Oncol 2022; 12:1004502. [DOI: 10.3389/fonc.2022.1004502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 10/11/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeTo systematically assess the multiparametric MRI clear cell likelihood score (ccLS) algorithm for the classification of small renal masses (SRM).MethodsWe conducted an electronic literature search on Web of Science, MEDLINE (Ovid and PubMed), Cochrane Library, EMBASE, and Google Scholar to identify relevant articles from 2017 up to June 30, 2022. We included studies reporting the diagnostic performance of the ccLS for characterization of solid SRM. The bivariate model and hierarchical summary receiver operating characteristic (HSROC) model were used to pool sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR−), and diagnostic odds ratio (DOR). The quality evaluation was performed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool.ResultsA total of 6 studies with 825 renal masses (785 patients) were included in the current meta-analysis. The pooled sensitivity and specificity for cT1a renal masses were 0.80 (95% CI 0.75–0.85) and 0.74 (95% CI 0.65–0.81) at the threshold of ccLS ≥4, the pooled LR+, LR−, and DOR were 3.04 (95% CI 2.34-3.95), 0.27 (95% CI 0.22–0.33), and 11.4 (95% CI 8.2-15.9), respectively. The area under the HSROC curve was 0.84 (95% CI 0.81–0.87). For all cT1 renal masses, the pooled sensitivity and specificity were 0.80 (95% CI 0.74–0.85) and 0.76 (95% CI 0.67–0.83).ConclusionsThe ccLS had moderate to high accuracy for identifying ccRCC from other RCC subtypes and with a moderate inter-reader agreement. However, its diagnostic performance remain needs multi-center, large cohort studies to validate in the future.
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15
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Schieda N, Davenport MS, Silverman SG, Bagga B, Barkmeier D, Blank Z, Curci NE, Doshi A, Downey R, Edney E, Granader E, Gujrathi I, Hibbert RM, Hindman N, Walsh C, Ramsay T, Shinagare AB, Pedrosa I. Multicenter Evaluation of Multiparametric MRI Clear Cell Likelihood Scores in Solid Indeterminate Small Renal Masses. Radiology 2022; 303:590-599. [PMID: 35289659 PMCID: PMC9794383 DOI: 10.1148/radiol.211680] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Background Solid small renal masses (SRMs) (≤4 cm) represent benign and malignant tumors. Among SRMs, clear cell renal cell carcinoma (ccRCC) is frequently aggressive. When compared with invasive percutaneous biopsies, the objective of the proposed clear cell likelihood score (ccLS) is to classify ccRCC noninvasively by using multiparametric MRI, but it lacks external validation. Purpose To evaluate the performance of and interobserver agreement for ccLS to diagnose ccRCC among solid SRMs. Materials and Methods This retrospective multicenter cross-sectional study included patients with consecutive solid (≥25% approximate volume enhancement) SRMs undergoing multiparametric MRI between December 2012 and December 2019 at five academic medical centers with histologic confirmation of diagnosis. Masses with macroscopic fat were excluded. After a 1.5-hour training session, two abdominal radiologists per center independently rendered a ccLS for 50 masses. The diagnostic performance for ccRCC was calculated using random-effects logistic regression modeling. The distribution of ccRCC by ccLS was tabulated. Interobserver agreement for ccLS was evaluated with the Fleiss κ statistic. Results A total of 241 patients (mean age, 60 years ± 13 [SD]; 174 men) with 250 solid SRMs were evaluated. The mean size was 25 mm ± 8 (range, 10-39 mm). Of the 250 SRMs, 119 (48%) were ccRCC. The sensitivity, specificity, and positive predictive value for the diagnosis of ccRCC when ccLS was 4 or higher were 75% (95% CI: 68, 81), 78% (72, 84), and 76% (69, 81), respectively. The negative predictive value of a ccLS of 2 or lower was 88% (95% CI: 81, 93). The percentages of ccRCC according to the ccLS were 6% (range, 0%-18%), 38% (range, 0%-100%), 32% (range, 60%-83%), 72% (range, 40%-88%), and 81% (range, 73%-100%) for ccLSs of 1-5, respectively. The mean interobserver agreement was moderate (κ = 0.58; 95% CI: 0.42, 0.75). Conclusion The clear cell likelihood score applied to multiparametric MRI had moderate interobserver agreement and differentiated clear cell renal cell carcinoma from other solid renal masses, with a negative predictive value of 88%. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Mileto and Potretzke in this issue.
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Affiliation(s)
- Nicola Schieda
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa. Ottawa, Ontario, Canada
| | | | - Stuart G. Silverman
- Department of Radiology, Brigham and Women’s Hospital. Harvard Medical School Boston, MA
| | - Barun Bagga
- Department of Radiology, NYU Langone Medical Center. New York, NY, USA
| | - Daniel Barkmeier
- Department of Radiology, University of Michigan. Ann Arbor, MI, USA
| | - Zane Blank
- Department of Radiology. University of Nebraska Medical Center. Omaha, Nebraska
| | - Nicole E Curci
- Department of Radiology, University of Michigan. Ann Arbor, MI, USA
| | - Ankur Doshi
- Department of Radiology. NYU Langone Medical Center. New York, NY, USA
| | - Ryan Downey
- Department of Radiology. University of Nebraska Medical Center. Omaha, Nebraska
| | - Elizabeth Edney
- Department of Radiology. University of Nebraska Medical Center. Omaha, Nebraska
| | - Elon Granader
- Department of Radiology. University of Nebraska Medical Center. Omaha, Nebraska
| | - Isha Gujrathi
- Department of Radiology, Brigham and Women’s Hospital. Harvard Medical School Boston, MA
| | - Rebecca M. Hibbert
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa. Ottawa, Ontario, Canada
| | - Nicole Hindman
- Department of Radiology. NYU Langone Medical Center, New York, NY, USA
| | - Cynthia Walsh
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa. Ottawa, Ontario, Canada
| | - Tim Ramsay
- Ottawa Hospital Research Institute. Ottawa, Ontario, Canada
| | - Atul B. Shinagare
- Department of Radiology, Brigham and Women’s Hospital. Harvard Medical School Boston, MA
| | - Ivan Pedrosa
- University of Texas Southwestern Medical Center. Dallas, TX
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16
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Bosniak Classification Version 2019: A CT-Based Update for Radiologists. CURRENT RADIOLOGY REPORTS 2022. [DOI: 10.1007/s40134-022-00397-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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17
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Rasmussen R, Sanford T, Parwani AV, Pedrosa I. Artificial Intelligence in Kidney Cancer. Am Soc Clin Oncol Educ Book 2022; 42:1-11. [PMID: 35580292 DOI: 10.1200/edbk_350862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Artificial intelligence is rapidly expanding into nearly all facets of life, particularly within the field of medicine. The diagnosis, characterization, management, and treatment of kidney cancer is ripe with areas for improvement that may be met with the promises of artificial intelligence. Here, we explore the impact of current research work in artificial intelligence for clinicians caring for patients with renal cancer, with a focus on the perspectives of radiologists, pathologists, and urologists. Promising preliminary results indicate that artificial intelligence may assist in the diagnosis and risk stratification of newly discovered renal masses and help guide the clinical treatment of patients with kidney cancer. However, much of the work in this field is still in its early stages, limited in its broader applicability, and hampered by small datasets, the varied appearance and presentation of kidney cancers, and the intrinsic limitations of the rigidly structured tasks artificial intelligence algorithms are trained to complete. Nonetheless, the continued exploration of artificial intelligence holds promise toward improving the clinical care of patients with kidney cancer.
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Affiliation(s)
- Robert Rasmussen
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Thomas Sanford
- Department of Urology, Upstate Medical University, Syracuse, NY
| | - Anil V Parwani
- Department of Pathology, The Ohio State University, Columbus, OH
| | - Ivan Pedrosa
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX.,Department of Urology, The University of Texas Southwestern Medical Center, Dallas, TX.,Advanced Imaging Research Center, The University of Texas Southwestern Medical Center, Dallas, TX
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18
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Growth Kinetics and Progression Rate of Bosniak Classification, Version 2019 III and IV Cystic Renal Masses on Imaging Surveillance. AJR Am J Roentgenol 2022; 219:244-253. [PMID: 35293234 DOI: 10.2214/ajr.22.27400] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Background: Active surveillance is increasingly used as first-line management for localized renal masses. Triggers for intervention primarily reflect growth kinetics, which are poorly investigated for cystic masses defined by Bosniak classification version 2019 (v2019). Objective: To determine growth kinetics and incidence rates of progression of class III and IV cystic renal masses, as defined by Bosniak classification v2019. Methods: This retrospective study included 105 patients (68 men, 37 women; median age, 67 years) with 112 Bosniak v2019 class III or IV cystic renal masses on baseline renal-mass protocol CT or MRI examinations from January 2005 to September 2021. Mass dimensions were measured. Progression was defined as any of: linear growth rate (LGR) ≥5 mm per year (representing clinical guideline threshold for intervention), volume doubling time <1 year, T category increase, or N1 or M1 disease. Class III and IV masses were compared. Time-to-progression was estimated using Kaplan-Meier curve analysis. Results: At baseline, 58 masses were class III and 54 were class IV. Median follow-up was 406 days. Median LGR was for class III masses 0.0 mm per year [interquartile range (IQR) -1.3 to 1.8] and for class IV masses 2.3 mm per year (IQR 0.0¬¬-5.7) (p<.001). LGR exceeded 5 mm per year in 4 (7%) class 3 masses and 15 (28%) class IV masses (p=.005). Two patients, both with class IV masses, developed distant metastases. Incidence rate of progression was for class III masses 11.0 (95% CI 4.5-22.8) and for class IV masses 73.6 (95% CI 47.8-108.7) per 100,000 person-days of follow-up. Median time-to-progression was undefined for class III mases given small number of progression events and 710 days for class IV masses. Hazard ratio of progression for class IV relative to class III masses was 5.1 (95% CI 2.5-10.8) (p<.001). Conclusion: During active surveillance of cystic masses evaluated using Bosniak classification v2019, class IV masses grew faster and were more likely to progress than class III masses. Clinical Impact: In comparison with current active surveillance guidelines that treat class III and IV masses similarly, future iterations may incorporate relatively more intensive surveillance for class IV masses.
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