1
|
Virarkar MK, Mileto A, Vulasala SSR, Ananthakrishnan L, Bhosale P. Dual-Energy Computed Tomography Applications in the Genitourinary Tract. Radiol Clin North Am 2023; 61:1051-1068. [PMID: 37758356 DOI: 10.1016/j.rcl.2023.05.007] [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] [Indexed: 10/03/2023]
Abstract
By virtue of material differentiation capabilities afforded through dedicated postprocessing algorithms, dual-energy CT (DECT) has been shown to provide benefit in the evaluation of various diseases. In this article, we review the diagnostic use of DECT in the assessment of genitourinary diseases, with emphasis on its role in renal stone characterization, incidental renal and adrenal lesion characterization, retroperitoneal trauma, reduction of radiation, and contrast dose and cost-effectiveness potential. We also discuss future perspectives of the DECT scanning mode, including the use of novel contrast injection strategies and photon-counting detector computed tomography.
Collapse
Affiliation(s)
- Mayur K Virarkar
- Department of Radiology, University of Florida College of Medicine, Clinical Center, C90, 2nd Floor, 655 West 8th Street, Jacksonville, FL 32209, USA
| | - Achille Mileto
- Department of Radiology, Mayo Clinic, Mayo Building West, 2nd Floor, 200 First Street SW, Rochester, MN, 55905, USA
| | - Sai Swarupa R Vulasala
- Department of radiology, University of Florida College of Medicine, Clinical Center, C90, 2nd Floor, 655 West 8th Street, Jacksonville, FL, 32209, USA.
| | - Lakshmi Ananthakrishnan
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, USA
| | - Priya Bhosale
- Department of Diagnostic Radiology, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 1479, Houston, TX 77030, USA
| |
Collapse
|
2
|
Schawkat K, Krajewski KM. Insights into Renal Cell Carcinoma with Novel Imaging Approaches. Hematol Oncol Clin North Am 2023; 37:863-875. [PMID: 37302934 DOI: 10.1016/j.hoc.2023.05.002] [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] [Indexed: 06/13/2023]
Abstract
This article presents a comprehensive overview of new imaging approaches and techniques for improving the assessment of renal masses and renal cell carcinoma. The Bosniak classification, version 2019, as well as the clear cell likelihood score, version 2.0, will be discussed as new imaging algorithms using established techniques. Additionally, newer modalities, such as contrast-enhanced ultrasound, dual energy computed tomography, and molecular imaging, will be discussed in conjunction with emerging radiomics and artificial intelligence techniques. Current diagnostic algorithms combined with newer approaches may be an effective way to overcome existing limitations in renal mass and RCC characterization.
Collapse
Affiliation(s)
- Khoschy Schawkat
- Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA; Harvard Medical School
| | - Katherine M Krajewski
- Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA; Harvard Medical School; Dana-Farber Cancer Institute, 440 Brookline Avenue, Building MA Floor L1 Room 04AC, Boston, MA 02215, USA.
| |
Collapse
|
3
|
French AFU Cancer Committee Guidelines - Update 2022-2024: management of kidney cancer. Prog Urol 2022; 32:1195-1274. [DOI: 10.1016/j.purol.2022.07.146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 07/14/2022] [Accepted: 07/18/2022] [Indexed: 11/17/2022]
|
4
|
Chen X, Yang B, Li J, Zhu J, Ma X, Chen D, Hu Z, Men K, Dai J. A deep-learning method for generating synthetic kV-CT and improving tumor segmentation for helical tomotherapy of nasopharyngeal carcinoma. Phys Med Biol 2021; 66. [PMID: 34700300 DOI: 10.1088/1361-6560/ac3345] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 10/26/2021] [Indexed: 12/11/2022]
Abstract
Objective:Megavoltage computed tomography (MV-CT) is used for setup verification and adaptive radiotherapy in tomotherapy. However, its low contrast and high noise lead to poor image quality. This study aimed to develop a deep-learning-based method to generate synthetic kilovoltage CT (skV-CT) and then evaluate its ability to improve image quality and tumor segmentation.Approach:The planning kV-CT and MV-CT images of 270 patients with nasopharyngeal carcinoma (NPC) treated on an Accuray TomoHD system were used. An improved cycle-consistent adversarial network which used residual blocks as its generator was adopted to learn the mapping between MV-CT and kV-CT and then generate skV-CT from MV-CT. A Catphan 700 phantom and 30 patients with NPC were used to evaluate image quality. The quantitative indices included contrast-to-noise ratio (CNR), uniformity and signal-to-noise ratio (SNR) for the phantom and the structural similarity index measure (SSIM), mean absolute error (MAE), and peak signal-to-noise ratio (PSNR) for patients. Next, we trained three models for segmentation of the clinical target volume (CTV): MV-CT, skV-CT, and MV-CT combined with skV-CT. The segmentation accuracy was compared with indices of the dice similarity coefficient (DSC) and mean distance agreement (MDA).Mainresults:Compared with MV-CT, skV-CT showed significant improvement in CNR (184.0%), image uniformity (34.7%), and SNR (199.0%) in the phantom study and improved SSIM (1.7%), MAE (24.7%), and PSNR (7.5%) in the patient study. For CTV segmentation with only MV-CT, only skV-CT, and MV-CT combined with skV-CT, the DSCs were 0.75 ± 0.04, 0.78 ± 0.04, and 0.79 ± 0.03, respectively, and the MDAs (in mm) were 3.69 ± 0.81, 3.14 ± 0.80, and 2.90 ± 0.62, respectively.Significance:The proposed method improved the image quality of MV-CT and thus tumor segmentation in helical tomotherapy. The method potentially can benefit adaptive radiotherapy.
Collapse
Affiliation(s)
- Xinyuan Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Bining Yang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Jingwen Li
- Cloud Computing and Big Data Research Institute, China Academy of Information and Communications Technology, People's Republic of China
| | - Ji Zhu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Xiangyu Ma
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Deqi Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Zhihui Hu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Kuo Men
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| |
Collapse
|
5
|
Çamlıdağ İ. Compatibility of true and virtual unenhanced attenuation in rapid kV-switching dual energy CT. ACTA ACUST UNITED AC 2020; 26:95-100. [PMID: 32116219 DOI: 10.5152/dir.2019.19345] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
PURPOSE We aimed to evaluate whether virtual unenhanced images (VUI) generated from nephrographic phase on rapid kV-switching dual energy CT (rsDECT) can replace true unenhanced images (TUI) by comparing attenuation values of various intraabdominal structures. METHODS In this retrospective study, 142 patients had unenhanced and nephrographic phase dual energy CT images. Attenuation values of the aorta, liver,spleen, pancreas, bilateral renal parenchyma, inferior vena cava, gallbladder and paraspinal muscle on TUI and VUI were recorded. Frequencies of organs who had more than 10 and 20 HU of attenuation difference were also calculated. RESULTS A total of 1224 ROIs were sampled. No statistically significant differences were found between TUA and VUA of the aorta, spleen and pancreas. The other structures had significant differences (P < 0.001). Correlation between measurements were weak to moderate (r=0.17-0.72). 20% of organs had more than 10 HU difference and 5% had more than 20 HU difference between TUI and VUI. CONCLUSION rsDECT based VUI does not seem to be an ideal surrogate for TUI.
Collapse
Affiliation(s)
- İlkay Çamlıdağ
- Department of Radiology, Ondokuz Mayıs University, Samsun, Turkey
| |
Collapse
|
6
|
Thiravit S, Brunnquell C, Cai LM, Flemon M, Mileto A. Use of dual-energy CT for renal mass assessment. Eur Radiol 2020; 31:3721-3733. [PMID: 33210200 DOI: 10.1007/s00330-020-07426-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 08/11/2020] [Accepted: 10/14/2020] [Indexed: 12/22/2022]
Abstract
Although dual-energy CT (DECT) may prove useful in a variety of abdominal imaging tasks, renal mass evaluation represents the area where this technology can be most impactful in abdominal imaging compared to routinely performed contrast-enhanced-only single-energy CT exams. DECT post-processing techniques, such as creation of virtual unenhanced and iodine density images, can help in the characterization of incidentally discovered renal masses that would otherwise remain indeterminate based on post-contrast imaging only. The purpose of this article is to review the use of DECT for renal mass assessment, including its benefits and existing limitations. KEY POINTS: • If DECT is selected as the scanning mode for most common abdominal protocols, many incidentally found renal masses can be fully triaged within the same exam. • Virtual unenhanced and iodine density DECT images can provide additional information when renal masses are discovered in the post-contrast-only setting. • For renal mass evaluation, virtual unenhanced and iodine density DECT images should be interpreted side-by-side to troubleshoot pitfalls that can potentially lead to erroneous interpretation.
Collapse
Affiliation(s)
- Shanigarn Thiravit
- Department of Radiology, University of Washington School of Medicine, 1959 NE Pacific Street, Box 357115, Seattle, WA, 98195, USA.,Division of Diagnostic Radiology, Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Christina Brunnquell
- Department of Radiology, University of Washington School of Medicine, 1959 NE Pacific Street, Box 357115, Seattle, WA, 98195, USA
| | - Larry M Cai
- Department of Radiology, University of Washington School of Medicine, 1959 NE Pacific Street, Box 357115, Seattle, WA, 98195, USA
| | - Mena Flemon
- Department of Radiology, University of Washington School of Medicine, 1959 NE Pacific Street, Box 357115, Seattle, WA, 98195, USA
| | - Achille Mileto
- Department of Radiology, University of Washington School of Medicine, 1959 NE Pacific Street, Box 357115, Seattle, WA, 98195, USA.
| |
Collapse
|
7
|
Bensalah K, Bigot P, Albiges L, Bernhard J, Bodin T, Boissier R, Correas J, Gimel P, Hetet J, Long J, Nouhaud F, Ouzaïd I, Rioux-Leclercq N, Méjean A. Recommandations françaises du Comité de cancérologie de l’AFU – actualisation 2020–2022 : prise en charge du cancer du rein. Prog Urol 2020; 30:S2-S51. [DOI: 10.1016/s1166-7087(20)30749-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
|
8
|
Çamlıdağ İ, Nural MS, Kalkan C, Danacı M. Discrimination of papillary renal cell carcinoma from benign proteinaceous cyst based on iodine and water content on rapid kV-switching dual-energy CT. ACTA ACUST UNITED AC 2020; 26:390-395. [PMID: 32755880 DOI: 10.5152/dir.2020.19483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
PURPOSE We aimed to evaluate whether rapid kV-switching dual energy CT (rsDECT) can discriminate between papillary renal cell carcinoma (RCC) and benign proteinaceous cysts (BPCs) based on iodine and water content. METHODS Twenty-four patients with histopathologically proven papillary RCC and 38 patients with 41 BPCs were retrospectively included. Patients with BPCs were eligible for inclusion when the cysts were stable in size and appearance for at least 2 years or proved to be a cyst on ultrasound or MRI. All patients underwent delayed phase (70-90 s) rsDECT. Iodine and water content of each lesion was measured on the workstation. RESULTS Of papillary RCC patients, 4 (16%) were female and 20 (84%) were male. Mean tumor size was 39±20 mm. Mean iodine and water content was 2.08±0.7 mg/mL and 1021±14 mg/mL, respectively. Of BPC patients, 9 were female and 29 were male. Mean cyst size was 20±7 mm. Mean iodine and water content was 0.82±0.4 mg/mL and 1012±14 mg/mL, respectively. There were significant differences between iodine and water contents of papillary RCCs and BPCs (P < 0.001). The best cutoff of iodine content for differentiating papillary RCC from BPC was 1.21 mg/mL (area under the curve [AUC]=0.97, P < 0.001, sensitivity 96%, specificity 88%, positive predictive value [PPV] 82%, negative predictive value [NPV] 97%, accuracy 91%,); the best cutoff of water content was 1015.5 mg/mL (AUC=0.68, P = 0.016, sensitivity 83%, specificity 56%, PPV 52%, NPV 85%, accuracy 66%). CONCLUSION An iodine content threshold of 1.21 mg/mL accurately differentiates papillary RCC from BPCs on a single postcontrast rsDECT. Despite having a high sensitivity, water content has inferior diagnostic accuracy.
Collapse
Affiliation(s)
- İlkay Çamlıdağ
- Department of Radiology, Ondokuz Mayıs University School of Medicine, Samsun, Turkey
| | - Mehmet Selim Nural
- Department of Radiology, Ondokuz Mayıs University School of Medicine, Samsun, Turkey
| | - Cihan Kalkan
- Department of Radiology, Ondokuz Mayıs University School of Medicine, Samsun, Turkey
| | - Murat Danacı
- Department of Urology, Ondokuz Mayıs University School of Medicine, Samsun, Turkey
| |
Collapse
|