Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Abraham Ari Hakimi
 -- 2517 2023-03-29 03:33:59 |
2 update references and layout
Rita Xu
Meta information modification 2517 2023-03-29 03:48:50 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Calderon, L.P.; Eismann, L.; Reese, S.W.; Reznik, E.; Hakimi, A.A. Imaging-Based Biomarkers in Renal Cell Carcinoma. Encyclopedia. Available online: https://encyclopedia.pub/entry/42597 (accessed on 27 July 2024).
Calderon LP, Eismann L, Reese SW, Reznik E, Hakimi AA. Imaging-Based Biomarkers in Renal Cell Carcinoma. Encyclopedia. Available at: https://encyclopedia.pub/entry/42597. Accessed July 27, 2024.
Calderon, Lina Posada, Lennert Eismann, Stephen W. Reese, Ed Reznik, Abraham Ari Hakimi. "Imaging-Based Biomarkers in Renal Cell Carcinoma" Encyclopedia, https://encyclopedia.pub/entry/42597 (accessed July 27, 2024).
Calderon, L.P., Eismann, L., Reese, S.W., Reznik, E., & Hakimi, A.A. (2023, March 29). Imaging-Based Biomarkers in Renal Cell Carcinoma. In Encyclopedia. https://encyclopedia.pub/entry/42597
Calderon, Lina Posada, et al. "Imaging-Based Biomarkers in Renal Cell Carcinoma." Encyclopedia. Web. 29 March, 2023.
Imaging-Based Biomarkers in Renal Cell Carcinoma
Edit
This entry is adapted from the peer-reviewed paper 10.3390/cancers15020354

Cross-sectional imaging is the standard diagnostic tool to determine underlying biology in renal masses, which is crucial for subsequent treatment. Standard CT imaging is limited in its ability to differentiate benign from malignant disease. Therefore, various modalities have been investigated to identify imaging-based parameters to improve the noninvasive diagnosis of renal masses and renal cell carcinoma (RCC) subtypes. MRI was reported to predict grading of RCC and to identify RCC subtypes, and has been shown in a small cohort to predict the response to targeted therapy. Dynamic imaging is promising for the staging and diagnosis of RCC. PET/CT radiotracers, such as 18F-fluorodeoxyglucose (FDG), 124I-cG250, radiolabeled prostate-specific membrane antigen (PSMA), and 11C-acetate, have been reported to improve the identification of histology, grading, detection of metastasis, and assessment of response to systemic therapy, and to predict oncological outcomes.

imaging renal cell carcinoma biomarker

1. Introduction

Renal cell carcinoma (RCC) has an incidence of 12 per 100,000 in North America, and a peak incidence at the age of 60–70 years [1]. RCC incidence continues to rise, with an estimated 79,000 new cases and 13,920 deaths from RCC in 2022 in the United States alone [2]. The most common histologic subtype of renal cell carcinoma is clear-cell renal cell carcinoma (ccRCC), with five-year survival rates declining by stage of the disease. In the industrial world, the incidence of localized RCC continues to rise, with almost 70% of tumors being detected incidentally [3][4], secondary to increased utilization of abdominal imaging.
Current challenges in the treatment of renal cell carcinoma include diagnostic uncertainty, which leads to both under- and overtreatment of the disease. Conventional cross-sectional imaging techniques do not allow for the discrimination of malignant tumor sub-types, nor can they differentiate between benign lesions. Additionally, current imaging gives researchers opaque insight into patients with metastatic disease and its response to and progression with therapeutics. In recent years, research has focused on improved imaging techniques to enhance diagnostic precision and prognosis in patients with renal tumors. Advancements in the field have been multi-factorial, from enhancements in current ultrasound and cross-sectional imaging technologies, nuclear medicine studies, and the field of radiomics, which infers renal mass insights from radiologic data.

2. Magnetic Resonance Imaging (MRI)

Multiparametric MRI (mpMRI) allows for the evaluation of anatomic as well as functional characteristics of renal masses [5]. Specifically, diffusion MRI and perfusion MRI have been studied as imaging tools to aid in differentiating tumor histology or subtype, and assessing the response to treatment [6]. MRI has been proposed as an alternative to computed tomography (CT), which is limited in its ability to identify benign lesions such as fat-poor angiomyolipomas (AMLs) and oncocytomas [7][8]. Diffusion-weighted imaging (DWI) quantifies the mobility of protons that are associated with water (Brownian motion). Tissue that is highly cellular, such as tumors, restricts water molecules’ movement, and thus appears as a high-intensity signal on DWI, and has a low apparent diffusion coefficient (ADC) [9]. A systematic review and meta-analysis including four studies that used DWI to differentiate between malignant and non-malignant lesions showed DWI to have 86% sensitivity and 78% specificity. In this meta-analysis, DWI used to differentiate high-grade and low-grade RCCs had an area under the curve (AUC) of 83%, reflecting moderately accurate test performance. However, there were no standardized criteria to compare radiological findings to different imaging modalities or pathological specimens [10]. These values are comparable to those associated with CT scans, where the sensitivity has been reported to be 88% and the specificity 75% [11]. Using diffusion MRI, parenchymal wash index, and ADC ratio were correlated with clear-cell RCC Fuhrman grade, with a pooled sensitivity and specificity of DWI to differentiate between high and low grades of 78% and 86%, respectively [12][13].
Perfusion MRI, which assesses tissue perfusion at the micropapillary level, offers the possibility of improving performance characteristics. There are three main types of perfusion MRI: dynamic contrast-enhanced (DCE), dynamic susceptibility contrast (DSC), and arterial spin labeling (ASL) [6][14]. DCE and DSC calculate changes in signal intensity before and after intravenous gadolinium contrast injection, which measures perfusion parameters. ASL does not require intravenous contrast, and measures perfusion by detecting water protons in the blood [14]. Using ASL perfusion MRI, Lanzman et al. compared pre-operative MRI perfusions of 42 patients with various types of renal masses. The RCC histology was associated with different mean perfusion levels, with papillary RCC having lower perfusion levels than all other RCC types, and oncocytomas having significantly higher perfusion levels than RCCs [15].
Differentiating fat-poor AML and RCC based solely on imaging is a known challenge. In a systematic review and meta-analysis by Wilson et al., MRI was found to be 83% sensitive and 90% specific for the detection of fat-poor AMLs, with an AUC of 0.93 [16]. In a retrospective study of 109 renal masses, Kay et al. developed an MRI diagnostic algorithm comprising 11 MR imaging features to determine the most likely histology of a renal mass. They found a sensitivity and specificity of 85% and 76%, respectively, for predicting clear cell histology, and 80% and 94%, respectively, for predicting papillary histology. Their algorithm, however, was weak in predicting chromophobe, oncocytoma, and fat-poor AML histologies [17]. Using this algorithm, Canvasser and colleagues developed a clear cell likelihood scale of 1 (less likely) to 5 (most likely), and found a sensitivity of 78% and a specificity of 90% for scores of 4 and 5 [18]. The clear cell likelihood scale was evaluated in a larger retrospective cohort of 454 renal masses, and the authors found a 93% positive predictive value for a score of 5, and a sensitivity and specificity of predicting clear-cell RCC of 91% and 56%, respectively, for scores of 4 and 5 [19]. Although these scales do not provide insight into tumor aggressiveness, they may be used to help select treatment for small renal masses, and to determine candidates for surveillance [5].
There is a growing interest in using mpMRI, not only to predict renal mass histology and behavior, but also to assess response to therapy. In a prospective mixed cohort of treatment-naïve and exposed patients, Tsai et al. evaluated changes in tumor ASL MRI perfusion as a measure of response to sunitinib and pazopanib treatment for metastatic RCC. Perfusion on MRI imaging, as evaluated by objective response rate, was compared among 6 responders and 11 non-responders at multiple time points during treatment and up to disease progression. Responders had a higher baseline tumor perfusion than non-responders (404 mL/100 g/min vs. 199 mL/100 g/min; p = 0.02), suggesting this could aid in identifying responders to therapy with tyrosine kinase inhibitors [20]. In a prospective, randomized, double-blinded trial that compared sorafenib and placebo, DCE MRI was also evaluated as a pharmacodynamic biomarker of response to sorafenib in metastatic RCC. Of the 44 patients with two available MRIs for comparison, two DCE parameters (area under the contrast concentration versus time curve 90 s after contrast injection [IAUC90], and volume transfer constant of contrast agent [Ktrans]) were evaluated. Although patients with high baseline Ktrans had better progression-free survival (PFS) compared to patients with low baseline Ktrans (log-rank p = 0.027), there was no significant association between change in IAUC90 and Ktrans with PFS [21].
In summary, while multiple studies have evaluated the use of MRI to predict the histology and grade of renal masses, and to assess response to treatment in metastatic RCC, they were generally small studies that used a variety of non-standardized mpMRI metrics [14]. Future studies are needed to validate the use of these metrics and demonstrate their usefulness in clinical scenarios.

3. Contrast-Enhanced Ultrasound

Ultrasound is a widely used diagnostic tool, and in many settings is the first modality used to evaluate renal pathologies. Focal lesions, hydronephrosis, and vascular pathologies can be identified, whereas benign lesions cannot be reliably distinguished from malignancies by conventional ultrasound [22]. Therefore, contrast-enhanced ultrasound (CEUS) has been proposed to visualize RCC characteristics. The contrast agent used for ultrasound is based on microbubbles, and amplifies the signal of microvascular structures [23]. CEUS has been shown to highly differentiate RCC from oncocytoma and angiomyolipoma [24][25]. In one study, combining CEUS parameters showed a 93% sensitivity and 100% specificity for renal malignancies [26]. Furthermore, a study of 85 patients with 93 renal masses showed that peak intensity and time to peak intensity in CEUS differed between clear-cell RCC, chromophobe RC and papillary RCC [27]. Additional studies have shown specific enhancement characteristics compared to clear-cell RCC [28]. CEUS has also been used in the diagnostic setting. For example, Lamuraglia et al., in 2006, showed that CEUS holds predictive value in metastatic RCC patients treated with the multi-kinase inhibitor sorafenib [29]. Similarly, Williams et al. reported significant changes in CEUS along with anti-angiogenic therapy of metastatic RCC, although CEUS parameters did not correlate with progression-free survival or best response rate to therapy [30]. Current research focuses on the assessment of CEUS to predict the response to immunotherapy in metastatic RCC (NCT05206942).
In summary, CEUS is a diagnostic modality that offers potential advantages in the characterization of renal masses, including enhanced diagnostic performance, characterization of renal mass histologic subtypes, its low cost, its low barrier to access, and the absence of ionizing radiation. Additional studies are needed in larger settings to validate these findings, including understanding performance characteristics in patients with different habitus.

4. Positron Emission Tomography–Computed Tomography (PET/CT)

Molecular or nuclear imaging studies rely on in vivo visualizations of biological processes at a cellular and molecular level, using radiopharmaceutical compounds that bind to a molecule of interest [5]. In RCC specifically, nuclear imaging allows for the identification of not only anatomic locations, but also for molecular pathways and processes that are associated with specific histologic features and tumor behavior. Multiple positron emission tomography (PET) radiotracers have been developed and studied as both prognostic and predictive biomarkers in RCC [6][31].

4.1. 18F-Fluorodeoxy-Glucose (FDG) PET/CT

While 18F-fluorodeoxy-glucose (FDG)-PET is the most common and well-known radiotracer used in other cancers, it has limited applicability in RCC due to its variable activity in primary and metastatic tumors, as well as physiologic uptake in normal renal parenchyma [5]. In a meta-analysis of 14 studies that assessed this modality in advanced RCC, the pooled sensitivity and specificity of FDG-PET/CT were 62% and 88%, respectively, for renal lesions, and 79% and 90%, respectively, for extrarenal lesions [32]. Despite variable uptake at the individual lesion level, the maximum standardized uptake value (SUVmax) of lesions in patients with advanced RCC has been independently associated with overall survival (OS) and PFS [33][34]. FDG-PET/CT activity has been proposed as a surrogate for tumor aggressiveness, as it has also been correlated with higher Fuhrman grade, TNM stage, and sarcomatoid features, and can aid in the prediction of progression and in clinical decision making [35][36][37][38]. Additionally, the detection of metastatic or recurrent sites was evaluated in a recent meta-analysis that included 14 studies [39]. The pooled sensitivity was described with 0.86, and specificity with 0.88 [39]. Accordingly, FDG-PET may be a useful re-staging tool for RCC, but current evidence is mostly based on retrospective studies, and lacks prospective investigations [39]. Hou et al. focused on the clinical value of FDG-PET in papillary RCC, and reported a similar sensitivity of 81% in the primary lesion and 100% in recurrent lesions [40]. These preliminary retrospective studies are promising, and need to be confirmed in larger prospective studies. Limitations to the use of FDG-PET/CT include practicality, cost, and variable results across multiple studies [41].

4.2. 124I-cG250 (124I-Girentuximab)

Girentuximab, formerly known as antibody cG250, is one of the most promising nuclear imaging methodologies in the characterization of solid renal masses [5]. It selectively binds to carbonic anhydrase IX (CA-IX), a protein that is overexpressed in VHL-mutated pathways in response to hypoxic conditions, and is expressed in 95 to 100% of clear-cell RCCs [6][42][43]. A multi-center phase III trial, the REDECT trial, evaluated the diagnostic efficacy of 124I-girentuximab PET/CT and contrast-enhanced CT (CECT) in identifying clear-cell RCC in patients with indeterminate renal masses that were scheduled for surgical resection. Imaging was performed 2–6 days after intravenous administration of girentuximab, and prior to surgical resection. Imaging readings were classified as clear-cell RCC and non-clear-cell RCC, which were then compared to final surgical pathologies. In 195 patients that had imaging and pathology available for analysis, the average sensitivity and specificity were 86.2% and 85.9%, respectively, for girentuximab-PET/CT, and 75.5% and 46.8%, respectively, for CECT. Furthermore, the inter-reader agreement was higher for girentuximab PET/CT [44]. Although the limitations of this study included a bias in patient selection, using only pre-surgical candidates, nevertheless, it provides the most accurate validation of pathology with imaging using PET/CT. Different radiotracers targeting CA-IX are currently being studied to improve clinical practice to reduce the long half-life of girentuximab. The molecule F-VM4-037, which is reported to have an 18-minute plasma half-life, has been studied in a phase II trial to allow same-day imaging [45]. Although the performance characteristics of this approach appear to be promising, logistics and timing remain ongoing barriers and, additionally, advancements in the technology will need to be validated in order for it to be used in clinical practice.
There is currently a prospective, open-label, multi-center phase III trial evaluating the performance characteristics of girentuximab (an anti-CAIX monoclonal antibody) labelled with 89Zr, to evaluate indeterminate renal masses to differentiate clear-cell RCC from other renal masses (ZIRCON Trial; NCT03849118). Its preliminary results were reported recently, and exceed the predetermined sensitivity and specificity study targets, with the imaging agent delivering 86% sensitivity and 87% specificity [46]. The phase I study showed in all ten cases a good toxicity profile, and was able to differentiate between clear-cell RCC and non-clear-cell RCC renal mass [47]. This technology is also being examined for diagnostic and therapeutic purposes in the STARLITE 2 Phase II study which evaluates the efficacy of Lu177 conjugated to girentuximab + nivolumab (anti-PD-1) systemic therapy. In a theranostic approach, Girentuximab could be labelled with 177Lu, a beta- emitter, that could induce single-strand DNA breaks into RCC cells. These agents are promising, and future research will focus on their incorporation into clinical practice.

4.3. Prostate-Specific Membrane Antigen (PSMA)–Targeted PET/CT

Prostate-specific membrane antigen (PSMA) is a cell surface protein that is overexpressed in prostate cancer, as well as in the neovasculature of some solid tumors, including RCC. PSMA-targeted imaging was first described in metastatic RCC by Demirci et al. in 2014 [48]. Small studies have reported the sensitivity of F-DCFPyL PSMA PET/CT in detecting distant metastases to range from 88.9% to 94.7%, compared with 66.7% to 78.0% for conventional CT scans [49][50][51]. For localized renal masses, Golan et al. found that the mean SUVmax of 68Ga-PSMA-11 PET/CT was significantly higher in malignant as compared to benign lesions, and its washout coefficient K2 was significantly lower in cancerous tissue [52]. Gao et al. reported that SUVmax of the same tracer could effectively differentiate high vs. low (WHO/SIUP grade I-II vs. III-IV) grade in 36 cases of clear-cell RCC. Furthermore, 68Ga-PSMA-11 PET/CT could predict the presence of adverse histopathological characteristics, such as necrosis and sarcomatoid and rhabdoid features, with an AUC of 0.89 [53]. Both of these studies, however, have small sample sizes, and are not consistent with prior studies that show a high-background signal, limiting the evaluation of primary masses [54][55]. In general, most studies of PSMA-targeted PET/CT have included mostly clear-cell RCC, but the few non-clear-cell RCC lesions evaluated by this approach have shown lower uptake than surrounding renal parenchyma [55]. In particular, a meta-analysis described that PSMA PET/CT may also be suitable for chromophobe RCC, due to its relevant PSMA expression [56]. In contrast, only 13.6% of papillary RCC demonstrate a PSMA expression and therefore, FDG PET is the preferred dynamic imaging modality [56][57]. Based on the inconsistency of PSMA uptake in non-clear-cell RCC, PSMA PET is not appropriate for staging RCC subtypes other than clear-cell and chromophobe RCCs [56][58].

References

  1. Volpe, A.; Patard, J.J. Prognostic factors in renal cell carcinoma. World J. Urol. 2010, 28, 319–327.
  2. Cancer.Net. Cancer.Net: Kidney Cancer: Statistics. 2022. Available online: https://www.cancer.net/cancer-types/kidney-cancer/statistics (accessed on 1 January 2023).
  3. Capitanio, U.; Bensalah, K.; Bex, A.; Boorjian, S.A.; Bray, F.; Coleman, J.; Gore, J.L.; Sun, M.; Wood, C.; Russo, P. Epidemiology of Renal Cell Carcinoma. Eur. Urol. 2019, 75, 74–84.
  4. Partin, A.W.; Dmochowski, R.R.; Kavoussi, L.R.; Peters, C.A. Campbell-Walsh-Wein Urology, 12th ed.; Chapter 57; Elsevier: Amsterdam, The Netherlands, 2020; ISBN 9780323546423.
  5. Roussel, E.; Capitanio, U.; Kutikov, A.; Oosterwijk, E.; Pedrosa, I.; Rowe, S.P.; Gorin, M.A. Novel Imaging Methods for Renal Mass Characterization: A Collaborative Review. Eur. Urol. 2022, 81, 476–488.
  6. Farber, N.J.; Kim, C.J.; Modi, P.K.s.; Hon, J.D.; Sadimin, E.T.; Singer, E.A. Renal cell carcinoma: The search for a reliable biomarker. Transl. Cancer Res. 2017, 6, 620–632.
  7. Johnson, D.C.; Vukina, J.; Smith, A.B.; Meyer, A.M.; Wheeler, S.B.; Kuo, T.M.; Tan, H.J.; Woods, M.E.; Raynor, M.C.; Wallen, E.M.; et al. Preoperatively misclassified, surgically removed benign renal masses: A systematic review of surgical series and United States population level burden estimate. J. Urol. 2015, 193, 30–35.
  8. Sasaguri, K.; Takahashi, N. CT and MR imaging for solid renal mass characterization. Eur. J. Radiol. 2018, 99, 40–54.
  9. Gilet, A.G.; Kang, S.K.; Kim, D.; Chandarana, H. Advanced renal mass imaging: Diffusion and perfusion MRI. Curr. Urol. Rep. 2012, 13, 93–98.
  10. Kang, S.K.; Zhang, A.; Pandharipande, P.V.; Chandarana, H.; Braithwaite, R.S.; Littenberg, B. DWI for Renal Mass Characterization: Systematic Review and Meta-Analysis of Diagnostic Test Performance. Am. J. Roentgenol. 2015, 205, 317–324.
  11. Vogel, C.; Ziegelmüller, B.; Ljungberg, B.; Bensalah, K.; Bex, A.; Canfield, S.; Giles, R.H.; Hora, M.; Kuczyk, M.A.; Merseburger, A.S.; et al. Imaging in Suspected Renal-Cell Carcinoma: Systematic Review. Clin. Genitourin. Cancer 2019, 17, e345–e355.
  12. Cornelis, F.; Tricaud, E.; Lasserre, A.S.; Petitpierre, F.; Bernhard, J.C.; Le Bras, Y.; Yacoub, M.; Bouzgarrou, M.; Ravaud, A.; Grenier, N. Multiparametric magnetic resonance imaging for the differentiation of low and high grade clear cell renal carcinoma. Eur. Radiol. 2015, 25, 24–31.
  13. Woo, S.; Suh, C.H.; Kim, S.Y.; Cho, J.Y.; Kim, S.H. Diagnostic Performance of DWI for Differentiating High- From Low-Grade Clear Cell Renal Cell Carcinoma: A Systematic Review and Meta-Analysis. AJR Am. J. Roentgenol. 2017, 209, W374–W381.
  14. Wu, Y.; Kwon, Y.S.; Labib, M.; Foran, D.J.; Singer, E.A. Magnetic Resonance Imaging as a Biomarker for Renal Cell Carcinoma. Dis. Mark. 2015, 2015, 648495.
  15. Lanzman, R.S.; Robson, P.M.; Sun, M.R.; Patel, A.D.; Mentore, K.; Wagner, A.A.; Genega, E.M.; Rofsky, N.M.; Alsop, D.C.; Pedrosa, I. Arterial spin-labeling MR imaging of renal masses: Correlation with histopathologic findings. Radiology 2012, 265, 799–808.
  16. Wilson, M.P.; Patel, D.; Murad, M.H.; McInnes, M.D.F.; Katlariwala, P.; Low, G. Diagnostic Performance of MRI in the Detection of Renal Lipid-Poor Angiomyolipomas: A Systematic Review and Meta-Analysis. Radiology 2020, 296, 511–520.
  17. Kay, F.U.; Canvasser, N.E.; Xi, Y.; Pinho, D.F.; Costa, D.N.; Diaz de Leon, A.; Khatri, G.; Leyendecker, J.R.; Yokoo, T.; Lay, A.H.; et al. Diagnostic Performance and Interreader Agreement of a Standardized MR Imaging Approach in the Prediction of Small Renal Mass Histology. Radiology 2018, 287, 543–553.
  18. Canvasser, N.E.; Kay, F.U.; Xi, Y.; Pinho, D.F.; Costa, D.; de Leon, A.D.; Khatri, G.; Leyendecker, J.R.; Yokoo, T.; Lay, A.; et al. Diagnostic Accuracy of Multiparametric Magnetic Resonance Imaging to Identify Clear Cell Renal Cell Carcinoma in cT1a Renal Masses. J. Urol. 2017, 198, 780–786.
  19. Steinberg, R.L.; Rasmussen, R.G.; Johnson, B.A.; Ghandour, R.; De Leon, A.D.; Xi, Y.; Yokoo, T.; Kim, S.; Kapur, P.; Cadeddu, J.A.; et al. Prospective performance of clear cell likelihood scores (ccLS) in renal masses evaluated with multiparametric magnetic resonance imaging. Eur. Radiol. 2021, 31, 314–324.
  20. Tsai, L.L.; Bhatt, R.S.; Strob, M.F.; Jegede, O.A.; Sun, M.R.M.; Alsop, D.C.; Catalano, P.; McDermott, D.; Robson, P.M.; Atkins, M.B.; et al. Arterial Spin Labeled Perfusion MRI for the Evaluation of Response to Tyrosine Kinase Inhibition Therapy in Metastatic Renal Cell Carcinoma. Radiology 2021, 298, 332–340.
  21. Hahn, O.M.; Yang, C.; Medved, M.; Karczmar, G.; Kistner, E.; Karrison, T.; Manchen, E.; Mitchell, M.; Ratain, M.J.; Stadler, W.M. Dynamic contrast-enhanced magnetic resonance imaging pharmacodynamic biomarker study of sorafenib in metastatic renal carcinoma. J. Clin. Oncol. 2008, 26, 4572–4578.
  22. Sidhar, K.; McGahan, J.P.; Early, H.M.; Corwin, M.; Fananapazir, G.; Gerscovich, E.O. Renal Cell Carcinomas. J. Ultrasound Med. 2016, 35, 311–320.
  23. Sidhu, P.S.; Cantisani, V.; Dietrich, C.F.; Gilja, O.H.; Saftoiu, A.; Bartels, E.; Bertolotto, M.; Calliada, F.; Clevert, D.A.; Cosgrove, D.; et al. The EFSUMB Guidelines and Recommendations for the Clinical Practice of Contrast-Enhanced Ultrasound (CEUS) in Non-Hepatic Applications: Update 2017 (Long Version). Ultraschall Med. 2018, 39, e2–e44.
  24. Xu, Z.F.; Xu, H.X.; Xie, X.Y.; Liu, G.J.; Zheng, Y.L.; Lu, M.D. Renal cell carcinoma and renal angiomyolipoma: Differential diagnosis with real-time contrast-enhanced ultrasonography. J. Ultrasound Med. 2010, 29, 709–717.
  25. Barr, R.G.; Peterson, C.; Hindi, A. Evaluation of indeterminate renal masses with contrast-enhanced US: A diagnostic performance study. Radiology 2014, 271, 133–142.
  26. Tufano, A.; Drudi, F.M.; Angelini, F.; Polito, E.; Martino, M.; Granata, A.; Di Pierro, G.B.; Kutrolli, E.; Sampalmieri, M.; Canale, V.; et al. Contrast-Enhanced Ultrasound (CEUS) in the Evaluation of Renal Masses with Histopathological Validation-Results from a Prospective Single-Center Study. Diagnostics 2022, 12, 1209.
  27. Sun, D.; Wei, C.; Li, Y.; Lu, Q.; Zhang, W.; Hu, B. Contrast-Enhanced Ultrasonography with Quantitative Analysis allows Differentiation of Renal Tumor Histotypes. Sci. Rep. 2016, 6, 35081.
  28. Wei, S.; Tian, F.; Xia, Q.; Huang, P.; Zhang, Y.; Xia, Z.; Wu, M.; Yang, B. Contrast-enhanced ultrasound findings of adult renal cell carcinoma associated with Xp11.2 translocation/TFE3 gene fusion: Comparison with clear cell renal cell carcinoma and papillary renal cell carcinoma. Cancer Imaging 2019, 20, 1.
  29. Lamuraglia, M.; Escudier, B.; Chami, L.; Schwartz, B.; Leclère, J.; Roche, A.; Lassau, N. To predict progression-free survival and overall survival in metastatic renal cancer treated with sorafenib: Pilot study using dynamic contrast-enhanced Doppler ultrasound. Eur. J. Cancer 2006, 42, 2472–2479.
  30. Williams, R.; Hudson, J.M.; Lloyd, B.A.; Sureshkumar, A.R.; Lueck, G.; Milot, L.; Atri, M.; Bjarnason, G.A.; Burns, P.N. Dynamic Microbubble Contrast-enhanced US to Measure Tumor Response to Targeted Therapy: A Proposed Clinical Protocol with Results from Renal Cell Carcinoma Patients Receiving Antiangiogenic Therapy. Radiology 2011, 260, 581–590.
  31. Krajewski, K.M.; Shinagare, A.B. Novel imaging in renal cell carcinoma. Curr. Opin. Urol. 2016, 26, 388–395.
  32. Wang, H.Y.; Ding, H.J.; Chen, J.H.; Chao, C.H.; Lu, Y.Y.; Lin, W.Y.; Kao, C.H. Meta-analysis of the diagnostic performance of FDG-PET and PET/CT in renal cell carcinoma. Cancer Imaging 2012, 12, 464–474.
  33. Kayani, I.; Avril, N.; Bomanji, J.; Chowdhury, S.; Rockall, A.; Sahdev, A.; Nathan, P.; Wilson, P.; Shamash, J.; Sharpe, K.; et al. Sequential FDG-PET/CT as a biomarker of response to Sunitinib in metastatic clear cell renal cancer. Clin. Cancer Res. 2011, 17, 6021–6028.
  34. Nakaigawa, N.; Kondo, K.; Tateishi, U.; Minamimoto, R.; Kaneta, T.; Namura, K.; Ueno, D.; Kobayashi, K.; Kishida, T.; Ikeda, I.; et al. FDG PET/CT as a prognostic biomarker in the era of molecular-targeting therapies: Max SUVmax predicts survival of patients with advanced renal cell carcinoma. BMC Cancer 2016, 16, 67.
  35. Singh, H.; Arora, G.; Nayak, B.; Sharma, A.; Singh, G.; Kumari, K.; Jana, S.; Patel, C.; Pandey, A.K.; Seth, A.; et al. Semi-quantitative F-18-FDG PET/computed tomography parameters for prediction of grade in patients with renal cell carcinoma and the incremental value of diuretics. Nucl. Med. Commun. 2020, 41, 485–493.
  36. Zhu, H.; Zhao, S.; Zuo, C.; Ren, F. FDG PET/CT and CT Findings of Renal Cell Carcinoma With Sarcomatoid Differentiation. AJR Am. J. Roentgenol. 2020, 215, 645–651.
  37. Zhao, Y.; Wu, C.; Li, W.; Chen, X.; Li, Z.; Liao, X.; Cui, Y.; Zhao, G.; Liu, M.; Fu, Z. 2-FDG PET/CT parameters associated with WHO/ISUP grade in clear cell renal cell carcinoma. Eur. J. Nucl. Med. Mol. Imaging 2021, 48, 570–579.
  38. Nakajima, R.; Abe, K.; Kondo, T.; Tanabe, K.; Sakai, S. Clinical role of early dynamic FDG-PET/CT for the evaluation of renal cell carcinoma. Eur. Radiol. 2016, 26, 1852–1862.
  39. Ma, H.; Shen, G.; Liu, B.; Yang, Y.; Ren, P.; Kuang, A. Diagnostic performance of 18F-FDG PET or PET/CT in restaging renal cell carcinoma: A systematic review and meta-analysis. Nucl. Med. Commun. 2017, 38, 156–163.
  40. Hou, G.; Zhao, D.; Jiang, Y.; Zhu, Z.; Huo, L.; Li, F.; Cheng, W. Clinical utility of FDG PET/CT for primary and recurrent papillary renal cell carcinoma. Cancer Imaging 2021, 21, 25.
  41. Caldarella, C.; Muoio, B.; Isgrò, M.A.; Porfiri, E.; Treglia, G.; Giovanella, L. The role of fluorine-18-fluorodeoxyglucose positron emission tomography in evaluating the response to tyrosine-kinase inhibitors in patients with metastatic primary renal cell carcinoma. Radiol. Oncol. 2014, 48, 219–227.
  42. Weng, S.; DiNatale, R.G.; Silagy, A.; Mano, R.; Attalla, K.; Kashani, M.; Weiss, K.; Benfante, N.E.; Winer, A.G.; Coleman, J.A.; et al. The Clinicopathologic and Molecular Landscape of Clear Cell Papillary Renal Cell Carcinoma: Implications in Diagnosis and Management. Eur. Urol. 2021, 79, 468–477.
  43. Stillebroer, A.B.; Mulders, P.F.; Boerman, O.C.; Oyen, W.J.; Oosterwijk, E. Carbonic anhydrase IX in renal cell carcinoma: Implications for prognosis, diagnosis, and therapy. Eur. Urol. 2010, 58, 75–83.
  44. Divgi, C.R.; Uzzo, R.G.; Gatsonis, C.; Bartz, R.; Treutner, S.; Yu, J.Q.; Chen, D.; Carrasquillo, J.A.; Larson, S.; Bevan, P.; et al. Positron emission tomography/computed tomography identification of clear cell renal cell carcinoma: Results from the REDECT trial. J. Clin. Oncol. 2013, 31, 187–194.
  45. Turkbey, B.; Lindenberg, M.L.; Adler, S.; Kurdziel, K.A.; McKinney, Y.L.; Weaver, J.; Vocke, C.D.; Anver, M.; Bratslavsky, G.; Eclarinal, P.; et al. PET/CT imaging of renal cell carcinoma with (18)F-VM4-037: A phase II pilot study. Abdom. Radiol. 2016, 41, 109–118.
  46. Conroy, R. 89Zr-DFO-Girentuximab PET Agent Meets Specificity and Sensitivity End Points in Clear Cell RCC. Available online: https://www.cancernetwork.com/view/89zr-dfo-girentuximab-pet-agent-meets-specificity-and-sensitivity-end-points-in-clear-cell-rcc (accessed on 2 January 2023).
  47. Merkx, R.I.J.; Lobeek, D.; Konijnenberg, M.; Jiménez-Franco, L.D.; Kluge, A.; Oosterwijk, E.; Mulders, P.F.A.; Rijpkema, M. Phase I study to assess safety, biodistribution and radiation dosimetry for (89)Zr-girentuximab in patients with renal cell carcinoma. Eur. J. Nucl. Med. Mol. Imaging 2021, 48, 3277–3285.
  48. Demirci, E.; Ocak, M.; Kabasakal, L.; Decristoforo, C.; Talat, Z.; Halaç, M.; Kanmaz, B. 68Ga-PSMA PET/CT imaging of metastatic clear cell renal cell carcinoma. Eur. J. Nucl. Med. Mol. Imaging 2014, 41, 1461–1462.
  49. Gorin, M.A.; Rowe, S.P.; Hooper, J.E.; Kates, M.; Hammers, H.J.; Szabo, Z.; Pomper, M.G.; Allaf, M.E. PSMA-Targeted 18F-DCFPyL PET/CT Imaging of Clear Cell Renal Cell Carcinoma: Results from a Rapid Autopsy. Eur. Urol. 2017, 71, 145–146.
  50. Rowe, S.P.; Gorin, M.A.; Hammers, H.J.; Som Javadi, M.; Hawasli, H.; Szabo, Z.; Cho, S.Y.; Pomper, M.G.; Allaf, M.E. Imaging of metastatic clear cell renal cell carcinoma with PSMA-targeted ¹⁸F-DCFPyL PET/CT. Ann. Nucl. Med. 2015, 29, 877–882.
  51. Meyer, A.R.; Carducci, M.A.; Denmeade, S.R.; Markowski, M.C.; Pomper, M.G.; Pierorazio, P.M.; Allaf, M.E.; Rowe, S.P.; Gorin, M.A. Improved identification of patients with oligometastatic clear cell renal cell carcinoma with PSMA-targeted. Ann. Nucl. Med. 2019, 33, 617–623.
  52. Golan, S.; Aviv, T.; Groshar, D.; Yakimov, M.; Zohar, Y.; Prokocimer, Y.; Nadu, A.; Baniel, J.; Domachevsky, L.; Bernstine, H. Dynamic 68Ga-PSMA-11 PET/CT for the Primary Evaluation of Localized Renal Mass: A Prospective Study. J. Nucl. Med. 2021, 62, 773–778.
  53. Gao, J.; Xu, Q.; Fu, Y.; He, K.; Zhang, C.; Zhang, Q.; Shi, J.; Zhao, X.; Wang, F.; Guo, H. Comprehensive evaluation of 68Ga-PSMA-11 PET/CT parameters for discriminating pathological characteristics in primary clear-cell renal cell carcinoma. Eur. J. Nucl. Med. Mol. Imaging 2021, 48, 561–569.
  54. Muselaers, S.; Erdem, S.; Bertolo, R.; Ingels, A.; Kara, Ö.; Pavan, N.; Roussel, E.; Pecoraro, A.; Marchioni, M.; Carbonara, U.; et al. PSMA PET/CT in Renal Cell Carcinoma: An Overview of Current Literature. J. Clin. Med. 2022, 11, 1829.
  55. Sawicki, L.M.; Buchbender, C.; Boos, J.; Giessing, M.; Ermert, J.; Antke, C.; Antoch, G.; Hautzel, H. Diagnostic potential of PET/CT using a 68Ga-labelled prostate-specific membrane antigen ligand in whole-body staging of renal cell carcinoma: Initial experience. Eur. J. Nucl. Med. Mol. Imaging 2017, 44, 102–107.
  56. Urso, L.; Castello, A.; Rocca, G.C.; Lancia, F.; Panareo, S.; Cittanti, C.; Uccelli, L.; Florimonte, L.; Castellani, M.; Ippolito, C.; et al. Role of PSMA-ligands imaging in Renal Cell Carcinoma management: Current status and future perspectives. J. Cancer Res. Clin. Oncol. 2022, 148, 1299–1311.
  57. Toyama, Y.; Werner, R.A.; Ruiz-Bedoya, C.A.; Ordonez, A.A.; Takase, K.; Lapa, C.; Jain, S.K.; Pomper, M.G.; Rowe, S.P.; Higuchi, T. Current and future perspectives on functional molecular imaging in nephro-urology: Theranostics on the horizon. Theranostics 2021, 11, 6105–6119.
  58. Yin, Y.; Campbell, S.P.; Markowski, M.C.; Pierorazio, P.M.; Pomper, M.G.; Allaf, M.E.; Rowe, S.P.; Gorin, M.A. Inconsistent Detection of Sites of Metastatic Non-Clear Cell Renal Cell Carcinoma with PSMA-Targeted DCFPyL PET/CT. Mol. Imaging Biol. 2019, 21, 567–573.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Urology & Nephrology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Lina Posada Calderon
,
Lennert Eismann
,
Stephen W. Reese
,
Ed Reznik
,
Abraham Ari Hakimi
View Times: 286
Revisions: 2 times (View History)
Update Date: 29 Mar 2023
Table of Contents
    1000/1000
    Hot Most Recent
    Video Production Service
    Feedback