Magnetic Resonance Imaging as a Biomarker for Renal Cell Carcinoma

Wu, Yan; Kwon, Young Suk; Labib, Mina; Foran, David J.; Singer, Eric A.

doi:10.1155/2015/648495

Cited by 33 publications

(24 citation statements)

References 52 publications

(64 reference statements)

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“…Hence there is a need for preoperative prediction of tumor subtype and grade using non-invasive tools like imaging. Diffusion-weighted imaging (DWI) and perfusion MRI were the first modalities to be used for this purpose (10,11).…”

Section: Introductionmentioning

confidence: 99%

Role of MR texture analysis in histological subtyping and grading of renal cell carcinoma: a preliminary study

et al. 2019

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The study evaluated the usefulness of magnetic resonance imaging (MRI) texture parameters in differentiating clear cell renal carcinoma (CC-RCC) from non-clear cell carcinoma (NC-RCC) and in the histological grading of CC-RCC. Material and Methods: After institutional ethical approval, this retrospective study analyzed 33 patients with 34 RCC masses (29 CC-RCC and five NC-RCC; 19 low grade and 10 high grade CC-RCC), who underwent MRI between January 2011 and December 2012 on a 1.5-T scanner (Avanto, Siemens, Erlangen, Germany). The MRI protocol included T2-weighted imaging (T2WI), diffusion-weighted imaging [DWI; at b 0, 500 and 1000 s/mm 2 with apparent diffusion coefficient (ADC) maps] and T1-weighted pre and postcontrast [corticomedullary (CM) and nephrographic (NG) phase] acquisition. MR texture analysis (MRTA) was performed using the TexRAD research software (Feedback Medical Ltd., Cambridge, UK) by a single reader who placed free-hand polygonal region of interest (ROI) on the slice showing the maximum viable tumor. Filtration histogrambased texture analysis was used to generate six first-order statistical parameters [mean intensity, standard deviation (SD), mean of positive pixels (MPP), entropy, skewness and kurtosis] at five spatial scaling factors (SSF) as well as on the unfiltered image. Mann-Whitney test was used to compare the texture parameters of CC-RCC vs. NC-RCC, and high grade vs. low grade CC-RCC. P-value < 0.05 was considered significant. A 3-step feature selection was used to obtain the best texture metrics for each MRI sequence and included the receiver operating characteristic (ROC) curve analysis and Pearson's correlation test. Results: The best performing texture parameters in differentiating CC-RCC from NC-RCC for each sequence included (area under the curve in parentheses): entropy at SSF 4 (0.807) on T2WI, SD at SSF 4 (0.814) on DWI b500, SD at SSF 6 (0.879) on DWI b1000, mean at SSF 0 (0.848) on ADC, skewness at SSF 2 (0.854) on T1WI and skewness at SSF 3 (0.908) on CM phase. In differentiating high from low grade CC-RCC, the best parameters were: entropy at SSF 6 (0.823) on DWI b1000, mean at SSF 3 (0.889) on CM phase and MPP at SSF 5 (0.870) on NG phase. Conclusion: Several MR texture parameters showed excellent diagnostic performance (AUC> 0.8) in differentiating CC-RCC from NC-RCC, and high grade from low grade CC-RCC. MRTA could serve as a useful non-invasive tool for this purpose.

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Section: Introductionmentioning

confidence: 99%

Role of MR texture analysis in histological subtyping and grading of renal cell carcinoma: a preliminary study

et al. 2019

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“…Multi-parametric MRI offers important advantages over CT for assessing the biological effect of radiotherapy, most notably by assessing tissue perfusion using dynamic contrast enhanced MRI [ 10 – 12 ] and for characterising diffusion using diffusion weighted imaging (DWI) [ 13 ]. Studies into DWI and DCE MRI for RCC have been increasing in recent years as described in a number of review articles [ 14 – 16 ], particularly for their potential as treatment response biomarkers to targeted therapies indicating the presence of radiation-induced oedema, changes in cellularity, changes in vascularity and necrosis. Currently, however, no MRI-derived biomarkers are used in routine clinical practice for primary or metastatic RCC and no studies have been performed to investigate the use of mpMRI for assessing response to SABR treatment of RCC.…”

Section: Introductionmentioning

confidence: 99%

Diffusion weighted and dynamic contrast enhanced MRI as an imaging biomarker for stereotactic ablative body radiotherapy (SABR) of primary renal cell carcinoma

et al. 2018

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PurposeTo explore the utility of diffusion and perfusion changes in primary renal cell carcinoma (RCC) after stereotactic ablative body radiotherapy (SABR) as an early biomarker of treatment response, using diffusion weighted (DWI) and dynamic contrast enhanced (DCE) MRI.MethodsPatients enrolled in a prospective pilot clinical trial received SABR for primary RCC, and had DWI and DCE MRI scheduled at baseline, 14 days and 70 days after SABR. Tumours <5cm diameter received a single fraction of 26 Gy and larger tumours received three fractions of 14 Gy. Apparent diffusion coefficient (ADC) maps were computed from DWI data and parametric and pharmacokinetic maps were fitted to the DCE data. Tumour volumes were contoured and statistics extracted. Spearman’s rank correlation coefficients were computed between MRI parameter changes versus the percentage tumour volume change from CT at 6, 12 and 24 months and the last follow-up relative to baseline CT.ResultsTwelve patients were eligible for DWI analysis, and a subset of ten patients for DCE MRI analysis. DCE MRI from the second follow-up MRI scan showed correlations between the change in percentage voxels with washout contrast enhancement behaviour and the change in tumour volume (ρ = 0.84, p = 0.004 at 12 month CT, ρ = 0.81, p = 0.02 at 24 month CT, and ρ = 0.89, p = 0.001 at last follow-up CT). The change in mean initial rate of enhancement and mean Ktrans at the second follow-up MRI scan were positively correlated with percent tumour volume change at the 12 month CT onwards (ρ = 0.65, p = 0.05 and ρ = 0.66, p = 0.04 at 12 month CT respectively). Changes in ADC kurtosis from histogram analysis at the first follow-up MRI scan also showed positive correlations with the percentage tumour volume change (ρ = 0.66, p = 0.02 at 12 month CT, ρ = 0.69, p = 0.02 at last follow-up CT), but these results are possibly confounded by inflammation.ConclusionDWI and DCE MRI parameters show potential as early response biomarkers after SABR for primary RCC. Further prospective validation using larger patient cohorts is warranted.

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“…Perfusion MRI (pMRI) and diffusion MRI are the two primary techniques with which these aims have been studied (18). …”

Section: Imaging Biomarkersmentioning

confidence: 99%

“…The largest series was reported by de Bazelaire et al , who imaged patients with ASL at 1-and 4-month after tyrosine kinase inhibitor therapy and found that early tumor blood flow changes predicted clinical outcome (20). While the application of pMRI is promising in the management of RCC patients, the technical aspects and expertise required to consistently obtain high quality images remain obstacles to the routine implementation of pMRI in clinical practice (Figure 1) (18). …”

Section: Imaging Biomarkersmentioning

confidence: 99%

Renal cell carcinoma: the search for a reliable biomarker

et al. 2017

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One particular challenge in the treatment of kidney tumors is the range of histologies and tumor phenotypes a renal mass can represent. A kidney tumor can range from benign (e.g., oncocytoma) to a clinically indolent malignancy (e.g., papillary type I, chromophobe) to aggressive disease [e.g., papillary type II or high-grade clear cell renal cell carcinoma (ccRCC)]. Even among various subtypes, kidney cancers are genetically diverse with variable prognoses and treatment response rates. Therefore, the key to proper treatment is the differentiation of these subtypes. Currently, a wide array of diagnostic, prognostic, and predictive biomarkers exist that may help guide the individualized care of kidney cancer patients. This review will discuss the various serum, urine, imaging, and immunohistological biomarkers available in practice.

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Magnetic Resonance Imaging as a Biomarker for Renal Cell Carcinoma

Cited by 33 publications

References 52 publications

Role of MR texture analysis in histological subtyping and grading of renal cell carcinoma: a preliminary study

Role of MR texture analysis in histological subtyping and grading of renal cell carcinoma: a preliminary study

Diffusion weighted and dynamic contrast enhanced MRI as an imaging biomarker for stereotactic ablative body radiotherapy (SABR) of primary renal cell carcinoma

Renal cell carcinoma: the search for a reliable biomarker

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