TY  - JOUR
JF  - Abdominal Radiology
A1  - Goyal, A
A1  - Razik, A
A1  - Kandasamy, D
A1  - Seth, A
A1  - Das, P
A1  - Ganeshan, B
A1  - Sharma, R
KW  - Fuhrman grade
KW  -  Magnetic resonance imaging
KW  -  Radiomics
KW  -  Renal cell carcinoma
KW  -  Texture analysis
N2  - Purpose: The study evaluated the usefulness of magnetic resonance imaging (MRI) texture parameters in differentiating clear cell renal carcinoma (CC-RCC) from non-clear cell carcinoma (NC-RCC) and in the histological grading of CC-RCC. /

Materials and methods: After institutional ethical approval, this retrospective study analyzed 33 patients with 34 RCC masses (29 CC-RCC and five NC-RCC; 19 low-grade and 10 high-grade CC-RCC), who underwent MRI between January 2011 and December 2012 on a 1.5-T scanner (Avanto, Siemens, Erlangen, Germany). The MRI protocol included T2-weighted imaging (T2WI), diffusion-weighted imaging [DWI; at b 0, 500 and 1000 s/mm2 with apparent diffusion coefficient (ADC) maps] and T1-weighted pre and postcontrast [corticomedullary (CM) and nephrographic (NG) phase] acquisition. MR texture analysis (MRTA) was performed using the TexRAD research software (Feedback Medical Ltd., Cambridge, UK) by a single reader who placed free-hand polygonal region of interest (ROI) on the slice showing the maximum viable tumor. Filtration histogram-based texture analysis was used to generate six first-order statistical parameters [mean intensity, standard deviation (SD), mean of positive pixels (MPP), entropy, skewness and kurtosis] at five spatial scaling factors (SSF) as well as on the unfiltered image. Mann?Whitney test was used to compare the texture parameters of CC-RCC versus NC-RCC, and high-grade versus low-grade CC-RCC. P value?<?0.05 was considered significant. A 3-step feature selection was used to obtain the best texture metrics for each MRI sequence and included the receiver-operating characteristic (ROC) curve analysis and Pearson?s correlation test. /

Results: The best performing texture parameters in differentiating CC-RCC from NC-RCC for each sequence included (area under the curve in parentheses): entropy at SSF 4 (0.807) on T2WI, SD at SSF 4 (0.814) on DWI b500, SD at SSF 6 (0.879) on DWI b1000, mean at SSF 0 (0.848) on ADC, skewness at SSF 2 (0.854) on T1WI and skewness at SSF 3 (0.908) on CM phase. In differentiating high from low-grade CC-RCC, the best parameters were: entropy at SSF 6 (0.823) on DWI b1000, mean at SSF 3 (0.889) on CM phase and MPP at SSF 5 (0.870) on NG phase. /

Conclusion: Several MR texture parameters showed excellent diagnostic performance (AUC?>?0.8) in differentiating CC-RCC from NC-RCC, and high-grade from low-grade CC-RCC. MRTA could serve as a useful non-invasive tool for this purpose.
ID  - discovery10080383
UR  - https://doi.org/10.1007/s00261-019-02122-z
SN  - 2366-0058
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
TI  - Role of MR texture analysis in histological subtyping and grading of renal cell carcinoma: a preliminary study
AV  - public
SP  - 3336
VL  - 44
Y1  - 2019/10//
EP  - 3349
ER  -