PURPOSE:Usefulness of biexponentially fitted signal attenuation at different b-values for differentiating the histological characteristics of renal tumors.MATERIALS AND METHODS:A total of 26 patients with 28 renal masses (histologically proven: 20 clear cell renal cell carcinomas [ccRCC], three transitional cell carcinomas, two oncocytomas, and one papillary RCC) and 30 volunteers with healthy kidneys were examined at 1.5 Tesla using an echo-planar DWI sequence. Using the IVIM model, we calculated the perfusion fraction f and the diffusion coefficient D. Furthermore, the ADC was obtained. These tumor parameters were compared to healthy renal tissue nonparametrically, and a receiver operating characteristic (ROC) analysis was performed.RESULTS:Healthy renal parenchyma showed higher ADC and D values (p<0.001) than ccRCC (ADC 1.95±0.10 [SD] μm2/ms, f 18.32±2.52%, and D 1.88±0.11 μm2/ms versus ADC 1.45±0.38 μm2/ms, f 18.59±6.16%, and D 1.34±0.38 μm2/ms). When detecting malignancies the area under the curve for D was higher than for ADC. The f values for ccRCC were higher (p<0.001) than for non-ccRCC (ADC 1.52±0.47 μm2/ms, f 8.44±1.24%, and D 1.30±0.18 μm2/ms). Both f and D correlated with ccRCC grading.CONCLUSION:IVIM imaging is able to provide reliable diffusion values in the human kidney and may enhance the accuracy of tumor diagnosis. The D value was the best parameter to distinguish renal tumors from healthy renal tissue. The f value is promising for determining the histological subgroups.

Diffusion weighted imaging (DWI) derived apparent diffusion coefficient (ADC) is currently used in identifying and post-therapy followup of several types of tumours. In brain tumours in particular ADC values are known to correlate inversely to tumour cellularity and high and low malignant areas can be distinguished based on ADC values.The average ADC value increases after successful chemotherapy, radiotherapy or a combination of both and is used as a surrogate marker for treatment response.More recently DWI derived ADC has been used to differentiate pancreatic cancer from healthy pancreatic tissue although with some limitations. A second DWI derived parameter, the perfusion fraction f has also shown promise in classifying pancreatic lesions. This parameter is estimated using special multiple b-value prototypes and the IVIM model.The main purpose of our project was to develop a software platform to assist radiologists in studying cancerous lesions by quantifying and mapping these two DWI derived parameters: ADC and perfusion fraction f. The platform we developed automatically calculates and maps the ADC and IVIM-model perfusion fraction f values from raw diffusion data.Furthermore, the software enables the automated delineation and ADC quantification of tissue sections in a fast, objective, user independent manner and has so far been applied to successfully delineating brain tumours. The perfusion fraction f mapping capabilities have so far been successfully applied to delineate pancreatic cancer lesions from healthy tissue. Further studies are in preparation to apply this software tool to study both ADC and perfusion fraction f in other types of cancerous lesions.