Do the variations in ROI placement technique have influence for prostate ADC measurements?

Recommendations for improved reproducibility of ADC derivation on behalf of the Elekta MRI-linac consortium image analysis working group

BACKGROUND AND PURPOSE

The apparent diffusion coefficient (ADC), a potential imaging biomarker for radiotherapy response, needs to be reproducible before translation into clinical use. The aim of this study was to evaluate the multi-centre delineation- and calculation-related ADC variation and give recommendations to minimize it.

MATERIALS AND METHODS

Nine centres received identical diffusion-weighted and anatomical magnetic resonance images of different cancerous tumours (adrenal gland, pelvic oligo metastasis, pancreas, and prostate). All centres delineated the gross tumour volume (GTV), clinical target volume (CTV), and viable tumour volume (VTV), and calculated ADCs using both their local calculation methods and each of the following calculation conditions: b-values 0-500 vs. 150-500 s/mm2, region-of-interest (ROI)-based vs. voxel-based calculation, and mean vs. median. ADC variation was assessed using the mean coefficient of variation across delineations (CVD) and calculation methods (CVC). Absolute ADC differences between calculation conditions were evaluated using Friedman's test. Recommendations for ADC calculation were formulated based on observations and discussions within the Elekta MRI-linac consortium image analysis working group.

RESULTS

The median (range) CVD and CVC were 0.06 (0.02-0.32) and 0.17 (0.08-0.26), respectively. The ADC estimates differed 18% between b-value sets and 4% between ROI/voxel-based calculation (p-values < 0.01). No significant difference was observed between mean and median (p = 0.64). Aligning calculation conditions between centres reduced CVC to 0.04 (0.01-0.16). CVD was comparable between ROI types.

CONCLUSION

Overall, calculation methods had a larger impact on ADC reproducibility compared to delineation. Based on the results, significant sources of variation were identified, which should be considered when initiating new studies, in particular multi-centre investigations.

Diagnostic utility of three Tesla diffusion tensor imaging in prostate cancer: correlation with Gleason score values

Background

Preoperative assessment of prostate cancer (PCa) aggressiveness is a prerequisite to provide specific management options. The Gleason score (GS) obtained from prostatic biopsy or surgery is crucial for the evaluation of PCa aggressiveness and personalized treatment planning. Diffusion tensor imaging (DTI) provides valuable information about microstructural properties of prostatic tissue. The most common prostate DTI measures are the fractional anisotropy (FA) and median diffusivity (MD) can give more information regarding the biophysical characteristics of prostate tissue. We aimed to explore the correlation of these DTI parameters with GS levels in PCa patients that can affect the management protocol of PCa.

Results

The computed area under curve (AUC) of the FA values used to differentiate cancer patients from control group was (0.90) with cutoff point to differentiate both groups were ≥ 0.245. The computed sensitivity, specificity, positive and negative predictive values were (84%, 80%, 95.5%, and 50%), respectively, with accuracy 83.3%. FA showed high positive correlation with Gleason score (p value < 0.001). Median diffusivity (MD) showed negative correlation with GS with statistically significant results (p value  = 0.013). PCa fiber bundles were dense, orderly arranged, without interruption in the low grade, and slightly disorganized in the intermediate group. However, in the high-grade group, the fiber bundles were interrupted, irregularly arranged, and absent at the site of cancerous foci.

Conclusions

Combined quantitative parameter values (FA and MD values) and parametric diagrams (FA and DTI maps) can be utilized to evaluate prostate cancer aggressiveness and prognosis, helping in the improvement of the management protocol of PCa patients.