Background: Diffusion-weighted magnetic resonance imaging (DW-MRI) and the derived apparent diffusion coefficient (ADC) value has potential for monitoring tumor response to radiotherapy (RT). Method used for segmentation of volumes with reduced diffusion will influence both volume size and observed distribution of ADC values. This study evaluates: 1) different segmentation methods; and 2) how they affect assessment of tumor ADC value during RT.
Material and methods: Eleven patients with locally advanced cervical cancer underwent MRI three times during their RT: prior to start of RT (PRERT), two weeks into external beam RT (WK2RT) and one week prior to brachytherapy (PREBT). Volumes on DW-MRI were segmented using three semi-automatic segmentation methods: "cluster analysis", "relative signal intensity (SD4)" and "region growing". Segmented volumes were compared to the gross tumor volume (GTV) identified on T2-weighted MR images using the Jaccard similarity index (JSI). ADC values from segmented volumes were compared and changes of ADC values during therapy were evaluated.
Results: Significant difference between the four volumes (GTV, DWIcluster, DWISD4 and DWIregion) was found (p < 0.01), and the volumes changed significantly during treatment (p < 0.01). There was a significant difference in JSI among segmentation methods at time of PRERT (p < 0.016) with region growing having the lowest JSIGTV (mean± sd: 0.35 ± 0.1), followed by the SD4 method (mean± sd: 0.50 ± 0.1) and clustering (mean± sd: 0.52 ± 0.3). There was no significant difference in mean ADC value compared at same treatment time. Mean tumor ADC value increased significantly (p < 0.01) for all methods across treatment time.
Conclusion: Among the three semi-automatic segmentations of hyper-intense intensities on DW-MR images implemented, cluster analysis and relative signal thresholding had the greatest similarity to the clinical tumor volume. Evaluation of mean ADC value did not depend on segmentation method.