DYNAMIC MRI OF INVASIVE BREAST-CANCER - ASSESSMENT OF 3 REGION-OF-INTEREST ANALYSIS-METHODS

Purpose: In this study, three region-of-interest (ROI) analysis method s based on operator-defined and semiautomated sampling of pharmacokine tic breast maps of contrast uptake are described. The observer variabi lity and impact of the methods on the estimated enhancement characteri stics of invasive cancer are also presented. Method: Fifty-four women with invasive breast cancer underwent dynamic Gd-DTPA-enhanced MRI. RO Is were drawn by two observers on parametric images obtained from comp artmental modeling of the dynamic data. Three methods were used: (a) A n irregular ROI was drawn to include as much of the enhancing part of the tumor as possible (large ROI); (b) a 12 pixel circular ROI was pla ced at the most rapidly enhancing part of the large region (small ROI) ; and (c) a computer algorithm interrogated the large region pixel by pixel using a 9 pixel square mask and selected the region with the hig hest mean parameter value (semiautomated ROI). Results: Significant ob server variability and bias were found in the enhancement measurements using the large ROI method. There was no observer bias associated wit h the other methods, but the variability of the small ROI method was s ubstantial. An almost perfect observer agreement was achieved using th e semiautomated method. The small and semiautomated ROI methods produc ed significantly higher enhancement ratios than the large ROI method, especially in grade III carcinomas. Conclusion: Variability is inheren t in subjective ROI analysis, but the semiautomated method of ROI sele ction and sampling of parameter images of the breast is an efficient a nd reliable alternative that may allow better standardization of the M R technique.