Improving suspicious breast lesion characterization using semi-automatic lesion fractional volume washout kinetic...

Although breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) demonstrates high sensitivity for malignant tumor detection, a major limitation is the relative low specificity, resulting in many false-positive diagnoses of suspicious lesions (BI-RADS assessment of 4 or 5) in clinical practice and consequently producing a relatively low positive predictive value (PPV) for biopsies. The most enhanced areas in the malignant tumors show a typical washout (WO) kinetic feature for the postcontrast signal intensity time courses and also correlate with microvessel density. Benign proliferative breast diseases can also produce the WO curve, yielding an equivocal kinetic behavior for the benign lesions and rendering their diagnoses as suspicious lesions in clinical practice. Considering that tumor angiogenesis is essential to an aggressive cancer tumor growth, the authors hypothesize that the WO volume fraction, i.e., the total volume of the WO voxels that demonstrate the WO curve within the tumor, is relatively large for malignant tumors in comparison to that for benign lesions. In this study, the authors present a lesion fractional volume WO kinetic analysis for improving the characterization of suspicious breast lesions.

A method to automatically detect the boundary of a manually selected contrast-enhanced lesion was introduced and tested, utilizing the signal intensity difference between the contrast-enhanced lesion and its surrounding tissues. The kinetic features of the postcontrast signal intensity time courses were quantitatively analyzed voxel-by-voxel with emphasis on the examination of the WO behavior. The WO volume fraction relative to the whole lesion volume was introduced and tested as a biomarker for improving the characterization of suspicious breast lesions. The sample for this test consisted of 28 suspicious lesions with correlative histopathology reports available. The lesions included 10 malignant tumors and 18 benign lesions, yielding a 35.7% PPV of the biopsies.

The semi-automatic method produced an objective volume of interest for each lesion with voxelwise-quantified kinetic features. With an optimal choice of kinetic analysis, the mean and standard deviation of the WO volume fraction were 59.1 ± 13.1 (%) with the range from 41.0% to 80.7% for the malignant tumors and 31.4 ± 20.5 (%) with the range between 3.3% and 71.6% for the benign lesions, respectively. The WO volume fraction was significantly larger (p < 0.0004) for the malignant tumors than for the benign lesions. While maintaining the same sensitivity for malignant tumors, using the WO volume fraction as an additional biomarker would characterize 14 out of the 18 benign lesions as benign, potentially resulting in an 100% improvement rate in the PPV of the biopsies (from 35.7% to 71.4%) and consequently a 77.8% reduction rate in potentially unnecessary biopsies (from 18 to 4).

The significantly larger WO volume fraction for the malignant tumors was probably related to the increased vascularity associated with tumor angiogenesis. The results suggest that the WO volume fraction biomarker has potential to improve the computer-based assessment of breast MRI by greatly increasing the PPV of breast biopsies and potentially significantly reducing the number of unnecessary biopsies without compromising sensitivity.