Spatial-resolution optimization of 3D high-frequency quantitative ultrasound methods to detect metastatic regions in human lymph nodes

Proper staging and treatment of cancer require accurate detection of lymph-node metastases, but current histological methods fail to detect small, but clinically significant metastases. We used novel 3D quantitative ultrasound (QUS) methods to identify metastatic regions in freshly excised lymph nodes from cancer patients. Individual lymph nodes were scanned in 3D using a 26-MHz, single-element, F2 transducer with a 12-mm focal length. QUS methods quantified the backscatter coefficient to yield four estimates in cylindrical regions of interest (ROIs) having equal lengths and diameters ranging from 0.4 to 1 mm. To optimize the tradeoff between QUS-estimate quality and the spatial resolution of the estimates, the effect of ROI size on estimate bias and variance was investigated using a database of 101 lymph nodes of colorectal-cancer patients. Estimates were combined using linear-discriminant approaches and ROC curves were computed to assess classification performance. A Bayesian approach was used to convert the discriminant scores to 3D cancer-probability estimates throughout each lymph node. Analysis indicated that ROIs with a 0.8-mm length and diameter improved spatial resolution and minimally degraded estimate quality with an average variance increase of <20% for each estimate. The area under the ROC curve remained greater than 0.92 for all ROI sizes. Our QUS methods potentially can reduce the rate of false-negative determinations drastically by efficiently guiding pathologists to suspicious regions in lymph nodes, and having the best possible spatial resolution while retaining adequate estimate quality is critical.