Development and evaluation of a robust algorithm for computer-assisted detection of sentinel lymph node micrometastases

Increasing the sectioning rate for breast sentinel lymph nodes can increase the likelihood of detecting micrometastases. To make serial sectioning feasible, we have developed an algorithm for computer-assisted detection (CAD) with digitized lymph node sections.

K-means clustering assigned image pixels to one of four areas in a colourspace (representing tumour, unstained background, counterstained background and microtomy artefacts). Four filters then removed 'false-positive' pixels from the tumour cluster. A set of 43 sections containing tumour (a total of 259 foci) and 59 sections negative for malignancy was defined by two pathologists, using light microscopy, and CAD was applied. For the clinically relevant task of identifying the largest focus in each section (micrometastasis in 22/43 sections), the sensitivity and specificity were 100%. Isolated tumour cells (ITCs) were identified in one slide initially considered to be negative. Identification of all 259 foci yielded sensitivities of 57.5% for ITCs (<0.200 mm), 89.5% for micrometastases, and 100% for larger metastases, with one false-positive. Reduced sensitivity was ascribed to variable staining. Nine additional metastases (<0.01-0.3 mm) that were not initially identified were detected by CAD.

This algorithm is well suited to the task of sentinel lymph node evaluation and may enhance the detection of occult micrometastases.