Imaging Research, Sunnybrook Health Sciences Centre, Department of Surgical Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada.
Histopathology. 2011 Jul;59(1):116-28. doi: 10.1111/j.1365-2559.2011.03896.x.
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.
提高乳腺前哨淋巴结的切片率可以增加检测微转移的可能性。为了使连续切片成为可能，我们开发了一种用于数字化淋巴结切片的计算机辅助检测（CAD）算法。
K-均值聚类将图像像素分配到颜色空间的四个区域之一（代表肿瘤、未染色背景、染色背景和切片伪影）。然后，四个滤波器从肿瘤簇中去除“假阳性”像素。两组病理学家使用光学显微镜定义了一组包含肿瘤（共 259 个病灶）和 59 个无恶性病变的切片（共 43 个切片），并应用 CAD。对于在每个切片中识别最大病灶（22/43 个切片中的微转移）的临床相关任务，敏感性和特异性均为 100%。在最初被认为是阴性的一张切片中，鉴定出了一个孤立的肿瘤细胞（ITC）。鉴定所有 259 个病灶的敏感性分别为 ITCs（<0.200mm）的 57.5%、微转移的 89.5%和更大转移的 100%，假阳性率为 1 个。敏感性降低归因于染色的可变性。通过 CAD 还检测到另外 9 个（<0.01-0.3mm）未被初始识别的转移灶。
该算法非常适合前哨淋巴结评估任务，并且可能增强对隐匿性微转移的检测。
