Nakayama R, Watanabe R, Namba K, Takeda K, Yamamoto K, Katsuragawa S, Doi K
Department of Radiology, Mie University School of Medicine, 2-174 Edobashi, Tsu, 514-8507, Japan.
Methods Inf Med. 2007;46(6):716-22.
Our purpose was to evaluate the potential usefulness of the nearest neighbor case which was assumed to be the similar case in a CAD scheme for determining the histological classification of clustered microcalcifications.
Our database consisted of current and previous magnification mammograms obtained from 93 patients before and after three-month follow-up examination. It included 11 invasive carcinomas, 19 noninvasive carcinomas of the comedo type, 25 non-invasive carcinomas of the noncomedo type, 23 mastopathies, and 15 fibroadenomas. Six objective features on clustered microcalcifications were first extracted from each of the current and the previous images. The nearest neighbor case was then identified by the Euclidean distance in the previous and current feature-space. The histological classification of an unknown new case in question was assumed to be the same as that of the nearest neighbor case which has the shortest Euclidean distance in our database.
The classification accuracies were 90.9% for invasive carcinoma, 89.5% for noninvasive carcinoma of the comedo type, 96.0% for noninvasive carcinoma of the noncomedo type, 82.6% for mastopathy, and 93.3% for fibroadenoma. These results were substantially higher than those with our previous CAD scheme.
The nearest neighbor criterion was useful in a CAD scheme for determining the histological classification.
我们的目的是评估在计算机辅助检测（CAD）方案中，将最近邻病例假定为相似病例对于确定簇状微钙化的组织学分类的潜在有用性。
我们的数据库包含从93名患者在三个月随访检查前后获得的当前和先前的放大乳腺X线照片。它包括11例浸润性癌、19例粉刺型非浸润性癌、25例非粉刺型非浸润性癌、23例乳腺病和15例纤维腺瘤。首先从当前和先前的每张图像中提取关于簇状微钙化的六个客观特征。然后通过先前和当前特征空间中的欧几里得距离确定最近邻病例。在我们的数据库中，假定有问题的未知新病例的组织学分类与具有最短欧几里得距离的最近邻病例相同。
浸润性癌的分类准确率为90.9%，粉刺型非浸润性癌为89.5%，非粉刺型非浸润性癌为96.0%，乳腺病为82.6%，纤维腺瘤为93.3%。这些结果显著高于我们先前的CAD方案。
最近邻标准在用于确定组织学分类的CAD方案中是有用的。
