Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Comput Methods Programs Biomed. 2013 Jul;111(1):84-92. doi: 10.1016/j.cmpb.2013.03.017. Epub 2013 Apr 29.
The information from radiologists was utilized in the proposed computer-aided diagnosis (CAD) for breast tumor classification. The ultrasound (US) database used in this study contained 166 benign and 78 malignant masses. For each mass, six quantitative feature sets were used to describe the radiologists' grading of six Breast Imaging Reporting and Data System (BI-RADS) categories including shape, orientation, margins, lesion boundary, echo pattern, and posterior acoustic features on breast US. The descriptive abilities were between 76% and 82% and the predicted descriptors were then used for tumor classification. Using receiver operating characteristic curve for evaluation, the area under curve (AUC) of the proposed CAD was slightly better than that of a conventional CAD based on the combination of all quantitative features (0.96 vs. 0.93, p=0.18). The partial AUC over 90% sensitivity of the proposed CAD was significantly better than that of the conventional CAD (0.90 vs. 0.76, p<0.05). In conclusion, the computer-aided analysis with qualitative information from radiologists showed a promising result for breast tumor classification.
该研究使用的超声（US）数据库包含 166 个良性和 78 个恶性肿块。对于每个肿块，使用六个定量特征集来描述放射科医生对六个乳房成像报告和数据系统（BI-RADS）类别（形状、方向、边缘、病变边界、回声模式和乳房 US 的后声特征）的分级。描述能力在 76%到 82%之间，然后使用预测描述符进行肿瘤分类。使用接收者操作特性曲线进行评估，所提出的 CAD 的曲线下面积（AUC）略优于基于所有定量特征组合的传统 CAD（0.96 与 0.93，p=0.18）。所提出的 CAD 在超过 90%灵敏度的部分 AUC 明显优于传统 CAD（0.90 与 0.76，p<0.05）。总之，放射科医生定性信息的计算机辅助分析在乳腺肿瘤分类方面显示出了很有前景的结果。
