摘要
Architectural distortion is an important ultrasonographic indicator of breast cancer. However, it is difficult for clinicians to determine whether a given lesion is malignant because such distortions can be subtle in ultrasonographic images. In this paper, we report on a study to develop a computerized scheme for the histological classification of masses with architectural distortions as a differential diagnosis aid. Our database consisted of 72 ultrasonographic images obtained from 47 patients whose masses had architectural distortions. This included 51 malignant (35 invasive and 16 non-invasive carcinomas) and 21 benign masses. In the proposed method, the location of the masses and the area occupied by them were first determined by an experienced clinician. Fourteen objective features concerning masses with architectural distortions were then extracted automatically by taking into account subjective features commonly used by experienced clinicians to describe such masses. The k-nearest neighbors (k-NN) rule was finally used to distinguish three histological classifications. The proposed method yielded classification accuracy values of 91.4% (32/35) for invasive carcinoma, 75.0% (12/16) for noninvasive carcinoma, and 85.7% (18/21) for benign mass, respectively. The sensitivity and specificity values were 92.2% (47/51) and 85.7% (18/21), respectively. The positive predictive values (PPV) were 88.9% (32/36) for invasive carcinoma and 85.7% (12/14) for noninvasive carcinoma whereas the negative predictive values (NPV) were 81.8% (18/22) for benign mass. Thus, the proposed method can help the differential diagnosis of masses with architectural distortions in ultrasonographic images.
Architectural distortion is an important ultrasonographic indicator of breast cancer. However, it is difficult for clinicians to determine whether a given lesion is malignant because such distortions can be subtle in ultrasonographic images. In this paper, we report on a study to develop a computerized scheme for the histological classification of masses with architectural distortions as a differential diagnosis aid. Our database consisted of 72 ultrasonographic images obtained from 47 patients whose masses had architectural distortions. This included 51 malignant (35 invasive and 16 non-invasive carcinomas) and 21 benign masses. In the proposed method, the location of the masses and the area occupied by them were first determined by an experienced clinician. Fourteen objective features concerning masses with architectural distortions were then extracted automatically by taking into account subjective features commonly used by experienced clinicians to describe such masses. The k-nearest neighbors (k-NN) rule was finally used to distinguish three histological classifications. The proposed method yielded classification accuracy values of 91.4% (32/35) for invasive carcinoma, 75.0% (12/16) for noninvasive carcinoma, and 85.7% (18/21) for benign mass, respectively. The sensitivity and specificity values were 92.2% (47/51) and 85.7% (18/21), respectively. The positive predictive values (PPV) were 88.9% (32/36) for invasive carcinoma and 85.7% (12/14) for noninvasive carcinoma whereas the negative predictive values (NPV) were 81.8% (18/22) for benign mass. Thus, the proposed method can help the differential diagnosis of masses with architectural distortions in ultrasonographic images.
作者
Akiyoshi Hizukuri
Ryohei Nakayama
Emi Honda
Yumi Kashikura
Tomoko Ogawa
Akiyoshi Hizukuri;Ryohei Nakayama;Emi Honda;Yumi Kashikura;Tomoko Ogawa(Mizuho Information & Research Institute, Inc., Information and Communication Research Division, Tokyo, Japan;Department of Electronic and Computer Engineering, Ritsumeikan University, Kusatsu, Japan;Department of Radiology, Japanese Red Cross Medical Center, Tokyo, Japan;Department of Breast Surgery, Mie University School of Medicine, Tsu, Japan)
