- 期刊
- OpenAccess
腫瘤影像形狀特徵多變數邏輯式回歸分析塵用於乳房固質結節的超音波診斷
Multiple Logistic Regression Analysis of Tumor Shape Features for Ultrasound Breast Cancer Diagnosis
摘要
臨床超音波影像在診斷時需仰賴醫師的多年經驗做判斷，其中有許多主觀的判斷因素，易受醫師經驗的影響，也缺乏經驗交流的管道。所以，我們希望能利用多變數分析技術，將資深醫師多年的診斷經驗轉為電腦輔助分析診斷的工具。在本研究中，我們針對超音波腫瘤影像形狀特徵建立多變數分析模型，輔助超音波診斷乳房固質結節之良惡性。 我們收集123例經病理證實的乳房固質結節的超音波影像(76例浸潤性乳管癌和47例纖維線瘤)，經醫師目視分類其形狀，並加以編碼，而後針對腫瘤良惡性及形狀編碼進行多變數邏輯式迴歸分析。根據回歸分析結果，其預測腫瘤良惡性的準確度為92.7%，敏感度為89.5%，特異度為97.9％。 本研究依據醫師的專業經驗進行影像形狀特徵分類，進一步結合多變數分析方法，建立一套電腦輔助診斷系統，可協助將醫師的診斷經驗轉為可供電腦記錄、分析及診斷的檢驗法則，可做為醫師診斷時參考的依據，協助醫師進行乳房固質結節的超音波診斷。
並列摘要
The implementation of ultrasound image diagnosis greatly depends on the experience of physicians. Therefore, the diagnostic process is highly depends on the physician, and is also lacking the experience sharing media and reference standard in the diagnostic process. In order to establish the experience-sharing platform, in this research, we developed a computer-aided diagnosis system based on the tumor shapes. To establish multivariate analysis model of tumor shape features for ultrasound of breast cancer diagnosis, we evaluated 123 ultrasound images of pathologically proven solid breast tumors (76 infiltrative ductal carcinomas and 47 fibroadenomas). The shapes of tumors were classified and coded by experienced radiologists, and a multiple logistic regression algorithm was used to classify the tumor as benign or malignant. The accuracy of this model for classifying malignancies was 92.7 %, the sensitivity was 89.5 %, and the specificity was 97.9%. According to the experience of the radiologists and the regression analysis algorithm, we established a computer-aided diagnosis system applied to ultrasound of solid breast tumors. This system helps differentiate solid breast tumors with relatively high accuracy and can be of significant aid for inexperienced operators to avoid misdiagnoses.