COMPUTER AIDED DIAGNOSIS IN BREAST IMAGING

描述（申请人摘要中的逐字记录）：筛查乳腺X线摄影时 已被证明是早期发现乳腺癌的有效方法 癌症，目前，5- 30%的乳腺癌女性有一个marnmogram 这被解释为正常。据报道，解释错误 (when放射科医生看到癌症，但报告为良性）是原因 54%的癌症被遗漏。此外，只有10%至40%的妇女 做一个活组织检查，实际上是乳腺癌;由于活组织检查很昂贵， 对患者造成侵入性和创伤性。此外，还有大量 观察者间对乳腺X线摄影病变解释的变异性。的 这项研究的长期目标是开发和评估计算机辅助 乳腺多模态成像的诊断和预后方法。的 要检验的主要假设是， 乳房X线摄影、乳房超声和MR图像的计算机化分析，沿着 与临床数据，应产生改进的方法（a）区分 恶性和良性病变之间的差异，即，诊断和（B）预测 预后目标是建立包含乳房X光照片的数据库， 超声和MR图像沿着临床信息，恶性/良性 状态和患者结局;开发计算机化方法， 表征基本的形态、纹理、超声和血管 病变的特征;并评估这些方法在 区分恶性和良性病变，并预测患者 预后预计这项研究的结果将有助于 放射科医生/肿瘤科医生在确定恶性肿瘤的可能性， 预测患者预后。拟议的工作是新颖的，因为这样一个 用于计算机辅助诊断的综合系统尚未被尝试。 我们相信，结合多模态成像和 临床信息，患者总体结局将改善。

DESCRIPTION (Verbatim from Applicant's Abstract): While screening mammography has been shown to be an effective method for the early detection of breast cancer, currently, 5-30 percent of women with breast cancer have a marnmogram that is interpreted as normal. It has been reported that interpretation errors (when the radiologist sees the cancer but reports it as benign) are the cause of 54 percent of missed cancers. In addition, only 10-40 percent of women who have a biopsy actually have breast cancer; with biopsies being expensive, invasive and traumatic to the patient. In addition, there is large inter-observer variability in the interpretation of mammographic lesions. The long-term goal of this research is to develop and evaluate computer-aided diagnosis and prognosis methods for multi-modality imaging of the breast. The main hypotheses to be tested are that, combined information from the computerized analysis of mammography, breast ultrasound, and MR images, along with clinical data, should yield improved methods for (a) distinguishing between malignant and benign lesions, i.e., diagnosis and (b) predicting prognosis. The objectives are to create databases containing mammogram, ultrasound, and MR images along with clinical information, malignant/benign status, and patient outcomes; to develop computerized methods for characterizing the essential morphological, textural, sonographic, and vascular features of the lesions; and to evaluate the accuracy of these methods in distinguishing between malignant and benign lesions and in predicting patient prognosis. It is expected that the results from this research will aid radiologists/oncologists in determining the likelihood of malignancy and in predicting patient prognosis. The proposed work is novel in that such a comprehensive system for computer-aided diagnosis has not yet been attempted. We believe that with the combined information from multimodality imaging and clinical information, overall patient outcome will improve.