COMPUTER AIDED DIAGNOSIS IN BREAST IMAGING

描述(摘自申请者摘要)：筛查乳房X光检查时 已被证明是一种早期检测乳房的有效方法 癌症，目前，患有乳腺癌的女性中有5%-30%做了X光检查 这被解读为正常现象。据报道，口译错误 (当放射科医生看到癌症但报告为良性时)是原因 54%的漏诊癌症。此外，只有10%-40%的女性 活组织检查实际上患有乳腺癌；由于活组织检查很昂贵， 对病人具有侵入性和创伤性。此外，还有很大的 乳房X光检查病变解释中的观察者间变异性。这个 这项研究的长期目标是开发和评估计算机辅助 乳腺多模式成像的诊断和预后方法。这个 需要检验的主要假设是，来自 乳房X光照相、乳房超声和磁共振图像的计算机分析 根据临床数据，应该产生改进的方法来区分(A) 恶性和良性病变之间的区别，即诊断和(B)预测 预后。目标是创建包含乳房X光照片的数据库， 超声、磁共振图像和临床信息，恶性/良性 状态和患者结果；开发计算机化方法 基本的形态、质地、超声和血管的特征 病变的特征；并评估这些方法在 良恶性病变的鉴别及对患者的预测 预后。预计这项研究的结果将有助于 放射科医生/肿瘤学家在确定恶性肿瘤的可能性和在 预测患者的预后。拟议的工作是新颖的，因为这样一种 尚未尝试建立全面的计算机辅助诊断系统。 我们认为，结合来自多模式成像的信息和 临床信息显示，总体患者预后将有所改善。

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.