COMPUTERIZED MAMMOGRAPHIC LESION DESCRIPTION

计算机乳房 X 光检查病变描述

• 批准号: 6514127
• 负责人: JOHN J HEINE
• 金额: $ 14.5万
• 依托单位: UNIVERSITY OF SOUTH FLORIDA
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-05-01 至 2004-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6514127
• 关键词: artificial intelligence; bioimaging /biomedical imaging; breast neoplasms; computer assisted diagnosis; diagnosis design /evaluation; diagnosis quality /standard; human data; image processing; information display; mammography

DESCRIPTION (Verbatim from Applicant's Abstract): Mammography is recognized as an important means to reduce breast cancer mortality. However, its accuracy is limited, both in sensitivity (some cancers are missed) and specificity (many non-cancer cases are referred for invasive procedures). This proposal aims at improving the effectiveness of breast cancer screening by the discovery of measurements that can be taken from digital mammograms, and the design of classifiers that result in an automatic computer-generated description of suspicious areas. In particular, classifiers for the standardized lexicon for mass shape, mass margin and mass density, as well as breast composition will be designed. These automaticaily generated descriptions are aimed at increasing the specificity of mammography by providing the radiologist with a probability of rnalignancy for the lesion. The descriptions should also be helpful in conjunction with computer programs that detect suspicious areas, by rejecting those detected areas that do not likely represent cancer (false positive reduction). In addition, automatic description of a marnmographic iesion will reduce reader variability and may heip in training radiologists to use the standardized lexicon. This project will first develop a segmentation program, that finds the border of a mammographic mass. The approach will be a multi-stage knowledge guided system. Measurements to be taken from digital mammograms make use of this border, and include some that have been reported in the literature as well as newly proposed measurements. As the classification problem for mammographic masses is very difficult, hybrid classifiers will be constructed that take advantage of specific abilities of multiple classifiers. A rule based system will be developed to arrive at an aggregate decision for each of the BIRADS descriptions. Finally, these descriptions will be used in a classifier to determine the likelihood of malignancy. The outcome of this project would be the tools to develop an aid to non-expert mammographers to define the characteristics of mammographic masses and help in decisions to biopsy a mammographic lesion, but can also be applied to help enhance sensitivity through computer assisted diagnosis, and in educational tools.

描述（逐字摘自申请人摘要）：乳房X线照相术被认为是 降低乳腺癌死亡率的重要手段。然而，它的准确度是 敏感性（一些癌症被遗漏）和特异性（许多 非癌症病例被转介进行侵入性手术）。该提案旨在 通过发现提高乳腺癌筛查的有效性 可以从数字乳房X光检查中进行测量，以及设计 分类器会产生计算机自动生成的描述 可疑区域。特别是标准化词典的分类器 肿块形状、肿块边缘和肿块密度以及乳房成分将被 设计的。 这些自动生成的描述旨在增加 乳房X线照相术的特异性，为放射科医生提供以下可能性： 病变的恶性程度。这些描述也应该有帮助 与检测可疑区域的计算机程序结合，通过拒绝 那些检测到的区域不太可能代表癌症（假阳性 减少）。此外，自动描述乳房X线造影病变将 减少读者的可变性，并可能有助于培训放射科医生使用 标准化词典。 该项目将首先开发一个分割程序，找到边界 乳房 X 光检查肿块。该方法将是多阶段的知识引导 系统。从数字乳房 X 光检查中进行的测量利用了这一点 边界，并包括一些文献中报道过的内容以及 新提出的测量。作为乳腺X线摄影的分类问题 质量非常困难，将构建混合分类器 多个分类器的特定能力的优势。基于规则的系统 将制定每个 BIRADS 的总体决策 描述。最后，这些描述将在分类器中使用 确定恶性肿瘤的可能性。 该项目的成果将是为非专家开发辅助工具 乳腺 X 光检查人员定义乳腺 X 光检查肿块的特征并帮助 决定对乳房 X 光检查病变进行活检，但也可用于帮助 通过计算机辅助诊断和教育提高敏感性 工具。

项目成果

New statistical learning theory paradigms adapted to breast cancer diagnosis/classification using image and non-image clinical data.

新的统计学习理论范式适用于使用图像和非图像临床数据进行乳腺癌诊断/分类。

• DOI: 10.1504/ijfipm.2008.020183
• 发表时间: 2008
• 期刊: International journal of functional informatics and personalised medicine
• 作者: LandJr,WalkerH;Heine,JohnJ;Raway,Tom;Mizaku,Alda;Kovalchuk,Nataliya;Yang,JackY;Yang,MaryQu
• 通讯作者: Yang,MaryQu