Add-on module for ONCAD for diagnosing infiltrating lobular carcinoma

用于诊断浸润性小叶癌的 ONCAD 附加模块

• 批准号: 7480573
• 负责人: ALAN I. PENN
• 金额: $ 10.01万
• 依托单位: ALAN PENN AND ASSOCIATES, INC.
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-11 至 2010-02-10
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7480573
• 关键词: Algorithms; Biopsy; Breast; Diagnosis; Exhibits; Kinetics; Lobular Carcinoma; Magnetic Resonance Imaging; Mammography; Morphology; Research; Research Methodology; System; Training; base; breast lesion; clinically significant; improved; malignant breast neoplasm; radiologist

DESCRIPTION (provided by applicant): The specific aim of this study is to develop an add-on module for the ONCAD breast MRI CAD system that will provide radiologists with additional assistance when they have suspicion of the presence of infiltrating lobular carcinoma (ILC). The proposed research will investigate the possibility of developing, training and evaluating an ILC- specific algorithm that uses both kinetics and morphology to increase the sensitivity of ONCAD to ILC. This ILC-specific algorithm will enable the radiologist to further evaluate cases in which ILC is suspected and to improve sensitivity on this subset of breast cancers. Infiltrating lobular carcinoma (ILC), which is found in a clinically significant fraction of breast cancers, is difficult to detect clinically or with mammography and frequently fails to exhibit suspicious kinetics on MRI. In prior studies, ONCAD, the only FDA-approved CAD system for MRI that marks breast lesions that have suspicious morphologies, identified 7 of 8 biopsy-proven cases of ILC. These results provide preliminary evidence that the ONCAD algorithms may provide a basis of researching methods of identifying sufficient percentage of ILCs to be clinically significant. This ILC-specific algorithm will enable the radiologist to further evaluate cases in which ILC is suspected and to improve sensitivity on this subset of breast cancers.

描述（由申请人提供）：本研究的具体目的是为ONCAD乳腺MRI CAD系统开发一个附加模块，当放射科医生怀疑浸润性小叶癌（ILC）存在时，该模块将为放射科医生提供额外的帮助。拟议的研究将探讨开发、训练和评估ILC特异性算法的可能性，该算法使用动力学和形态学来提高ONCAD对ILC的敏感性。这种ILC特异性算法将使放射科医生能够进一步评估疑似ILC的病例，并提高对这类乳腺癌的敏感性。浸润性小叶癌（ILC）是临床上乳腺癌的重要组成部分，很难在临床或乳房x光检查中发现，并且在MRI上经常不能表现出可疑的动力学。在先前的研究中，ONCAD是fda批准的唯一用于MRI的CAD系统，用于标记具有可疑形态的乳腺病变，在8例活检证实的ILC病例中识别出7例。这些结果提供了初步证据，表明ONCAD算法可以为确定具有临床意义的足够百分比的ilc的研究方法提供基础。这种ILC特异性算法将使放射科医生能够进一步评估疑似ILC的病例，并提高对这类乳腺癌的敏感性。