COMPUTER AIDED DIAGNOSIS OF BREAST CANCER INVASION

说明：本研究的目的是开发一种计算机辅助 预测乳腺病变恶性程度和侵袭性的诊断(CADx)系统 根据梅地卡的调查结果。人工神经网络(ANN)技术将 用于预测乳房X光检查可疑的病变是否为良性 原位癌，或浸润性癌。 ANN输入将从现有的可用信息中获得，例如 病史和放射科医生对病变形态的描述 遵循ACR乳房影像报告和数据系统(BI-RADS)。人工神经网络 非常适合这种诊断任务，因为像人类一样，神经网络可以 学习如何准确而有力地执行诊断任务 适当的培训范例。 拟议研究的具体目标是：(1)开发使用 乳房X光检查和病史检查可预测乳腺癌的恶性程度和侵袭性 前瞻性收集的患者数据库中的乳腺病变；(2)提炼 通过优化输入结果的数量来提高CADx系统的准确性 并研究更复杂的网络体系结构，研究是 成本效益。(3)对CADx系统进行临床评价。 图形用户界面，并使用它来回顾评估 系统性能。 在初步研究中，人工神经网络准确地预测了96 活检证实的乳腺癌，使用BI-RADS发现和患者年龄作为 输入调查结果。 这个提议的直接好处是一种非侵入性的计算机辅助 仅提供以前可用的信息的诊断系统 通过活组织检查。该系统可以帮助乳房X光摄影师和外科医生 为乳房病变患者制定手术计划，并可能降低成本 以及不必要的手术活检的发病率。

DESCRIPTION: The purpose of this study is to develop a computer-aided diagnosi (CADx) system to predict breast lesion malignancy and invasion based on medica findings. Artificial neural network (ANN) techniques will be used to predict whether mammographically suspect lesions are benign, in situ cancer, or invasive cancer. The ANN inputs will be derived from existing, available information such as patient history and radiologists descriptions of lesion morphology following the ACR Breast Imaging Reporting and Data System (BI-RADS). ANNs are well suited for this diagnostic task because, like humans, ANNs can be taught to perform diagnostic tasks accurately and robustly when given appropriate training examples. The specific aims of the proposed study are to: (1) Develop ANNs that use mammography and history findings to predict malignancy and invasion of breast lesions among a prospectively collected patient database; (2) Refine the accuracy of the CADx system by optimizing the number of input findings and investigating more complex network architectures, and study is cost-effectiveness. (3) Evaluate the CADx system clinically, by developing a graphical user interface and using it to retrospectively evaluate the systems performance. In preliminary studies, an ANN accurately predicted invasion among 96 biopsy-proven breast cancers, using BI-RADS findings and patient age as input findings. The immediate benefit of this proposal is a noninvasive computer-aided diagnosis system which provides information previously available only through biopsy. This system can assist mammographers and surgeons in surgical planning for patients with breast lesions, and may reduce the cost and morbidity of unnecessary surgical biopsies.