Feasibility of CT in high risk breast cancer patients

乳房x光检查用于筛查无症状妇女的乳腺癌，乳房x光检查发现的典型乳腺癌直径约为11毫米。在这个小范围内，切除病变对大多数女性来说都是治愈乳腺癌的结果。然而，有一小部分女性（约占所有乳腺癌患者的5%）在遗传上易患乳腺癌（BRCA1和BCRA2基因），在这些女性中，需要更积极的检测方法。除了BRCA1和BRCA2携带者乳腺癌风险特别高外，乳房密度极高的女性患乳腺癌的风险更高（由于乳房密度高，比值比为4比6），而乳房x光检查对这些女性的敏感性较低。对于这些高风险类别的妇女，大多数乳房致密，乳房x线摄影成像效果不佳，需要一种具有更好的病变检测性能（对比度分辨率）的成像方式。而超声波依靠的对比机制不如x射线可靠——这就是为什么它们不被用于筛查。然而，计算机断层扫描（CT）确实依赖于x射线对比机制，但其对比度分辨率约为投影乳房x线摄影的10倍。CT非常能够识别3-5mm范围内的软组织病变，这种病变的体积比乳房x光检查发现的平均11mm的病变小10到50倍。因此，对于高危患者，CT具有比乳房x光检查更早发现乳腺癌的潜力。在这项可行性研究中，我们建议通过计算机模拟技术结合尸体乳房和乳房切除术标本的CT，深入研究专用乳房CT的潜力。蒙特卡罗研究将用于充分评估乳腺CT的腺体剂量，影像学研究将用于确定最佳CT采集的要求。利用CT扫描大约10个乳房切除标本的乳房病变，将开发一个乳房肿瘤模型。该肿瘤模型将与一系列约20具尸体乳腺CT数据集一起进行广泛的ROC研究。计算机观察者将用于定义CT和乳房x线摄影的Az与肿瘤直径曲线。人类观察员将被用来验证和校准更广泛的计算机观察员的结果。这项研究的结果应该让我们清楚地认识到乳腺CT作为降低乳腺癌高危人群乳腺癌死亡率的工具的潜力。

Mammography is used to screen asymptomatic women for breast cancer, and typical breast cancer found using mammography is approximately 11 mm in diameter. At this small size, removal of lesion results in breast cancer cure in the majority of women. However, there is a small class of women (about 5% of all breast cancers) who are genetically predisposed to breast cancer (BRCA1 and BCRA2 genes), and in these women, more aggressive detection methods are needed. In addition to BRCA1 and BRCA2 carriers who are at extraordinary risk of breast cancer, women with extremely dense breasts are at higher risk from breast cancer (by virtue of their dense breasts with odds ratio from 4 to 6), and mammography is less sensitive in these women. For women in these high-risk categories, most of whom have dense breasts that are poorly imaged by mammography, an imaging modality with better lesion detectability performance (contrast resolution) is needed. While ultrasound rely on contrast mechanisms that are less reliable than X-ray contrast-that is why they are not used for screening. However, computed tomography (CT) does depend upon x-ray contrast mechanisms, but has about 10 times the contrast resolution as projection mammography. CT is very capable of identifying soft tissue lesions in the 3-5mm range- Such lesions are 10 to 50 times smaller in volume than the average 11mm lesion found by mammography. Therefore, CT has great potential for much earlier detection of breast cancer than mammography for high-risk patients. In this feasibility study, we propose to throughly investigate the potential of dedicated breast CT using computer simulation techniques coupled with CT of cadaver breasts and mastectomy specimens. Monte Carlo studies will be used to fully evaluate the glandular dose of breast CT, and imaging studies will be used to define the requirements of optimal CT acquisition. Using CT scans of breast lesions from about 10 mastectomy specimens, a breast tumor model will be developed. The tumor model will be used with a series of about 20 cadaver breast CT data sets to conduct extensive ROC studies. Computer observers will be used to define the Az versus tumor diameter curves for both CT and mammography. Human observers will be used to validate and calibrate the more extensive computer observer results. The results of this investigation should provide a clear understanding of the potential of breast CT as a tool to reduce breast cancer mortality in the population of women with dire risk of breast cancer.