利用物理信息神经网络和表面温度数据检测乳腺癌的变革性方法

I. Perez-Raya, Carlos Gutierrez, S. Kandlikar
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引用次数: 0

摘要

早期发现是预防乳腺癌最有效的方法。乳房 X 射线照相术是一种成熟的 X 射线技术,用于对 40 岁以上的妇女进行每年一次或每两年一次的筛查。由于致密的乳腺组织有时会掩盖 X 光图像中的癌症,因此约有 10% 的受检妇女会被召回并接受额外的辅助检查,如超声波、数字乳腺断层扫描或磁共振成像(MRI)。这些方法都有一些缺点,如额外的辐射剂量、过度诊断和高昂的费用。最近提出了一种新的并发多光谱成像方法,通过利用乳房表面温度数据和反物理信息神经网络算法,消除了高召回率的问题。多光谱成像不使用任何有害辐射,如 X 射线,无接触,也不需要压迫乳房。它已在 23 名患者身上得到验证,为提高癌细胞的检测能力提供了一种经济有效的解决方案。据估计,它能将召回率从目前的 10%大幅降低,并相应减少活检次数。这种辅助方法建立在乳腺 X 射线照相术的基础上,依靠癌细胞较高的新陈代谢率,提供了一种安全的辅助方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Transformative Approach for Breast Cancer Detection Using Physics-Informed Neural Network and Surface Temperature Data
Early detection is the most effective defense against breast cancer. Mammography is a well-established X-ray based technique that is used for annual or biennial screening of women above age of 40. Since the dense breast tissue sometimes obscures the cancer in an x-ray image, about 10% of screened women are recalled and undergo additional adjunctive modalities, such as ultrasound, digital breast tomosynthesis or magnetic resonance imaging (MRI). These modalities have drawbacks such as additional radiation dosage, overdiagnosis and high cost. A new concurrent multi-spectral imaging approach is recently presented to eliminate the high recall rates by utilizing the breast surface temperature data with an inverse physics-informed neural network algorithm. The multi-spectral imaging does not use any harmful radiations, such as x-rays, is contact-less and does not require breast compression. It has been validated in 23 patients and offers a cost-effective solution to provide improved detection capability of cancerous cells. It is estimated to reduce the recall rates significantly from the current 10%, with a corresponding reduction in the biopsies. This adjunctive approach builds on the strength of mammography and offers a safe adjunct by relying on the higher metabolic rates of cancer cells.
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