Denisov Sergey, Blinchevsky Benjamin, Friedman Jonathan, Gerbelli Barbara, Ajeer Ash, Adams Lois, Greenwood Charlene, Rogers Keith, Mourokh Lev, Lazarev Pavel
Matur UK Ltd., 5 New Street Square, London EC4A 3TW, UK.
Institut de Chimie Physique, UMR8000, CNRS, Université Paris-Saclay, Bât. 349, 91405 Orsay, France.
Cancers (Basel). 2024 Jul 9;16(14):2499. doi: 10.3390/cancers16142499.
With breast cancer being one of the most widespread causes of death for women, there is an unmet need for its early detection. For this purpose, we propose a non-invasive approach based on X-ray scattering. We measured samples from 107 unique patients provided by the Breast Cancer Now Tissue Biobank, with the total dataset containing 2958 entries. Two different sample-to-detector distances, 2 and 16 cm, were used to access various structural biomarkers at distinct ranges of momentum transfer values. The biomarkers related to lipid metabolism are consistent with those of previous studies. Machine learning analysis based on the Random Forest Classifier demonstrates excellent performance metrics for cancer/non-cancer binary decisions. The best sensitivity and specificity values are 80% and 92%, respectively, for the sample-to-detector distance of 2 cm and 86% and 83% for the sample-to-detector distance of 16 cm.
乳腺癌是女性最常见的死因之一,因此对其早期检测存在未满足的需求。为此,我们提出了一种基于X射线散射的非侵入性方法。我们测量了由“乳腺癌现在组织生物样本库”提供的107名独特患者的样本,总数据集包含2958个条目。使用了2厘米和16厘米两种不同的样品到探测器距离,以在不同的动量转移值范围内获取各种结构生物标志物。与脂质代谢相关的生物标志物与先前研究的结果一致。基于随机森林分类器的机器学习分析在癌症/非癌症二元决策方面表现出优异的性能指标。对于2厘米的样品到探测器距离,最佳灵敏度和特异性值分别为80%和92%,对于16厘米的样品到探测器距离,分别为86%和83%。