Screening ovarian cancers with Raman spectroscopy of blood plasma coupled with machine learning data processing
Screening ovarian cancers with Raman spectroscopy of blood plasma coupled with machine learning data processing
复制标题
利用血浆拉曼光谱结合机器学习数据处理来筛查卵巢癌
DOI：
10.1016/j.saa.2021.120355
复制
发表时间：
2021-09-13
影响因子：
4.4
通讯作者：
Yu, Jin
中科院分区：
文献类型：
--
作者：
Chen, Fengye;Sun, Chen;Yu, Jin
卵巢癌的死亡率与其早期发现率低密切相关。在寻找有效诊断方法的过程中，血液的拉曼光谱特征作为一种有前途的技术，允许简单、快速、微创和成本有效地检测癌症，特别是卵巢癌。虽然拉曼光谱已被证明是有效的检测卵巢癌相对于正常对照，二元分类仍然是理想的相对于真实的临床实践。这项工作考虑了95名最初怀疑患有卵巢癌并最终确诊为癌症或囊肿的女性患者。此外，79名正常对照完成了样本的集合。这样的样品收集为我们提出了一个研究案例，其中三元分类应该用所收集的血液样品的拉曼光谱与合适的光谱数据处理算法相结合来实现。从医学和数据的角度来看，囊肿病例的出现大大缩短了不同人群之间的距离，使他们的区别更加困难，因为中间囊肿病例可以共享癌症和正常病例的特定特征。经过适当的光谱预处理后，我们首先证明了三种样品的拉曼光谱之间的不同行为的证据。这种差异在高维空间中被进一步可视化，其中癌症和正常病例的数据点被单独聚类，而囊肿病例的数据被分散到分别由癌症和正常病例占据的区域中。最后，我们开发并测试了基于机器学习算法的三元分类模型的集合，其中包含2个后续的二元分类步骤，允许识别
The mortality of ovarian cancer is closely related to its poor rate of early detection. In the search of an efficient diagnosis method, Raman spectroscopy of blood features as a promising technique allowing simple, rapid, minimally-invasive and cost-effective detection of cancers, in particular ovarian cancer. Although Raman spectroscopy has been demonstrated to be effective to detect ovarian cancers with respect to normal controls, a binary classification remains idealized with respect to the real clinical practice. This work considered a population of 95 woman patients initially suspected of an ovarian cancer and finally fixed with a cancer or a cyst. Additionally, 79 normal controls completed the ensemble of samples. Such sample collection proposed us a study case where a ternary classification should be realized with Raman spectroscopy of the collected blood samples coupled with suitable spectroscopic data treatment algorithms. In the medical as well as data points of view, the appearance of the cyst case considerably reduces the distances among the different populations and makes their distinction much more difficult, since the intermediate cyst case can share the specific features of the both cancer and normal cases. After a proper spectrum pretreatment, we first demonstrated the evidence of different behaviors among the Raman spectra of the 3 types of samples. Such difference was further visualized in a high dimensional space, where the data points of the cancer and the normal cases are separately clustered, whereas the data of the cyst case were scattered into the areas respectively occupied by the cancer and normal cases. We finally developed and tested an ensemble of models for a ternary classification with 2 consequent steps of binary classifications, based on machine learning algorithms, allowing identification with