Raman optical identification of renal cell carcinoma via machine learning
Raman optical identification of renal cell carcinoma via machine learning
复制标题
通过机器学习拉曼光学识别肾细胞癌
DOI：
10.1016/j.saa.2021.119520
复制
发表时间：
2021-02-12
影响因子：
4.4
通讯作者：
Ye, Jian
中科院分区：
文献类型：
--
作者：
He, Chang;Wu, Xiaorong;Ye, Jian
高病理肿瘤淋巴结转移（pTNM）分期分级或Fuhrman分级表明肾细胞癌（RCC）的肿瘤学结局较差。早期诊断和筛查这些RCC并相应地调整手术计划对患者非常有益。拉曼光谱是分子水平上高度特异性的指纹光谱，非常适合于无标记和非侵入性的癌症诊断。本文建立了一种基于拉曼光谱的支持向量机（SVM）模型，用于离体准确区分人肾肿瘤与正常组织和脂肪，准确率为92.89%。该模型还可用于确定肿瘤边界，与病理染色分析结果一致。该方法还可用于肾肿瘤亚型和分级的分类，准确率分别为86.79%和89.53%。因此，我们证明了拉曼光谱在快速和准确的临床诊断肾癌的巨大潜力。(C)2021爱思唯尔有限公司版权所有。
High pathologic tumor-node-metastasis (pTNM) stage grade or Fuhrman grade indicates poor oncological outcome in renal cell carcinoma (RCC). Early diagnosis and screening of these RCCs and adjust surgical planning accordingly are greatly beneficial to patients. Raman spectroscopy is a highly specific fingerprint spectrum on molecular level, pretty appropriate for label-free and noninvasive cancer diagnosis. In this work we established a Raman spectrum-based supporting vector machine (SVM) model to accurately ex vivo distinguish human renal tumor from normal tissues and fat with an accuracy of 92.89%. The model can also be used to determine tumor boundary, showing consistent results to pathological staining analysis. This method can be additionally used to accomplish the classification purposes of renal tumor subtypes and grades with an accuracy of 86.79% and 89.53%, respectively. Therefore, we prove that Raman spectroscopy has great potential in the rapid and accurate clinical diagnosis of renal cancers. (C) 2021 Elsevier B.V. All rights reserved.