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基于高性能计算的用于结直肠癌检测的拉曼光谱优化预处理。

Optimised Pre-Processing of Raman Spectra for Colorectal Cancer Detection Using High-Performance Computing.

机构信息

Department of Physics, 7759Swansea University, Swansea, UK.

Blackett Laboratory, 4615Imperial College London, London, UK.

出版信息

Appl Spectrosc. 2022 Apr;76(4):496-507. doi: 10.1177/00037028221088320. Epub 2022 Mar 29.

Abstract

Spectral pre-processing is an essential step in data analysis for biomedical diagnostic applications of Raman spectroscopy, allowing the removal of undesirable spectral contributions that could mask biological information used for diagnosis. However, due to the specificity of pre-processing for a given sample type and the vast number of potential pre-processing combinations, optimisation of pre-processing via a manual "trial and error" format is often time intensive with no guarantee that the chosen method is optimal for the sample type. Here we present the use of high-performance computing (HPC) to trial over 2.4 million pre-processing permutations to demonstrate the optimisation on the pre-processing of human serum Raman spectra for colorectal cancer detection. The effect of varying pre-processing order, using extended multiplicative scatter correction, spectral smoothing, baseline correction, binning and normalization was considered. Permutations were assessed on their ability to detect patients with disease using a random forest (RF) algorithm trained with 102 patients (510 spectra) and independently tested with a set of 439 patients (1317 spectra) in a primary care patient cohort. Optimising via HPC enables improved performance in diagnostic abilities, with sensitivity increasing by 14.6%, specificity increasing by 6.9%, positive predictive value increasing by 3.4%, and negative predictive value increasing by 2.4% when compared to a standard pre-processing optimisation. Ultimate values of these metrics are very important for diagnostic adoption, and once diagnostics demonstrate good accuracy these types of optimisations can make a significant difference to roll-out of a test and demonstrating advantages over existing tests. We also provide tips/recommendations for pre-processing optimisation without the use of HPC. From the HPC permutations, recommendations for appropriate parameter constraints for conducting a more basic pre-processing optimisation are also detailed, thus helping model development for researchers not having access to HPC.

摘要

光谱预处理是生物医学诊断应用中拉曼光谱数据分析的重要步骤,它允许去除可能掩盖用于诊断的生物信息的不需要的光谱贡献。然而,由于预处理对于给定的样本类型的特异性以及潜在的预处理组合的数量众多,通过手动“反复试验”格式进行预处理的优化通常需要大量时间,并且不能保证所选方法对于样本类型是最优的。在这里,我们使用高性能计算 (HPC) 来尝试超过 240 万种预处理排列,以演示用于结直肠癌检测的人血清拉曼光谱预处理的优化。考虑了改变预处理顺序、使用扩展乘性散射校正、光谱平滑、基线校正、-bin 和归一化的效果。使用随机森林 (RF) 算法评估排列的能力,该算法使用 102 名患者(510 个光谱)进行训练,并在初级保健患者队列中使用 439 名患者(1317 个光谱)的一组进行独立测试。通过 HPC 进行优化可以提高诊断能力的性能,与标准预处理优化相比,敏感性提高 14.6%,特异性提高 6.9%,阳性预测值提高 3.4%,阴性预测值提高 2.4%。这些指标的最终值对于诊断的采用非常重要,一旦诊断表现出良好的准确性,这些类型的优化就可以对测试的推出和展示相对于现有测试的优势产生重大影响。我们还提供了无需使用 HPC 进行预处理优化的提示/建议。从 HPC 排列中,还详细说明了进行更基本的预处理优化的适当参数约束的建议,从而帮助没有访问 HPC 的研究人员进行模型开发。

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