Engineering Department, Lancaster University , Lancaster , UK.
Department of Medical Oncology, Airedale NHS Foundation Trust, Airedale General Hospital , Steeton , UK.
Expert Rev Mol Diagn. 2019 Oct;19(10):929-940. doi: 10.1080/14737159.2019.1659727. Epub 2019 Sep 8.
: Artificial intelligence (AI) and machine learning (ML) approaches in combination with Raman spectroscopy (RS) to obtain accurate medical diagnosis and decision-making is a way forward for understanding not only the chemical pathway to the progression of disease, but also for tailor-made personalized medicine. These processes remove unwanted affects in the spectra such as noise, fluorescence and normalization, and help in the optimization of spectral data by employing chemometrics. : In this study, breast cancer tissues have been analyzed by RS in conjunction with principal component (PCA) and linear discriminate (LDA) analyses. Tissue microarray (TMA) breast biopsies were investigated using RS and chemometric methods and classified breast biopsies into luminal A, luminal B, HER2, and triple negative subtypes. : Supervised and unsupervised algorithms were applied on biopsy data to explore intra and inter data set biochemical changes associated with lipids, collagen, and nucleic acid content. LDA predicted specificity accuracy of luminal A, luminal B, HER2, and triple negative subtypes were 70%, 100%, 90%, and 96.7%, respectively. : It is envisaged that a combination of RS with AI and ML may create a precise and accurate real-time methodology for cancer diagnosis and monitoring.
人工智能（AI）和机器学习（ML）方法与拉曼光谱（RS）相结合，以获得准确的医学诊断和决策，这是理解疾病进展的化学途径的一种方法，也是为个性化医疗定制的方法。这些过程消除了光谱中的不需要的影响，如噪声、荧光和归一化，并通过化学计量学帮助优化光谱数据。在这项研究中，乳腺癌组织通过 RS 与主成分（PCA）和线性判别（LDA）分析结合进行分析。使用 RS 和化学计量学方法研究组织微阵列（TMA）乳腺活检，并将乳腺活检分为 luminal A、luminal B、HER2 和三阴性亚型。监督和无监督算法应用于活检数据，以探索与脂质、胶原和核酸含量相关的内在和数据集间的生化变化。LDA 预测 luminal A、luminal B、HER2 和三阴性亚型的特异性准确性分别为 70%、100%、90%和 96.7%。预计 RS 与 AI 和 ML 的结合可能会为癌症诊断和监测创造一种精确和准确的实时方法。
