Raman spectroscopy and machine learning for the classification of breast cancers

Zhang, Lihao; Li, Chengjian; Peng, Di; Yi, Xiaofei; He, Shuai; Li, Fengxiang; Zheng, Xiangtai; Huang, Wei E.; Zhao, Liang; Huang, Xia

doi:10.1016/j.saa.2021.120300

Cited by 91 publications

(61 citation statements)

References 52 publications

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“…The current study revealed an increased amount of lipids in the two TNBC BL1 cell lines (MDA-MB-468 and HCC-1143), which is in accordance with the infrared and Raman data formerly obtained by the authors for the TNBC MSL MDA-MB-231 cell line [ 33 ] that evidenced a high lipid content, particularly regarding glycerophospholipids known to be augmented in cancer cells [ 51 ]. In addition, other studies have reported an increase in the lipid content in breast cancer cells as compared to normal breast cells [ 43 , 52 ]. Several authors have shown a correlation between the rise in cytoplasmic lipid droplets and the increase in cancer aggressiveness [ 38 , 52 , 53 ]—the number of lipid droplets in non-malignant breast cells being reported as two- and four times lower than in non-TNBC and TNBC cells, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…They are label-free, deliver chemical image maps apart from average biochemical information and can be reliably applied to both in vitro and in vivo conditions. Hence, there is an increasing number of reported studies on the application of infrared and Raman spectroscopies to monitor cells, both live and fixed (e.g., healthy from cancer discrimination and metabolic response to drugs) and tissues (e.g., cancer diagnosis and treatment monitoring) [ 4 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ]. Thanks to their distinctive characteristics and ease of automation, these techniques constitute an improved approach for cancer detection when compared to the currently used diagnostic methods such as the gold standard histopathological assessment (which lacks sensitivity and specificity at an early stage of the disease).…”

Section: Introductionmentioning

confidence: 99%

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Who’s Who? Discrimination of Human Breast Cancer Cell Lines by Raman and FTIR Microspectroscopy

Santos

Martins

Carvalho

et al. 2022

Cancers

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(1) Breast cancer is presently the leading cause of death in women worldwide. This study aims at identifying molecular biomarkers of cancer in human breast cancer cells, in order to differentiate highly aggressive triple-negative from non-triple-negative cancers, as well as distinct triple-negative subtypes, which is currently an unmet clinical need paramount for an improved patient care. (2) Raman and FTIR (Fourier transform infrared) microspectroscopy state-of-the-art techniques were applied, as highly sensitive, specific and non-invasive methods for probing heterogeneous biological samples such as human cells. (3) Particular biochemical features of malignancy were unveiled based on the cells’ vibrational signature, upon principal component analysis of the data. This enabled discrimination between TNBC (triple-negative breast cancer) and non-TNBC, TNBC MSL (mesenchymal stem cell-like) and TNBC BL1 (basal-like 1) and TNBC BL1 highly metastatic and low-metastatic cell lines. This specific differentiation between distinct TNBC subtypes—mesenchymal from basal-like, and basal-like 1 with high-metastatic potential from basal-like 1 with low-metastatic potential—is a pioneer result, of potential high impact in cancer diagnosis and treatment.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Who’s Who? Discrimination of Human Breast Cancer Cell Lines by Raman and FTIR Microspectroscopy

Santos

Martins

Carvalho

et al. 2022

Cancers

View full text Add to dashboard Cite

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“…Zhang et al interrogated five breast cancer and one healthy breast cell lines with RS [ 39 ], using PCA-DFA (Discriminant Factor Analysis) and PCA-SVM to classify spectra. The latter technique in particular was well able to separate healthy from cancerous cell lines with an accuracy of 99.0%.…”

Section: Resultsmentioning

confidence: 99%

“…Of those studies splitting data at the level of subject or sample, the accuracies were: 84.4%, 99%, 81.8%, 98.3%, 83%, 87%, 92.9%, 81.3% and 62.5% [ 20 , 22 , 24 , 30 , 31 , 32 , 34 , 35 , 40 ]. Of those studies in which the level of split was not explicitly stated, the accuracies were: 86.0%, 96.7%, 80.2%, 99.0%, 92.0%, 90.5%, 99.5%, 98.8%, 96.9% and 99.0% [ 16 , 17 , 18 , 21 , 27 , 33 , 36 , 37 , 38 , 39 ]. No attempt has been made to statistically compare these groups, as might be performed during a meta-analysis, as the various study aims and methodologies are too heterogeneous to make such comparisons statistically valid.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature

et al. 2022

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Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited their routine use in clinical settings. Traditional machine learning models have been used to help exploit this information, but recent advances in deep learning have the potential to improve the field. However, there are a number of potential pitfalls with both traditional and deep learning models. We conduct a literature review to ascertain the recent machine learning methods used to classify cancers using Raman spectral data. We find that while deep learning models are popular, and ostensibly outperform traditional learning models, there are many methodological considerations which may be leading to an over-estimation of performance; primarily, small sample sizes which compound sub-optimal choices regarding sampling and validation strategies. Amongst several recommendations is a call to collate large benchmark Raman datasets, similar to those that have helped transform digital pathology, which researchers can use to develop and refine deep learning models.

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“…Previous studies have shown these breast cancer subtypes have unique Raman features ( 16 , 69 ). Machine learning was conducted to determine the extent to which these features can be used to segregate these subtypes and be augmented using the metabolic dimension of excess methionine.…”

Section: Methodsmentioning

confidence: 99%

Imaging Sub-Cellular Methionine and Insulin Interplay in Triple Negative Breast Cancer Lipid Droplet Metabolism

et al. 2022

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Triple negative breast cancer (TNBC) is a particularly aggressive cancer subtype that is difficult to diagnose due to its discriminating epidemiology and obscure metabolome. For the first time, 3D spatial and chemometric analyses uncover the unique lipid metabolome of TNBC under the tandem modulation of two key metabolites – insulin and methionine - using non-invasive optical techniques. By conjugating heavy water (D2O) probed Raman scattering with label-free two-photon fluorescence (TPF) microscopy, we observed altered de novo lipogenesis, 3D lipid droplet morphology, and lipid peroxidation under various methionine and insulin concentrations. Quantitative interrogation of both spatial and chemometric lipid metabolism under tandem metabolite modulation confirms significant interaction of insulin and methionine, which may prove to be critical therapeutic targets, and proposes a powerful optical imaging platform with subcellular resolution for metabolic and cancer research.

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Raman spectroscopy and machine learning for the classification of breast cancers

Cited by 91 publications

References 52 publications

Who’s Who? Discrimination of Human Breast Cancer Cell Lines by Raman and FTIR Microspectroscopy

Who’s Who? Discrimination of Human Breast Cancer Cell Lines by Raman and FTIR Microspectroscopy

Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature

Imaging Sub-Cellular Methionine and Insulin Interplay in Triple Negative Breast Cancer Lipid Droplet Metabolism

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