Infrared micro-spectroscopy coupled with multivariate and machine learning techniques for cancer classification in tissue: a comparison of classification method, performance, and pre-processing technique

Ferguson, Dougal; Henderson, Alex; McInnes, Elizabeth F.; Lind, Rob; Wildenhain, Jan; Gardner, Peter

doi:10.1039/d2an00775d

Cited by 11 publications

(9 citation statements)

References 54 publications

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“…Statistical analysis utilised the pingouin package (v. 0.5.2) [ 47 ]. All classification algorithms utilised the F 1 score as the metric of performance [ 48 ]. The following sections detail each of the methodologies which were employed.…”

Section: Methodsmentioning

confidence: 99%

Application of Advanced Non-Linear Spectral Decomposition and Regression Methods for Spectroscopic Analysis of Targeted and Non-Targeted Irradiation Effects in an In-Vitro Model

Slattery

Nguyen

Shields

et al. 2022

IJMS

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Irradiation of the tumour site during treatment for cancer with external-beam ionising radiation results in a complex and dynamic series of effects in both the tumour itself and the normal tissue which surrounds it. The development of a spectral model of the effect of each exposure and interaction mode between these tissues would enable label free assessment of the effect of radiotherapeutic treatment in practice. In this study Fourier-transform Infrared microspectroscopic imaging was employed to analyse an in-vitro model of radiotherapeutic treatment for prostate cancer, in which a normal cell line (PNT1A) was exposed to low-dose X-ray radiation from the scattered treatment beam, and also to irradiated cell culture medium (ICCM) from a cancer cell line exposed to a treatment relevant dose (2Gy). Various exposure modes were studied and reference was made to previously acquired data on cellular survival and DNA double strand break damage. Spectral analysis with manifold methods, linear spectral fitting, non-linear classification and non-linear regression approaches were found to accurately segregate spectra on irradiation type and provide a comprehensive set of spectral markers which differentiate on irradiation mode and cell fate. The study demonstrates that high dose irradiation, low-dose scatter irradiation and radiation-induced bystander exposure (RIBE) signalling each produce differential effects on the cell which are observable through spectroscopic analysis.

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Section: Methodsmentioning

confidence: 99%

Application of Advanced Non-Linear Spectral Decomposition and Regression Methods for Spectroscopic Analysis of Targeted and Non-Targeted Irradiation Effects in an In-Vitro Model

Slattery

Nguyen

Shields

et al. 2022

IJMS

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“…Of alternative technologies considered, Fourier‐transform infrared (FTIR) microspectroscopy has been recently utilised in a range of biomedical settings 11–13 . This technology allows for imaging of sample specimens at thousands of infrared (IR) wavelengths simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Prediction of prognosis in oral squamous cell carcinoma using infrared microspectroscopy

Whitley,

Ellis,

Triantafyllou

et al. 2024

Cancer Medicine

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BackgroundEstimation of prognosis of oral squamous cell carcinoma (OSCC) is inaccurate prior to surgery, only being effected following subsequent pathological analysis of the primary tumour and excised lymph nodes. Consequently, a proportion of patients are overtreated, with an increase in morbidity, or undertreated, with inadequate margins and risk of recurrence. We hypothesise that it is possible to accurately characterise clinical outcomes from infrared spectra arising from diagnostic biopsies. In this first step, we correlate survival with IR spectra derived from the primary tumour.MethodsInfrared spectra were collected from tumour tissue from 29 patients with OSCC and subject to classification modelling.ResultsThe model had a median AUROC of 0.89 with regard to prognosis, a median specificity of 0.83, and a hazard ratio of 6.29 in univariate Cox proportional hazard modelling.ConclusionThe data suggest that FTIR spectra may be a useful early biomarker of prognosis in OSCC.

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“…Although IR spectra are difficult to interpret by direct inspection, significant progress has been made by the application of machine learning techniques. [1][2][3][4][5][6] Both FTIR and Raman spectroscopy have been used to study oral squamous cell carcinoma (OSCC) (see Byrne et al 7 for a review). In particular, Fukuyama et al 8 reported that the FTIR spectra of normal tissue have stronger contributions from keratin and collagen than abnormal tissue.…”

Section: Introductionmentioning

confidence: 99%

Metric-based analysis of FTIR data to discriminate tissue types in oral cancer

Ellis

Ingham

Whitley

et al. 2023

Analyst

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Infrared micro-spectroscopy coupled with multivariate and machine learning techniques for cancer classification in tissue: a comparison of classification method, performance, and pre-processing technique

Abstract: The visual detection, classification, and differentiation of cancers within tissues of clinical patients is an extremely difficult and time-consuming process with severe diagnosis implications. To this end, many computational approaches...

Cited by 11 publications

References 54 publications

Application of Advanced Non-Linear Spectral Decomposition and Regression Methods for Spectroscopic Analysis of Targeted and Non-Targeted Irradiation Effects in an In-Vitro Model

Application of Advanced Non-Linear Spectral Decomposition and Regression Methods for Spectroscopic Analysis of Targeted and Non-Targeted Irradiation Effects in an In-Vitro Model

Prediction of prognosis in oral squamous cell carcinoma using infrared microspectroscopy

Metric-based analysis of FTIR data to discriminate tissue types in oral cancer

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