Detection of acquired radioresistance in breast cancer cell lines using Raman spectroscopy and machine learning

Tipatet, Kevin Saruni; Davison-Gates, Liam; Tewes, Thomas J.; Fiagbedzi, Emmanuel Kwasi; Elfick, Alistair; Neu, Björn; Downes, Andrew

doi:10.1039/d1an00387a

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Cited by 7 publications

(1 citation statement)

References 56 publications

(62 reference statements)

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“…The pre-processing steps for the Raman spectra were carried out as previously described. 24 This performs 4 types of pre-processing on spectral data. Raw spectra were first filtered by applying a spectral window 600–1700 cm −1 to select the ‘fingerprint’ region relating to biomolecules.…”

Section: Methodsmentioning

confidence: 99%

Raman spectroscopy of brain and skin tissue in a minipig model of Huntington's disease

Tipatet,

Du Boulay,

Muir

et al. 2024

Anal. Methods

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Section: Methodsmentioning

confidence: 99%

Raman spectroscopy of brain and skin tissue in a minipig model of Huntington's disease

Tipatet,

Du Boulay,

Muir

et al. 2024

Anal. Methods

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Subtype‐Specific Detection in Stage Ia Breast Cancer: Integrating Raman Spectroscopy, Machine Learning, and Liquid Biopsy for Personalised Diagnostics

Tipatet,

Hanna,

Davison‐Gates

et al. 2024

Journal of Biophotonics

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This study explores the integration of Raman spectroscopy (RS) with machine learning for the early detection and subtyping of breast cancer using blood plasma samples. We performed detailed spectral analyses, identifying significant spectral patterns associated with cancer biomarkers. Our findings demonstrate the potential for classifying the four major subtypes of breast cancer at stage Ia with an average sensitivity and specificity of 90% and 95%, respectively, and a cross‐validated macro‐averaged area under the curve (AUC) of 0.98. This research highlights efforts to integrate vibrational spectroscopy with machine learning, enhancing cancer diagnostics through a non‐invasive, personalised approach for early detection and monitoring disease progression. This study is the first of its kind to utilise RS and machine learning to classify the four major breast cancer subtypes at stage Ia.

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Self-assembled Cr2O3@nanogel/Au nanozymes to simulate peroxidase activity as a H2O2 sensor

Xia¹,

Tang²,

Jiang³

et al. 2023

Spectrochimica Acta Part A: Molecular and Biomolecular Spectros

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Detection of acquired radioresistance in breast cancer cell lines using Raman spectroscopy and machine learning

Abstract: PCA–LDA scatter plot for Raman spectra of wild-type (circles) and radioresistant (traingles) breast cancer cell lines. An accuracy of 100% is achieved in classifying radioresistant from wild-type for all 198 spectra in the test set (open markers).

Cited by 7 publications

References 56 publications

Raman spectroscopy of brain and skin tissue in a minipig model of Huntington's disease

Raman spectroscopy of brain and skin tissue in a minipig model of Huntington's disease

Subtype‐Specific Detection in Stage Ia Breast Cancer: Integrating Raman Spectroscopy, Machine Learning, and Liquid Biopsy for Personalised Diagnostics

Self-assembled Cr2O3@nanogel/Au nanozymes to simulate peroxidase activity as a H2O2 sensor

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