Photodiagnosis and photodynamic therapy · 2026
Fourier transform infrared spectra and machine learning to detect low HER2 expression in breast cancer plasma.
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Klongkleaw Kanjana, Chatchawal Patutong, Tippayawat Patcharaporn, Techasen Anchalee, Wongwattanakul Molin
Аннотация
BACKGROUND: In patients with breast cancer, tumors showing low human epidermal growth factor receptor 2 (HER2) expression may not demonstrate clinical benefits from chemotherapy. Since traditional diagnostic methods for detecting HER2 expression require invasive tissue biopsies, we propose a less invasive approach that combines attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy with machine learning to detect breast cancer with low plasma HER2 expression.
METHODS: The leftover heparinized plasma with low HER2 expression from 55 breast cancer patients and 32 healthy controls were performed with ATR-FTIR spectrometer. The ten protocol was applied to preprocessed data analysis. Then, machine learning models such as partial least squares-discriminant analysis (PLS-DA) and neural network were performed. The analytical performance was calculated for accuracy, sensitivity and specificity of the predicted model of detection.
RESULTS: The infrared spectra of low HER2 expression from 55 breast cancer samples and 32 control samples were obtained and analyzed in the 1400 - 1000 cm-1, which is related to the HER2 extracellular domain structure. The neural network models achieved higher discriminative accuracy, sensitivity, and specificity at 78%, while PLS-DA showed 65% accuracy, 71% sensitivity, and 56% specificity.
CONCLUSIONS: This approach has the potential to detect low HER2 expression in less invasive samples. However, validation through larger-scale clinical trials should be considered to achieve more efficiency.
METHODS: The leftover heparinized plasma with low HER2 expression from 55 breast cancer patients and 32 healthy controls were performed with ATR-FTIR spectrometer. The ten protocol was applied to preprocessed data analysis. Then, machine learning models such as partial least squares-discriminant analysis (PLS-DA) and neural network were performed. The analytical performance was calculated for accuracy, sensitivity and specificity of the predicted model of detection.
RESULTS: The infrared spectra of low HER2 expression from 55 breast cancer samples and 32 control samples were obtained and analyzed in the 1400 - 1000 cm-1, which is related to the HER2 extracellular domain structure. The neural network models achieved higher discriminative accuracy, sensitivity, and specificity at 78%, while PLS-DA showed 65% accuracy, 71% sensitivity, and 56% specificity.
CONCLUSIONS: This approach has the potential to detect low HER2 expression in less invasive samples. However, validation through larger-scale clinical trials should be considered to achieve more efficiency.