Plasma ATR-FTIR spectroscopy combined with machine learning for nasopharyngeal carcinoma classification

Nasopharyngeal carcinoma (NPC) is often diagnosed at advanced stages, creating a need for minimally invasive approaches to support detection. This study evaluated plasma attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy, combined with machine learning, to distinguish NPC from clinically healthy individuals.

Plasma samples from 51 histologically confirmed NPC cases and 51 age- and sex-matched clinically healthy controls were analyzed across 4000-600 cm^-1. Differences at 21 spectral peaks were assessed using the Mann-Whitney U test. Seven machine-learning algorithms were evaluated using the full spectrum, fingerprint region, selected peaks, and low-rank spectral representations with repeated cross-validation and exploratory age- and sex-stratified analyses.

Thirteen peaks showed significant between-group differences in median absorbance (p < 0.01). Despite substantial overlap in the original spectral distributions, model performance in the complete cohort increased when selected peaks and low-rank spectral representations were used: the best case AUCs were 0.6303 ± 0.0384 for the full spectrum, 0.6330 ± 0.0333 for the fingerprint region, 0.7066 ± 0.0452 for selected peaks, and 0.7404 ± 0.0422 for the low-rank representation. The neural network model achieved the best overall performance using low-rank features, with an accuracy (ACC) of 0.7115 ± 0.0456. Exploratory subgroup estimates varied across age- and sex-defined subsets. However, due to limited subgroup sizes and variability in the performance estimates, interpretation and generalizability was cautioned.

Plasma ATR-FTIR spectroscopy combined with machine learning showed moderate discrimination between NPC and clinically healthy controls, with the highest performance obtained using low-rank spectral features and a feedforward neural network. Larger independent cohorts are needed for validation.