Abstract
In recent years, the diagnosis of brain tumors has been investigated with attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy on dried human serum samples to eliminate spectral interferences of the water component, with promising results. This research evaluates ATR-FTIR on both liquid and air-dried samples to investigate "digital drying" as an alternative approach for the analysis of spectra obtained from liquid samples. Digital drying approaches, consisting of water subtraction and least-squares method, have demonstrated a greater random forest (RF) classification performance than the air-dried spectra approach when discriminating cancer vs control samples, reaching sensitivity values higher than 93.0% and specificity values higher than 83.0%. Moreover, quantum cascade laser infrared (QCL-IR) based spectroscopic imaging is utilized on liquid samples to assess the implications of a deep-penetration light source on disease classification. The RF classification of QCL-IR data has provided sensitivity and specificity amounting to 85.1% and 75.3% respectively.
Highlights
In 2018, the global cancer burden was reported to amount to over 18 million new cases with almost 10 million deaths; worldwide, during their lifetime, one man in five and one woman in six develop cancer, which is lethal for one man in eight and one woman in eleven [1]
Notwithstanding the high standard deviation (SD) values of both test set (TS) specificity outputs, the models produced high precision and F1 score values above 90%, suggesting stability and reliability of both models
Both classifications were based on a wide range of spectral features, but only the classification of the airdried dataset used the spectral features of both amide I and amide II (1700-1500 cm−1) to discriminate the disease state
Summary
In 2018, the global cancer burden was reported to amount to over 18 million new cases with almost 10 million deaths; worldwide, during their lifetime, one man in five and one woman in six develop cancer, which is lethal for one man in eight and one woman in eleven [1]. Using PRFFECT v2, the spectral range was reduced to the 1800 to 1000 cm−1 region, and baseline correction (second derivative with a window size of five points) and vector normalization were applied to all the spectra, as in the air-dried dataset. The classification model of liquid spectra showed lower results; sensitivity and specificity amounted to 89.9% and 81.2% respectively.
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