Structural and spectral morphometry and diagnosis of lung tumors

Kong, Xin; Wang, Fuli; Guo, Shanshan; Wang, Hanqiu; Lü, Zhiwei; Xu, Cong; Guan, Yu; Kong, Lingnan; Li, Jing; Wei, Gongxiang; Liu, Huiqiang

doi:10.1016/j.infrared.2022.104229

Cited by 3 publications

(1 citation statement)

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“…The Fourier-transform infrared spectroscopy (FTIR) is widely used in cancer screening, characterized as a simple, label-free, non-invasive technique. Many potential IMFs for disease diagnostics have been reported (e.g., brain [ 14 ], lung [ 15 ], prostate [ 16 ], ovarian [ 17 ], and breast [ 18 ] cancers; metabolic changes related to health monitoring [ 19 , 20 ]). However, whether IMFs can be systematically used to identify DTC type, subtype and stage remain poorly understood, impeding their applicability for DTC.…”

Section: Introductionmentioning

confidence: 99%

Fast and Deep Diagnosis Using Blood-Based ATR-FTIR Spectroscopy for Digestive Tract Cancers

et al. 2022

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Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) of liquid biofluids enables the probing of biomolecular markers for disease diagnosis, characterized as a time and cost-effective approach. It remains poorly understood for fast and deep diagnosis of digestive tract cancers (DTC) to detect abundant changes and select specific markers in a broad spectrum of molecular species. Here, we present a diagnostic protocol of DTC in which the in-situ blood-based ATR-FTIR spectroscopic data mining pathway was designed for the identification of DTC triages in 252 blood serum samples, divided into the following groups: liver cancer (LC), gastric cancer (GC), colorectal cancer (CC), and their different three stages respectively. The infrared molecular fingerprints (IMFs) of DTC were measured and used to build a 2-dimensional second derivative spectrum (2D-SD-IR) feature dataset for classification, including absorbance and wavenumber shifts of FTIR vibration peaks. By comparison, the Partial Least-Squares Discriminant Analysis (PLS-DA) and backpropagation (BP) neural networks are suitable to differentiate DTCs and pathological stages with a high sensitivity and specificity of 100% and averaged more than 95%. Furthermore, the measured IMF data was mutually validated via clinical blood biochemistry testing, which indicated that the proposed 2D-SD-IR-based machine learning protocol greatly improved DTC classification performance.

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