IR Spectroscopy and Linear Support Vector Machine Analysis of Colorectal Liver Metastasis
James V. Coe, Heather C. Allen, Charles. L. Hitchcock, Edward W. Martin
TL;DR
This paper uses infrared spectroscopy and machine learning to analyze liver metastasis from colorectal cancer and develop a faster cancer detection probe.
Contribution
The study introduces a Decision Contribution Spectrum derived from IR spectroscopy to aid in tumor/nontumor classification and design a faster cancer probe.
Findings
A Decision Contribution Spectrum was derived to represent average spectral contributions in tumor/nontumor classification.
A linear SVM model was trained using Leave-One-Case-Out to avoid overtraining and enable simple feature selection.
Results suggest potential for a fast mid-IR cancer probe with reduced wavenumber range and increased intervals.
Abstract
The Colorectal Liver Metastasis (CLM) library contains 756,096 full range Fourier transform infrared (FTIR) microscope imaging spectra of 14 frozen tissue sections from 7 different consenting patients with liver metastasis of colorectal origin. A subset of 30 windows was defined in predominant tumor or nontumor regions for the training or testing of linear support vector machine (SVM) models. Since the number of consenting patients was small, the primary purpose of this work was to establish a physical chemistry perspective on the infrared (IR) spectroscopy of metastatic liver cancer using the large number of available spectra. The linear SVM model was trained with a Leave-One-Case-Out strategy to avoid case leakage, minimize overtraining, and offer simple feature selection. The primary result is the derivation and measurement of a spectral form for the average contribution to the…
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Taxonomy
TopicsSpectroscopy Techniques in Biomedical and Chemical Research · Spectroscopy and Chemometric Analyses · Hepatocellular Carcinoma Treatment and Prognosis