Intrinsic feature between malignant tumor cells and human normal leukocytes with statistical decision tree analysis via Raman spectroscopy

TL;DR

This paper presents a novel approach combining statistical decision tree analysis with Raman spectroscopy to accurately distinguish between malignant tumor cells and normal leukocytes, achieving over 94% accuracy.

Contribution

It introduces a new method integrating decision tree analysis with Raman spectroscopy for rapid, accurate differentiation of tumor cells and leukocytes, highlighting key spectral features.

Findings

Achieved 94.43% classification accuracy.

Identified adenine and amide I as key spectral markers.

Provided a fast spectral identification technique.

Abstract

In this study, the combination of a developing data mining technique called statistical decision tree analysis method and Raman spectroscopy was proposed to differentiate human normal leukocytes from malignant tumor cells. Statistical results obtained indicate this method possesses an admirable performance of a mean classification accuracy of 94.43% on the one hand, base adenine and amide I are recognized as potential characterizations of main- and subintrinsic biological difference in between on the other hand. Moreover, these certain Raman bands reflecting intrinsic physiological differences can be directionally extracted from whole fingerprint spectra and then provide a fast and accurate manipulation for spectrum identification.