Detecting collagen by machine learning improved photoacoustic spectral analysis for breast cancer diagnostics: feasibility studies with murine models

TL;DR

This study demonstrates that machine learning-enhanced photoacoustic spectral analysis can non-invasively detect collagen, a key biomarker, with promising accuracy for early breast cancer diagnosis in murine models.

Contribution

It introduces a novel machine learning approach to improve photoacoustic spectral analysis for collagen detection, enhancing non-invasive breast cancer diagnostics.

Findings

Achieved 72% diagnostic accuracy using optimal spectra

Identified collagen-dominated wavebands with genetic algorithms

Showed superior performance over full-range spectra

Abstract

Collagen, a key structural component of the extracellular matrix, undergoes significant remodeling during carcinogenesis. However, the important role of collagen levels in breast cancer diagnostics still lacks effective in vivo detection techniques to provide a deeper understanding. This study presents photoacoustic spectral analysis improved by machine learning as a promising non-invasive diagnostic method, focusing on exploring collagen as a salient biomarker. Murine model experiments revealed more profound associations of collagen with other cancer components than in normal tissues. Moreover, an optimal set of feature wavelengths was identified by a genetic algorithm for enhanced diagnostic performance, among which 75% were from collagen-dominated absorption wavebands. Using optimal spectra, the diagnostic algorithm achieved 72% accuracy, 66% sensitivity, and 78% specificity, surpassing full-range spectra by 6%, 4%, and 8%, respectively. The proposed photoacoustic methods examine the feasibility of offering valuable biochemical insights into existing techniques, showing great potential for early-stage cancer detection.