Molecular breast cancer subtype identification using photoacoustic spectral analysis and machine learning at the biomacromolecular level

Jiayan Li; Yingna Chen; Wanli Ye; Mengjiao Zhang; Jingtao Zhu; Wenxiang Zhi; Qian Cheng

doi:10.1016/j.pacs.2023.100483

Molecular breast cancer subtype identification using photoacoustic spectral analysis and machine learning at the biomacromolecular level

Photoacoustics. 2023 Mar 29:30:100483. doi: 10.1016/j.pacs.2023.100483. eCollection 2023 Apr.

Authors

Jiayan Li¹, Yingna Chen¹, Wanli Ye¹, Mengjiao Zhang¹, Jingtao Zhu², Wenxiang Zhi³, Qian Cheng^{1

4}

Affiliations

¹ Institute of Acoustics, School of Physics Science and Engineering, Tongji University, Shanghai, China.
² School of Physics Science and Engineering, Tongji University, Shanghai, China.
³ Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China.
⁴ Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, Shanghai, China.

Abstract

Breast cancer threatens the health of women worldwide, and its molecular subtypes largely determine the therapy and prognosis of patients. However, an uncomplicated and accurate method to identify subtypes is currently lacking. This study utilized photoacoustic spectral analysis (PASA) based on the partial least squares discriminant algorithm (PLS-DA) to identify molecular breast cancer subtypes at the biomacromolecular level in vivo. The area of power spectrum density (APSD) was extracted to semi-quantify the biomacromolecule content. The feature wavelengths were obtained via the variable importance in projection (VIP) score and the selectivity ratio (Sratio), to identify the biomarkers. The PASA achieved an accuracy of 84%. Most of the feature wavelengths fell into the collagen-dominated absorption waveband, which was consistent with the histopathological results. This paper proposes a successful method for identifying molecular breast cancer subtypes and proves that collagen can be treated as a biomarker for molecular breast cancer subtyping.

Keywords: Biomacromolecules; Breast cancer; Machine learning; Molecular subtypes; Photoacoustic spectral analysis.