Discriminating Breast Cancer Patients by Spectral Analysis of Arterial Pulse Waveforms.

Abstract

Background/aim: Breast cancer (BC) remains a major health threat, highlighting the need for accessible screening. Characteristics of the cardiovascular system can be altered in BC patients. This study explored a noninvasive method combining arterial pulse-waveform analysis and classification to compare arterial pulse-waveform characteristics between BC patients (n = 51)) and age-matched controls (n = 76)).

Methods: From one-minute radial waveform recordings, 40 harmonic indices (amplitude proportions, phase angles, and their variability) were derived. Significant differences in pulse-waveform indices were observed between groups, suggesting altered pulse-wave transmission conditions in BC patients. Two classifiers were compared: machine learning and a novel pulse-distribution analysis (PDA).

Results: PDA demonstrated an acceptable discrimination with an AUC of 0.74, outperforming machine learning (AUC up to 0.58). Subgroup analysis by clinical factors such as TNM stage showed variable performance, with AUCs ranging from 0.66-0.93. Discrimination appeared to improve with disease progression (AUC 0.86 for stage IV), suggesting that tumor-induced vascular alterations may affect pulse-wave transmission.

Conclusion: The primary contribution of this work is the identification of distinct pulse-waveform signatures in BC patients, providing a physiological basis for future development of noninvasive screening tools. The proposed method, characterized by its low cost and ease of use, warrants further validation in larger cohorts.