Low-Cost Commercial Integrated Spectral Sensor for Revealing Breast Cancer Margins

Breast malignancy (BM) poses a significant global health challenge, impacting millions worldwide. Precise detection of BM margins is essential in clinical pathology assessments, aiding in surgical margin delineation to effectively reduce recurrence risks. Our proposal involves implementing a cost-effective commercial optical imaging system tailored for distinguishing between malignant and normal tissues using infrared (IR) wavelengths. This system incorporates a low-cost, small and non-invasive spectral sensor (NeoSpectra-Micro) based on Fourier transform IR principles utilizing monolithic MEMS technology, operating within the 1350 to 2500 nm range. Employing this setup, we captured spectral signatures from 30 malignant and 30 normal tissue samples sourced from diverse patient cohorts, analyzing them with detector software. Subsequently, we normalized the data by subtracting the mean value to eliminate offset and scaled it to a range of (-1 to 1) by dividing by the maximum value. These steps enable clear differentiation between normal and BM tissues based solely on pattern, minimizing amplitude effects. Diffused reflected spectra exhibited notable visual differences between BM and normal tissues across three ranges: 1580–1630 nm, 1720–1740 nm, and 2150–2205 nm. Through data analysis, we identified optimal wavelength ranges of 1588 nm, 1722 nm, and 2195 nm, representing maximum differences between normal and tumor tissues. This study underscores the IR-optical sensor's potential as an effective adjunct to clinical pathology. Nonetheless, integration into current protocols necessitates large-scale clinical studies with statistically significant sample sizes.