Characterization of Collagen Fiber Organization in Breast Cancer via Model-Free Multiscale pSHG Image Analysis.

purposeAlterations in collagen micro-architecture are hallmarks of tumor progression. Conventional polarization second-harmonic generation (pSHG) analyses rely on rigid symmetry assumptions that often fail in heterogeneous tissue microenvironments. We present a fully automated model-free, multiscale, computational framework designed for the unbiased quantification of complex collagen organization in breast cancer tissue.

methodsBreast tumor and adjacent perilesional tissues were imaged using a custom pSHG microscope and analyzed at micro- and meso-scale levels. Collagen centerlines were extracted via U-Net-based segmentation to estimate fiber orientations, while global alignment was quantified using 2D-FFT angular spectra. Structural organization was characterized with model-free descriptors, including scalar and biaxial order parameters and semi-variogram-based spatial autocorrelation.

resultsAcross a limited proof-of-concept dataset (

conclusionsThis seminal framework provides a robust and assumption-free methodology to extract quantitative collagen descriptors across spatial scales. By integrating deep learning, frequency-domain analysis, and spatial statistics, it captures both local and long-range organizational features, supporting collagen architecture as a potential quantitative multiscale biomarker of tumor-associated extracellular matrix remodeling.