1：Experimental Design and Method Selection:

The study uses a 3D Mueller-matrix polarimetry technique based on differential matrix decomposition and digital holographic reconstruction. The theoretical framework involves Mueller matrix decomposition into polarized and depolarized parts using differential matrices.

2：Sample Selection and Data Sources:

Blood samples from 18 healthy donors and 18 patients with prostate cancer were used. Each sample film was prepared by applying a drop of blood to a glass substrate and drying at room temperature, with optical thickness between

3：68 and 75 and depolarization degree between 41% and 52%. List of Experimental Equipment and Materials:

He-Ne laser (λ=

4：6328 μm), collimator, beam splitter, optical mirrors, polarizers, quarter wave plates (Achromatic True Zero-Order Waveplate), polarizer (B + W Kaesemann XS-Pro Polarizer MRC Nano), strain-free microobjective (Nikon CFI Achromat P, focal length 30 mm, numerical aperture 1, magnification 4×), digital camera (The Imaging Source DMK 41AUAS, monochrome 1/2" CCD, Sony ICX205AL, resolution 1280×960, light-sensitive pad size 7600×6200 μm, sensitivity 05 lx, dynamic range 8 bit, SNR 9 bit), personal computer, glass substrates. Experimental Procedures and Operational Workflow:

The optical setup involves forming a laser beam, splitting it into irradiating and reference beams, passing through polarization filters to create specific polarization states (0°, 90°, 45°, right-circular), illuminating the sample, recording interference patterns with a digital camera through a polarizer-analyzer, applying Fourier transform and inverse Fourier transform to reconstruct complex amplitudes in different phase planes, calculating Stokes vectors and Mueller matrices, and determining diffuse tomograms using logarithmic matrix algorithms.

5：Data Analysis Methods:

Statistical moments (mean, dispersion, skewness, kurtosis) of the diffuse tomograms were calculated using MATLAB software (operators 'hist', 'mean', 'STD', 'skewness', 'excess') to analyze distributions and differentiate between healthy and cancerous samples, with sensitivity, specificity, and balanced accuracy metrics computed.