Work

K-Means Cluster Analysis of Multiphotometric Mid-Infrared Absorption Maps for Label-Free Delineation of Biochemically Distinct Tissue Compartments in Head and Neck Squamous Cell Carcinoma

Year: 2026

Type: article

Abstract: Conventional histopathological diagnostics rely on morphological assessment of stained tissue sections, requiring extensive sample preparation and subjective expert interpretation. Mid-infrared (MIR) imaging offers a complementary approach by providing spatially resolved, label-free access to the intrinsic biochemical composition of tissue without exogenous contrast agents. This work introduces a preprocessing and analysis pipeline for multiphotometric MIR data, applied to formalin-fixed, paraffin-embedded tissue sections from two patients with histopathologically confirmed head and neck squamous cell carcinoma. Combining differential scattering correction, sub-pixel channel registration, and automated tissue segmentation with unsupervised K-Means clustering, the pipeline achieves label-free discrimination of biochemically distinct tissue compartments. K-Means clustering identified four distinct clusters, of which three corresponded to tissue compartments with protein-to-lipid ratios tentatively consistent with epithelial, tumor-associated, and stromal compartments. The resulting cluster maps showed partial spatial correspondence with mIF reference stainings targeting epithelial and stromal markers, supporting the potential of this approach for label-free tissue characterization in digital pathology.

Source: Applied Sciences

DOI: 10.3390/app16168242