Nanoscale Prognosis of Colorectal Cancer Metastasis from AFM Image Processing of Histological Sections

SIMPLE SUMMARY: This work advances colorectal cancer (CRC) metastatic prognosis by identifying morphological metastatic markers from image processing of atomic force microscopy (AFM) images of CRC histological sections. High orders of variograms of residuals of Gaussian-filtered images define metastatic/non-metastatic thresholds with 97.7 nm spatial resolution. The metastatic/non-metastatic differentiation defines irreversible hierarchical and complexity levels. ABSTRACT: Early ascertainment of metastatic tumour phases is crucial to improve cancer survival, formulate an accurate prognostic report of disease advancement, and, most importantly, quantify the metastatic progression and malignancy state of primary cancer cells with a universal numerical indexing system. This work proposes an early improvement to metastatic cancer detection with 97.7 nm spatial resolution by indexing the metastatic cancer phases from the analysis of atomic force microscopy images of human colorectal cancer histological sections. The procedure applies variograms of residuals of Gaussian filtering and theta statistics of colorectal cancer tissue image settings. This methodology elucidates the early metastatic progression at the nanoscale level by setting metastatic indexes and critical thresholds based on relatively large histological sections and categorising the malignancy state of a few suspicious cells not identified with optical image analysis. In addition, we sought to detect early tiny morphological differentiations indicating potential cell transition from epithelial cell phenotypes of low metastatic potential to those of high metastatic potential. This metastatic differentiation, which is also identified in higher moments of variograms, sets different hierarchical levels for metastatic progression dynamics.