Multifractal analysis of cellular ATR-FTIR spectrum as a method for identifying and quantifying cancer cell metastatic levels.

Cancer is a leading cause of mortality today. Sooner a cancer is detected, the more effective is the treatment. Histopathological diagnosis continues to be the gold standard worldwide for cancer diagnosis, but the methods used are invasive, time-consuming, insensitive, and still rely to some degree on the subjective judgment of pathologists. Recent research demonstrated that Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy can be used to determine the metastatic potential of cancer cells by evaluating their membrane hydration. In the current study, we demonstrate that the conversion of ATR-FTIR spectra using multifractal transformation generates a unique number for each cell line's metastatic potential. Applying this technique to murine and human cancer cells revealed a correlation between the metastatic capacity of cancer cells within the same lineage and higher multifractal value. The multifractal spectrum value was found to be independent of the cell concentration used in the assay and unique to the tested lineage. Healthy cells exhibited a smaller multifractal spectrum value than cancer cells. Further, the technique demonstrated the ability to detect cancer progression by being sensitive to the proportional change between healthy and cancerous cells in the sample. This enables precise determination of cancer metastasis and disease progression independent of cell concentration by comparing the measured spectroscopy derived multifractal spectrum value. This quick and simple technique devoid of observer bias can transform cancer diagnosis to a great extent improving public health prognosis worldwide.

Fractal and textural imaging identify new subgroups of patients with colorectal cancer based on biophysical properties of the cancer cells.

Colorectal cancer (CRC) can been sub-divided, based on the generation of tertiary lymphoid structures (TLS), into CRC with a Crohn's like lymphoid reaction (CLR) representing de novo formation of TLSs or CRC lacking TLSs that show Diffuse Inflammatory infiltration (DII). The association between TLS, early treatment initiation and longer survival highlights the need for deeper patient stratification that could lead to more targeted therapies. We hypothesized that such stratification might be achieved by using digital image analyses. Here we retrospectively analyzed 35 CRC patient samples classified as CLR or DII by digital analysis, focusing on the parameters Fractal dimension, Lacunarity and the textural features Angular second momentum, Correlation, Inverse difference momentum and Entropy. Significant differences in the grades of these parameters between the two patient groups provided preliminary data that additional biophysical information can divide CRC into at least 3 subgroups which encompass CLR and DII. Additional studies are needed to test if this sub-classification aids in the selection of targeted therapy for patients with CRC.