Liver metastasis early detection using fMRI based statistical model

Abstract

We present a novel method for computer aided early detection of liver metastases. The method used fMRI-based statistical modeling to characterize colorectal hepatic metastases and follow their early hemodynamical changes. Changes in hepatic hemodynamics were evaluated from T2*-W fMRI images acquired during the breathing of air, air-CO2, and carbogen. A classification model was built to differentiate between metastatic and healthy liver tissue. The model was constructed from 128 validated fMRI samples of metastatic and healthy mice liver tissue using histogram-based features and SVM classification engine. The model was subsequently tested with a set of 32 early, non-validated fMRI samples. Our model yielded an accuracy of 84.38% with 80% precision.