Automatic characterization of liver tumors from multi-phase CT images

Abstract

Distinguishing the types of liver tumors is important for determining treatment strategy. Dynamic contrast-enhanced computed tomography (DCE-CT), which captures images at multiple timings after injecting contrast medium, provides essential characteristics to distinguish tumor types without biopsy. However, recognizing such characteristics takes a lot of time for radiologists because it requires to distinguish ambiguous image features comparing multiple images. Although several studies have proposed systems that classify tumor types from DCE-CT images, these systems usually output only classification results without the basis of classification such as the tumor characteristics. In this study, we propose a novel liver tumor characterization system that analyzes multi-phase DCE-CT images to help radiologists classify tumor types. We defined a list of eight essential tumor characteristics that radiologists commonly find to distinguish tumor types such as hepatocellular carcinomas (HCC), hemangiomas, and metastases. To deal with variable number of input images, we propose three deep neural network classification models that can take both two- and three-phase DCE-CT images as input. Using a dataset consisting of 3,318 tumors with labeled characteristics, each model was trained to classify the eight characteristics and validated. Evaluation results showed high discrimination performance exceeding 91% in ROC-AUC on average.