Three-Dimensional Neural Network to Automatically Assess Liver Tumor Burden Change on Consecutive Liver MRIs.

Abstract

Tumor response to therapy is often assessed by measuring change in liver lesion size between consecutive MRIs. However, these evaluations are both tedious and time-consuming for clinical radiologists. In this study, we sought to develop a convolutional neural network to detect liver metastases on MRI and applied this algorithm to assess change in tumor size on consecutive examinations. We annotated a data set of 64 patients with neuroendocrine tumors who underwent at least two consecutive liver MRIs with gadoxetic acid. We then developed a 3-D neural network using a U-Net architecture with ResNet-18 building blocks that first detected the liver and then lesions within the liver. Liver lesion labels for each examination were then matched in 3-D space using an iterative closest point algorithm followed by Kuhn-Munkres algorithm. We developed a deep learning algorithm that detected liver metastases, co-registered the detected lesions, and then assessed the interval change in tumor burden between two multiparametric liver MRI examinations. Our deep learning algorithm was concordant in 91% with the radiologists' manual assessment about the interval change of disease burden. It had a sensitivity of 0.85 (95% confidence interval (95% CI): 0.77; 0.93) and specificity of 0.92 (95% CI: 0.87; 0.96) to classify liver segments as diseased or healthy. The mean DICE coefficient for individual lesions ranged between 0.73 and 0.81. Our algorithm displayed high agreement with human readers for detecting change in liver lesions on MRI, offering evidence that artificial intelligence-based detectors may perform these tasks as part of routine clinical care in the future.