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

Abstract

Background: 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.

Purpose: 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.

Methods: 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.

Results: 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.

Conclusions: 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.