Development and Validation of Suspicious Liver Lesion Deep Learning Algorithm for Computed Tomography

Jacob Johnson, Kaneel Senevirathne, Lawrence Ngo
2021 · doi:10.1101/2021.12.15.21267873
preprint OA: closed CC-BY-4.0
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Abstract

In this work, we report the results of a deep-learning based liver lesion detection algorithm. While several liver lesion segmentation and classification algorithms have been developed, none of the previous work has focused on detecting suspicious liver lesions. Furthermore, their generalizability remains a pitfall due to their small sample size and sample homogeneity. Here, we developed and validated a highly generalizable deep-learning algorithm for detection of suspicious liver lesions. The algorithm was trained and tested on a diverse dataset containing CT exams from over 2,000 hospital sites in the United States. Our final model achieved an AUROC of 0.84 with a specificity of 0.99 while maintaining a sensitivity of 0.33.

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License: CC-BY-4.0