Deep learning–based algorithm to detect primary hepatic malignancy in multiphase CT of patients at high risk for HCC

Of late, deep learning-based algorithms have been successfully applied to various medical imaging modalities, ranging from chest radiographs to head CT scans. Compared to other body parts, there is a paucity of data regarding the application of deep learning-based algorithms in the liver. This can be attributed to the following reasons: First, unlike other body parts usually relying on single-phase images, analyzing multiple-phase images of CT or MRI is essential in order to characterize focal hepatic lesions, which requires integration of the complex information; second, the complex vascular anatomy of the liver and its adjacent organs may mimic abnormalities in the liver. To overcome such obstacles, we developed and evaluated a deep learning-based model to detect hepatic malignancies based on multichannel integration and automatic segmentation of the liver using Mask R-CNN. Our model showed high (84.8%) sensitivity with less than 5 false positives per CT scan on the test set. We believe that our study sheds light on the application of deep learning-based algorithms to detect primary hepatic tumors which have continued to increase in their incidences worldwide. Key points Image processing, including multichannel integration of multiphase CT and automatic liver segmentation, enabled the application of a deep learning-based model to detect primary hepatic malignancy. Our model exhibited a sensitivity of 84.8% with a false-positive rate of 4.80 per CT scan. Article: Deep learning-based algorithm to detect primary hepatic malignancy in multiphase CT of patients at high risk for HCC Authors: Dong Wook Kim, Gaeun Lee, So Yeon Kim, Geunhwi Ahn, June-Goo Lee, Seung Soo Lee, Kyung Won Kim, Seong Ho Park, Yoon Jin Lee & Namkug Kim