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

• Published in: European radiology Vol. 31; no. 9; pp. 7047 - 7057
• Main Authors: Kim, Dong Wook, Lee, Gaeun, Kim, So Yeon, Ahn, Geunhwi, Lee, June-Goo, Lee, Seung Soo, Kim, Kyung Won, Park, Seong Ho, Lee, Yoon Jin, Kim, Namkug
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Computed tomography; Deep learning; Diagnostic Radiology; Hepatocellular carcinoma; Image processing; Image segmentation; Imaging; Imaging Informatics and Artificial Intelligence; Integration; Internal Medicine; Interventional Radiology; Liver cancer; Machine learning; Malignancy; Medical imaging; Medicine; Medicine & Public Health; Model testing; Multiphase; Neural networks; Neuroradiology; Radiology; Risk; Test sets; Tomography; Tuning; Ultrasound; Deep learning; Hepatocellular carcinoma; Computer-assisted radiographic image interpretation; X-ray computed tomography; Artificial intelligence
• ISSN: 0938-7994; 1432-1084; 1432-1084
• DOI: 10.1007/s00330-021-07803-2

Objectives To develop and evaluate a deep learning–based model capable of detecting primary hepatic malignancies in multiphase CT images of patients at high risk for hepatocellular carcinoma (HCC). Methods A total of 1350 multiphase CT scans of 1280 hepatic malignancies (1202 HCCs and 78 non-HCCs) in 1320 patients at high risk for HCC were retrospectively analyzed. Following the delineation of the focal hepatic lesions according to reference standards, the CT scans were categorized randomly into the training (568 scans), tuning (193 scans), and test (589 scans) sets. Multiphase CT information was subjected to multichannel integration, and livers were automatically segmented before model development. A deep learning–based model capable of detecting malignancies was developed using a mask region–based convolutional neural network. The thresholds of the prediction score and the intersection over union were determined on the tuning set corresponding to the highest sensitivity with < 5 false-positive cases per CT scan. The sensitivity and the number of false-positives of the proposed model on the test set were calculated. Potential causes of false-negatives and false-positives on the test set were analyzed. Results This model exhibited a sensitivity of 84.8% with 4.80 false-positives per CT scan on the test set. The most frequent potential causes of false-negatives and false-positives were determined to be atypical enhancement patterns for HCC (71.7%) and registration/segmentation errors (42.7%), respectively. Conclusions The proposed deep learning–based model developed to automatically detect primary hepatic malignancies exhibited an 84.8% of sensitivity with 4.80 false-positives per CT scan in the test set. 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.