Published online Jul 7, 2020. doi: 10.3748/wjg.v26.i25.3660
Peer-review started: March 16, 2020
First decision: April 25, 2020
Revised: May 8, 2020
Accepted: June 4, 2020
Article in press: June 4, 2020
Published online: July 7, 2020
The accurate classification of focal liver lesions (FLLs) is essential to properly guide treatment options and predict prognosis. Dynamic contrast-enhanced computed tomography (DCE-CT) is still the cornerstone in the exact classification of FLLs due to its noninvasive nature, high scanning speed, and high-density resolution. Since their recent development, convolutional neural network-based deep learning techniques has been recognized to have high potential for image recognition tasks.
To develop and evaluate an automated multiphase convolutional dense network (MP-CDN) to classify FLLs on multiphase CT.
A total of 517 FLLs scanned on a 320-detector CT scanner using a four-phase DCE-CT imaging protocol (including precontrast phase, arterial phase, portal venous phase, and delayed phase) from 2012 to 2017 were retrospectively enrolled. FLLs were classified into four categories: Category A, hepatocellular carcinoma (HCC); category B, liver metastases; category C, benign non-inflammatory FLLs including hemangiomas, focal nodular hyperplasias and adenomas; and category D, hepatic abscesses. Each category was split into a training set and test set in an approximate 8:2 ratio. An MP-CDN classifier with a sequential input of the four-phase CT images was developed to automatically classify FLLs. The classification performance of the model was evaluated on the test set; the accuracy and specificity were calculated from the confusion matrix, and the area under the receiver operating characteristic curve (AUC) was calculated from the SoftMax probability outputted from the last layer of the MP-CDN.
A total of 410 FLLs were used for training and 107 FLLs were used for testing. The mean classification accuracy of the test set was 81.3% (87/107). The accuracy/specificity of distinguishing each category from the others were 0.916/0.964, 0.925/0.905, 0.860/0.918, and 0.925/0.963 for HCC, metastases, benign non-inflammatory FLLs, and abscesses on the test set, respectively. The AUC (95% confidence interval) for differentiating each category from the others was 0.92 (0.837-0.992), 0.99 (0.967-1.00), 0.88 (0.795-0.955) and 0.96 (0.914-0.996) for HCC, metastases, benign non-inflammatory FLLs, and abscesses on the test set, respectively.
MP-CDN accurately classified FLLs detected on four-phase CT as HCC, metastases, benign non-inflammatory FLLs and hepatic abscesses and may assist radiologists in identifying the different types of FLLs.
Core tip: We developed and evaluated a deep learning-based convolutional neural network (CNN) to classify focal liver lesions (FLLs) on multiphase computed tomography. The most important highlight of the current study is that, to the best of our knowledge, this study is the first to employ four-channel input data to preserve the dynamic enhancement properties. The combination of the lesion's dynamic enhancement pattern with a CNN can imitate the image diagnosis of radiologists and is expected to improve diagnostic accuracy. It was interesting to note that the accuracy and specificity of differentiating each category from others were high. This model may become an efficient tool to assist radiologists in the classification of FLLs.