Feasibility of automatic detection of small hepatocellular carcinoma (≤2 cm) in cirrhotic liver based on pattern matching and deep learning

Abstract

Background and objective. Early detection of hepatocellular carcinoma (HCC) is crucial for clinical management. Current studies have reported large HCC detections using automatic algorithms, but there is a lack of research on automatic detection of small HCCs (sHCCs). This study is to investigate the feasibility of automatic detection of sHCC (≤2 cm) based on pattern matching and deep learning (PM-DL) model. Materials and methods. A retrospective study included 5376 image sets from 56 cirrhosis patients (28 sHCC patients with 32 pathologically confirmed lesions and 28 non-HCC cirrhosis patients) in the training-validation cohort to build and validate the model through five-fold cross-validation. In addition, an external test cohort including 6144 image sets from 64 cirrhosis patients (32 sHCC patients with 38 lesions and 32 non-HCC cirrhosis patients) was applied to further verify the generalization ability of the model. The proposed PM-DL model consisted of three main steps: 3D co-registration and liver segmentation, screening of suspicious lesions on diffusion-weighted imaging images based on pattern matching algorithm, and identification/segmentation of sHCC lesions on dynamic contrast-enhanced images with convolutional neural network. Results. The PM-DL model achieved a sensitivity of 89.74% and a positive predictive value of 85.00% in the external test cohort for per-lesion analysis. No significant difference was observed in volumes (P = 0.13) and the largest sizes (P = 0.89) between manually delineated and segmented lesions. The DICE coefficient reached 0.77 ± 0.16. Similar performances were identified in the validation cohort. Moreover, the PM-DL model outperformed Liver Imaging Reporting and Data System (LI-RADS) in sensitivity (probable HCCs: LR-5 or LR-4, P = 0.18; definite HCCs: LR-5, P < 0.001), with a similar high specificity for per-patient analysis. Conclusion. The PM-DL model may be feasible for accurate automatic detection of sHCC in cirrhotic liver.

Imaging diagnosis of hepatocellular carcinoma: Future directions with special emphasis on hepatobiliary magnetic resonance imaging and contrast-enhanced ultrasound

“…One study developed a deep-learning algorithm for detecting HCCs on HBP images, which showed comparable performance to less-experienced radiologists on external datasets (sensitivity and specificity, 87% vs. 86% and 93% vs. 92%, respectively) [ 127 ]. Other studies also showed relatively good performance of deep-learning algorithms for detecting HCCs on CT (sensitivity, 92.0% and false-positive rate, 13.7%) or MRI (sensitivity, 89.7% and positive predictive value, 85.0% for lesions ≤2 cm) [ 128 , 129 ]. Regarding LI-RADS, one study reported that deep learning methods on MRI can distinguish between LR-3 and LR-4 or LR-5 lesions with an accuracy of 90% in the test set [ 130 ].…”

Automatic segmentation of hepatocellular carcinoma on dynamic contrast-enhanced MRI based on deep learning

“…Zheng et al (2022) performed automatic liver tumor segmentation based on threedimensional convolution and convolutional long short-term memory-based model. Additionally, diffusionweighted imaging and multi-phasic DCE were used to develop a workflow based on pattern recognition and neural network by Zheng et al (2021). The late hepatocellular phase of DCE MRI was also used for deep learningbased liver tumor segmentation by Hänsch et al (2022).…”

Progress of MRI Radiomics in Hepatocellular Carcinoma

“…Recently, Zhu et al(56) studied the application value of histogram features on IVIM-DWI in the differential diagnosis of HCC. They found that the histogram parameters of IVIM-DWI could distinguish hepatic hemangiomas, hepatic cysts, and HCC and that the volume of the pseudodiffusion coefficient and perfusion fraction had better diagnostic value than other histogram parameters(56).In recent years, DL technology has been developed and has achieved excellent performance in the classification of hepatic lesions(65)(66)(67)(68)(69)(70)(71).Hamm CA et al (65) developed a proof-ofconcept convolutional neural network (CNN)-based DL system and classified 494 hepatic lesions from six categories on MRI.…”