Both updated in 2018, the EASL and LI-RADS criteria are currently the most widely used diagnostic criteria for HCC. However, concerns have been raised for both criteria regarding their applicability in Asian cohort and with hepatobiliary-specific contrast agents. Advances in radiomics have led to improved tumor-heterogeneity quantification and may assist in liver lesion characterization [9]. In this prospective study, we found that the multi-sequence-based MR radiomics signature, the LI-RADS v2018 and the EASL v2018 demonstrated comparable diagnostic accuracies for HCC in high-risk patients.
First, we constructed a multi-sequence-based MR radiomics signature in the training cohort and compared its diagnostic accuracy with EASL and LI-RADS criteria exclusively in the validation cohort to eliminate the effect of overfitting. We found that the AUCs of the radiomics signature were similar to EASL and LI-RADS criteria irrespective of lesion size and the presence of underlying cirrhosis. Notably, in non-cirrhotic patients, the radiomics signature demonstrated 100% specificity, which was significantly higher than both EASL (p = 0.008) and LI-RADS (p = 0.045) criteria, with an excellent AUC of 0.923. Since HBV chronic infection is currently the leading risk factor for HCC in Asian countries [3] and in this context many HCCs can develop without cirrhosis, the radiomics signature may play a pivotal role in increasing the diagnostic specificity and overall accuracy for these patients. However, the radiomics signature was less sensitive than EASL criteria, particularly in cirrhotic livers and for lesions≤2 cm, and these might have been explained by the fact that radiomics signatures constructed in small lesions could not usually provide sufficient biological information in a reliable fashion, as many such small lesions have not developed in the full spectrum [19].
Extracted from clinical radiologic images, radiomics features can indicate the gene expression profiles of HCC [20] and reveal key phenotypic characteristics including tumor growth and vascular invasion [21,22,23]. In our multi-sequence-based radiomics signature, most extracted imaging features belonged to the gray-level co-occurrence matrix (61%, 11/18) and run-length matrix (28%, 5/18) categories. Gray-level co-occurrence matrix parameters can depict tumor texture described by pixel spatial relationships [24]. Run-length matrix features enable evaluation of the complex 3D structures labelled with the same grey level values and have been reported to indicate HCC aggressiveness on Gd-EOB-DTPA-enhanced MR imaging [19]. However, the one-to-one correlations between numerous radiomics features and complex tumor biology processes are still unclear and need to be explored in further studies.
Interestingly, we found that the radiomics-clinical model incorporating predictive clinical markers showed no diagnostic benefit compared with the sole radiomics signature. This finding highlighted the central role of imaging examinations in HCC diagnostic workflow and indicated that clinical markers may provide limited information for liver lesion characterization in high-risk patients.
Afterwards, we compared the performances between EASL and LI-RADS criteria in the combined cohort comprising all patients. Both criteria demonstrated similar diagnostic accuracies irrespective of lesion size and the underlying cirrhosis status, which were in line with the study of Ronot et al [25]. However, despite that both EASL and LI-RADS were developed and modified in order to be nearly 100% specific, we reported relatively low specificities of both criteria. These results were not in accordance with previous studies [25,26,27,28], in which the specificities of previous EASL and LI-RADS criteria reached up to 87.6–98.6% [25, 26] and 83.6–100% [25,26,27,28], respectively.
Therefore, we explored origins of the restricted specificities on a per-lesion level. Among all false-positive cases, 9 (Fig. 4) were misclassified by both EASL and LI-RADS criteria (cHCC-CCA: n = 3; ICCA: n = 2; neuroendocrine tumor: n = 2; inflammatory pseudotumor: n = 1; angioleiomyolipoma: n = 1), 7 exclusively by EASL criteria (ICCA: n = 5; cHCC-CCA: n = 1; dysplastic nodule: n = 1) and 1 exclusively by LI-RADS criteria (ICCA). 85% (6/7) of the false-positive lesions misdiagnosed exclusively by EASL criteria presented the “targetoid appearance”, a target-like imaging morphology as a result of the highly cellular peripheral area surrounding the central fibrotic/ischemic stroma according to LI-RADS criteria [5]. This feature is highly indicative of ICCA, cHCC-CCA and other non-HCC malignancies. In our study, the “targetoid appearance” was significantly more common in non-HCC malignancies (75.0%) than in HCCs (7.5–9.8%) (both p < 0.001), as previously reported [7, 29]. Thus, a possible approach to improve the specificity of EASL criteria for HCC is to eliminate the effect of the “targetoid appearance” from the diagnostic algorithm.
However, neither EASL nor LI-RADS criteria demonstrated satisfactory specificities even after eliminating the effect of the “targetoid appearance”, particularly in differentiating between HCC and non-HCC malignancies in cirrhotic patients. One possible explanation was that 49% (112/229) of the included lesions were>5 cm. As larger lesions are more likely to demonstrate significant intratumoral heterogeneity and atypical imaging features, differential diagnosis of these tumors can be particularly challenging due to considerable clinical and imaging overlaps. By subgroup analysis, we reported the lowest specificities for both EASL and LI-RADS criteria in nodules>5 cm, which might have affected the overall diagnostic results substantially. Another likely explanation for the limited specificities was that 64% (134/211) of the included patients were cirrhotic, and small duct type ICCAs and cHCC-CCAs, can mimic HCCs in cirrhotic patients [30,31,32]. Similarly, Choi et al reported a relatively low specificity (87%) for LI-RADS v2017 in differentiating between HCC, ICCA and cHCC-CCA in HBV-predominant patients [32]. As both EASL and LI-RADS were developed in Western countries, where hepatitis C virus infection is the most important risk factor for HCC [2, 4], the diagnostic dilemma caused by these mimickers in chronic HBV patients may not be well addressed by either EASL or LI-RADS criteria.
In summary, the radiomics signature demonstrated comparable AUC for HCC with the v2018 EASL and LI-RADS but significantly higher specificity in non-cirrhotic patients, which may be clinically beneficial for patient with chronic HBV infection. However, the sensitivity of it was limited and the diagnostic results were difficult to interpret. In addition, radiomics results are prone to overfitting and the influence of imaging collection and modality variation [33, 34]. Thus, one of the key aspects of applying radiomics results in daily clinical practices is optimal acquisition and integration of curated data in a standardized and reproducible manner.
The EASL criterion is currently the most widely used diagnostic criteria for HCC. It is sensitive for small lesions, easy to apply and does not require the use of advanced imaging techniques. However, its accuracy might be restricted by relatively low specificity. LI-RADS empowers HCC probability assessment by integrating various imaging features with standardized interpretation and reporting. However, the diagnostic performances of LI-RADS were suboptimal in our HBV-predominant cohort. Apart from the geographical discrepancies of HCC between Western and Eastern cohorts, another possible explanation for the suboptimal performance of LI-RADS in this study might be the fact that LI-RADS was predominantly designed for MR using extracellular contrast agents instead of Gd-EOB-DTPA. Therefore, further tailoring of the system in Asian cohort using Gd-EOB-DTPA is necessary to optimize patient management. In addition, all LI-RADS ancillary features are weighted equally and optional, but some features (e.g. hepatobiliary phase hypointensity and restricted diffusion) may merit more emphasis or weighting [35]. Notably, combining LR-4 with LR-5 [26, 27] might be a possible approach to improve the sensitivity of LI-RADS in Eastern cohort.
This study has several limitations. First, the consecutive prospective cohort consisted of limited numbers of non-HCC and small HCC lesions. The small sample sizes of these specific categories of hepatic nodules might introduce significant selection bias to our diagnostic results. However, only patients with reliable pathological results were included, and many patients with small HCCs or non-HCC lesions were excluded because they were not candidates for surgery (e.g., some non-HCC benign lesions), received alternative therapies (e.g., ablation for small HCCs) or did not have conclusive histopathologic results. However, a different study design, such as using either histopathologic diagnosis or imaging follow-up as the reference standard might provide a larger number of these lesions. Second, we did not conduct multicenter external validation for the radiomics models due to dramatic variations in MR imaging protocols and surgical procedures across different centers. To overcome this limitation, we assessed the performance of the radiomics-clinical model in an independent validation cohort in our center. However, further prospective studies with multicenter large-scale external validation are warranted to assess the reproducibility and generalizability of the reported findings.