Clinically, the imaging findings of HCC and FNH showing iso-or hyperintensity in the HBP are similar, it is difficult for preoperative subjective diagnosis. Gd-EOB-DTPA enhanced MRI is a common clinical examination, especially the HBP imaging findings can be more accurate in the differential diagnosis of benign and malignant liver tumor. Radiomics has been widely used in the study of liver disease. For example, Yu et al.24 established intra-tumoral and peritumoral radiomics models to predict vessels encapsulating tumor clusters (VETC) in HCC, and Wu et al.25 found that MRI radiomics models may be useful for discriminating dual-phenotype hepatocellular carcinoma (DPHCC) from non-DPHCC before surgery. In addition, compared to conventional machine learning methods, deep learning26,27 has emerged as a state-of-the-art machine learning method in many areas. The advantage of our research lies in the combination of conventional machine learning and deep learning methods.
Our results showed that the AUC of the combined deep learning model were higher than that of other models. Although the difference was not statistically significant between the combined deep learning model and other models, the AUC value of the combined deep learning model was higher than other models. What’s more, the decision curve indicated that the combined deep learning model had the highest clinical benefit, indicating that deep leaning models had higher diagnostic efficacy than conventional and delta radiomics models.
Delta radiomics28, consisting in the analysis of feature variation at different acquisition time points, demonstrated potential utility for differential diagnosis in oncology. In this study, we extracted radiomics features of images at different scanning time points and subtracted the values of same features of each phase to obtain the delta-radiomics features. Finally, we established Pre-PP and AP-PP delta radiomics models using the features obtained from the Pre, AP and PP images. As well-differentiated HCC may not show the typical enhancement mode, while FNH usually significantly enhances in the AP and the degree of enhancement in the PP is close to normal liver parenchyma, resulting in a greater difference in texture features between Pre and AP than between AP and PP. That might explain why the AUCs of Pre-AP models were higher than those of AP-PP models. However, delta radiomics models did not perform as well as deep learning models in this study.
Generally speaking, the combined deep learning model had the highest identification efficiency because the model included conventional radiomics and deep learning features of AP, PP and HBP images, containing more comprehensive information. It can explain why deep leaning combined model had a better AUC value in discriminating HCC from FNH showing iso-or hyperintensity in the HBP. Therefore, deep learning methods combined radiomics diagnosis models may be a potential tool for the identification of HCC and FNH showing iso-or hyperintensity in the HBP, which is helpful for the clinical decision.
Cui et al.29 have developed a deep learning-based radiomics nomogram model (DLRN) in order to predict the early response to neoadjuvant chemotherapy in gastric cancer. Based on enhanced CT original images, the VOIs were delineated at the maximum level of the lesions, the radiomics and deep learning features were extracted and the model was established. DLRN performed well in the training, internal and external validation cohorts. It may be due to the irregular morphology of gastric cancer lesions in CT images with irregular boundaries, so this study extracted 2D features of the largest layer of the lesions. Compared with this study, VOIs of our study delineated the entire 3D lesion and contained more information, making the study results more comprehensive and objective.
At present, there are a few reports on the identification of HCC and FNH. Ding et al.15 chose eight radiomics features and four clinical factors (age, sex, HBsAg, and enhancement pattern) for the establishment of radiomics, clinical and combined models. The results of this study showed that the AUC of the combined model was significantly higher than that of the clinical or imaging model in both the training cohort (0.984 vs.0.937) and the validation cohort (0.972 vs.0.903), providing a non-invasive quantitative method for the differentiation between HCC and FNH in non-cirrhotic liver, with high predictive value. However, the cases of FNH included in this study were confirmed partly by pathological biopsy and partly by radiology diagnosis based on the European Association for the Study of the Liver (EASL) Clinical Practice Guidelines. The advantage of our study is that all the included FNH cases were confirmed by pathological biopsy. As a matter of fact, the difficulty in differentiating HCC and FNH is not whether both of them occur in the background of non-cirrhosis, but in the case of well differentiation of HCC, the imaging findings of the two are difficult to distinguish. As a multicenter study, we focused on the differentiation between HCC and FNH showing iso-or hyperintensity in the HBP, and established differential diagnosis models. What’s more, we set up an external test cohort to verify the repeatability of our models. However, the discrimination efficiency of the combined deep learning model in our study (internal validation cohort, AUC = 0.965) was lower than that of the clinical-radiomics model of Ding’s study (validation cohort, AUC = 0.972), the reason may be the small number of cases in our study.
Kitao Azusa et al.12analysed the imaging findings of dynamic CT and gadoxetic acid–enhanced MRI of hyperintense HCCs, FNHs and FNH-like nodule. The result showed that ADC ratio (P = 0.03) and arterial phase enhancement and washout pattern at dynamic CT (P = 0.04) were the independent factors for differentiation between hyperintense HCC and FNH. Although this study highlighted the identification of HCC and FNH showing hyperintensity in the HBP, the results were based on qualitative analysis, relying on subjective visual judgment of lesion enhancement patterns. Our study employed radiomics to investigate objective and quantitative features in higher dimensions of images, eliminating reliance on visual observations and enhancing preoperative identification accuracy.
Limitations
First of all, as a retrospective study with small sample size, we will continue to collect relevant cases in the future. What’s more, clinical features were not the focus of this study, if the number of cases can be expanded, clinical and radiomics can be combined for further study. Finally, only the images of Pre, AP, PP and HBP were segmented in this study, the other sequences, such as ADC, DWI or T2, could be added later to improve the multi-parameter study.