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diagnostic capability of US for early identification of HCC would be highly beneficial from a clinical point
of view. Radiomics has a potential role in this regard, although the evidence is very scarce: one single study
by Yao et al. explored radiomic analysis in 177 subjects with a liver lesion who underwent B-mode US, shear
wave elastography, and shear wave viscosity imaging; 2,560 features were extracted and five radiomic
models were constructed to differentiate between benign and malignant lesions and to diagnose HCC . The
[9]
areas under the curve (AUCs) were 0.94 for differentiation between benign and malignant lesions and 0.97
for classification of the malignant subtype. Although CT and MR are frequently performed after an initial
insufficient US examination of the liver, their role in screening has not been investigated, as both are
unsuitable due to radiation burden and high cost, respectively. Nine of the 54 studies included in the
systematic review by Harding-Theobald et al. examined aspects of HCC diagnosis based on radiomics
analysis, but most of them focused on the differentiation between hemangiomas from HCC, which is a
simple task for trained Radiologists; moreover, these studies were of low methodological quality, with a RQS
[10]
ranging from 5 to 10 . Dankerl et al. trained a computer-aided diagnosis (CAD) system based on CT
texture analysis in 372 patients with 2,325 liver lesions . The diagnostic performance of the CAD system
[11]
for differentiation between benign and malignant lesions and between cysts, hemangiomas, and metastases
was high, with AUCs of 94.5% for lesion type and 91.4% for lesion histology; overall, the CAD system
performed better than three radiologists blinded to clinical information and with access only to CT images.
Using the European Association for the Study of the Liver (EASL) guidelines, Mokrane et al. retrospectively
[12]
examined 178 patients with cirrhosis with radiologically indeterminate, biopsy-proven liver nodules . 142
and 36 patients were randomly chosen for discovery and validation cohorts, respectively. Nodules were
segmented on CT images, and 13,920 features were extracted. The radiomic signature was trained,
calibrated, and validated using machine learning for differentiation between HCC and non-HCC nodules: a
single radiomics feature had an AUC of 0.70 and 0.66 in the two cohorts. Laino et al. reviewed 11 studies on
automatic classification of liver nodules using the Liver Imaging Reporting and Data System (LI-RADS)
criteria: all studies demonstrated that radiomics performs well in the classification of liver nodules,
[13]
sometimes better than human evaluation, reaching an AUC of 0.98 either on CT or MR .

Prognostic stratification
Prognostic stratification is the most challenging and fascinating application of radiomics. This can be
obtained either by correlating radiomics features to known adverse pathological features or by directly
comparing radiomics features to overall survival (OS) and recurrence-free survival (RFS). Tumor grade has
a role in determining the prognosis of HCC patients, as disease recurrence and metastasis are more likely to
develop in high-grade HCCs . Mao et al. and Wu et al. reported high AUC values for combined
[16]
[14]
[15]
clinical and radiomics models for HCC grading prediction (0.801 and 0.800, respectively). Microvascular
invasion (MVI) has an impact on survival outcomes and disease recurrence. Studies conducted on the role
of radiomics in predicting MVI reported AUC values ranging from 0.76 to 0.94 [10,17-20] , with MR-based
studies having the highest diagnostic value in predicting MVI. Another negative prognostic factor in HCC is
vessels encapsulated tumor clusters (VETC), which are histologically defined as the presence of vessels
marked with CD34 completely encapsulating neoplastic clusters. Yu et al. developed a predictive model
based on contrast-enhanced MR images obtained after gadolinium ethoxy benzylic diethylenetriamine
pentaacetic acid (Gd-EOB-DTPA) injection, with AUC of 0.972 for preoperative prediction of VETC .
[21]
Finally, HCCs expressing biliary-specific markers such as CK19 have a higher rate of nodal metastasis and
worse prognosis after surgery . Wang et al. developed a radiomics-based model derived from MR images
[22]
with sensitivity and specificity values of 0.818 and 0.974 in the training cohort and 0.769 and 0.818 in the
validation cohort . Disease recurrence after surgery negatively affects patients’ prognosis. According to 17
[23]
studies included in the meta-analysis by Harding-Theobald et al., radiomic models can predict early
recurrence after surgery, with AUCs ranging from 0.71 to 0.86 . The radiomics model developed by Ji et al.
[10]
to predict early disease recurrence outperformed non-radiomics models and commonly used staging