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systems in terms of predictive performance (C-index ≥ 0.77) and prediction error (integrated Brier
[24]
score ≤ 0.14) . The most advanced applications of radiomics incorporate immuno-molecular and genomic
characteristics of HCC, which may aid in treatment stratification. Hectors et al. reported that qualitative and
quantitative radiomics features were significantly correlated with several immunohistochemical markers
such as CD3, CD68, CD31, PD-L1, PD1 and CTLA4 .
[25]
Response to treatment
Several radiological criteria have been proposed to assess treatment response in HCC patients, such as
Response Evaluation Criteria In Solid Tumors (RECIST) and modified RECIST (mRECIST). Radiomics
might enable a standardized, early and comprehensive evaluation of treatment response, with implications
for patient management. Most studies were applied to patients who underwent loco-regional treatments for
HCC . Kim et al. demonstrated that post-TACE OS could be predicted with a hazard ratio (HR) of 19.8 by
[10]
combining clinical (Child-Pugh score, alphafetoprotein, and tumor size) and radiomics features (surface
area-to-volume ratio, kurtosis, median, size zone variability) . Other studies reported similar results,
[26]
indicating that radiomics features extracted from pre-treatment imaging were able to predict treatment
[27]
response after TACE . Histogram analysis of apparent diffusion coefficient (ADC) maps seems to predict
TACE response, as reported by Wu et al. and Shaghaghi et al. . Yang et al. developed a radiomics
[29]
[30]
[28]
score composed of four features that independently affected recurrence after ablation at 1, 2, and 3 years
with AUCs of 0.79/0.72, 0.72/0.61, and 0.71/0.64 in the train and validation groups, respectively.
CHOLANGIOCARCINOMA
Radiomics may help in diagnosis and treatment of biliary tumors. Fiz et al. included in their meta-analysis
[31]
27 studies with more than 3,600 patients . Most studies focused on mass-forming intrahepatic
cholangiocarcinoma (iCC). Radiomics predicted nodal metastases (AUC = 0.73-0.90, accuracy = 0.69-0.83),
tumor grade (AUC = 0.68-0.89, accuracy = 0.70-0.82), and survival (C-index = 0.67-0.89); moreover,
radiomics features allowed differentiation of iCC from HCC, combined HCC-iCC, and inflammatory
lesions (AUC = 0.80). Differentiation of iCC from combined forms and atypical HCC may be difficult at
imaging, but differentiation is necessary to enable optimal treatment decisions.

Diagnosis
Liu et al. aimed to differentiate combined HCC-CC from iCC and HCC using MRI and CT radiomics
features . MRI-based radiomics features were the best for differentiation of HCC-CC from non-HCC-CC
[32]
(AUC = 0.77). Zhou et al. aimed to differentiate HCC-CC from mass-forming iCC by extracting radiomics
features from contrast-enhanced MR images: a radiomics nomogram including alpha-fetoprotein,
coexistent liver disease, and radiomics signature had an AUC value of 0.945 in the training cohort and 0.897
in the validation cohort .
[33]

Staging
Even using high-end preoperative imaging, understaging is a problem, as a substantial proportion of
patients with cholangiocarcinoma (CC) have occult metastases detected only on resection specimens or
through early recurrence after resection. Moreover, positive resection margins are not uncommon. For
these reasons, preoperative risk stratification should be as precise as possible to better stratify patients’
prognoses. Ji et al. developed a radiomics signature with significant association with nodal metastases in a
cohort of 155 patients (test cohort = 103 patients; validation cohort = 52 patients); by adding CA 19.9 levels
[34]
to the radiomics signature, the model had AUC of 0.846 and 0.892 in the two cohorts, respectively . Chu
et al. included 203 iCC patients from two centers . Clinical and CT-derived radiomics features were
[35]
selected to develop two models predictive of unbeneficial surgery (i.e., with macroscopic residual tumor or
definitely unresectable). The radiomic model had higher AUC than the clinical model (0.804 vs. 0.590; P =