Page 20 - Read Online

Page 8 of 16 Zhang et al. Hepatoma Res 2020;6:30 I http://dx.doi.org/10.20517/2394-5079.2020.17

Multiple morphological imaging biomarkers including non-smooth tumor margins, irregular rim-like
arterial phase hyperenhancement, tumor multifocality, and “mosaic” architecture, are reported to be highly
correlated with MVI in HCC [62-64] . DWI and its-derived technologies have also shown great potential in
detecting MVI in HCC. The mean and minimum ADC values of HCCs with MVI are reported to be lower
[65]
than those of HCCs without MVI . In addition, a higher MK value on DKI and irregular rim enhancement
[66]
pattern are highly correlated with the presence of MVI . Similar results were reported in our previous
[37]
study [Figure 1]. The mechanism underlying these results may be associated with the formation of a more
complex microenvironment induced by MVI, such as the presence of tumor cell proliferation, necrosis,
[67]
or inflammatory damage .Therefor, a greater packed cell structure and more irregular, heterogeneous
environments are likely to occur in HCC with MVI, resulting in increased tissue diffusion, which manifest
as increased MK [66,68] . Furthermore, hypo-intensity on HBP images on Gd-EOB-DTPA enhanced-MRI in
conjunction with other clinical indicators has been proven to further improve the prediction of MVI in
HCC, and is superior in predicting early recurrence and the survival rate in HCC patients [69,70] .

AI, a branch of computer science, has emerged as a new technology to study and develop the theory,
technology and application systems for simulating, extending and expanding human intelligence. In recent
years, AI has had huge potential in medical imaging and attracted considerable attention in a range of fields
from tumor diagnosis to outcomes prediction. Radiomics, and its derived analyses such as texture analysis,
are noninvasive methods for the prediction of tumor heterogeneity, and have shown favorable predictive
accuracy of MVI status in patients with HCC [71-74] . Radiomics signatures associated with tumor size and
intra-tumoral heterogeneity were reported to be the top-ranked indicators for MVI prediction. In addition, a
radiomics nomogram incorporated with the clinical and radiological features outperform the combination of
[73]
clinical and radiological features for predicting MVI and the clinical outcomes of HCC patients .
INTRACELLULAR FAT ACCUMULATION
Intracellular fat accumulation, a common morphological characteristic of HCC, occurs in the context of
ischemia and hypoxia dueto decreased portal vein and nontumoral artery flow, and insufficient unpaired
[75]
arteries . Thus, intracellular fat accumulation is gradually increased in low-grade dysplastic nodules, high-
grade dysplastic nodules, and early-stage HCC [76,77] . However, with regard to intra-tumoral fatty infiltration
in poorly differentiated HCC, controversy exists [75,78] .

Chemical-shift MRI is the most commonly used technique to monitor the presence of intra-tumoral fat
infiltration [Figure 2]. There is a close relationship between intracellular fatty change and MVI in HCC. MVI
is more likely to occur in non-containing fatty HCCs, which means intra-tumoral fat infiltration suggests
[79]
[80]
a lower risk for MVI in HCC . Moreover, Kubota et al. , reported that macro-vesicular steatosis HCC
has a better prognosis with less portal vein invasion and a lower cumulative risk of recurrence than micro-
vesicular steatosis HCC. More related research is necessary for detecting intra-tumoral fat infiltration,
and predicting the prognosis of HCC with different histopathological characteristics in the early stage of
hepatocarcinogenesis, so as to optimize disease management and promote personalized treatment.

INVASIVE GROWTH PATTERN
Invasive growth, defined as the invasion of cancer cells into adjacent tissues and the vascular lymphatic
system, is correlated with tumor metastasis and poor prognosis. Aberrant regulation of cell migration
[81]
contributes to the progression of cancer cell invasion .

Tumors with a permeative growth pattern on CT/MRI images are frequently termed “infiltrative”, which
manifest as lesions with indistinguishable margins on CT/MRI images. The Liver Imaging Reporting and
Data System was developed to standardize the interpretation, reporting, and evaluation of patients at risk
of developing HCC, and provides an explicit interpretation of the features of “infiltrative appearance” in