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De Robertis et al. Art Int Surg 2023;3:166-79 https://dx.doi.org/10.20517/ais.2023.18 Page 172

0.043) and reached 84.6% sensitivity and 77.1% specificity values. In the study by Qin et al., 274 patients who
underwent contrast-enhanced CT and resection were divided into training (n = 167), internal validation
[36]
(n = 70) and external validation (n = 37) cohorts . 18,120 radiomics features and 48 clinical and radiologic
characteristics were analyzed. A model based on tumor differentiation, nodal metastasis, preoperative CA
19.9 level, tumor enhancement, the “shrink score” (i.e., features extracted from a ROI comprising 50% of the
entire tumor area on arterial, portal and delayed contrast phases) had an AUC of 0.883 and performed
better than clinical and radiomic models (AUCs 0.792-0.805); the model had an accuracy of 0.826, which
[38]
[37]
was higher than AJCC 8th , MSKCC , and Gazzaniga staging systems (AUCs, 0.641, 0.617, and 0.581,
[39]
respectively).
Prediction of prognosis
Some studies have been published concerning the possibility of predicting the prognosis of patients with
CC. The largest series in this regard was reported by a multicenter, retrospective study by Park et al.,
including 345 patients with mass-forming iCC who underwent surgery . A clinical-radiologic model
[40]
including infiltrative margins, multifocality, periductal infiltration, extrahepatic infiltration, and nodal
metastases had similar performance in predicting RFS compared to the radiomics model (C-index, 0.65 vs.
0.68; P = 0.43). A clinical-radiological-radiomics model performed better than the clinical-radiologic model
(C-index, 0.71; P = 0.01), with similar performance to commonly used postoperative prognostic systems to
predict RFS (C-index, 0.71-0.73 vs. 0.70-0.73; P > 0.05) and OS (C-index, 0.68-0.71 vs. 0.64-0.74; P > 0.05).
Zhang et al. developed an MRI-based texture signature predictive for the immunophenotyping and OS of
patients with iCC: 78 patients were divided into two cohorts (inflamed iCC, n = 26; non-inflamed iCC,
n = 52) based on CD8+ T cells density; arterial phase MR images were analyzed . A combination of three
[41]
wavelets and one 3D feature were able to discriminate immunophenotyping (AUC = 0.919).

Treatment selection and response to treatment
The radiomic feature Wavelet-HLH_firstorder_Median was associated with OS, with a C-index of 0.70. CT
texture analysis can quantify vascularization and homogeneity of iCC, providing useful information in
identifying optimal candidates for trans-arterial radioembolization (TARE), as reported by Mosconi et al.:
in this study, analysis was retrospectively performed in 55 pre-TARE CT scans; iCCs showing objective
response after TARE had a higher mean histogram values (P < 0.001), GLCM homogeneity (P = 0.005) and
GLCM correlation (P = 0.030), and lower kurtosis (P = 0.043), Grey-level co-occurrence matrix (GLCM)
[42]
contrast (P = 0.004), and GLCM dissimilarity (P = 0.005) at the pre-TARE CT scan .
LIVER METASTASES
Radiomics is a promising method to predict disease recurrence and survival and improve personalized
treatment in patients with liver metastases (LM), according to three systematic reviews [43-45] . A very
important issue was reported by Kelahan et al., as inter-reader reproducibility of CT radiomics features
demonstrated tumor-size dependence, and this could explain result variability among the studies .
[46]

Prediction of Kirsten Rat Sarcoma Virus gene (KRAS) status
Kirsten Rat Sarcoma Virus gene (KRAS) mutation is associated with worse prognosis; on the other hand,
KRAS inhibitors are more and more commonly used for the treatment of metastatic colorectal cancer.
Predicting KRAS status with non-invasive methods would be, therefore, clinically useful. A meta-analysis
by Jia et al. reported on the prediction of the KRAS status in patients with colorectal LM, with pooled
sensitivity, specificity and AUC values of 0.80/0.78, 0.80/0.84 and 0.87/0.86 in the training and validation
cohorts, respectively .
[45]