Published online Jan 14, 2021. doi: 10.3748/wjg.v27.i2.189
Peer-review started: November 2, 2020
First decision: December 3, 2020
Revised: December 7, 2020
Accepted: December 16, 2020
Article in press: December 16, 2020
Published online: January 14, 2021
Processing time: 69 Days and 24 Hours
The point at which transarterial chemoembolization (TACE) should be continued or stopped is currently not addressed by any guideline to our knowledge. Repeated TACE cycles are, however, associated with an increase of related side effects and liver damage, potentially preventing an even greater survival advantage. In the era of personalized oncology, radiomics has allowed digitally encrypted medical images to be transformed into high-throughput quantitative features that provide information on patient prognosis.
In previous studies, patients with high Assessment for Retreatment with Transarterial Chemoembolization (ART) or α-fetoprotein, Barcelona Clinic Liver Cancer, Child-Pugh, and Response score (ABCR) scores tended to have a poor prognosis. Nonetheless, in terms of predictive ability, neither score was reliable enough to allow for clinical decision-making. Although previous studies have shown the prognostic value of computed tomography (CT) radiomic features for different cancer sites, there is scarcity of multi-centre radiomics research on TACE refractoriness.
The purpose of this study was to develop and validate a CT-based radiomics nomogram for the pre-treatment prediction of TACE refractoriness.
Our study consisted of a training dataset (n = 137) and an external validation dataset (n = 81) of patients with clinically/pathologically confirmed hepatocellular carcinoma who underwent repeated TACE from March 2009 to March 2016. The radiomics features were retrospectively extracted from preoperative CT images of the arterial phase. The radiomics signature was built by least absolute shrinkage and selection operator (LASSO) regression. The CT-based radiomics nomogram incorporating clinical risk factors was built by multivariable logistic regression analysis. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. We used the concordance index to conduct head-to-head comparisons of the radiomics nomogram with the other four models (ART score, ABCR score, CT-based radiomics signature, and clinical model).
Eight features were selected to build the radiomics signature using the LASSO regression model. The CT-based radiomics nomogram included the radiomics score (HR = 3.9, 95% confidence interval: 3.1-8.8, P < 0.001) and four clinical factors and classified patients into high-risk (score > 3.5) and low-risk (score ≤ 3.5) groups with markedly different prognoses (overall survival: 12.3 mo vs 23.6 mo, P < 0.001). The accuracy of the nomogram was considerably higher than that of the other four models (ART score, ABCR score, CT-based radiomics signature, and clinical model). The calibration curve and decision curve analyses verified the usefulness of the CT-based radiomics nomogram for clinical practice.
The CT-based radiomics nomogram is valuable in preoperatively predicting TACE refractoriness, which may aid interventional radiologist in determining the optimal treatment approach.
First, additional information, such as gene sequence data or the molecular pathway, might be necessary for better interpretation of radiomics features, and this issue is left for future research. Second, larger prospective multicenter studies are needed to externally validate our newly constructed model in the future.