Published online Mar 15, 2024. doi: 10.4251/wjgo.v16.i3.857
Peer-review started: October 30, 2023
First decision: December 21, 2023
Revised: December 26, 2023
Accepted: January 29, 2024
Article in press: January 29, 2024
Published online: March 15, 2024
Processing time: 134 Days and 7.4 Hours
Recently, vessels encapsulating tumor clusters (VETC) was considered as a distinct pattern of tumor vascularization which can primarily facilitate the entry of the whole tumor cluster into the bloodstream in an invasion independent manner, and was regarded as an independent risk factor for poor prognosis in hepatocellular carcinoma (HCC).
To develop and validate a preoperative nomogram using contrast-enhanced computed tomography (CECT) to predict the presence of VETC+ in HCC.
We retrospectively evaluated 190 patients with pathologically confirmed HCC who underwent CECT scanning and immunochemical staining for cluster of differentiation 34 at two medical centers. Radiomics analysis was conducted on intratumoral and peritumoral regions in the portal vein phase. Radiomics features, essential for identifying VETC+ HCC, were extracted and utilized to develop a radiomics model using machine learning algorithms in the training set. The model’s performance was validated on two separate test sets. Receiver operating characteristic (ROC) analysis was employed to compare the identified performance of three models in predicting the VETC status of HCC on both training and test sets. The most predictive model was then used to constructed a radiomics nomogram that integrated the independent clinical-radiological features. ROC and decision curve analysis were used to assess the performance characteristics of the clinical-radiological features, the radiomics features and the radiomics nomogram.
The study included 190 individuals from two independent centers, with the majority being male (81%) and a median age of 57 years (interquartile range: 51-66). The area under the curve (AUC) for the combined radiomics features selected from the intratumoral and peritumoral areas were 0.825, 0.788, and 0.680 in the training set and the two test sets. A total of 13 features were selected to construct the Rad-score. The nomogram, combining clinical-radiological and combined radiomics features could accurately predict VETC+ in all three sets, with AUC values of 0.859, 0.848 and 0.757. Decision curve analysis revealed that the radiomics nomogram was more clinically useful than both the clinical-radiological feature and the combined radiomics models.
This study demonstrates the potential utility of a CECT-based radiomics nomogram, incorporating clinical-radiological features and combined radiomics features, in the identification of VETC+ HCC.
Core Tip: Vessels encapsulating tumor clusters (VETC) is an independent risk factor for poor prognosis in hepatocellular carcinoma (HCC) and currently determined only on histologic examination after surgical resection. We evaluated 190 patients with pathologically confirmed HCC and constructed a machine learning-based contrast-enhanced computed tomography radiomics model, performed canonical screening of features and multiple validations, and confirmed robustness on various data resources. The radiomics model showed remarkable performance in predicting the VETC subtype, and the results were reproducible, demonstrating that the approach may be applied to other patient samples. Radiomics could provide valuable information for assisting clinicians in pretreatment decision-making.