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
Vessels encapsulating tumor clusters (VETC) is an independent risk factor for poor prognosis in hepatocellular carcinoma (HCC) and patients with VETC+ HCC show shorter overall survival and disease-free survival and are more prone to progression and metastasis relative to patients with VETC- HCC. So far, VETC is currently determined only on histologic examination after surgical resection.
Preoperative diagnosis of VETC status in HCC is of great significance for predicting the prognosis of HCC patients and determining treatment strategies.
This study aimed to develop and validate a preoperative nomogram based on contrast-enhanced computed tomography (CECT) scanning combined with radiomics and clinical-radiological features to provide a preoperative reference for accurate prediction of VETC status in patients with HCC.
This was a retrospective, diagnostic study conducted from January 2017 to March 2023, at two centers. The study included 190 (training set: 106; internal test set: 47; external test set: 37) HCC patients who underwent CECT. Variance threshold, SelectKBest, the least absolute shrinkage and selection operator algorithm and multivariable logistic regression analysis were used to select the useful features and transform them into models. Receiver operating characteristic analysis was employed to compare the identified performance of models in predicting the VETC status of HCC on both training and test sets.
Among 190 individuals used for radiomics modeling, with the majority being male (81%) and a median age of 57 years (interquartile range: 51-66), 94 (49%) were confirmed to have the VETC subtype. The nomogram model included clinical-radiological features and 13 radiomics features and showed good performance for predicting the VETC subtype, with area under the curves of 0.859, 0.848, and 0.757 in the training set, internal test set, and external test set, respectively. The radiomics nomogram outperformed any clinical-radiological feature and the combined radiomics models in terms of clinical predictive abilities, according to a decision curve analysis.
The findings of this research indicate that a nomogram, developed using clinical-radiological features and combined radiomics features, holds the capability to accurately forecast the VETC status of HCC.
Our findings may be useful for preoperative identification of VETC subtype in HCC, which could help select HCC patients with poor prognosis, early recurrence, and sorafenib benefit.