Frontiers in oncology · 2026
Preoperative prediction of microvascular invasion in hepatocellular carcinoma ≤5 cm based on contrast-enhanced ultrasound features and LI-RADS categorization: a multicenter study.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zhang Qian, Pang Chuan, Liu Zhilong, Wang Ruining, Fan Wenwen, Zhao Yufang, Zhang Yanjing, Liang Ping, Liu Liping
Аннотация
OBJECTIVES: To investigate the predictive value of contrast-enhanced ultrasound (CEUS) features combined with Liver Imaging Reporting and Data System (LI-RADS) categorization for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) ≤5 cm.
METHODS: This multicenter retrospective study enrolled adult patients with HCC ≤5 cm who underwent CEUS between January 2018 and December 2025. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic analysis were employed to screen risk factors and establish the MVI prediction model. Three models were developed: a clinical model, an ultrasound model, and a combined model. The performance of the combined model was evaluated and validated using the area under the receiver operating characteristic (AUC), calibration curves, decision curve analysis (DCA), and the Hosmer-Lemeshow test.
RESULTS: A total of 261 patients with HCC ≤5 cm were enrolled. Patients were divided into a derivation cohort (n=209) and an external validation cohort (n=52). 85 patients (32.57%) were MVI-positive. LASSO regression and multivariate analysis revealed that AFP, tumor margin, enhanced homogeneity, mosaic, and LI-RADS were significantly associated with MVI. The combined model showed an AUC of 0.880 (95% CI: 0.832-0.929) in the derivation cohort and 0.832 (95% CI: 0.703-0.960) in the external validation cohort. Calibration curves revealed excellent agreement between the model's predicted probability of MVI and the actual observed outcomes. DCA confirmed the excellent net benefits.
CONCLUSION: This model can noninvasive preoperative prediction of MVI risk in patients with HCC ≤5 cm, offering a reliable reference for clinicians in developing personalized treatment strategies.
METHODS: This multicenter retrospective study enrolled adult patients with HCC ≤5 cm who underwent CEUS between January 2018 and December 2025. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic analysis were employed to screen risk factors and establish the MVI prediction model. Three models were developed: a clinical model, an ultrasound model, and a combined model. The performance of the combined model was evaluated and validated using the area under the receiver operating characteristic (AUC), calibration curves, decision curve analysis (DCA), and the Hosmer-Lemeshow test.
RESULTS: A total of 261 patients with HCC ≤5 cm were enrolled. Patients were divided into a derivation cohort (n=209) and an external validation cohort (n=52). 85 patients (32.57%) were MVI-positive. LASSO regression and multivariate analysis revealed that AFP, tumor margin, enhanced homogeneity, mosaic, and LI-RADS were significantly associated with MVI. The combined model showed an AUC of 0.880 (95% CI: 0.832-0.929) in the derivation cohort and 0.832 (95% CI: 0.703-0.960) in the external validation cohort. Calibration curves revealed excellent agreement between the model's predicted probability of MVI and the actual observed outcomes. DCA confirmed the excellent net benefits.
CONCLUSION: This model can noninvasive preoperative prediction of MVI risk in patients with HCC ≤5 cm, offering a reliable reference for clinicians in developing personalized treatment strategies.