Research Progress in Predicting Hepatocellular Carcinoma with Portal Vein Tumour Thrombus in the Era of Artificial Intelligence

Figures & data

Table 1 Research Progress in Prediction of HCC Portal Vein Cancer Thrombus

Author/Trial/Year	Design	Patients	Model	Statistical Approach	Forecast Content	Forecast Effectiveness
Huang et al 2022^Citation45	RT	105vs.26	Nomogram based on rad score, clinical features and dosimetric parameters	Multivariate regression analysis	OS after radiotherapy in HCC patients with PVTT	C Index: 0.73 (95% CI, 0.67–0.79), AUC: 0.71 (95% CI, 0.62–0.79)
Cheng et al 2023^Citation46	RT	69vs.31	Random survival forest model, image omics model		OS in patients with HCC and PVTT were treated by transarterial chemoembolization with drug-eluted strains	C index: Training cohort: 0.759; Verification queue: 0.730
Cheng et al 2023^Citation46	RT	69vs.31	Clinical model, imaging model		OS in patients with HCC and PVTT were treated by transarterial chemoembolization with drug-eluted strains	C index: Training cohort: 0.814; Verification queue: 0.792
Wu et al 2020^Citation47	RT	70	Multivariate Cox regression risk model	Multivariate Cox regression analysis	OS in PVTT patients undergoing stereotactic radiotherapy	Experimental group: AUC=0.859 (CI: 0.770–0.948) Comparison group: AUC=0.761 (CI: 0.641–0.881)
Zhang et al 2023^Citation48	RT	94	Predictive models integrating clinical and MRI features	Univariate and multivariate Cox regression analysis	OS after surgical resection in PVTT patients undergoing preoperative enhanced MRI	High risk group vs low risk group (11.7vs.25.0 months, p<0.001)
Fu et al 2021^Citation49	RT	281vs.85	Optimal model combining clinical/radiological factors and imaging omics features	Multi-task deep learning network	The probability of future large vessel invasion in HCC patients without vascular invasion	Training set AUC: 0.877 Verification set AUC: 0.836 Time to great vessel invasion: Training: hazard ratio [HR] = 0.073, 95% confidence interval [CI]: 0.032 ~ 0.167, p < 0.001, verification: HR = 0.090, 95% CI: 0.022 ~ 0.366, p < 0.001) Overall survival (training: HR = 0.344, 95% CI: 0.246 ~ 0.547, p < 0.001, verification: HR = 0.489, 95% CI: 0.279 ~ 0.859, p = 0.003)
Wei et al 2021^Citation50	RT	154vs,72	Integrated clinical-imaging model	Cox regression analysis	The probability of future large vessel invasion in HCC patients without vascular invasion	Training set AUC: 0.986 Verification set AUC: 0.979

Huang YM, Wang TE, Chen MJ, et al. Radiomics-based nomogram as predictive model for prognosis of hepatocellular carcinoma with portal vein tumor thrombosis receiving radiotherapy. Front Oncol. 2022;12:906498. doi:10.3389/fonc.2022.906498

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Cheng S, Hu G, Jin Z, Wang Z, Xue H. CT-based radiomics nomogram for prediction of survival after transarterial chemoembolization with drug-eluting beads in patients with hepatocellular carcinoma and portal vein tumor thrombus. Eur Radiol. 2023;33(12):8715–8726. doi:10.1007/s00330-023-09830-7

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Wu K, Shui Y, Sun W, Lin S, Pang H. Utility of radiomics for predicting patient survival in hepatocellular carcinoma with portal vein tumor thrombosis treated with stereotactic body radiotherapy. Front Oncol. 2020;10:569435. doi:10.3389/fonc.2020.569435

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