A Nomogram Estimation for the Risk of Microvascular Invasion in Hepatocellular Carcinoma Patients Meeting the Milan Criteria

Pan, Chenggeng; Liu, Xi; Zou, Bei; Chin, Wenjie; Zhang, Weichen; Ye, Yufu; Liu, Yuanxing; Yu, Jun

doi:10.1080/08941939.2021.1893411

Cited by 2 publications

(1 citation statement)

References 40 publications

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“…Recently, there have been many studies to predict the presence of MVI preoperatively and the risk of developing MVI in patients with HCC, and many prediction models have been constructed. In particular, some research institutions have developed models for imaging and radiology [10][11][12][13][14][15][16][17][18]. But so far no research institution has developed models to predict the presence of MVI in rHCC patients before surgery.…”

Section: Introductionmentioning

confidence: 99%

A nomogram for preoperative prediction of microvascular invasion in ruptured hepatocellular carcinoma

Xia

Zhang

Ndhlovu

et al. 2023

European Journal of Gastroenterology &Amp; Hepatology

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Background and aim Microvascular invasion (MVI) is defined as the presence of micrometastatic cancer cell emboli in hepatic vessels, including small vessels, and at present, researchers believe that is an important factor for early postoperative recurrence and survival. Here, we developed and validated a preoperative predictive model for the presence of MVI in patients with ruptured hepatocellular carcinoma (rHCC). Methods We retrospectively collected data for 210 rHCC patients who underwent staged hepatectomy at Wuhan Tongji Hospital, and 91 patients who underwent staged hepatectomy at Zhongshan People’s Hospital between January 2010 and March 2021. Then, the former was used as the training cohort and the latter was used as the validation cohort. Logistic regression was used to screen for variables associated with MVI, and these variables were used to construct nomograms. We used R software to assess the discrimination, calibration ability, as well as clinical efficacy of nomograms. Results Multivariate logistic regression analysis identified four risk factors independently associated with MVI: max tumor length [odds ratio (OR) = 1.385; 95% confidence interval (CI), 1.072–1.790], number of tumors (OR = 2.182; 95% CI, 1.129–5.546), direct bilirubin (OR = 1.515; 95% CI, 1.189–1.930), and alpha-fetoprotein (cutoff = 400 ng/mL) (OR = 2.689; 95% CI, 3.395–13.547). Nomograms were built from the four variables and they were tested for discrimination and calibration, and the results were good. Conclusion We developed and validated a preoperative predictive model for the presence of MVI in patients with ruptured HCC. This model can help clinicians identify patients at risk of MVI and make better treatment options.

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