Background and AimsMost microvascular invasion (MVI)-predicting models have not considered MVI classification, and thus do not reflect true MVI effects on prognosis of patients with hepatocellular carcinoma (HCC). We aimed to develop a novel MVI-predicting model focused on MVI classification, hoping to provide useful information for clinical treatment strategy decision-making.MethodsA retrospective study was conducted with data from two Chinese medical centers for 800 consecutive patients with HCC (derivation cohort) and 250 matched patients (external validation cohort). MVI-associated variables were identified by ordinal logistic regression. Predictive models were constructed based on multivariate analysis results and validated internally and externally. The models’ discriminative ability and calibration ability were examined.ResultsFour factors associated independently with MVI: tumor diameter, tumor number, serum lactate dehydrogenase (LDH) ≥ 176.58 U/L, and γ-glutamyl transpeptidase (γ-GGT). Area under the curve (AUC)s for our M2, M1, and M0 nomograms were 0.864, 0.648, and 0.782. Internal validation of all three models was confirmed with AUC analyses in D-sets (development datasets) and V-sets (validation datasets) and C-indices for each cohort. GiViTI calibration belt plots and Hosmer-Lemeshow (HL) chi-squared calibration values demonstrated good consistency between observed frequencies and predicted probabilities for the M2 and M0 nomograms. Although the M1 nomogram was well calibrated, its discrimination was poor.ConclusionWe developed and validated MVI prediction models in patients with HCC that differentiate MVI classification and may provide useful guidance for treatment planning.

Background Hepatocellular carcinoma (HCC) is a prevalent and aggressive malignancy closely related to background chronic liver disease. This study aimed to explore predictive factors associated with background liver fibrosis burden in patients with HCC and sought to construct a practical predictive model for clinical use. Methods This large two-center retrospective cohort study evaluated data from Chinese medical centers. Uni- and multivariate ordinal logistic regression analyses were performed to identify variables associated with liver fibrosis stages. Predictive models based on variables identified by multivariate analysis were established in the Derivation Cohort and subjected to internal and external validation. Model performance was evaluated for discriminative and calibration abilities. Results Multivariate ordinal logistic regression analysis identified liver fibrosis severity score (LFSS), portal hypertension (PH) severity, plateletcrit (PCT) and model for end-stage liver disease-sodium (MELD-Na) as independent predictors of liver fibrosis stage in HCC patients. Nomograms that integrated these factors disclosed that the area under receiver operating characteristic curves (AUROCs) to predict S1 in the Derivation and External Validation cohorts were 0.850 and 0.919, respectively. Internal validation disclosed C-indexes of 0.823 and 0.833 in the Derivation and External Validation cohorts, respectively, indicating that the nomogram had good and excellent performance for distinguishing between S1 and non-S1 patients. Nomogram performance in the Derivation and External Validation cohorts, respectively, was fair and good to predict stage S2 (AUROCs 0.726, 0.806; C-indexes 0.713, 0.791); poor for S3 (AUROCs 0.648, 0.698; C-indexes 0.616, 0.666); good for S4 (AUROCs 0.812, 0.824; C-indexes 0.804, 0.792); and good for S3+S4 (AUROCs 0.806, 0.840; C-indexes 0.795, 0.811). Conclusion We propose new predictive models for the staging of background liver fibrosis in patients with HCC that can be implemented into clinical practice as important complements to hepatic imaging to inform HCC management strategy.

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