Integrated Nomograms for Preoperative Prediction of Microvascular Invasion and Lymph Node Metastasis Risk in Hepatocellular Carcinoma Patients
The aim of the present work is to develop and validate accurate preoperative nomograms to predict microvascular invasion (MVI) and lymph node metastasis (LNM) in hepatocellular carcinoma.
Patients and Methods
A total of 268 patients with resected hepatocellular carcinoma (HCC) were divided into a training set (n = 180), in an earlier period, and a validation set (n = 88), thereafter. Risk factors for MVI and LNM were assessed based on logistic regression. Blood signatures were established using the least absolute shrinkage and selection operator algorithm. Nomograms were constructed by combining risk factors and blood signatures. Performance was evaluated using the training set and validated using the validation set. The clinical values of the nomograms were measured by decision curve analysis.
The risk factors for MVI were hepatitis B virus (HBV) DNA loading, portal hypertension, Barcelona liver clinic (BCLC) stage, and three computerized tomography (CT) imaging features, namely tumor number, size, and encapsulation, while only BCLC stage, Child–Pugh classification, and tumor encapsulation were associated with LNM. The nomogram incorporating both risk factors and blood signatures achieved better performance in predicting MVI in the training and validation sets (C-indexes of 0.828 and 0.804) than the LNM nomogram (C-indexes of 0.765 and 0.717). Calibration curves also demonstrated a good fit. The decision curves indicate significant clinical usefulness.
The novel validated nomograms for HCC patients presented herein are noninvasive preoperative tools that can effectively predict the individualized risk of MVI and LNM, and this predictive power can aid doctors in explaining the illness for patient counseling.
This work was supported by the National Natural Science Foundation of China (Nos. 81572407, 81602112, 81672405), Young Teachers Training Program of Sun Yat-sen University (19ykpy112), Key Project of the Natural Science Foundation of Guangdong Province, China (No. 4210016041), Science and Technology Program of Guangdong Province, China (Nos. 2015A030313096, 2016A030313184), Natural Science Foundation of Guangzhou, China (No. 4250016043), a grant (163) from the Key Laboratory of Malignant Tumor Molecular Mechanism and Translational Medicine of Guangzhou Bureau of Science and Information Technology, a grant (KLB09001) from the Key Laboratory of Malignant Tumor Gene Regulation and Target Therapy of Guangdong Higher Education Institutes, and a grant from the Guangdong Science and Technology Department (2017B030314026).
The authors declare that they have no conflicts of interest related to this manuscript.
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