Radiomics and nomogram of magnetic resonance imaging for preoperative prediction of microvascular invasion in small hepatocellular carcinoma

Figure 1 Flow diagram of the study cohort. A total of 415 participants were included in this multi-center study.

Figure 2 Flow diagram for the radiomics of machine learning. A: Construct radiomics models, the volume of interest was delineated by experienced radiologists and three-dimensional images were formed, extracting quantitative features by software; B: Pathologic examination, firstly obtaining specimens of small hepatocellular carcinoma tissue, and then taking pathologic diagnosis for microvascular invasion; C: Data cleaning and dimensions reduction; D: Establishing the model for predicting microvascular invasion by machine learning. LR: Logistic regression; KNN: K-Nearest neighbor; SVM: Support vector machine; RF: Random forest.

Figure 3 Receiver operating characteristic curve of different radiomics models for diagnosis microvascular invasion in small hepatocellular carcinoma (testing set). A: T1 weighted imaging [area under curve (AUC) was 0.776; 95% confidence interval (CI): 0.611-0.895]; B: T2 weighted imaging [AUC, 0.813; 95% confidence interval (CI): 0.651-0.922]; C: Diffusion weighted imaging (AUC, 0.971; 95%CI: 0.858-0.999); D: Arterial phase (AUC, 0.788; 95%CI: 0.642-0.894); E: Portal vein phase (AUC, 0.790; 95%CI: 0.630-0.904); F: Hepatobiliary phase (AUC, 0.990; 95%CI: 0.911-1.000). T1W1: T1 weighted imaging; T2W2: T2 weighted imaging; AUC: Area under curve; DWI: Diffusion weighted imaging; AP: Arterial phase; PVP: Portal vein phase; HBP: Hepatobiliary phase.

Figure 4 Nomogram of diffusion weighted imaging radiomics model to predict microvascular invasion in patients with small hepatocellular carcinoma. A: The nomogram was developed with radiomics signature and clinicoradiological factors. A vertical line was drawn according to the value of radiomics scores to determine the corresponding value of points. Similarly, the points of tumor markers were determined. The total points were the sum of the two points above. Finally, a vertical line was made according to the value of the total points to determine the probability of microvascular invasion (MVI); B: Validity of the predictive performance of the nomogram in estimating the risk of MVI presence in the training cohort; C: Validity of the predictive performance of the nomogram in estimating the risk of MVI presence in the validation cohort. AFP: Alpha-fetoprotein; DWI: Diffusion weighted imaging; Rad_score: Radiomics signatures score.

Figure 5 Decision curve analysis. A and B: Decision curve analysis of the prediction model for training (A) and testing (B) cohort. Y-axis represents the net benefit, which is calculated by gaining true positives and deleting false positives. The X-axis is the probability threshold. The curve of the radiomics and combined nomogram over the clinical features that integrated AFP and radiological signatures showed the greatest benefit. AFP: Alpha-fetoprotein; Rad_score: Radiomics signatures score.