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ORIGINAL RESEARCH

Multiple Machine-Learning Fusion Model Based on Gd-EOB-DTPA-Enhanced MRI and Aminotransferase-to-Platelet Ratio and Gamma-Glutamyl Transferase-to-Platelet Ratio to Predict Microvascular Invasion in Solitary Hepatocellular Carcinoma: A Multicenter Study

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Pages 427-442 | Received 05 Dec 2023, Accepted 20 Feb 2024, Published online: 29 Feb 2024

Figures & data

Figure 1 Workflow and design of the study.

Figure 1 Workflow and design of the study.

Figure 2 Qualitative and quantitative features of HCC. Peritumoral enhancement (a); radiologic capsule ((b) complete, (c) incomplete); non-smooth margins on HBP (d); smooth margins on HBP (e); quantitative measurement in HBP (f): ROIs (mean area 100 mm2) were applied to measure the tumor signal intensity (SI), normal liver SI and right spinal muscle SI for three times on the tumors largest level of the axial HBP; peritumoral hypointensity in HBP (g).

Abbreviations: HBP, hepatobiliary phases; HCC, hepatocellular carcinoma; SI, signal intensity; ROI, regions of interest.
Figure 2 Qualitative and quantitative features of HCC. Peritumoral enhancement (a); radiologic capsule ((b) complete, (c) incomplete); non-smooth margins on HBP (d); smooth margins on HBP (e); quantitative measurement in HBP (f): ROIs (mean area 100 mm2) were applied to measure the tumor signal intensity (SI), normal liver SI and right spinal muscle SI for three times on the tumors largest level of the axial HBP; peritumoral hypointensity in HBP (g).

Table 1 The Definitions and Best Cutoff Values of Derivative Biomarkers and MR Quantitative Features for Predicting MVI in Solitary HCC

Table 2 The Baseline Characteristics of the HCC Patients in Different Cohorts

Table 3 Seven Most Important Features Were Selected Through Stepwise Forward Multivariate Logistic Regression

Figure 3 Feature selection path plots of tenfold cross-validation (A) and LASSO regression (B). The dotted vertical line represents the log (Lambda) value (Lambda=0.02257) corresponding to the number of variables of the minimum binomial deviation, where eleven features were included. Heatmaps of correlation coefficients for seven important features (C). Sort the importance of Shapley values for seven important parameters (D).

Abbreviations: APRI, aminotransferase-to-platelet ratio; GPR, gamma-glutamyl transferase-to-platelet ratio; PLT, platelet count; HBPratio3, (liver signal intensitytumor signal intensity)/liver signal intensity.
Figure 3 Feature selection path plots of tenfold cross-validation (A) and LASSO regression (B). The dotted vertical line represents the log (Lambda) value (Lambda=0.02257) corresponding to the number of variables of the minimum binomial deviation, where eleven features were included. Heatmaps of correlation coefficients for seven important features (C). Sort the importance of Shapley values for seven important parameters (D).

Table 4 Comparison of the Prediction Performance of Multiple Machine-Learning Models for Predicting MVI in Solitary HCC

Figure 4 Comparison of the ROC curves of the six machine-learning models for predicting MVI in training set (A), internal validation set (B), external validation set (C) and whole dataset set (D).

Abbreviations: ROC, the receiver operating characteristic curve; MVI, microvascular invasion.
Figure 4 Comparison of the ROC curves of the six machine-learning models for predicting MVI in training set (A), internal validation set (B), external validation set (C) and whole dataset set (D).

Figure 5 ROC curves of the ENS model for predicting MVI in training set (A), internal validation set (B), external validation set (C) and whole dataset set (D). The calibration curves of ENS in training set (E), internal validation set (F), external validation set (G) and whole dataset set (H). Decision curve analysis of ENS in training set (I), internal validation set (J), external validation set (K) and whole dataset set (L).

Abbreviations: ROC, the receiver operating characteristic curve; MVI, microvascular invasion; ENS, the ensemble model.
Figure 5 ROC curves of the ENS model for predicting MVI in training set (A), internal validation set (B), external validation set (C) and whole dataset set (D). The calibration curves of ENS in training set (E), internal validation set (F), external validation set (G) and whole dataset set (H). Decision curve analysis of ENS in training set (I), internal validation set (J), external validation set (K) and whole dataset set (L).

Figure 6 Comparison of the clinical net benefit of the seven models for predicting MVI in training set (A), internal validation set (B), external validation set (C) and whole dataset set (D).

Abbreviations: RF, Random forest; LR, Logistic regression; Tree, Decision Tree; Xgboost, eXtreme Gradient Boosting; SVM, Support Vector Machine; Knn, K-nearest neighbor; ENS, the ensemble model; MVI, microvascular invasion.
Figure 6 Comparison of the clinical net benefit of the seven models for predicting MVI in training set (A), internal validation set (B), external validation set (C) and whole dataset set (D).

Table 5 The ENS Model Diagnostic Performance for MVI of Different Sizes HCC

Figure 7 Kaplan–Meier survival curve plots according to pathological results (A) and ENS prediction results (B). The ENS model achieved excellent stratification for MVI high-risk status vs low-risk status of one- (C), two- (D), and three-years RFS (E).

Abbreviations: ENS, the ensemble model; MVI, microvascular invasion; RFS, recurrence-free survival.
Figure 7 Kaplan–Meier survival curve plots according to pathological results (A) and ENS prediction results (B). The ENS model achieved excellent stratification for MVI high-risk status vs low-risk status of one- (C), two- (D), and three-years RFS (E).