Preoperative Prediction of a Rare and Highly Aggressive Subtype of Hepatocellular Carcinoma Based on Multimodal Imaging and Clinical Indicators

Abstract

Purpose: To develop and validate a reliable preoperative non-invasive diagnostic model for dual-phenotype hepatocellular carcinoma (DPHCC) by integrating multimodal imaging and clinical indicators, thereby facilitating clinical decision-making.

Patients and methods: 222 pathologically confirmed patients (61 with DPHCC, 161 with non-DPHCC) were retrospectively enrolled in this study and randomly assigned to training and validation cohorts in an 8:2 ratio. Serological and multimodal imaging characteristics were analyzed. Univariate and multivariate logistic regression analyses identified independent DPHCC predictors and built a nomogram. Model performance and clinical utility were assessed by receiver operating characteristic (ROC) and decision curve analysis (DCA) curve respectively. The calibration curve was used to verify the model. Recurrence-free survival (RFS) was assessed using Kaplan-Meier and Log rank tests.

Results: In multivariate analysis, age (OR=0.91; P < 0.001), LDH (OR=1.03; P=0.002), PT (OR=0.14; P < 0.001), AFP (OR=4.04; P=0.019), Adler grade (OR=0.17; P=0.037), non-enhancing area (OR=8.30; P=0.004), arterial phase hyperenhancement (OR=0.12; P=0.015) and enhancing capsule (OR=0.32; P=0.04) were independent predictors of DPHCC. The nomogram achieved a robust predictive performance with C-index (0.92 vs 0.87) and accuracy (0.87 vs 0.86) in the training and validation cohorts. In addition, the calibration curve and DCA also showed good model performance. DPHCC patients had significantly lower RFS than non-DPHCC patients (P = 0.037).

Conclusion: A nomogram was established for non-invasive prediction of DPHCC risk utilizing multimodal imaging combined with clinical indicators to help achieve personalized treatment.

Keywords: combined model; contrast enhanced magnetic resonance imaging; contrast enhanced ultrasound; dual-phenotype hepatocellular carcinoma; hepatocellular carcinoma; nomogram.

Figure 1

The flowchart of patient inclusion and exclusion.

Figure 2

A 77-year-old woman with NDPHCC in the left outer lobe, and the mass was indicated by the blue arrow. (A) Grayscale ultrasound showed a hypoechoic, homogeneous and regular tumor. (B) Color Doppler imaging showed a grade III blood flow. (C) The arterial phase of CEUS showed uniform and significant enhancement. (D) The delayed phase showed low enhancement. (E) T2-weighted image showed a mass with higher signal. (F) Diffusion-weighted image (b = 500) showed a high signal mass. (G) The CEMRI showed significantly enhancement in arterial phase. (H) The delayed phase showed low enhancement and enhancing capsule.

Figure 3

A 51-year-old woman with DPHCC in the right posterior lobe, and the mass was indicated by the blue arrow. (A) Grayscale ultrasound showed a hypoechoic, heterogeneous and irregular tumor. (B) Color Doppler imaging showed a grade I blood flow. (C) The arterial phase of CEUS showed uneven hyper-enhancement and non-enhancing area. (D) The delayed phase showed low enhancement and non-enhancing area. (E) T2-weighted image showed a moderately high signal mass with slightly higher signal margins. (F) Diffusion-weighted image (b = 500) showed a high signal ring in the periphery of the mass and a slightly higher signal in the center. (G) The arterial phase showed rim-APHE. (H) The portal venous phase showed mild decreased enhancement.

Figure 4

Nomogram for DPHCC prediction. The column chart for predicting DPHCC was created based on the 8 predictors above. To use a nomogram, place each patient’s value on each variable axis and draw a line up to determine the number of points received for each variable value. The sum of these numbers is located on the total point axis and a line is drawn down on the bottom axis to determine the probability of DPHCC.

Figure 5

Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) of the training and validation cohorts. (A) ROC curve of the training cohort. (B) ROC curve of the validation cohort. (C) DCA of the training cohort. (D) DCA of the validation cohort. The nomogram had good discriminative performance, and the area under ROC curve (AUC) in the training and validation cohorts were 0.92 and 0.87, respectively. The DCA showed that when the threshold probabilities for patients in our study were 2–100% and 9%-100%, respectively, using a nomogram to predict DPHCC was more beneficial than treating all patients or treating none.

Figure 6

Calibration curves comparing predicted and actual DPHCC probabilities. (A) Calibration curve of the nomogram in the training cohort. (B) Verify the calibration curve of the nomogram in the validation cohorts. The calibration curve describes the agreement between the predicted DPHCC probability and the observed DPHCC results. The X-axis represents the predicted probability of DPHCC. The Y-axis represents the actual DPHCC probability. The long-dotted line represents the perfect prediction of the ideal model. The short-dashed line indicates the predicted value of the column graph, and the solid line indicates the estimated value after bootstrap correction. A well-calibrated curve for a nomogram will be close to the ideal line.