Novel model combining contrast-enhanced ultrasound with serology predicts hepatocellular carcinoma recurrence after hepatectomy

Value of Multimodal Data From Clinical and Sonographic Parameters in Predicting Recurrence of Hepatocellular Carcinoma After Curative Treatment

OBJECTIVE

The objective of the work described here was to assess the value of the combination of pre-operative multimodal data-including clinical data, contrast-enhanced ultrasound (CEUS) information and liver stiffness measurement (LSM) based on 2-D shear wave elastography (SWE)-in predicting early (within 1 y) and late (after 1 y) recurrence of hepatocellular carcinoma (HCC) after curative treatment.

METHODS

We retrospectively included 101 patients with HCC who met the Milan criteria and received curative treatment. The multimodel data from clinical parameters, LSM by 2-D SWE and CEUS enhancement patterns were collected. The association between different variables in HCC recurrence was accessed using a Cox proportional hazard model. On the basis of the independent factors of early recurrence, models with different source variables were established (Clinical Model, CEUS-Clinical Model, SWE-Clinical Model, CEUS-SWE-Clinical Model). The goodness-of-fit of models was evaluated and the performance trends of different models were calculated by time-dependent area under the curve (AUC).

RESULTS

Two-dimensional SWE, CEUS enhancement patterns and clinical parameters (spleen length, multiple tumors, α-fetoprotein, albumin and prothrombin time) were independently associated with early recurrence (all p values <0.05). Multiple tumors and a decrease in albumin independently contributed to the late recurrence. The model fit of CEUS-SWE-Clinical Model was superior to other models in predicting early recurrence (all p values <0.05). The AUCs of the CEUS-Clinical Model were higher from 2 mo to 7 mo, while the SWE-Clinical Model had higher AUCs from 9 mo to 12 mo.

CONCLUSION

CEUS enhancement patterns and 2-D SWE were independent predictors of HCC early recurrence as the two factors contributed to the predictive performance at different times. The multimodal model, which included diverse data in predicting early HCC recurrence, had the best goodness-of-fit.

Prediction of early recurrence of HCC after hepatectomy by contrast-enhanced ultrasound-based deep learning radiomics

Objective

This study aims to evaluate the predictive model based on deep learning (DL) and radiomics features from contrast-enhanced ultrasound (CEUS) to predict early recurrence (ER) in patients with hepatocellular carcinoma (HCC).

Methods

One hundred seventy-two patients with HCC who underwent hepatectomy and followed up for at least 1 year were included in this retrospective study. The data were divided according to the 7:3 ratios of training and test data. The ResNet-50 architecture, CEUS-based radiomics, and the combined model were used to predict the early recurrence of HCC after hepatectomy. The receiver operating characteristic (ROC) curve and calibration curve were drawn to evaluate its diagnostic efficiency.

Results

The CEUS-based radiomics ROCs of the “training set” and “test set” were 0.774 and 0.763, respectively. The DL model showed increased prognostic value, the ROCs of the “training set” and “test set” were 0.885 and 0.834, respectively. The combined model ROCs of the “training set” and “test set” were 0.943 and 0.882, respectively.

Conclusion

The deep learning radiomics model integrating DL and radiomics features from CEUS was used to predict ER and achieve satisfactory performance. Its diagnostic efficiency is significantly better than that of the single model.