Abstract
Background
Accurate preoperative identification of the microvascular invasion (MVI) can relieve
the pressure from personalized treatment adaptation and improve the poor prognosis
for hepatocellular carcinoma (HCC). This study aimed to develop and validate a novel
multimodal deep learning (DL) model for predicting MVI based on multi-parameter magnetic
resonance imaging (MRI) and contrast-enhanced computed tomography (CT).
Methods
A total of 397 HCC patients underwent both CT and MRI examinations before surgery.
We established the radiological models (RCT, RMRI) by support vector machine (SVM), DL models (DLCT_ALL, DLMRI_ALL, DLCT + MRI) by ResNet18. The comprehensive model (CALL) involving multi-modality DL features and clinical and radiological features was
constructed using SVM. Model performance was quantified by the area under the receiver
operating characteristic curve (AUC) and compared by net reclassification index (NRI)
and integrated discrimination improvement (IDI).
Results
The DLCT + MRI model exhibited superior predicted efficiency over single-modality models, especially
over the DLCT_ALL model (AUC: 0.819 vs. 0.742, NRI > 0, IDI > 0). The DLMRI_ALL model improved the performance over the RMRI model (AUC: 0.794 vs. 0.766, NRI > 0, IDI < 0), but no such difference was found
between the DLCT_ALL model and RCT model (AUC: 0.742 vs. 0.710, NRI < 0, IDI < 0). Furthermore, both the DLCT + MRI and CALL models revealed the prognostic power in recurrence-free survival stratification (P < 0.001).
Conclusion
The proposed DLCT + MRI model showed robust capability in predicting MVI and outcomes for HCC. Besides, the
identification ability of the multi-modality DL model was better than any single modality,
especially for CT.
Keywords
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Article info
Publication history
Published online: September 05, 2022
Accepted:
August 30,
2022
Received in revised form:
August 22,
2022
Received:
June 21,
2022
Identification
Copyright
© 2022 Published by Elsevier Ltd.
