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Objectives <p>This study employs dynamic contrast-enhanced MRI (DCE-MRI) to noninvasively predict the proliferative subtype of hepatocellular carcinoma (HCC). This subtype is marked by high tumor proliferation and aggressive clinical behavior. We developed a deep learning prediction model that employs a dynamic radiomics workflow and self-supervised learning (SSL). The model analyzes temporal and spatial patterns in DCE-MRI data to identify the proliferative subtype efficiently and accurately. Our goal is to improve diagnostic precision and guide personalized treatment planning.</p> Methods <p>This retrospective study included 381 HCC patiephonnts who underwent curative resection at two medical centers. The cohort was divided into the training (<i>n</i> = 220), internal (<i>n</i> = 93), and external (<i>n</i> = 68) test sets. A DL model was developed using DCE-MRI of the primary tumor. Class activation mapping was used to interpret HCC proliferation in HCC.</p> Results <p>The pHCC-SSL model performed well in predicting HCC proliferation, with a training set AUC) of 1.00, an internal test set AUC of 0.91, and an external test set AUC of 0.94. Without SSL pre-training, the AUC for internal and external testing decreased to 0.81 and 0.80, respectively. The predictive performance of the derived model was superior to that of the current single-sequence model.</p> Conclusions <p>The pHCC-SSL model employs dynamic radiomics and a two-stage training approach to efficiently predict HCC proliferation from multi-sequence DCE-MRI, surpassing traditional single-stage models in accuracy and speed.</p> Critical relevance statement <p>Our study introduces the pHCC-SSL model, a self-supervised deep learning approach using DCE-MRI that enhances the diagnostic accuracy of HCC subtypes, significantly advancing clinical radiology by enabling personalized treatment strategies.</p> Key Points <p><UnorderedList Mark="Bullet"> <ItemContent> <p>The proposed model enables noninvasive identification of HCC with high proliferation and aggressive behavior.</p> </ItemContent> <ItemContent> <p>SSL improves lesion differentiation by reducing redundancy and enhancing feature diversity.</p> </ItemContent> <ItemContent> <p>Dynamic feature extraction captures vascular infiltration, aiding preoperative metastasis risk assessment.</p> </ItemContent> </UnorderedList></p> Graphical Abstract <p></p>

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A deep learning model based on self-supervised learning for identifying subtypes of proliferative hepatocellular carcinoma from dynamic contrast-enhanced MRI

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月17日

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  • Hui Qu,
  • Shuairan Zhang,
  • Xuedan Li,
  • Yuan Miao,
  • Yuxi Han,
  • Ronghui Ju,
  • Xiaoyu Cui,
  • Yiling Li

摘要

Objectives

This study employs dynamic contrast-enhanced MRI (DCE-MRI) to noninvasively predict the proliferative subtype of hepatocellular carcinoma (HCC). This subtype is marked by high tumor proliferation and aggressive clinical behavior. We developed a deep learning prediction model that employs a dynamic radiomics workflow and self-supervised learning (SSL). The model analyzes temporal and spatial patterns in DCE-MRI data to identify the proliferative subtype efficiently and accurately. Our goal is to improve diagnostic precision and guide personalized treatment planning.

Methods

This retrospective study included 381 HCC patiephonnts who underwent curative resection at two medical centers. The cohort was divided into the training (n = 220), internal (n = 93), and external (n = 68) test sets. A DL model was developed using DCE-MRI of the primary tumor. Class activation mapping was used to interpret HCC proliferation in HCC.

Results

The pHCC-SSL model performed well in predicting HCC proliferation, with a training set AUC) of 1.00, an internal test set AUC of 0.91, and an external test set AUC of 0.94. Without SSL pre-training, the AUC for internal and external testing decreased to 0.81 and 0.80, respectively. The predictive performance of the derived model was superior to that of the current single-sequence model.

Conclusions

The pHCC-SSL model employs dynamic radiomics and a two-stage training approach to efficiently predict HCC proliferation from multi-sequence DCE-MRI, surpassing traditional single-stage models in accuracy and speed.

Critical relevance statement

Our study introduces the pHCC-SSL model, a self-supervised deep learning approach using DCE-MRI that enhances the diagnostic accuracy of HCC subtypes, significantly advancing clinical radiology by enabling personalized treatment strategies.

Key Points

The proposed model enables noninvasive identification of HCC with high proliferation and aggressive behavior.

SSL improves lesion differentiation by reducing redundancy and enhancing feature diversity.

Dynamic feature extraction captures vascular infiltration, aiding preoperative metastasis risk assessment.

Graphical Abstract