Multi-Region Radiomic Analysis Based on Multi-Sequence MRI Can Preoperatively Predict Microvascular Invasion in Hepatocellular Carcinoma

Lanmei Gao; Meilian Xiong; Xiaojie Chen; Zewen Han; Chuan Yan; Rongping Ye; Lili Zhou; Yueming Li

doi:10.3389/fonc.2022.818681

Multi-Region Radiomic Analysis Based on Multi-Sequence MRI Can Preoperatively Predict Microvascular Invasion in Hepatocellular Carcinoma

Front Oncol. 2022 Apr 27:12:818681. doi: 10.3389/fonc.2022.818681. eCollection 2022.

Authors

Lanmei Gao¹, Meilian Xiong¹, Xiaojie Chen¹, Zewen Han^{1

2}, Chuan Yan¹, Rongping Ye¹, Lili Zhou¹, Yueming Li^{1

3}

Affiliations

¹ Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
² The School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, Fujian, China.
³ Key Laboratory of Radiation Biology (Fujian Medical University), Fujian Province University, Fuzhou, Fujian, China.

Abstract

Objectives: Microvascular invasion (MVI) affects the postoperative prognosis in hepatocellular carcinoma (HCC) patients; however, there remains a lack of reliable and effective tools for preoperative prediction of MVI. Radiomics has shown great potential in providing valuable information for tumor pathophysiology. We constructed and validated radiomics models with and without clinico-radiological factors to predict MVI.

Methods: One hundred and fifteen patients with pathologically confirmed HCC (training set: n = 80; validation set: n = 35) who underwent preoperative MRI were retrospectively recruited. Radiomics models based on multi-sequence MRI across various regions (including intratumoral and/or peritumoral areas) were built using four classification algorithms. A clinico-radiological model was constructed individually and combined with a radiomics model to generate a fusion model by multivariable logistic regression.

Results: Among the radiomics models, the model based on T2WI and arterial phase (T2WI-AP model) in the volume of the liver-HCC interface (VOI_interface) exhibited the best predictive power, with AUCs of 0.866 in the training group and 0.855 in the validation group. The clinico-radiological model exhibited good efficacy (AUC: 0.819 and 0.717, respectively). The fusion model showed excellent predictive ability (AUC: 0.915 and 0.868, respectively), outperforming both the clinico-radiological and the T2WI-AP models in the training and validation sets.

Conclusion: The fusion model of multi-region radiomics achieves an enhanced prediction of the individualized risk estimation of MVI in HCC patients. This may be a beneficial tool for clinicians to improve decision-making in personalized medicine.

Keywords: hepatocellular carcinoma; machine learning; magnetic resonance imaging; microvascular invasion; nomogram; radiomics.