Super Resolution Reconstruction: A Strategy to Improve the Diagnostic Performance of Radiomics for Predicting Microvascular Invasion in Hepatocellular Carcinoma.
Authors
Affiliations (2)
Affiliations (2)
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China. Electronic address: [email protected].
Abstract
Microvascular invasion (MVI) is a key factor associated with high postoperative recurrence rate in patients with hepatocellular carcinoma (HCC). This study aims to evaluate the value of super-resolution (SR) reconstruction based on hepatobiliary phase (HBP) MR images for improving the accuracy of MVI prediction. A total of 544 patients with HCC from two medical centers between 2018 and 2025 were retrospectively analyzed, including 450 patients in the training set, 51 in the internal validation set, and 43 in the external test set. Based on a lesion-aware 3D Restormer transformer architecture, SR reconstruction was performed using HBP MR images to generate super-resolution MR images. Tumor segmentations were automatically performed using the nnU-Net architecture and manually corrected by an experienced abdominal radiologist before radiomics feature extraction. Radiomics features were then extracted from original and SR images. Radiomics models based on original and SR images were developed to predict MVI status. Three machine learning algorithms, including Logistic Regression, Random Forest, and LightGBM, were used for model development. A total of 851 radiomics features were extracted from the original and SR images, respectively. Redundant features were filtered, and 50 radiomics features were selected using LASSO regression with cross-validated. For MVI prediction, the pooled areas under the receiver operating curves (AUCs) of radiomics model based on the original images were 0.710, 0.764, and 0.614 in the training, validation and test sets, respectively. The corresponding AUCs of the radiomics model based on the SR images were 0.855, 0.798, and 0.827, respectively. In the test set, the AUC of the radiomics model based on the SR images was higher than that based on the original images (0.827 vs. 0.614, p <0.001). SR reconstruction can improve the diagnostic performance of radiomics models for predicting MVI in patients with HCC.