Wang Zi-Zheng, Song Shao-Ming, Zhang Gong, Chen Rui-Qiu, Zhang Zhuo-Chao, Liu Rong
Department of Hepatobiliary Surgery, Senior Department of Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing 100039, China.
The First School of Clinical Medicine, Lanzhou University, Lanzhou 730000, Gansu Province, China.
World J Gastroenterol. 2025 Sep 14;31(34):111541. doi: 10.3748/wjg.v31.i34.111541.
Deep learning-based super-resolution (SR) reconstruction can obtain high-quality images with more detailed information.
To compare multiparametric normal-resolution (NR) and SR magnetic resonance imaging (MRI) in predicting the histopathologic grade in hepatocellular carcinoma.
We retrospectively analyzed a total of 826 patients from two medical centers (training 459; validation 196; test 171). T2-weighted imaging, diffusion-weighted imaging, and portal venous phases were collected. Tumor segmentations were conducted automatically by 3D U-Net. Based on generative adversarial network, we utilized 3D SR reconstruction to produce SR MRI. Radiomics models were developed and validated by XGBoost and Catboost. The predictive efficiency was demonstrated by calibration curves, decision curve analysis, area under the curve (AUC) and net reclassification index (NRI).
We extracted 3045 radiomic features from both NR and SR MRI, retaining 29 and 28 features, respectively. For XGBoost models, SR MRI yielded higher AUC value than NR MRI in the validation and test cohorts (0.83 0.79; 0.80 0.78), respectively. Consistent trends were seen in CatBoost models: SR MRI achieved AUCs of 0.89 and 0.80 compared to NR MRI's 0.81 and 0.76. NRI indicated that the SR MRI models could improve the prediction accuracy by -1.6% to 20.9% compared to the NR MRI models.
Deep learning-based SR MRI could improve the predictive performance of histopathologic grade in HCC. It may be a powerful tool for better stratification management for patients with operable HCC.
基于深度学习的超分辨率（SR）重建能够获得具有更详细信息的高质量图像。
比较多参数常规分辨率（NR）和SR磁共振成像（MRI）在预测肝细胞癌组织病理学分级中的作用。
我们回顾性分析了来自两个医学中心的826例患者（训练组459例；验证组196例；测试组171例）。收集了T2加权成像、扩散加权成像和门静脉期图像。通过3D U-Net自动进行肿瘤分割。基于生成对抗网络，我们利用3D SR重建来生成SR MRI。通过XGBoost和Catboost开发并验证了放射组学模型。通过校准曲线、决策曲线分析、曲线下面积（AUC）和净重新分类指数（NRI）来证明预测效率。
我们从NR和SR MRI中分别提取了3045个放射组学特征，分别保留了29个和28个特征。对于XGBoost模型，在验证组和测试组中，SR MRI的AUC值均高于NR MRI（分别为0.83对0.79；0.80对0.78）。CatBoost模型也呈现出一致的趋势：SR MRI的AUC分别为0.89和0.80，而NR MRI为0.81和0.76。NRI表明，与NR MRI模型相比，SR MRI模型可将预测准确性提高-1.6%至20.9%。
基于深度学习的SR MRI可提高HCC组织病理学分级的预测性能。它可能是对可手术HCC患者进行更好分层管理的有力工具。
