Wang Fang, Cheng Ming, Du Binbin, Li Jing, Li Liming, Huang Wenpeng, Gao Jianbo
Department of Radiology, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Erqi, Zhengzhou, Henan, 450052, People's Republic of China.
Information Department, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China.
Insights Imaging. 2024 Mar 26;15(1):90. doi: 10.1186/s13244-024-01649-0.
We assessed the predictive capacity of computed tomography (CT)-enhanced radiomics models in determining microvascular invasion (MVI) for isolated hepatocellular carcinoma (HCC) ≤ 5 cm within peritumoral margins of 5 and 10 mm.
Radiomics software was used for feature extraction. We used the least absolute shrinkage and selection operator (LASSO) algorithm to establish an effective model to predict patients' preoperative MVI status.
The area under the curve (AUC) values in the validation sets for the 5- and 10-mm radiomics models concerning arterial tumors were 0.759 and 0.637, respectively. In the portal vein phase, they were 0.626 and 0.693, respectively. Additionally, the combined radiomics model for arterial tumors and the peritumoral 5-mm margin had an AUC value of 0.820. The decision curve showed that the combined tumor and peritumoral radiomics model exhibited a somewhat superior benefit compared to the traditional model, while the fusion model demonstrated an even greater advantage, indicating its significant potential in clinical application.
The 5-mm peritumoral arterial model had superior accuracy and sensitivity in predicting MVI. Moreover, the combined tumor and peritumoral radiomics model outperformed both the individual tumor and peritumoral radiomics models. The most effective combination was the arterial phase tumor and peritumor 5-mm margin combination. Using a fusion model that integrates tumor and peritumoral radiomics and clinical data can aid in the preoperative diagnosis of the MVI of isolated HCC ≤ 5 cm, indicating considerable practical value.
The radiomics model including a 5-mm peritumoral expansion is a promising noninvasive biomarker for preoperatively predicting microvascular invasion in patients diagnosed with a solitary HCC ≤ 5 cm.
• Radiomics features extracted at a 5-mm distance from the tumor could better predict hepatocellular carcinoma microvascular invasion. • Peritumoral radiomics can be used to capture tumor heterogeneity and predict microvascular invasion. • This radiomics model stands as a promising noninvasive biomarker for preoperatively predicting MVI in individuals.
我们评估了计算机断层扫描（CT）增强的放射组学模型在确定肿瘤周围边缘为5毫米和10毫米的孤立性肝细胞癌（HCC）≤5厘米微血管侵犯（MVI）方面的预测能力。
使用放射组学软件进行特征提取。我们使用最小绝对收缩和选择算子（LASSO）算法建立一个有效的模型来预测患者术前的MVI状态。
关于动脉期肿瘤的5毫米和10毫米放射组学模型在验证集中的曲线下面积（AUC）值分别为0.759和0.637。在门静脉期，它们分别为0.626和0.693。此外，动脉期肿瘤与肿瘤周围5毫米边缘的联合放射组学模型的AUC值为0.820。决策曲线显示，联合肿瘤和肿瘤周围放射组学模型与传统模型相比表现出略优的益处，而融合模型显示出更大的优势，表明其在临床应用中的巨大潜力。
肿瘤周围5毫米动脉期模型在预测MVI方面具有更高的准确性和敏感性。此外，联合肿瘤和肿瘤周围放射组学模型优于单独的肿瘤和肿瘤周围放射组学模型。最有效的组合是动脉期肿瘤与肿瘤周围5毫米边缘的组合。使用整合肿瘤和肿瘤周围放射组学及临床数据的融合模型有助于术前诊断≤5厘米的孤立性HCC的MVI，具有相当大的实用价值。
包括肿瘤周围5毫米扩展的放射组学模型是术前预测诊断为孤立性HCC≤5厘米患者微血管侵犯的有前景的非侵入性生物标志物。
• 从肿瘤5毫米距离处提取的放射组学特征能更好地预测肝细胞癌微血管侵犯。• 肿瘤周围放射组学可用于捕捉肿瘤异质性并预测微血管侵犯。• 该放射组学模型是术前预测个体MVI的有前景的非侵入性生物标志物。
