Wen Jingyu, Wang Xi, Xia Mingge, Wei Bowen, Yang Hongji, Hou Yifu
Department of Medical Insurance, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Abdom Radiol (NY). 2024 Jun;49(6):1905-1917. doi: 10.1007/s00261-023-04177-5. Epub 2024 Mar 7.
To evaluate the predictive value of tumor and peritumor radiomics in the fatty acid binding protein 4 (FABP4) expression levels and overall survival in patients with hepatocellular carcinoma.
The genomic data of HCC patients were obtained from The Cancer Genome Atlas. The Dual-area CT images of corresponding patients were downloaded from The Cancer Imaging Archive, for radiomics feature extraction, model construction and prognosis analysis. Simultaneously, using patients from Sichuan Provincial People's Hospital, the prognostic value of the radiomics model in HCC patients was validated.
In the TCIA database, the area under the curve (AUC) values of the volumes of interest (VOI) model in the training set and internal validation set were 0.812 and 0.754, respectively, and the AUC value of VOI in the training set and internal validation set were 0.866 and 0.779, respectively. In the VOI and the VOI model of the independent cohort, there were significant differences in OS between the high and low rad-score groups (P = 0.009, P = 0.021, respectively). Significant positive correlations can be observed between FABP4 expression and correlations with rad-score of VOI model (r = 0.691) and VOI model (r = 0.732) in the independent cohort.
Radiomics models of tumor and peritumor Dual-area CT images could predict stably the expression levels of FABP4 and may be helping in personalized treatment strategies.
评估肿瘤及瘤周放射组学在肝细胞癌患者脂肪酸结合蛋白4（FABP4）表达水平及总生存中的预测价值。
肝细胞癌患者的基因组数据来自癌症基因组图谱。从癌症影像存档库下载相应患者的双区域CT图像，用于放射组学特征提取、模型构建及预后分析。同时，使用四川省人民医院的患者对放射组学模型在肝细胞癌患者中的预后价值进行验证。
在TCIA数据库中，训练集和内部验证集中感兴趣体积（VOI）模型的曲线下面积（AUC）值分别为0.812和0.754，训练集和内部验证集中VOI的AUC值分别为0.866和0.779。在独立队列的VOI和VOI模型中，高、低放射评分组之间的总生存期存在显著差异（分别为P = 0.009，P = 0.021）。在独立队列中，可观察到FABP4表达与VOI模型（r = 0.691）和VOI模型（r = 0.732）的放射评分之间存在显著正相关。
肿瘤及瘤周双区域CT图像的放射组学模型能够稳定预测FABP4的表达水平，可能有助于制定个性化治疗策略。
