Hu Siqi, Kang Yinqian, Xie Yujie, Yang Ting, Yang Yuan, Jiao Ju, Zou Qiong, Zhang Hong, Zhang Yong
Department of Nuclear Medicine, The Third Affiliated Hospital of Sun Yat-Sen University, No. 600 Tianhe Road, Tianhe District, Guangzhou, 510630, China.
Department of Anesthesiology, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng East Road, Yuexiu District, Guangzhou, 510060, China.
Abdom Radiol (NY). 2023 Feb;48(2):532-542. doi: 10.1007/s00261-022-03722-y. Epub 2022 Nov 12.
To explore the potential of β-2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (F-FDG PET/CT) in the evaluation of macrotrabecular-massive (MTM) hepatocellular carcinoma (HCC) and to apply radiomics approach to build a radiomics nomogram for predicting MTM-HCC.
This study included 140 (training cohort:101; validation cohort:39) HCC patients who underwent preoperative F-FDG PET/CT at two institutions. The clinical features and tumor FDG metabolism measured by the tumor-to-liver ratio (TLR) via F-FDG PET/CT were retrospectively collected. Radiomics features were extracted from F-FDG PET/CT images and a radiomics score (Rad-score) was calculated. A radiomics nomogram was then constructed by combining Rad-score and independent clinical features and was assessed with a calibration curve. The performance of the radiomics nomogram, Rad-score and TLR was evaluated by receiver operating characteristic (ROC) curves and decision curve analysis (DCA).
A total of six top weighted radiomics features were selected from PET/CT images by the least absolute shrinkage and selection operator (LASSO) regression algorithm and were used to construct a Rad-score. Multivariate analysis identified Rad-score (OR = 2.183, P = 0.004), age ≤ 50 years (OR = 3.136, P = 0.036), AST > 40U/L (OR = 0.270, P = 0.017) and TLR (OR = 1.641, P = 0.049) as independent predictors of MTM-HCC. The radiomics nomogram had a higher area under the curves (AUCs) than the Rad-score and TLR for predicting MTM-HCC in both training (0.849 [95% CI 0.774-0.924] vs. 0.764 [95% CI 0.669-0.843], 0.763 [95% CI 0.668-0.842]) and validation (0.749 [95% CI 0.584-0.873] vs. 0.690 [95% CI 0.522-0.828], 0.541 [95% CI 0.374-0.701]) cohorts. DCA showed the radiomics nomogram to be more clinically useful than Rad-score and TLR.
Tumor FDG metabolism is significantly associated with MTM-HCC. A F-FDG PET/CT-based radiomics nomogram may be useful for preoperatively predicting the MTM subtype in primary HCC patients, contributing to pretreatment decision-making.
探讨β-2-[18F]氟-2-脱氧-D-葡萄糖正电子发射断层扫描/计算机断层扫描(F-FDG PET/CT)在评估大结节型-巨块型(MTM)肝细胞癌(HCC)中的潜力,并应用放射组学方法构建预测MTM-HCC的放射组学列线图。
本研究纳入了140例(训练队列:101例;验证队列:39例)在两家机构接受术前F-FDG PET/CT检查的HCC患者。回顾性收集临床特征以及通过F-FDG PET/CT测量的肿瘤与肝脏比值(TLR)所反映的肿瘤FDG代谢情况。从F-FDG PET/CT图像中提取放射组学特征并计算放射组学评分(Rad-score)。然后将Rad-score与独立临床特征相结合构建放射组学列线图,并通过校准曲线进行评估。通过受试者工作特征(ROC)曲线和决策曲线分析(DCA)评估放射组学列线图、Rad-score和TLR的性能。
通过最小绝对收缩和选择算子(LASSO)回归算法从PET/CT图像中总共选择了6个权重最高的放射组学特征,并用于构建Rad-score。多变量分析确定Rad-score(比值比[OR]=2.183,P=0.004)、年龄≤50岁(OR=3.136,P=0.036)、谷草转氨酶(AST)>40U/L(OR=0.270,P=0.017)和TLR(OR=1.641,P=0.049)为MTM-HCC的独立预测因素。在训练队列(0.849[95%可信区间(CI)0.774-0.924]对0.764[95%CI 0.669-0.843]、0.763[95%CI 0.668-0.842])和验证队列(0.749[95%CI 0.584-0.873]对0.690[95%CI 0.522-0.828]、0.541[95%CI 0.374-0.701])中,放射组学列线图在预测MTM-HCC方面的曲线下面积(AUC)高于Rad-score和TLR。DCA显示放射组学列线图在临床上比Rad-score和TLR更有用。
肿瘤FDG代谢与MTM-HCC显著相关。基于F-FDG PET/CT的放射组学列线图可能有助于术前预测原发性HCC患者的MTM亚型,为治疗前决策提供依据。