Department of Nuclear Medicine, The First Affiliated Hospital of Guangzhou Medical University, No. 151, Yanjiang Road, Yuexiu District, Guangzhou, 510000, Guangdong, China.
Nanfang PET Center, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Guangzhou, 510515, Guangdong Province, China.
Eur J Nucl Med Mol Imaging. 2021 Jul;48(8):2599-2614. doi: 10.1007/s00259-020-05119-9. Epub 2021 Jan 8.
As a reliable preoperative predictor for microvascular invasion (MVI) and disease-free survival (DFS) is lacking, we developed a radiomics nomogram of [F]FDG PET/CT to predict MVI status and DFS in patients with very-early- and early-stage (BCLC 0, BCLC A) hepatocellular carcinoma (HCC).
Patients (N = 80) with BCLC0-A HCC who underwent [F]FDG PET/CT before surgery were enrolled in this retrospective study and were randomized to a training cohort and a validation cohort. Texture features from patients obtained using Lifex software in the training cohort were subjected to LASSO regression to select the most useful predictive features of MVI and DFS. Then, the radiomics nomogram was constructed using the radiomics signature and clinical features and further validated.
To predict MVI, the [F]FDG PET/CT radiomics signature consisted of five texture features from the PET and six texture features from CT. The signature was significantly associated with MVI status in the training cohort (P = 0.001). None of the clinical features was independent predictors for MVI status (P > 0.05). The area under the curve value of the M-PET/CT model was 0.891 (95% CI: 0.799-0.984) in the training cohort and showed good discrimination and calibration. To predict DFS, the [F]FDG PET/CT radiomics nomogram (D-PET/CT model) and a clinicopathologic nomogram were built in the training cohort. The D-PET/CT model, which integrated the D-PET/CT radiomics signature with INR and TB, provided better predictive performance (C-index: 0.831, 95% CI: 0.761-0.900) and larger net benefits than the simple clinical model, as determined by decision curve analyses.
The newly developed [F]FDG PET/CT radiomics signature was an independent biomarker for the estimation of MVI and DFS in patients with very-early- and early-stage HCC. Moreover, PET/CT nomogram, which incorporated the radiomics signature of [F]FDG PET/CT and clinical risk factors in patients with very-early- and early-stage HCC, performed better for individualized DFS estimation, which might enable a step forward in precise medicine.
由于缺乏微血管侵犯 (MVI) 和无病生存 (DFS) 的可靠术前预测指标,我们开发了一种基于[F]FDG PET/CT 的放射组学生成模型,以预测非常早期和早期 (BCLC 0、BCLC A) 肝细胞癌 (HCC) 患者的 MVI 状态和 DFS。
本回顾性研究纳入了 80 例接受[F]FDG PET/CT 检查的 BCLC0-A HCC 患者,并将其随机分配到训练队列和验证队列。使用 Lifex 软件从训练队列中获取患者的纹理特征,然后进行 LASSO 回归,以选择对 MVI 和 DFS 最有用的预测特征。然后,使用放射组学特征和临床特征构建放射组学生成模型,并进一步验证。
为了预测 MVI,[F]FDG PET/CT 放射组学生成模型由来自 PET 的 5 个纹理特征和来自 CT 的 6 个纹理特征组成。该模型在训练队列中与 MVI 状态显著相关(P=0.001)。无临床特征是 MVI 状态的独立预测因素(P>0.05)。M-PET/CT 模型的曲线下面积值在训练队列中为 0.891(95% CI:0.799-0.984),显示出良好的区分度和校准度。为了预测 DFS,在训练队列中构建了[F]FDG PET/CT 放射组学生成模型(D-PET/CT 模型)和临床病理组学生成模型。D-PET/CT 模型将 D-PET/CT 放射组学特征与 INR 和 TB 相结合,通过决策曲线分析显示出更好的预测性能(C 指数:0.831,95% CI:0.761-0.900)和更大的净收益。
新开发的[F]FDG PET/CT 放射组学生成模型是评估非常早期和早期 HCC 患者 MVI 和 DFS 的独立生物标志物。此外,在非常早期和早期 HCC 患者中,结合[F]FDG PET/CT 放射组学特征和临床风险因素的 PET/CT 列线图在个体化 DFS 估计方面表现更好,这可能使精准医学向前迈进了一步。