School of Clinical Medicine, Guizhou Medical University, Guiyang, China.
Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Br J Radiol. 2023 Apr 1;96(1145):20220702. doi: 10.1259/bjr.20220702. Epub 2023 Feb 14.
We present a new artificial intelligence-powered method to predict 3-year hepatocellular carcinoma (HCC) recurrence by analysing the radiomic profile of contrast-enhanced CT (CECT) images that was validated in patient cohorts.
This retrospective cohort study of 224 HCC patients with follow-up for at least 3 years was performed at a single centre from 2012 to 2019. Two groups of radiomic signatures were extracted from the arterial and portal venous phases of pre-operative CECT. Then, the radiological model (RM), deep learning-based radiomics model (DLRM), and clinical & deep learning-based radiomics model (CDLRM) were established and validated in the area under curve (AUC), calibration curve, and clinical decision curve.
Comparison of the clinical baseline variables between the non-recurrence ( = 109) and recurrence group ( = 115), three clinical independent factors (Barcelona Clinic Liver Cancer staging, microvascular invasion, and α-fetoprotein) were incorporated into DLRM for the CDLRM construction. Among the 30 radiomic features most crucial to the 3 year recurrence rate, the selection from deep learning-based radiomics (DLR) features depends on CECT. through the Gini index. In most cases, CDLRM has shown superior accuracy and distinguished performance than DLRM and RM, with the 0.98 AUC in the training cohorts and 0.83 in the testing.
This study proposed that DLR-based CDLRM construction would be allowed for the predictive utility of 3-year recurrence outcomes of HCCs, providing high-risk patients with an effective and non-invasive method to possess extra clinical intervention.
This study has highlighted the predictive value of DLR in the 3-year recurrence rate of HCC.
通过分析增强 CT（CECT）图像的放射组学特征，我们提出了一种新的人工智能方法来预测 3 年肝细胞癌（HCC）复发，并在患者队列中进行了验证。
这是一项回顾性队列研究，纳入了 2012 年至 2019 年在单一中心接受至少 3 年随访的 224 例 HCC 患者。从术前 CECT 的动脉期和门静脉期提取两组放射组学特征。然后，在曲线下面积（AUC）、校准曲线和临床决策曲线中建立并验证放射学模型（RM）、基于深度学习的放射组学模型（DLRM）和临床与基于深度学习的放射组学模型（CDLRM）。
在无复发组（n=109）和复发组（n=115）之间比较了临床基线变量，将巴塞罗那临床肝癌分期、微血管侵犯和甲胎蛋白这三个独立的临床因素纳入 DLRM 中构建 CDLRM。在与 3 年复发率最相关的 30 个放射组学特征中，通过基尼指数，基于深度学习的放射组学（DLR）特征的选择取决于 CECT。在大多数情况下，CDLRM 比 DLRM 和 RM 具有更高的准确性和区分性能，在训练队列中的 AUC 为 0.98，在测试队列中的 AUC 为 0.83。
本研究表明，基于 DLR 的 CDLRM 构建可用于预测 HCC 3 年复发结局，为高危患者提供一种有效且非侵入性的方法，以便进行额外的临床干预。
本研究强调了 DLR 在 HCC 3 年复发率中的预测价值。
