Li Jia, Ma Yunhui, Yang Chunyu, Qiu Ganbin, Chen Jingmu, Tan Xiaoliang, Zhao Yue
Department of Oncology, Central People's Hospital of Zhanjiang, Zhanjiang, China.
Department of Radiology, The First School of Clinical Medicine, Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen, China.
Front Oncol. 2024 Feb 23;14:1277698. doi: 10.3389/fonc.2024.1277698. eCollection 2024.
This study aimed to evaluate the effectiveness of radiomics analysis with R2* maps in predicting early recurrence (ER) in single hepatocellular carcinoma (HCC) following partial hepatectomy.
We conducted a retrospective analysis involving 202 patients with surgically confirmed single HCC having undergone preoperative magnetic resonance imaging between 2018 and 2021 at two different institutions. 126 patients from Institution 1 were assigned to the training set, and 76 patients from Institution 2 were assigned to the validation set. A least absolute shrinkage and selection operator (LASSO) regularization was conducted to operate a logistic regression, then features were identified to construct a radiomic score (Rad-score). Uni- and multi-variable tests were used to assess the correlations of clinicopathological features and Rad-score with ER. We then established a combined model encompassing the optimal Rad-score and clinical-pathological risk factors. Additionally, we formulated and validated a predictive nomogram for predicting ER in HCC. The nomogram's discrimination, calibration, and clinical utility were thoroughly evaluated.
Multivariable logistic regression revealed the Rad-score, microvascular invasion (MVI), and α fetoprotein (AFP) level > 400 ng/mL as significant independent predictors of ER in HCC. We constructed a nomogram based on these significant factors. The areas under the receiver operator characteristic curve of the nomogram and precision-recall curve were 0.901 and 0.753, respectively, with an F1 score of 0.831 in the training set. These values in the validation set were 0.827, 0.659, and 0.808.
The nomogram that integrates the radiomic score, MVI, and AFP demonstrates high predictive efficacy for estimating the risk of ER in HCC. It facilitates personalized risk classification and therapeutic decision-making for HCC patients.
本研究旨在评估利用R2*图的放射组学分析在预测部分肝切除术后单发性肝细胞癌（HCC）早期复发（ER）方面的有效性。
我们进行了一项回顾性分析，纳入了202例经手术确诊为单发性HCC的患者，这些患者于2018年至2021年期间在两家不同机构接受了术前磁共振成像检查。来自机构1的126例患者被分配到训练集，来自机构2的76例患者被分配到验证集。采用最小绝对收缩和选择算子（LASSO）正则化进行逻辑回归，然后识别特征以构建放射组学评分（Rad-score）。采用单变量和多变量检验评估临床病理特征和Rad-score与ER的相关性。然后，我们建立了一个包含最佳Rad-score和临床病理危险因素的联合模型。此外，我们制定并验证了一个用于预测HCC患者ER的预测列线图。对列线图的辨别力、校准和临床实用性进行了全面评估。
多变量逻辑回归显示，Rad-score、微血管侵犯（MVI）和甲胎蛋白（AFP）水平>400 ng/mL是HCC患者ER的显著独立预测因素。我们基于这些显著因素构建了一个列线图。列线图的受试者操作特征曲线下面积和精确召回率曲线下面积在训练集中分别为0.901和0.753，F1评分为0.831。验证集中的这些值分别为0.827、0.659和0.808。
整合放射组学评分、MVI和AFP的列线图在估计HCC患者ER风险方面显示出较高的预测效能。它有助于对HCC患者进行个性化风险分类和治疗决策。
