Department of Hepatobiliary Pancreatic Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China.
Department of Radiology, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China.
World J Surg Oncol. 2021 Dec 12;19(1):344. doi: 10.1186/s12957-021-02459-0.
This study aimed to establish a radiomics-based nomogram for predicting severe (grade B or C) post-hepatectomy liver failure (PHLF) in patients with huge (≥ 10 cm) hepatocellular carcinoma (HCC).
One hundred eighty-six patients with huge HCC (training dataset, n = 131 and test dataset, n = 55) that underwent curative hepatic resection were included in this study. The least absolute shrinkage and selection operator (LASSO) approach was applied to develop a radiomics signature for grade B or C PHLF prediction using the training dataset. A multivariable logistic regression model was used by incorporating radiomics signature and other clinical predictors to establish a radiomics nomogram. Decision tree analysis was performed to stratify the risk for severe PHLF.
The radiomics signature consisting of nine features predicted severe PHLF with AUCs of 0.766 and 0.745 for the training and test datasets. The radiomics nomogram was generated by integrating the radiomics signature, the extent of resection and the model for end-stage liver disease (MELD) score. The nomogram exhibited satisfactory discrimination ability, with AUCs of 0.842 and 0.863 for the training and test datasets, respectively. Based on decision tree analysis, patients were divided into three risk classes: low-risk patients with radiomics score < -0.247 and MELD score < 10 or radiomics score ≥ - 0.247 but underwent partial resections; intermediate-risk patients with radiomics score < - 0.247 but MELD score ≥10; high-risk patients with radiomics score ≥ - 0.247 and underwent extended resections.
The radiomics nomogram could predict severe PHLF in huge HCC patients. A decision tree may be useful in surgical decision-making for huge HCC hepatectomy.
本研究旨在建立基于放射组学的nomogram 模型，以预测巨大（≥ 10cm）肝细胞癌（HCC）患者术后严重（B 级或 C 级）肝衰竭（PHLF）。
本研究纳入了 186 例接受根治性肝切除术治疗的巨大 HCC 患者（训练数据集，n = 131；测试数据集，n = 55）。采用最小绝对收缩和选择算子（LASSO）方法，利用训练数据集建立预测 B 级或 C 级 PHLF 的放射组学特征模型。通过纳入放射组学特征和其他临床预测因素，建立多变量逻辑回归模型，建立放射组学 nomogram。采用决策树分析对严重 PHLF 风险进行分层。
由 9 个特征组成的放射组学特征模型可预测严重 PHLF，其在训练数据集和测试数据集的 AUC 分别为 0.766 和 0.745。该放射组学 nomogram 是通过整合放射组学特征、切除范围和终末期肝病模型（MELD）评分生成的。该 nomogram 具有良好的区分能力，在训练数据集和测试数据集的 AUC 分别为 0.842 和 0.863。基于决策树分析，患者被分为三个风险等级：放射组学评分＜-0.247 且 MELD 评分＜10 或放射组学评分≥-0.247 但行部分切除术的低危患者；放射组学评分＜-0.247 但 MELD 评分≥10 的中危患者；放射组学评分≥-0.247 且行扩大切除术的高危患者。
放射组学 nomogram 可预测巨大 HCC 患者的严重 PHLF。决策树可能有助于巨大 HCC 肝切除术的手术决策。
