Wen Haoxiang, Liang Ruiming, Liu Xiaofei, Yu Yang, Lin Shuirong, Song Zimin, Huang Yihao, Yu Xi, Chen Shuling, Chen Lili, Qian Baifeng, Shen Jingxian, Xiao Han, Shen Shunli
Center of Hepato-Pancreatico-Biliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, People's Republic of China.
Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, Guangdong Province, People's Republic of China.
J Hepatocell Carcinoma. 2024 Nov 1;11:2145-2157. doi: 10.2147/JHC.S487370. eCollection 2024.
Predicting the pathological response after neoadjuvant conversion therapy for initially unresectable hepatocellular carcinoma (HCC) is essential for surgical decision-making and survival outcomes but remains a challenge. We aimed to develop a radiomics model to predict pathological responses.
We included 203 patients with HCC who underwent hepatectomy after neoadjuvant conversion therapy between 2015 and 2023 and separated them into a training set (100 patients from Center A) and a validation set (103 patients from Center B). Pathological complete response (pCR)-related radiomic features were extracted from the largest tumor layer in the arterial and portal vein phases of the CT. A synthetic minority oversampling technique (SMOTE) was used to balance the minority groups in the training set. The SMOTE radiomics model was constructed using a logistic regression model in the SMOTE training set and its performance was verified in the validation set.
The AUC of the preoperative modified response evaluation criteria in solid tumors (mRECIST) assessment for pCR was 0.656 and 0.589 in the training and validation sets, respectively. The SMOTE radiomics model was established based on ten radiomic features and showed good pCR-predictive performance in the SMOTE training set (AUC, 0.889; accuracy, 87.7%) and the validation set (AUC: 0.843, accuracy: 86.4%). The RFS of the radiomics-predicted-pCR group was significantly better than that of the predicted-non-pCR group in the training cohort ( 0.001, 2-year RFS: 69.5% and 30.1% respectively) and the validation cohort ( 0.012, 2-year RFS: 65.9% and 38.0% respectively).
The SMOTE radiomics model has great potential for predicting pathological response and evaluating RFS in patients with unresectable HCC after neoadjuvant conversion therapy.
预测初始不可切除肝细胞癌（HCC）新辅助转化治疗后的病理反应对于手术决策和生存结果至关重要，但仍然是一项挑战。我们旨在开发一种放射组学模型来预测病理反应。
我们纳入了2015年至2023年间接受新辅助转化治疗后行肝切除术的203例HCC患者，并将他们分为训练集（来自中心A的100例患者）和验证集（来自中心B的103例患者）。从CT动脉期和门静脉期的最大肿瘤层面提取与病理完全缓解（pCR）相关的放射组学特征。使用合成少数过采样技术（SMOTE）来平衡训练集中的少数群体。在SMOTE训练集中使用逻辑回归模型构建SMOTE放射组学模型，并在验证集中验证其性能。
在训练集和验证集中，术前实体瘤改良反应评估标准（mRECIST）对pCR评估的AUC分别为0.656和0.589。基于十个放射组学特征建立了SMOTE放射组学模型，该模型在SMOTE训练集（AUC，0.889；准确率，87.7%）和验证集（AUC：0.843，准确率：86.4%）中显示出良好的pCR预测性能。在训练队列（P < 0.001，2年无复发生存率分别为69.5%和30.1%）和验证队列（P = 0.012，2年无复发生存率分别为65.9%和38.0%）中，放射组学预测的pCR组的无复发生存期明显优于预测的非pCR组。
SMOTE放射组学模型在预测新辅助转化治疗后不可切除HCC患者的病理反应和评估无复发生存期方面具有巨大潜力。
